USDA Agricultural Marketing Service Dairy Program 

Regional Econometric Model Documentation

For Model Calibrated To

USDA Agricultural Projections to 2026

March 2018 

Economics Analysis Branch

Dairy Program
1

USDA-AMS Dairy Program Regional Econometric Model Documentation

Introduction

Dairy Program’s Economic Analysis Branch (EAB) maintains a dynamic regional econometric 

model of the U.S. dairy industry to support its economic analysis and forecasting responsibilities.

The model is comprehensive. It includes: the supply of milk; the allocation of butterfat and nonfat solids to fluid milk and the major manufactured dairy products; and consumer demand for 

milk and dairy products. The model’s supply and demand equations are estimated using 

historical annual data. The historic data capture changes in the marketplace, including policies 

and processing capacities. The model includes variables for the Federal Milk Marketing Order 

(FMMO) system, Dairy Economic Loss Assistance Payment Program (DELAP), and Milk 

Income Loss Contract (MILC) program. The Margin Protection Program – Dairy (MPP-D)

payouts also are estimated. However, the payments do not interact with the other model 

variables, because the program began recently in 2014 and the production response to the 

program is still unknown. The model is specified to generate long-term supply, demand, and 

price1projections that are consistent with USDA’s official baseline projections.

2 The official 

USDA baseline is modified for Federal order analyses by specifying Federal order milk 

marketings from national milk marketings. The model is estimated and simulated with SAS 

statistical software.

3

The model simultaneously forecasts annual regional milk production, regional fluid milk 

consumption, national manufactured dairy product consumption, regional dairy classification,

national dairy product prices, and regional farm milk prices sequentially along the time path of 

2016 – 2026. Butterfat and non-fat solids are allocated through the use of conversion factors 

consistent with farm milk and dairy products. Prices for dairy products, fluid milk, and farm milk

are solved within the model to achieve equilibrium conditions for supply and demand.

The model operates on three geographic levels: 1) supply regions, in which the milk is 

produced; 2) pools, in which milk is classified by various uses; and 3) national, in which the 

classified milk is processed into manufactured products and consumed. 

Supply Regions and Milk Production

Milk is produced in all fifty states. The states are grouped into fourteen supply regions: 

Appalachian (KY, NC, SC, TN, VA), Arizona, California, Central (CO, IA, IL, KS, NE, OK), 

Florida, Former Western (ID, NV, UT), Hawaii/Alaska, Mideast (IN, MI, OH, WV), Northeast 

 

1 All prices are discussed in real or relative terms. 

2 Dairy baseline forecasts are developed by an Interagency Commodity Estimates Committee at USDA. Intercept 

terms for the model are modified for each forecast year as needed to calibrate the model to approximate baseline 

forecasts. For information on USDA’s official baseline, see U. S. Department of Agriculture, Office of the Chief 

Economist, World Agricultural Outlook Board, Interagency Agricultural Projections Committee-Long-term 

Projections Report OCE-20167-1, February 2017. Available at: 

https://www.ers.usda.gov/webdocs/publications/82539/oce-2017-1.pdf?v=42788

3 See SAS Institute, Inc., Version 9.4 SAS/ETS User’s Guide
2

(CT, DE, MA, MD, ME, NJ, NH, NY, PA, RI, VT), Pacific Northwest (OR, WA), Southeast 

(AL, AR, GA, LA, MS, MO), Southwest (NM, TX), Upper Midwest (MN, ND, SD, WI), and the 

Unregulated West (MT, WY). The regions can be seen in Figure 1, presented below.

The regional supply of milk is estimated by taking the number of cows and multiplying by the 

amount of milk each cow produces. The cow numbers and the milk yield per cow are driven by 

different variables in each region. The regional cow numbers are functions of the producer milk 

price, feed costs, slaughter prices, non-farm earnings, and/or other variables. Milk production per 

cow is estimated as a function of milk prices, feed costs, and/or other variables. Producers 

respond to milk price changes relative to feed costs by adjusting milk cow numbers. Milk per 

cow is assumed to move in response to changes in all milk price relative to feed costs. The 

number of cows, milk per cow, and feed price data are reported at state level by NASS. Slaughter 

prices are reported by AMS Livestock Market News (LMN).

4 Non-farm earnings are reported by

the U.S. Department of Commerce Bureau of Economic Analysis (BEA). Number of cows and 

milk per cow are estimated using data from 1980 – 2015. Milk marketings are estimated as milk 

production less farm use.

The all-milk price estimates that drive milk production for each region are a function of the 

effective blend price of the pool which predominantly resembles the milk supply region. For 

example, Order 131 is the “predominant” pool for the Arizona supply region. If there is no 

predominant pool for a supply region, because the supply region is associated with an 

unregulated region, a neighboring pool’s blend price or all-milk price is used. All other pools for 

a given supply region are considered possible “supplemental” receivers of the milk supply. The 

all-milk prices are from NASS state all-milk data and are aggregated to the milk supply regions 

 

4 Because of differences in data reporting practices over time, the slaughter price is actually represented by different 

prices in different years. Currently, it is represented by the dressed domestic cutter (90 percent lean) live weight 

price. From 1991 – 2007, it was represented by the Sioux Falls, SD, boner price. Prior to 1991, it was represented by 

weighted average boner cow price.

Figure 1. States Included in Each Regional Supply Area
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using a weighted average of milk production in the region. The prices are estimated using data 

from 2000 – 2015 due to order reform. Prices are deflated by the Consumer Price Index (CPI) for 

all products as reported nationally by the Bureau of Labor Statistics, U.S. Department of Labor 

(BLS). The effective blend prices are calculated based on data reported by each FMMO’s Market 

Administrator (MA) office. Some equations include variables to adjust for unusual circumstances 

over the historical period. The equations related to the regional milk production estimates are in 

Tables 1 – 14.

5 The milk prices driving production are adjusted to reflect dairy support program 

payments. Dairy Market Loss Payments (MILC) and Dairy Economic Loss Assistance (DELAP) 

are included on a per-cwt basis.

6

Pools, Supply Allocation, and Compositional Regressions

Milk produced in each supply region is allocated to, or “pooled on,” one or more marketing 

areas, or “pools.” There are twelve pools in the model, comprised of the ten existing FMMOs, 

California,

7

and an unregulated area to handle the classification of products not otherwise 

covered.8 Figure 2, presented below, shows a map of the existing FMMO structure. The 

allocation of milk into various class uses, for production into consumer products, is estimated 

within these pools.

The sum of the allocations to each pool from a supply region must equal the milk produced in the 

supply region and cannot be less than zero. To ensure that milk movements to the pools from the 

supply regions sums to total production, compositional regressions are utilized to estimate the 

 

5 Tables are located at the end of the document.

6 Total monthly MILC Program state payments data are available from the Farm Service Agency (FSA) from 

October 2002 – May 2006. After May 2006, state MILC data from FSA on a monthly or calendar year basis are no 

longer available. State MILC data is estimated for periods of June 2006 - December 2007, and fiscal years 2009-

2015 assuming monthly state payments are proportional to the fiscal year state proportions. For calendar 2008 no 

MILC payments were made. Information on DELAP payments is reported by FSA.

7 Data for the California pool that would otherwise come from an MA office are available from the California 

Department of Food and Agriculture (CDFA).

8 The model accounts for the existence of Order 135 as a pool until 2005, after which it is considered to be part of 

the unregulated pool.

Figure 2. Areas Covered by Federal Milk Marketing Orders
4

movement of milk. The details of compositional regression estimation can be found in Aitchison

(1982); however, a brief explanation follows.9 Compositional regressions utilize a functional 

form that ensures that allocations to each pool are greater than zero and add up to the milk 

produced in the supply region. The adding up constraint is accomplished by estimating a ratio of 

each allocation over a designated “fill-up” value, with the ratio logged to satisfy the strict 

positivity constraint. The fill-up value acts to balance the equations as a residual variable might, 

but is not a residual in the traditional sense. Because the fill-up value is represented in each 

equation, it is not simply a leftover. Indeed, there is an implicit allocation equation in which the 

movement of milk to the predominant pool is estimated in relation to itself. However, this 

equation always equals one.

In the context of the regional model, compositional regressions are applied in the following 

manner: each supply region is associated with a predominant pool, as explained in the last 

section. Following Aitchison (1982), milk pooled on this pool is assumed to be the fill-up value.

Milk quantities moving to other pools, relative to the milk staying in the predominant pool, are

simultaneously estimated. Effective blend prices from each pool are assumed to be the driving 

factor, with prices based on MA and CDFA data. The producer milk marketed under each 

FMMO is based on AMS State of Origin data and CDFA unregulated Grade A marketings. 

The choice of the fill-up value for each supply region could be arbitrary, but the predominant 

pool is chosen for two reasons: one, it makes economic sense that milk will be chiefly utilized in 

the area in which transportation costs are minimized. Two, relative prices are assumed to be the 

driving factor in the allocation of milk to pools. By choosing the predominant pool as the fill-up 

value, the effective blend price of the other pools relative to the predominant pool’s effective 

blend price becomes the driving factor, representing the decision to pool milk on one pool or 

another.

 

9 Aitchison, J. 1982. “The Statistical Analysis of Compositional Data.” Journal of the Royal Statistical Society. 

Series B (Methodological), Vol. 44, No. 2., pp. 139 – 177. 

https://www.jstor.org/stable/2345821?seq=1#page_scan_tab_contents
5

As an example, a portion of Table 15, the Allocation of Northeast Milk to Pools, is reproduced 

below. The full table may be found at the end of this document. Milk from the Northeast supply 

region is estimated to go to one of four pools: Order 1, Order 5, Order 33, or the unregulated 

pool.

10 It should be noted that not all pools are explicitly estimated for each supply region. These 

specifications incorporate assumptions which follow historical transportation trends, i.e., milk 

produced in the Northeast is highly unlikely to be pooled on Order 124 (the Pacific Northwest 

order). In practical terms, the milk movements that are not historically observed or are extremely 

small (less than one percent of the pool’s supply or less than one percent of the supply region’s 

movements) are assumed to be zero. Order 1 is the Northeast region’s predominant pool. 

Therefore, the supply allocations to supplemental pools, such as Order 33, are estimated in ratio 

to the milk pooled on Order 1. Continuing to use Order 33 as an example supplemental pool, the 

primary driver for movements to Order 33 relative to movements to Order 1 is the ratio of the 

Order 33 over the Order 1 blend prices. This means that there must be a greater increase in Order 

33’s effective blend price than in Order 1’s to draw milk away from Order 1. 

Example: Allocation of Northeast Milk to Federal Orders

Dependent Variable Parameter

log (Northeast Milk to Order 5 Intercept

/ Northeast Milk to Order 1) log (Trend from 2000)

Dummy 2006-2007

lag (log (Order 5 Blend Price / Order 1 Blend Price))

log (Northeast Milk to Order 33 Intercept

/ Northeast Milk to Order 1) Dummy 2005-2007

lag (log (Order 33 Blend Price / Order 1 Blend 

Price))

log (Unregulated Northeast Milk Intercept

/ Northeast Milk to Order 1) Dummy 2004

Dummy 2006-2008

log (Order 1 Class I Price/ Order 1 Class III Price)

Dummy 2001

The milk movements to non-Federal order or California pools are allocated to an unregulated 

pool, which lacks a set of classified prices, and are estimated using a variety of data. The milk 

movements to unregulated areas are driven, depending on the supply region, by relative 

classified prices from the supply region’s predominant pool, percentage of classified utilization 

within the predominant pool, or a proxy unregulated pool price. Classified prices and classified 

utilizations are discussed in a later section, but all such data are based on MA data. Data for the 

supply allocation equations begin from order reform in 2000 and end with the most recently 

available annual data, 2015. The data for classified prices and classified utilization are regional. 

Since these are historic data, the data reflect regional changes in the orders’ policies, handlers’ 

marketing policies (such as base plans), plant capacities, transportation costs and demands for 

each class of milk. 

 

10 The Unregulated marketing area is not a “pool” in the strict sense of the word. However, for purposes of 

simplicity and to differentiate it from the Unregulated West supply region, here it is called a pool.
6

In certain supply regions, where milk is assumed to only go to two processing regions, the use of 

compositional regressions is unnecessary. In these milk supply regions, a logistic regression is 

used, in which the ratio of the percentages of raw milk allocated to each of the two pools is 

estimated. Given that the two percentages must sum to one, the estimated ratio can be solved

easily for each percentage. The percentages are multiplied by the milk supply region total to 

determine the pool allocations. The milk movement estimates from the supply regions to the 

pools are in Tables 15 – 28 (located at the end of this document, beginning on page 20).

Milk Classification and Consumer Products

After milk is produced in the supply regions, it is allocated to the various pools for bottling or 

processing into manufactured dairy products. Under the FMMO system, milk is classified based 

on how it is utilized:

Class I—fluid use

Class II—soft manufactured products (frozen products and other Class II)

Class III—cheese and dry whey

Class IV—butter, non-fat dry milk, whole dry milk, and canned milk.11

Because milk for fluid use is highly regional and commands the highest price, fluid use per 

capita is estimated first and separately from the other classes, driven by the Class I price within 

each pool. Some fluid demand equations may also include personal disposable income, the 

population of the U.S. under five years old, and/or other explanatory variables. Income data are 

available from BLS. Population data are available from the U.S. Census Bureau. Fluid use is 

estimated at the pool level based on MA data from 2000 – 2015. Fluid use is estimated for each 

of the ten Federal orders, California, and the unregulated pool. The USDA Economic Research 

Service provides National estimates for fluid milk use. The Class I fluid use in the unregulated 

pool is derived by subtracting the fluid use in the ten Federal orders and California (converted to 

FMMO Class I standards) from the National fluid use provided by Economic Research Service 

to create the historic unregulated fluid data. The unregulated fluid data are used to estimate the 

coefficients used to forecast unregulated fluid use in the model. The unregulated fluid use 

estimation is driven by income changes. The fluid use estimates are presented in Table 29. 

Butterfat and non-fat solids pounds required to produce the quantity of fluid milk demanded are 

calculated using conversion factors found in Table 30. 

The remaining milk is allocated to Class II, III, or IV using compositional regressions, as 

explained earlier. For the FMMOs, the fill-up value is Class II milk. Class III allocations are 

driven by national average cheddar cheese prices, national average dry whey prices, Class III 

prices at test for a given pool, and/or a weighted average of the prices of frozen dairy products 

and other Class II products, as reported by BLS. Class IV allocations are driven by national 

average butter prices, national average non-fat dry milk prices, and/or Class IV prices at test for a 

 

11 The term “canned milk” in this documentation refers to evaporated or sweetened condensed milk in consumertype packages.
7

given pool. All classified prices and class allocation variables are based on MA data, estimated 

from 2000 – 2015.

The structural form of the equations warrants the use of the input costs and own prices. However, 

in FMMOs, input costs are based on product prices. This may cause an issue of multicollinearity 

if the product prices are used as separate variables with the class prices. In order to avoid this 

issue, the product and class prices are included in the equations as ratios. These ratios capture the 

economic relationship between the input costs and own price that influences the class allocations. 

The Class III and IV allocations may also be driven by their respective lagged product-to-class 

price ratio. The FMMOs with equations that include these lags in their specifications are less 

flexible in switching their class utilization compared to FMMOs that have the current year 

product-to-class price ratio. Milk moves between classes in response to the product-to-class price 

ratios. This drives the milk to its highest value use. 

The equations for class III and IV allocation are estimated together using Seemingly Unrelated 

Regression (SUR). Even though the equations for class III and IV allocations are distinct in 

terms of factors affecting them, there may exist an underlying relationship across the two 

equations as they operate in the same economic environment. This underlying relationship is 

captured through cross correlation among the error terms from both equations. The Hausman test 

confirms the existence of cross equation error correlation. Therefore, SUR is an efficient 

estimator compared to OLS in this context and consequently the coefficients efficiently capture 

the historic relationship between Class II, III and IV for the forecast years.

Data for classification in the unregulated pool are unavailable. Fluid use in the unregulated pool 

estimation is driven by income and is classified as Class I. For the milk other than Class I in the 

unregulated pool, a weighted average breakdown of manufacturing classes are used. The 

manufacturing class breakdown in the Upper Midwest and in the remaining Federal Ordersother-than-the-Upper Midwest are weighted by the quantity of milk other than for fluid use from 

the Unregulated West and Former West production regions, respectively. A proportional 

breakdown of unregulated manufacturing milk based on the Federal orders and the UMW is 

chosen to allow changes in class utilizations based on prices changes over time.12 The FMMO 

non-fluid classification equation estimates are found in Tables 31 – 40. Classified butterfat, nonfat solids, and protein (where appropriate) are calculated by applying pool test values to 

classified milk estimates. Forecast test values are assumed to be an average of the pool test 

values from 2011 – 2015. 

The California pool has a different structure than the FMMO system. Total solids by 

classification, defined as the sums of butterfat and non-fat solids within each class, are estimated 

(rather than the total amount of milk allocated to each class), because milk pounds by 

classification are not reported. Class 2 remains the fill-up value. Class 3 solids are a function of 

the CPIs of frozen dairy products and other Class 2 dairy products, deflated by the CPI for all 

products. Class 4a nonfat solids are driven by the national average price of non-fat dry milk. 

 

12 A fixed percentage breakdown would not have allowed class utilization to change as the market conditions 

changed. Furthermore, California is not included in computing proportional breakdown of the manufacturing milk 

because the manufacturing milk breakdown in the unregulated pool would have been altered significantly by 

processors choosing not to pool under a California FMMO.
8

Class 4b nonfat solids are driven by the national average price of cheddar cheese and the CPI of 

other dairy products. The estimates for non-fluid classified milk allocation in the California 

marketing area can be found in Table 41. In the absence of a California Federal order, California 

classified solids are converted to their FMMO equivalents to account for classification 

differences. 

National Level Aggregations and Estimations

Manufacturing Allocation

Supply and demand for manufactured dairy products is handled at the national level. The 

manufactured milk in each class and their corresponding components are aggregated from the 

pools to create a national supply of milk, butterfat, and non-fat solids for each class. The 

aggregated class supplies are used to estimate the national manufactured product supplies. 

The aggregated Class II total milk solids are divided using a logistic regression to estimate the 

production of frozen products and other Class II products. The other Class II solids requirements 

were established in the historical data by the residual butterfat and non-fat solids left when 

accounting for all solids in Class I, III, IV, and total frozen products. Frozen products and other 

Class II products are treated as aggregations of their respective products. The proportions of the 

solids in frozen products for the forecast period are held at recent year averages. The percentage 

of Class II total milk solids used to manufacture frozen products relative to the percentage of 

Class II milk used to manufacture all other Class II products is estimated as a function of the 

price of frozen goods relative to the price of other dairy products and other variables. 

Class III milk is primarily used to produce cheese with dry whey being produced as a result of 

the cheese manufacturing process. Total cheese production is calculated by applying conversion 

factors based on the most recent three years’ average of the fat available for total cheese to the 

amount of total cheese production.13 American and other cheese production percentages are 

estimated with a logistical function which responds to the price of cheddar and the price of 

mozzarella cheese. The estimated production percentages are applied to the amount of total 

cheese produced to obtain pounds of American and other cheese production. Cheese production 

is assumed to use all necessary non-fat solids, with conversion factors determined in a similar 

manner to those used for cheese butterfat. Dry whey production is driven by its own price, the 

amount of cheese produced, and other variables. Dry whey has a separate production equation 

because more than sufficient whey is produced as a result of cheese manufacture to meet dry 

whey demand. The CPI for food is used in the production of whey to account for inflation. Food 

CPI data are obtained from BLS and are estimated using the CPI for all products in projection 

years. Butterfat and non-fat solids per product pound of dry whey are calculated using 

conversion factors. All the conversion factors can be found in Table 30. The conversion factors 

represent the pounds of solids required to create one pound of product. 

Class IV milk is allocated to the production of butter, non-fat dry milk, dry whole milk, and 

canned milk. Because dry whole milk and canned milk are relatively minor products, dry whole 

 

13 Non-fat dry milk and condensed skim milk used in cheese production are accounted for in this calculation.
9

milk’s production is assumed to be constant, and the production of canned milk is a function of 

that constant. For this reason, the production of dry whole milk and canned milk converted to fat 

and non-fat solids is taken first from the Class IV milk fat and non-fat solids supply. The 

remaining quantities of fat and non-fat solids that remain available are used for butter and nonfat dry milk. The bulk of remaining Class IV fat goes to the production of butter. Therefore, 

butter production is not explicitly estimated; rather a small portion of Class IV fat is allocated to 

the production of non-fat dry milk, and the rest is assumed to be used for butter. Butter 

production is assumed to take what is needed from non-fat solids, and all remaining non-fat 

solids are allocated for the production of non-fat dry milk. The production of butter is calculated 

by using the residual Class IV fat divided by a fat conversion factor for butter. The remaining 

non-fat solids needed are used to calculate the non-fat dry milk production using non-fat dry 

milk non-fat solids conversion factor. The fat-test for non-fat dry milk is indirectly calculated as 

a result in the model. The manufacturing allocation equation estimates can be found in Table 42.

To accurately account for butterfat and non-fat solids production, it is necessary to make some 

adjustments to avoid double counting the non-fat dry milk and condensed skim milk used in 

cheese production. Historical data used to account for duplication are taken for the most part 

from the American Dairy Products Institute (ADPI).14 For the forecast period, the proportion of 

non-fat dry milk used in cheese to total cheese production is estimated as a function of butter and 

cheese prices. Condensed skim milk used in cheese is estimated as an inverse function of non-fat 

dry milk used in cheese. Other types of duplication such as non-fat solids used for fluid milk 

fortification are accounted for as constant percentages of the applicable dairy product quantities 

produced.

Demand, Stocks, and Trade for Non-Fluid Dairy Products

Per capita demand functions for manufactured dairy products are estimated using product prices, 

per capita income, and other factors. Dairy product prices are deflated by the CPI for all products 

or the CPI for food. Per capita disposable income is deflated by the CPI for all products. Total 

consumption for each specific product or product aggregate is specified as per capita demand 

times the projected population for each year. National average wholesale prices for cheese, 

butter, non-fat dry milk, and dry whey are taken from Dairy Product Mandatory Reporting 

Program data. Equations in this section are based on the model used to estimate the national 

baseline.15 Adjustments for leap year are included in the forecast period. The estimates for per 

capita non-fluid product demand can be found in Table 43.

 

14 American Dairy Products Institute (2016) Dairy Products, Utilization and Production Trends

https://www.adpi.org/tabid/128/newsid545/49/Default.aspx

15 U.S. Department of Agriculture, Office of the Chief Economist, World Agricultural Outlook Board, OCE-2016-1

(2017 February) USDA Agricultural Projections to 2026

www.ers.usda.gov/webdocs/publications/82539/oce-2017-1.pdf?v=42788

USDA Agricultural Marketing Service Dairy Programs National Econometric Model Documentation (Model 

Calibrated to USDA Agricultural Baseline Projections to 2016 )

https://www.ams.usda.gov/sites/default/files/media/National%20Econometric%20Model%20Documentation%20apr

il%202007.pdf
10

Year-end stocks are estimated for American cheese, other cheese, butter, and non-fat dry milk.

Estimating ending stock values is complicated because of their volatility. For this reason a twostep process is used. First, average stock values are estimated, as seen in Table 44. For each year, 

this value is the simple average of the monthly ending stocks from the last half or last quarter of 

each year. For each equation, the average stock value has a negative relationship with product

demand. Second, year-end stocks are estimated from average stocks, reflecting the typical 

seasonal relationship that exists between average stocks and year-end stocks. Year-end stocks 

estimates are found in Table 45.

Imports and commercial exports for American cheese, other cheese, and butter are projected by 

the model, along with commercial exports of non-fat dry milk and dry whey. In observing the 

history of imports and exports of the various products included in the model, they appear to be 

the most price responsive. Imports and exports for all other dairy products are exogenous in the 

model. Cheese and butter imports are controlled to some extent by a tariff rate quota (TRQ) that 

allows limited imports at lower in-quota tariff rates and unlimited imports at higher over-quota 

tariff rates. Cheese and butter imports have usually exceeded the TRQ since it has been in place. 

The model assumes that the quota is filled each year, and thus only over-quota imports are 

estimated. Import quantity data are available from the Foreign Agriculture Service, and the 

equation is estimated using 1995 – 2015 data.16 Exports and over-quota imports are estimated as 

a function of the difference between the domestic product price and the free-on-board 

international price, represented by the Oceania price with regards to butter, cheese, and non-fat 

dry milk and the European Union price for dry whey. Trade equation estimates can be found in 

Table 46. 

Aggregated product supply is balanced against national consumer product demands, with price 

changing until a supply/demand balance is reached. In this manner, the prices estimated at the 

national level affect each pool’s effective blend price, which drive the all-milk prices that 

influence milk production, connecting the system. 

Price Relationships, Elasticities, and Statistics

Milk and dairy products, in aggregate, are expected to respond to changes in prices in a certain 

manner. Milk production variables (number of cows and yield-per-cow) and imports are 

expected to move in the same direction as domestic own prices, like the all-milk price: higher 

domestic prices will encourage farmers to produce more, while making foreign products more 

appealing to the consumer. Conversely, demand variables (e.g. per capita fluid use) and exports 

are expected to move in the opposite direction from domestic own prices: higher prices will 

decrease domestic consumption, while making domestic sales more appealing to producers. 

Competing prices, or those representing costs of production, such as the price of feed, are 

expected to have the opposite relationships. Income is expected to move in the same direction

with both supply and demand variables, with higher income meaning greater capacity for farm 

investment, as well as greater capacity to purchase dairy products. 

 

16 U.S. Department of Agriculture, Foreign Agricultural Service, Dairy Monthly Imports. Current report is available 

at: http://www.fas.usda.gov/data/dairy-monthly-imports
11

Parameter magnitudes vary based on specification, and they do not necessarily provide a clear 

picture of the impact of variable-in-question. To provide a clearer picture of the actual impact, 

each price and income variable have an additional statistic reported called the “elasticity”: It is 

the percent change in the left-hand side variable in response to a percent change in the right-hand 

side variable. For example, the Northeast supply region’s all-milk price is driven by the Order 1 

effective blend price (see Table 1). This price-price elasticity is 0.9124. This means that, for 

every 1 percent increase in the Order 1 effective blend price, the Northeast supply region’s allmilk price will increase by about 0.91 percent. The positive sign in the elasticity means that the 

all-milk price and the effective blend price move together, which follows expectations. The 

elasticities presented are averaged over the relevant data period for each equation. 

Statistical fit is represented by the R-Square for each equation. R-Square is the percent of 

variation in the data explained by the given equation, and therefore falls between 0 – 1. A higher 

R-Square is better, and represents how closely the model estimates historical data. Statistical 

significance is best represented by the p-value for each variable. The p-value is defined as the 

level of significance at which one can reject the null hypothesis that the variable is not 

significantly different from zero. In other words, it is a measure of confidence in the estimates 

the model produces: a smaller p-value indicates a higher level of statistical significance, and 

therefore greater confidence that the model produces reliable estimates. 

Only the equations that have estimated parameter coefficients are presented in this 

documentation. Equations that reflect pricing formulas and static conversion relationships are not 

presented here. They remain unchanged as long as the underlying policy is in effect. Any 

formula updates required due to a policy change(s) are incorporated into the model at the time of 

an impact analysis.

Summary

The Dairy Program’s Economic Analysis Branch maintains a regional econometric model of the 

U.S. dairy industry to support its economic analysis and forecasting responsibilities. The model’s

construction is regional and covers milk produced in all fifty States. It includes a framework to 

estimate the allocation and classification of milk under the FMMO system. It estimates the 

supply of classified milk solids, which are used to estimate product supplies through the use of 

logistic functions and conversion factors. The product supplies are balanced against demand for 

dairy products by varying prices until a balance is reached. The model’s responses to price and 

policy changes follow economic theory and are statistically validated. This documentation serves 

to outline the model’s sources, capabilities, and methods. The model is used for impact analyses, 

discussions of specific impacts are reserved for other publications.
12

Table 1: Northeast Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Northeast All Milk Price / CPI all) Intercept 0.2200 0.1073 2.05 0.0596 0.9744

log (Order 1 Blend Price at Test/ CPI all) 0.9124 0.0512 17.82 <.0001 0.9124

log (Northeast Number of Cows) Intercept 2.4986 0.5084 4.91 <.0001 0.9936

lag (log ((Northeast All Milk Price 0.0387 0.0144 2.69 0.0114 0.0387

+ Northeast Average Dairy Market Loss Payments

+ Average Dairy Economic Loss Assistance

Payments) / 16% Protein Feed Value))

Trend from 1980 -0.0045 0.0010 -4.57 <.0001

Dummy from 1980 to 1986 0.0345 0.0060 5.72 <.0001

lag (log (Northeast Number of Cows)) 0.6598 0.0663 9.95 <.0001

log (Northeast Milk Per Cow) Intercept 4.2184 1.0694 3.94 0.0005 0.9963

lag (log ((Northeast All Milk Price 0.0334 0.0143 2.34 0.0263 0.0334

+ Northeast Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value))

lag (log (Northeast Milk Per Cow)) 0.5480 0.1151 4.76 <.0001

Trend from 1980 0.0084 0.0022 3.83 0.0006

Dummy: Dairy Diversion Program -0.0255 0.0119 -2.14 0.0406

Dummy for years after 1999 -0.0254 0.0094 -2.70 0.0115

Table 2: Appalachian Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Appalachian All Milk Price / CPI all) Intercept 0.0933 0.0985 0.9500 0.3595 0.9857

log (Order 5 Blend Price at Test / CPI all) 0.9667 0.0459 21.0500 <.0001 0.9667

log (Appalachian Number of Cows) Intercept 24.0751 1.1941 20.16 <.0001 0.9833

lag (log (Appalachian Milk Per Cow)) -1.8866 0.1232 -15.31 <.0001

log ((Appalachian All Milk Price 0.1616 0.0541 2.99 0.0055 0.1616

+ Appalachian Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value)

Dummy for years after 1997 -0.1651 0.0347 -4.76 <.0001

log (Appalachian Milk Per Cow) Intercept 9.2114 0.0199 462.4100 <.0001 0.9927

lag (log(Appalachian All Milk Price 0.0502 0.0148 3.3800 0.0020 0.050225

+ Appalachian Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value)

Trend from 1980 0.0167 0.0005 30.6600 <.0001

Dummy for years after 1997 -0.0329 0.0100 -3.2900 0.0026

Dummy: Dairy Diversion Program -0.0625 0.0145 -4.3100 0.0002
13

Table 3: Florida Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Florida All Milk Price / CPI all) Intercept 0.1039 0.1117 0.93 0.3696 0.9878

log (Order 6 Blend Price at Test / CPI all) 0.9252 0.0495 18.69 <.0001 0.9252

Trend from 2000 0.0066 0.0012 5.40 0.0001

log (Florida Non-Farm Earnings Per Capita Intercept 20.2854 0.0842 240.94 <.0001 0.9944

/CPI all) log (Personal Disposable Income Per Capita / CPI all) 1.0430 0.0321 32.47 <.0001

Dummy for years after 2008 -0.1556 0.0120 -12.95 <.0001

log (Florida Number of Cows) Intercept 8.5314 1.9837 4.30 0.0002 0.9641

lag (log ((Florida All Milk Price 0.0742 0.0347 2.14 0.0409 0.0742

+ Florida Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value))

lag (log (Florida Number of Cows)) 0.7928 0.0601 13.20 <.0001

Dummy for years after 1985 0.0519 0.0187 2.78 0.0094

lag (log (Florida Non-Farm Earnings Per Capita / CPI all)) -0.3313 0.0770 -4.30 0.0002

log (Florida Milk Per Cow) Intercept 0.1913 0.2971 0.64 0.5246 0.9810

log ((Florida All Milk Price 0.0633 0.0306 2.06 0.0477 0.0633

+ Florida Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value)

lag (log (Florida Milk Per Cow)) 0.9738 0.0306 31.87 <.0001

Dummy for 1998 -0.0823 0.0232 -3.54 0.0013

Dummy for years after 2007 0.0374 0.0164 2.28 0.0299

Table 4: Southeast Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Southeast All Milk Price / CPI all) Intercept -0.0545 0.1234 -0.44 0.6657 0.9815

log (Order 7 Blend Price at Test / CPI all) 1.0107 0.0573 17.63 <.0001 1.0107

(Southeast Non-Farm Earnings Per Capita Intercept 56.2218 253.8000 0.22 0.8261 0.9936

/ CPI All) Personal Disposable Income Per Capita 273.1474 87.5364 3.12 0.0039

Dummy for years after 2008 -422.6370 139.9000 -3.02 0.0050

lag (Southeast Non-Farm Earnings Per Capita / CPI All) 0.6207 0.1163 5.34 <.0001

log (Southeast Number of Cows) Intercept 35.0848 1.8470 19.00 <.0001 0.9586

log ((Southeast All Milk Price 0.6353 0.0933 6.81 <.0001 0.6353

+ Southeast Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value)

Dummy for years 1980 to 1987 -0.3266 0.0667 -4.89 <.0001
14

lag(log (Southeast Non-Farm Earnings Per Capita / CPI all)) -3.1778 0.1974 -16.10 <.0001

log ((Southeast All Milk Price 0.3321 0.0965 3.44 0.0017 0.3321

+ Southeast Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) /Boning Cow Slaughter Price)

log (Southeast Milk Per Cow) Intercept 9.1370 0.0288 317.77 <.0001 0.9768

log ((Southeast All Milk Price 0.0936 0.0234 4.00 0.0003 0.0936

+ Southeast Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value)

Dummy from 1991 to 1995 0.0568 0.0113 5.04 <.0001

Trend from 1980 0.0149 0.0004 34.71 <.0001

Table 5: Upper Midwest Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Upper Midwest All Milk Price Intercept 0.2284 0.0540 4.23 0.0008 0.9944

/ CPI all) log (Order 30 Blend Price at Test / CPI all) 0.9188 0.0269 34.21 <.0001 0.9188

log (Upper Midwest Number of Cows) Intercept 0.2421 0.1447 1.67 0.1044 0.9596

lag (log (Upper Midwest Number of Cows)) 0.9540 0.0210 45.34 <.0001

lag (log ((Upper Midwest All Milk Price 0.0543 0.0168 3.23 0.0029 0.0543

+ Upper Midwest Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance Payments 

- 16% Protein Feed Value)/ CPI all))

Dummy for years after 2008 0.0297 0.0083 3.58 0.0012

log (Upper Midwest Milk Per Cow) Intercept 9.3245 0.0128 729.82 <.0001 0.9972

lag (log ((Upper Midwest All Milk Price 0.0277 0.0109 2.55 0.0160 0.0277

+ Upper Midwest Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance Payments)

/ 16% Protein Feed Value))

Trend from 1980 0.0200 0.0004 47.10 <.0001

Dummy for years after 1983 -0.0290 0.0078 -3.71 0.0008

Dummy for years after 2000 -0.0307 0.0074 -4.14 0.0003

Table 6: Central Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Central All Milk Price Intercept -4.3639 0.0638 -68.45 <.0001 0.9925

/ CPI all) log (Order 32 Blend Price at Test / CPI all) 0.9026 0.0314 28.76 <.0001 0.9026

log (Central Number of Cows) Intercept 0.5887 0.1958 3.01 0.0053 0.9895
15

lag (log ((Central All Milk Price 0.0385 0.0180 2.14 0.0405 0.0385

+ Central Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance Payments) 

/ 16% Protein Feed Value))

lag (log (Central Number of Cows)) 0.9079 0.0282 32.21 <.0001

Dummy for years after 1985 -0.0386 0.0113 -3.42 0.0018

Dummy for years after 2005 0.0243 0.0084 2.91 0.0067

log (Central Milk Per Cow) Intercept 0.2084 0.1508 1.38 0.1767 0.9920

lag (log ((Central All Milk Price 0.0448 0.0228 1.97 0.0581 0.0448

+ Central Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value))

lag (log (Central Milk Per Cow)) 0.9758 0.0157 62.13 <.0001

Dummy for years after 2008 * Trend from 2000 0.0016 0.0009 1.72 0.0963

Table 7: Mideast Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Mideast All Milk Price / CPI All) Intercept -4.4309 0.0645 -68.71 <.0001 0.9930

log (Order 33 Blend Price at Test / CPI All) 0.9367 0.0315 29.75 <.0001 0.9367

log (Mideast Number of Cows) Intercept 6.4857 0.1137 57.04 <.0001 0.9229

Dummy for years after 1988 -0.1220 0.0221 -5.53 <.0001

log ((Mideast All Milk Price 0.1576 0.0443 3.56 0.0012 0.1576

+ Mideast Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / CPI All)

Dummy from 1995 to 2004 -0.0997 0.0138 -7.20 <.0001

log (Mideast Milk Per Cow) Intercept 9.3281 0.0198 471.66 <.0001 0.9933

lag (log ((Mideast All Milk Price 0.0420 0.0166 2.54 0.0162 0.0420

+ Mideast Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value))

Trend from 1980 0.0194 0.0003 63.43 <.0001

Table 8: Pacific Northwest Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Pacific Northwest All Milk Price Intercept -4.4505 0.0688 -64.68 <.0001 0.9899

/ CPI All) log (Order 124 Blend Price at Test/ CPI All) 0.9448 0.0340 27.82 <.0001 0.9448

log (Pacific Northwest Number of Cows) Intercept 1.9192 0.2674 7.18 <.0001 0.9709
16

lag (log (Pacific Northwest Number of Cows)) 0.2893 0.0963 3.01 0.0055

lag (log ((Pacific Northwest All Milk Price 0.0245 0.0038 6.41 <.0001 0.0245

+ Pacific Northwest Average Dairy 

Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / CPI All))*Dummy for years after 2009

lag (log (Pacific Northwest Milk Per Cow)) 0.2264 0.0405 5.58 <.0001

Dummy from 1998 to 2001 -0.0333 0.0084 -3.96 0.0005

Dummy from 1992 to 1995 0.0372 0.0080 4.67 <.0001

Dummy from 1986 to 1989 -0.0350 0.0078 -4.48 0.0001

log (Pacific Northwest Milk Per Cow) Intercept 10.0508 0.0848 118.59 <.0001 0.9813

 log ((Pacific Northwest All Milk Price 0.0600 0.0237 2.53 0.0165 0.0600

+ Pacific Northwest Average Dairy 

Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value)

log (Producer Price Index for Fuel/GDP Deflator) -0.1075 0.0141 -7.64 <.0001

Trend from 1980 0.0175 0.0005 38.85 <.0001

Dummy for years after 2013 -0.0847 0.0164 -5.17 <.0001

Table 9: Southwest Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Southwest All Milk Price Intercept -4.5213 0.0689 -65.61 <.0001 0.9931

/ CPI All) log (Order 126 Blend Price at Test / CPI All) 0.9542 0.0334 28.61 <.0001 0.9542

log (Southwest Land Value / CPI All) Intercept -0.7335 0.3636 -2.02 0.0530 0.9845

lag (log (Southwest Land Value / CPI All)) 0.8784 0.0645 13.63 <.0001 0.8784

log (Personal Disposable Income Per Capita / CPI All) 0.6047 0.1334 4.53 <.0001 0.6047

lag (log (Southwest Number of Cows))*Dummy for years after 2010 -0.0023 0.0092 -0.25 0.8046

Dummy for years after 1986 -0.1598 0.0501 -3.19 0.0034

Dummy for years after 2009 -0.0091 0.0632 -0.14 0.8871

log (Southwest Number of Cows) Intercept -0.1540 0.1556 -0.99 0.3299 0.9601

lag (log ((Southwest All Milk Price 0.0730 0.0254 2.87 0.0072 0.0730

+ Southwest Average Dairy 

Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value))

lag (log (Southwest Number of Cows)) 1.0166 0.0220 46.26 <.0001

log (Southwest Milk Per Cow) Intercept 0.0182 0.1669 0.11 0.9140 0.9786

lag (log (Southwest Milk Per Cow)) 0.9964 0.0160 62.09 <.0001
17

log ((Southwest All Milk Price 0.0241 0.0136 1.77 0.0876 0.0241

+ Southwest Average Dairy 

Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) /U.S. Corn Price) 

Dummy for 1995 -0.0471 0.0191 -2.46 0.0202

Dummy for 2001 -0.0512 0.0196 -2.61 0.0142

Dummy for 2007 -0.0379 0.0195 -1.95 0.0615

Table 10: Arizona Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Arizona All Milk Price Intercept -4.5891 0.0387 -118.69 <.0001 0.9972

/ CPI all) log (Order 131 Blend Price at Test/ CPI All) 0.9933 0.0191 52.04 <.0001 0.9933

log (Arizona Number of Cows Intercept -30.9366 11.8651 -2.61 0.0139 0.9922

 - lag (Arizona Number of Cows)) log ((Arizona All Milk Price 6.8950 3.1209 2.21 0.0347 6.8950

+ Arizona Average Dairy Market Loss Payments

+ Average Dairy Economic Loss Assistance

Payments) / Boning Cow Slaughter Price)

Trend from 1980 0.1938 0.0719 2.69 0.0113

lag (log ((Arizona All Milk Price 7.6313 3.2040 2.38 0.0235 7.6313

+ Arizona Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value))

log (Arizona Milk Per Cow) Intercept 9.4414 0.0368 256.35 <.0001 0.9703

log ((Arizona All Milk Price 0.0875 0.0318 2.75 0.0098 0.0875

+ Arizona Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value)

Dummy for 1994 to 1997 0.0463 0.0166 2.79 0.0089

Trend from 1980 0.0210 0.0008 26.72 <.0001

Dummy for years after 2004 -0.0912 0.0198 -4.61 <.0001

Table 11: Former Western Order Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Former Western Order All Milk Intercept 0.1824 0.1091 1.67 0.1183 0.9870

Price / CPI All) log (California All Milk Price / CPI All) 0.9109 0.0566 16.09 <.0001 0.9109

log (Post-Order Reform Class II Price / CPI All) 0.0251 0.0088 2.86 0.0134 0.0251

* Dummy After 2010
18

log (Former Western Order Intercept -0.1648 0.0965 -1.71 0.0975 0.9920

Number of Cows) lag (log (Former Western Number of Cows)) 1.0229 0.0136 75.25 <.0001

lag (log ((Former Western Order All Milk Price 0.0538 0.0268 2.01 0.0536 0.0538

+ Former Western Order Average Dairy 

Market Loss Payments

+ Average Dairy Economic Loss Assistance

Payments) / 16% Protein Feed Value))

Dummy from 1994 to 2000 0.0466 0.0111 4.21 0.0002

log (Former Western Order Intercept 6.4193 0.3150 20.38 <.0001 0.9952

Milk Per Cow) log ((Former Western Order All Milk Price 0.0388 0.0177 2.20 0.0356 0.0388

+ Former Western Order Average Dairy 

Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value)

Trend from 1980 0.1708 0.0162 10.55 <.0001

lag (log (Former Western Order Milk Per Cow)) -0.0155 0.0017 -9.32 <.0001

* Trend from 2000

Table 12: Unregulated West Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Unregulated West All Milk Price Intercept 0.3577 0.1267 2.82 0.0135 0.9707

/ CPI All) log (Central Region All Milk Price / CPI All) 0.8165 0.0611 13.37 <.0001 0.8165

log (Unregulated West Intercept 0.1028 0.0855 1.20 0.2382 0.9687

Number of Cows) lag (log (Unregulated West Number of Cows)) 0.8133 0.0610 13.34 <.0001

lag (log ((Unregulated West All Milk Price 0.1123 0.0423 2.65 0.0124 0.1123

+ Unregulated West Average Dairy 

Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value))

log ((Unregulated West All Milk Price 0.1760 0.0673 2.62 0.0136 0.1760

+ Unregulated West Average Dairy 

Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / CPI All)

log (Unregulated West Milk Per Cow) Intercept 9.2792 0.0222 417.48 <.0001 0.9953

lag (log ((Unregulated West All Milk Price 0.0361 0.0175 2.06 0.0482 0.0361

+ Unregulated West Average Dairy 

Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value))

Dummy from 2006 to 2008 -0.0447 0.0104 -4.28 0.0002

lag (log (Unregulated West Milk Per Cow)*Dummy for years after 1999 0.0076 0.0011 6.62 <.0001

Trend from 1980 0.0172 0.0006 28.06 <.0001
19

Table 13: California Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (California All Milk Price Intercept -0.0031 0.0054 -0.58 0.5686 1.0000

/ CPI All) log (California Blend Price at Test / CPI All) 1.0007 0.0027 368.32 <.0001 1.0007

log (California Number of Cows) Intercept 0.1108 0.0996 1.11 0.2743 0.9670

log ((California All Milk Price 0.0324 0.0145 2.24 0.0325 0.0324

+ California Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value)

lag (log (California Number of Cows)) 0.9834 0.0128 76.73 <.0001

log(California Milk Per Cow) Intercept 4.2160 1.2402 3.40 0.0019 0.9740

lag (log ((California All Milk Price 0.0962 0.0441 2.18 0.0371 0.0962

+ California Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value))

lag (log (California Milk Per Cow)) 0.5573 0.1300 4.29 0.0002

Trend from 1980 0.0063 0.0019 3.26 0.0027

Dummy for 1994 0.0667 0.0206 3.25 0.0029

Table 14: Hawaii and Alaska Regional Milk Supply Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Hawaii and Alaska All Milk Price Intercept -0.7001 0.8163 -0.86 0.4094 0.8830

/ CPI All) log (Wholesale Cheddar Cheese Price / CPI All) 0.3255 0.1173 2.78 0.0180 0.3255

lag (log (Hawaii and Alaska All Milk Price / CPI All)) 0.7202 0.2050 3.51 0.0049 0.7202

Dummy for 2009 0.2389 0.0577 4.14 0.0017

log (Hawaii and Alaska Cows) Intercept -12.7522 2.2357 -5.70 <.0001 0.9836

log ((Hawaii and Alaska All Milk Price 0.6353 0.1145 5.55 <.0001 0.6353

+ Hawaii and Alaska Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / CPI All)

log (Hawaii and Alaska Milk Per Cow) 1.1658 0.2185 5.33 <.0001

lag (log (Hawaii and Alaska Cows)) 0.9469 0.0234 40.39 <.0001

log (Hawaii and Alaska Milk Per Cow) Intercept 7.4432 1.2785 5.82 <.0001 0.7524

lag (log ((Hawaii and Alaska All Milk Price 0.2312 0.0637 3.63 0.0011 0.2312

+ Hawaii and Alaska Average Dairy Market Loss Payments

+ Average Dairy Economy Loss Assistance

Payments) / 16% Protein Feed Value)

Dummy for years after 1985 0.0818 0.0261 3.13 0.0039

Dummy for years after 2003 -0.0824 0.0240 -3.44 0.0018

Dummy for 2008 -0.1584 0.0373 -4.25 0.0002

lag (log (Hawaii and Alaska Milk Per Cow)) 0.1348 0.1412 0.96 0.3474
20

Table 15: Allocation of Northeast Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log (Northeast Milk to Order 5) Intercept -3.4205 0.1854 -18.45 <.0001 0.7731

/ Northeast Milk to Order 1) log (Trend from 2000) -0.2260 0.0683 -3.31 0.007

Dummy from 2006 to 2007 0.3803 0.1022 3.72 0.0034

lag (log (Order 5 Blend Price at Test/ Order 1 Blend Price at Test)) 2.7801 1.2721 2.19 0.0514

log (Northeast Milk to Order 33 Intercept -2.1885 0.0339 -64.64 <.0001 0.8334

/ Northeast Milk to Order 1) Dummy from 2005 to 2007 0.2579 0.0355 7.27 <.0001

log (Order 33 Blend Price at Test / Order 1 Blend Price at Test) 0.7682 0.5080 1.51 0.1587

Dummy for years after 2012 -0.1553 0.0385 -4.03 0.002

log (Unregulated Northeast Milk Intercept -2.8794 0.0170 -169.7 <.0001 0.9154

/ Northeast Milk to Order 1) Dummy for 2004 0.3861 0.0475 8.13 <.0001

Dummy from 2006 to 2008 0.2087 0.0317 6.58 <.0001

log (Order 1 Class I Price at Test / Order 1 Class III Price at Test) -0.3131 0.1689 -1.85 0.0935

Dummy for 2001 0.2894 0.0481 6.02 0.0001

Table 16: Allocation of Appalachian Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log (Appalachia Milk to Order 1 Intercept -1.5687 0.4732 -3.31 0.0069 0.9273

/ Appalachia Milk to Order 5) log (Order 5 Blend Price at Test / CPI all) -0.5699 0.2202 -2.59 0.0252

Dummy for years after 2005 -0.7950 0.0646 -12.30 <.0001

Dummy for years after 2014 -1.4604 0.1819 -8.03 <.0001

log (Appalachia Milk to Order 7 Intercept 0.1381 0.3207 0.43 0.6769 0.9119

/ Appalachia Milk to Order 5) log (Order 5 Blend Price at Test / CPI all) -0.3674 0.0903 -4.07 0.0028

Dummy for years after 2006 0.2054 0.0259 7.92 <.0001

Dummy for 2012 0.1809 0.0446 4.06 0.0029

lag (log (Order 5 Blend Price at Test / CPI all)) -0.3266 0.1132 -2.88 0.0181

Dummy for years after 2014 -0.2429 0.0618 -3.93 0.0035

log (Unregulated Appalachia Milk Intercept -1.1975 0.2985 -4.01 0.0025 0.7558

/ Appalachia Milk to Order 5) log (Order 5 Class III Price at Test / Order 5 Class I Price at Test) 0.9453 0.1600 5.91 0.0001

Dummy for 2011 -0.4666 0.0534 -8.74 <.0001

lag(log(Order 5 Class I Price at Test / CPI all)) -0.4291 0.1440 -2.9800 0.0138

Dummy for 2014 -0.4601 0.1081 -4.26 0.0017

Table 17: Allocation of Florida Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

All Florida Milk is assumed to be used within either Order 6 or Order 7.

log (Percentage of Florida Milk to Order 7 Intercept -10.1194 2.2843 -4.43 0.0007 0.7427

/ 1 - Percentage of Florida Milk to log (Order 7 Blend Price at Test / CPI All) 2.6955 1.0662 2.53 0.0252

Order 7) Dummy for years after 2008 1.6997 0.2837 5.99 <.0001
21

Table 18: Allocation of Southeast Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log (Southeast Milk to Order 5 Intercept -2.9684 0.1343 -22.10 <.0001 0.8488

/ Southeast Milk to Order 7) log (Order 5 Blend Price at Test / Order 7 Blend Price at Test) 22.3350 9.1476 2.44 0.0327

Trend from 2000* Dummy for years after 2012 0.1253 0.0210 5.96 <.0001

lag (log (Order 6 Blend Price at Test / Order 7 Blend Price at Test) -8.3507 2.3306 -3.58 0.0043

 * Dummy for years after 2003)

log (Southeast Milk to Order 6 Intercept -2.5551 0.2346 -10.89 <.0001 0.8587

/ Southeast Milk to Order 7) lag (log (Order 6 Blend Price at Test / Order 7 Blend Price at Test)) 2.4650 1.0382 2.37 0.0369

* Dummy for years after 2002

Dummy for years after 2013 -0.5918 0.1366 -4.33 0.0012

log(Trend from 2000) 0.4725 0.0827 5.72 0.0001

log (Southeast Milk to Order 32 Intercept -1.5055 0.2137 -7.04 <.0001 0.7771

/ Southeast Milk to Order 7) log (Order 32 Blend Price at Test / Order 7 Blend Price at Test) 4.8555 1.5522 3.13 0.0096

Dummy for years after 2004 0.3031 0.0794 3.82 0.0029

Dummy from 2005 to 2006 0.1814 0.0885 2.05 0.0649

log (Unregulated Southeast Milk Intercept -3.6011 0.5581 -6.45 <.0001 0.7096

/ Southeast Milk to Order 7) log (Order 7 Class III Milk at Test / Order 7 Class I Milk at Test) 0.5415 0.2733 1.98 0.0731

Dummy for 2007 to 2008 0.3079 0.1319 2.33 0.0396

Dummy for 2010 0.5477 0.1686 3.25 0.0077

Table 19: Allocation of Upper Midwest Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log (Upper Midwest Milk to Order 32 Intercept -1.4350 0.7283 -1.97 0.0745 0.9691

/ Upper Midwest Milk to Order 30) lag (log (Order 32 Blend Price at Test 0.8409 0.3840 2.19 0.051

/ CPI All))

Dummy for years after 2007 -0.6306 0.2620 -2.41 0.0348

log (Trend from 2000) -1.1294 0.0959 -11.78 <.0001

log (Upper Midwest Milk to Order 33 Intercept -1.7775 0.0795 -22.36 <.0001 0.9353

/ Upper Midwest Milk to Order 30) lag (log (Order 33 Blend Price at Test 2.8317 0.8786 3.22 0.0073

 / Order 30 Blend Price at Test))

Dummy for years after 2005 -1.9600 0.2806 -6.99 <.0001

log (Unregulated Upper Midwest Milk Intercept -2.0976 0.1965 -10.68 <.0001 0.8650

/ Upper Midwest Milk to Order 30) Dummy from 2003 to 2004 1.3620 0.1758 7.75 <.0001

log (Order 30 Class III Milk at Test / Order 30 Class I Milk at Test) 2.3768 1.1241 2.11 0.0581

Dummy from 2007 to 2008 0.5388 0.2601 2.07 0.0626
22

Table 20: Allocation of Central Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log (Central Milk to Order 5 Intercept -6.9214 0.7187 -9.63 <.0001 0.8020

/ Central Milk to Order 32) Trend from 2000 0.1408 0.0344 4.09 0.0018

Dummy from 2004 to 2005 1.7385 0.3113 5.58 0.0002

lag (log (Order 5 Blend Price at Test / Order 32 Blend Price at Test)) 8.1443 3.7164 2.19 0.0508

log (Central Milk to Order 7 Intercept -3.0712 0.1334 -23.02 <.0001 0.8267

/ Central Milk to Order 32) log(Order 7 Blend Price at Test / Order 32 Blend Price at Test) 3.6907 1.0828 3.41 0.0067

Dummy from 2003 to 2004 0.3068 0.0667 4.60 0.001

Dummy for years after 2007 0.2337 0.0494 4.73 0.0008

Dummy for years after 2013 -0.2994 0.0659 -4.54 0.0011

log (Central Milk to Order 30 Intercept -2.5965 0.0798 -32.53 <.0001 0.8521

/ Central Milk to Order 32) lag (log (Order 30 Blend Price at Test 4.3265 1.4018 3.09 0.0094

/ Order 32 Blend Price at Test))

Dummy for years after 2003 0.8301 0.0702 11.82 <.0001

log (Central Milk to Order 126 Intercept -4.9776 0.3942 -12.63 <.0001 0.8076

/ Central Milk to Order 32) Dummy from 2006 to 2007 0.9649 0.1577 6.12 <.0001

Dummy for years after 2001 1.0584 0.3041 3.48 0.0051

lag (log (Order 126 Blend Price at Test 13.6844 3.7266 3.67 0.0037

/ Order 32 Blend Price at Test))

log (Unregulated Central Milk Intercept -1.9380 0.0534 -36.27 <.0001 0.8072

/ Central Milk to Order 32) log (Order 32 Class III Price at Test / Order 32 Class I Price at Test) 2.9097 0.6178 4.71 0.0008

Dummy for 2003 0.5163 0.1194 4.32 0.0015

Dummy from 2007 to 2008 0.5580 0.0822 6.79 <.0001

Dummy for 2009 0.3467 0.1159 2.99 0.0135

Table 21: Allocation of Mideast Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log (Mideast Milk to Order 5 Intercept -2.3383 0.2592 -9.02 <.0001 0.7117

/ Mideast Milk to Order 33) log (Order 5 Blend Price at Test / Order 33 Blend Price at Test) 2.4662 2.5458 0.97 0.3555

Dummy for years after 2012 -0.4014 0.1018 -3.94 0.0028

Dummy for years before 2003 -0.2921 0.1262 -2.31 0.0432

Dummy for years after 2006 0.1549 0.0872 1.78 0.1061

log (Mideast Milk to Order 7 Intercept -1.7331 0.4338 -4.00 0.0021 0.8643

/ Mideast Milk to Order 33) log(Order 7 Blend Price at Test / Order 33 Blend Price at Test) 4.0704 1.3517 3.01 0.0118

* Dummy After 2004

lag (log (Mideast Milk to Order 7 0.4628 0.1217 3.8 0.0029

/ Mideast Milk to Order 33))

Dummy for years after 2011 -0.3573 0.1073 -3.33 0.0067

log (Mideast Milk to Order 30 Intercept -5.7430 0.1669 -34.41 <.0001 0.8176
23

/ Mideast Milk to Order 33) lag (log (Order 30 Blend Price at Test 6.6855 2.5615 2.61 0.0243

/ Order 33 Blend Price at Test))

Dummy for years after 2007 * (Trend from 2000) 0.1329 0.0131 10.14 <.0001

Dummy for 2011 0.7086 0.3281 2.16 0.0537

log (Unregulated Mideast Milk Intercept -7.4595 0.6703 -11.13 <.0001 0.7634

/ Mideast Milk to Order 33) log (Former Western Order All Milk Price / CPI All) 2.4874 0.3323 7.49 <.0001

Dummy for 2005 -0.9007 0.1922 -4.69 0.0007

Dummy for 2003 0.3677 0.1986 1.85 0.0912

Table 22: Allocation of Pacific Northwest Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

Pacific Northwest Milk is assumed to be used within either an Unregulated Region or Order 124.

log (Percentage of Unregulated Pacific Intercept -1.8199 0.1269 -14.34 <.0001 0.8380

Northwest Milk log (Order 124 Class IV Price at Test -4.3897 1.0920 -4.02 0.0017

/ 1- Percentage of Unregulated Pacific / Order 124 Class I Price at Test)

Northwest Milk) Dummy for 2012 0.9354 0.3955 2.37 0.0357

Dummy for years after 2014 1.3969 0.3975 3.51 0.0043

Table 23: Allocation of Southwest Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log (Southwest Milk to Order 7 Intercept -2.5403 0.3285 -7.73 <.0001 0.9416

/ Southwest Milk to Order 126) lag (log (Order 7 Blend Price at Test 0.3384 0.1518 2.23 0.0476

/ CPI All))

Dummy for years after 2012 -1.0185 0.1562 -6.52 <.0001

Dummy from 2004 to 2007 0.4361 0.0449 9.70 <.0001

log (Southwest Milk to Order 32 Intercept -2.1627 0.1978 -10.93 <.0001 0.8274

/ Southwest Milk to Order 126) log (Order 32 Blend Price at Test / Order 126 Blend Price at Test) 11.1857 4.5635 2.45 0.0305

Dummy for years after 2007 0.6569 0.1387 4.74 0.0005

log (Southwest Milk to Order 131 Intercept -14.8376 2.9467 -5.04 0.0004 0.7972

/ Southwest Milk to Order 126) log (Order 131 Blend Price at Test / CPI All) 1.5799 0.7900 2.00 0.0708

*Dummy for years after 2007

log (Trend from 2000) 2.2920 0.9249 2.48 0.0307

Dummy for 2009 1.1436 0.5579 2.05 0.0650

log (Unregulated Southwest Milk Intercept -0.8488 0.1042 -8.15 <.0001 0.8135

/ Southwest Milk to Order 126) Dummy 2013 -0.8318 0.2288 -3.64 0.0039

Dummy from 2004 to 2006 -1.1434 0.3180 -3.60 0.0042

log (Order 126 Class III Price at Test 9.9190 1.9161 5.18 0.0003

/ Order 126 Class I Price at Test)
24

Table 24: Allocation of Arizona Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log (Arizona Milk to Order 126 Intercept -5.7360 0.9152 -6.27 <.0001 0.8025

/ Arizona Milk to Order 131) lag (log (Order 126 Blend Price at Test 22.7375 6.9471 3.27 0.0074

 / Order 131 Blend Price at Test))* Dummy After 2003

log (Trend from 2000) 1.0999 0.4828 2.28 0.0437

Dummy for years after 2006 -1.8132 0.4437 -4.09 0.0018

log (Unregulated Arizona Milk Intercept 0.2428 1.1154 0.22 0.8317 0.9229

/ Arizona Milk to Order 131) log (Order 131 Class I Price at Test / CPI all) -1.1985 0.5747 -2.09 0.0611

Dummy for years after 2008 -2.1117 0.6018 -3.51 0.0049

Dummy for 2004 to 2005 0.5635 0.1200 4.69 0.0007

Table 25: Allocation of Former Western Order Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

Almost 100% of the milk produced in the Former Western Region is allocated to the Unregulated pool.

Table 26: Allocation of Unregulated West Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

Milk in the Unregulated West Region is assumed to be used within either an Unregulated Area or Order 32. Intercept

log (Percentage of Unregulated West Intercept -4.4011 0.6854 -6.42 <.0001 0.9523

Milk to Order 32 log (Order 32 Blend Price at Test/ CPI All) 0.5084 0.3438 1.48 0.1630

/ 1 - Percentage of Unregulated Dummy for years after 2005 1.9026 0.1086 17.51 <.0001

West Milk to Order 32)

Table 27: Allocation of California Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log (California Milk to Order 131 Intercept -4.3189 0.5627 -7.68 <.0001 0.9137

/ California Milk used in California) lag (log (Order 131 Blend Price at Test 4.1865 1.8752 2.23 0.0473

/ California State Blend Price at Test))

Dummy from 2002 to 2005

lag (log (California Milk to Order 131 -2.0470 0.8724 -2.35 0.0387

/ California Milk used in California)) 0.2441 0.0986 2.48 0.0308

log (Unregulated California Milk Intercept -2.3640 0.3836 -6.16 <.0001 0.7412

/ California Milk used in California) lag (log (California State Blend Price at Test / CPI All)) -0.7627 0.2000 -3.81 0.0029

Dummy for 2009 0.4463 0.0845 5.28 0.0003

Dummy for 2015 0.3397 0.1180 2.88 0.015

Table 28: Allocation of Hawaii and Alaska Milk to Pools

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

All milk produced in Hawaii and Alaska is assumed to be allocated to the Unregulated Pool.
25

Table 29: Fluid Use Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Order 1 Fluid Use Per Capita) Intercept 4.0211 0.5371 7.49 <.0001 0.9841

log (Order 1 Class I Price at Test / CPI All) -0.0419 0.0207 -2.02 0.0710 -0.0419

Dummy for years after 2006 0.0424 0.0114 3.73 0.0039

lag (log (Personal Disposable Income / CPI All)) 0.4972 0.1942 2.56 0.0284 0.4972

Trend from 2000 -0.0222 0.0016 -13.77 <.0001

log (Order 5 Fluid Use Per Capita) Intercept -0.2113 0.3133 -0.67 0.5128 0.8921

log (Order 5 Class I Price at Test / CPI All) -0.0741 0.0294 -2.52 0.0268 -0.0741

lag (log (Order 5 Fluid Use Per Capita)) 1.0679 0.0607 17.60 <.0001

log (Order 6 Fluid Use Per Capita) Intercept 1.5729 1.0468 1.50 0.1611 0.9680

log (Order 6 Class I Price at Test/ CPI All) -0.0839 0.0348 -2.41 0.0345 -0.0839

Trend from 2000 -0.0059 0.0034 -1.74 0.1098

lag (log (Order 6 Fluid Use Per Capita)) 0.7348 0.2012 3.65 0.0038

log (Order 7 Fluid Use Per Capita) Intercept 5.5121 0.1181 46.69 <.0001 0.8861

lag(log (Order 7 Class I Price at Test / CPI All)) -0.1849 0.0554 -3.34 0.0066 -0.1849

Dummy for years after 2009 -0.1267 0.0144 -8.80 <.0001

Dummy for years 2003-2005 -0.0401 0.0180 -2.23 0.0472

log (Order 30 Fluid Use Per Capita) Intercept 5.6166 0.0694 80.92 <.0001 0.9274

lag (log (Order 30 Class I Price at Test / CPI All)) -0.1204 0.0367 -3.28 0.0065 -0.1204

Dummy for years after 2009 * Trend for 2000 -0.0089 0.0008 -11.40 <.0001

log (Order 32 Fluid Use Per Capita) Intercept 5.5238 0.0856 64.51 <.0001 0.9193

lag(log (Order 32 Class I Price at Test / CPI All)) -0.0952 0.0472 -2.02 0.0689 -0.0952

Dummy for years after 2009 -0.0671 0.0226 -2.97 0.0127 -0.0671

Trend from 2000 * Dummy for years after 2001 -0.0061 0.0026 -2.36 0.0377

log (Order 33 Fluid Use Per Capita) Intercept 1.6149 0.9358 1.73 0.1185 0.9615

log (Order 33 Class I Price at Test / CPI All) -0.0636 0.0251 -2.54 0.0319 -0.0636

lag (log (Order 33 Fluid Use Per Capita)) 0.4051 0.1303 3.11 0.0126

lag (log (Personal Disposable Income / CPI All)) 0.6199 0.2422 2.56 0.0307

Trend from 2000 -0.0125 0.0026 -4.87 0.0009

Dummy for 2011 0.0967 0.0149 6.49 0.0001

log (Order 124 Fluid Use Per Capita) Intercept -0.3491 0.1956 -1.78 0.1018 0.9760

log (Order 124 Class I Price at Test/ CPI All) -0.0626 0.0159 -3.95 0.0023 -0.0626

lag (log (Order 124 Fluid Per Capita)) 1.0841 0.0352 30.83 <.0001

Dummy for 2008 0.0413 0.0092 4.50 0.0009
26

log (Order 126 Fluid Use Per Capita) Intercept 0.0120 0.2156 0.06 0.9566 0.9733

log (Order 126 Class I Price at Test/ CPI All) -0.0468 0.0163 -2.87 0.0140 -0.0468

lag (log (Personal Disposable Income / CPI All)) 1.0139 0.0412 24.59 <.0001 1.0139

log (Order 131 Fluid Use Per Capita) Intercept 1.3222 0.5056 2.62 0.0240 0.9825

log (Order 131 Class I Price at Test/ CPI All) -0.0761 0.0303 -2.51 0.0290 -0.0761

lag (log (Order 131 Fluid Per Capita)) 0.7829 0.0928 8.44 <.0001

log (Trend from 2000) -0.0518 0.0205 -2.53 0.0282

log (California Fluid Use Per Capita) Intercept 5.5856 0.0815 68.50 <.0001 0.9308

lag(log (California Class I Price at Test / CPI All)) -0.1629 0.0432 -3.77 0.0031 -0.1629

Trend from 2000 * Dummy for years after 2001 -0.0131 0.0013 -10.02 <.0001

Dummy from 2008 to 2009 0.0808 0.0186 4.34 0.0012

log(Unregulated Fluid Intercept 1.6485 0.9735 1.69 0.1162 0.6769

Use Per Capita) lag (log (Personal Disposable Income / CPI All)) 1.2505 0.3477 3.60 0.0037 1.250462

Dummy form 2004 to 2005 0.1247 0.0582 2.14 0.0533

Dummy from 2007 to 2009 -0.1254 0.0365 -3.43 0.005
27

Table 30: Dairy Products Conversion Table

Solids Required per Product Unit

Products Butterfat Non-fat Solids

Producer Milk13.75 8.94

Butter 80.4 1.0

American Cheese233.7 87.2

Other Cheese228.6 87.8

Non-fat Dry Milk20.8 96.2

Canned Milk 7.9 18.5

Dry Whey 1.1 95.0

Dry Whole Milk 26.5 71.0

Fluid Milk21.9 8.9

1 The Butterfat and Non-fat Solids test for Producer Milk 

are a simple average over the forecasted years for the 

assumed tests

2 The Non-fat Solids test for American Cheese, Other 

Cheese, and Fluid Milk and the Butterfat test for Other 

Cheese, Non-fat Dry Milk, and Fluid Milk are estimated 

by the model. The Butterfat test for the numbers 

presented are simple averages of the results for the 

forecasted years.
28

Table 31: Federal Order 1 Non-Fluid Milk Use

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log ((Order 1 Class III Pooled Milk Intercept 0.5886 0.0355 16.58 <.0001 0.9299

+ Order 1 Class III Non-Pool Milk) log (Cheddar Cheese Wholesale Price Index 1.7423 0.2785 6.26 <.0001

/ (Order 1 Class II Pooled Milk / Order 1 Class III Price at Test Index)

+ Order 1 Class II Non-Pool Milk)) log (Weighted Class II CPI / Order 1 Class II Price at Test Index) -0.3813 0.1237 -3.08 0.0095

Dummy for years after 2002 -0.2411 0.0528 -4.56 0.0007

log ((Order 1 Class IV Pooled Milk Intercept -0.6737 0.0350 -19.26 <.0001 0.8347

+ Order 1 Class IV Non-Pool Milk) log (Grade-AA Butter Wholesale Price Index 0.7754 0.2263 3.43 0.0057

/ (Order 1 Class II Pooled Milk / Order 1 Class IV Price at Test Index)

+ Order 1 Class II Non-Pool Milk)) log (Non-Fat Dry Milk Wholesale Price Index 0.6948 0.3791 1.83 0.0941

/ Order 1 Class IV Price at Test Index)

Dummy for 2008 0.2444 0.1206 2.03 0.0676

Dummy for 2012 0.5456 0.1129 4.83 0.0005

Table 32: Federal Order 5 Non-Fluid Milk Use

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log ((Order 5 Class III Pooled Milk Intercept -0.5831 0.0478 -12.19 <.0001 0.8766

+ Order 5 Class III Non-Pool Milk) log (Cheddar Cheese Wholesale Price Index 1.4520 0.4035 3.60 0.0049

/ (Order 5 Class II Pooled Milk / Order 5 Class III Price at Test Index)

+ Order 5 Class II Non-Pool Milk)) log (Weighted Class II CPI / Order 5 Class II Price at Test Index) -0.8134 0.2170 -3.75 0.0038

Dummy from 2006 to 2008 -0.4456 0.0798 -5.58 0.0002

Dummy for years after 2013 -0.5032 0.1025 -4.91 0.0006

log ((Order 5 Class IV Pooled Milk Intercept -0.3459 0.0295 -11.71 <.0001 0.8540

+ Order 5 Class IV Non-Pool Milk) lag (log (Grade-AA Butter Wholesale Price Index 0.5278 0.1722 3.06 0.0119

/ (Order 5 Class II Pooled Milk / Cheddar Cheese Wholesale Price Index))

+ Order 5 Class II Non-Pool Milk)) Dummy for years after 2006 -0.2180 0.0478 -4.56 0.001

Dummy for years after 2011 0.1437 0.0555 2.59 0.0271

Dummy for 2014 0.2333 0.0865 2.70 0.0225

Table 33: Federal Order 6 Non-Fluid Milk Use

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log ((Order 6 Class III Pooled Milk Intercept -1.0479 0.0896 -11.70 <.0001 0.8261

+ Order 6 Class III Non-Pool Milk) log (Cheddar Cheese Wholesale Price Index 1.0093 0.3112 3.24 0.0078

/ (Order 6 Class II Pooled Milk / Order 6 Class III Price at Test Index)

+ Order 6 Class II Non-Pool Milk)) log (Weighted Class II CPI / Order 6 Class II Price at Test Index) -1.4491 0.4343 -3.34 0.0066

Dummy for years after 2011 -0.9088 0.1504 -6.04 <.0001

Dummy for 2001 -0.7853 0.2969 -2.65 0.0228
29

log ((Order 6 Class IV Pooled Milk Intercept -0.7847 0.0830 -9.45 <.0001 0.9020

+ Order 6 Class IV Non-Pool Milk) log (Grade-AA Butter Wholesale Price Index 0.6234 0.1516 4.11 0.0017

/ (Order 6 Class II Pooled Milk / Order 6 Class IV Price at Test Index)

+ Order 6 Class II Non-Pool Milk)) log (Non-Fat Dry Milk Wholesale Price Index 0.6517 0.1311 4.97 0.0004

/ Order 6 Class IV Price at Test Index)

Dummy from 2005 to 2012 0.4068 0.0569 7.15 <.0001

Dummy for years after 2001 -0.3275 0.0967 -3.39 0.0061

Table 34: Federal Order 7 Non-Fluid Milk Use

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log ((Order 7 Class III Pooled Milk Intercept 0.5279 0.0814 6.48 <.0001 0.7757

+ Order 7 Class III Non-Pool Milk) log (Cheddar Cheese Wholesale Price Index 4.0268 1.1345 3.55 0.0046

/ (Order 7 Class II Pooled Milk / Order 7 Class III Price at Test Index)

+ Order 7 Class II Non-Pool Milk)) log (Dry Whey Wholesale Price Index 0.4031 0.2321 1.74 0.1103

/ Order 7 Class III Price at Test Index)

Dummy from 2010 to 2011 0.6009 0.1721 3.49 0.0050

Dummy for 2014 -0.9645 0.2248 -4.29 0.0013

log ((Order 7 Class IV Pooled Milk Intercept -0.2440 0.0306 -7.98 <.0001 0.8598

+ Order 7 Class IV Non-Pool Milk) log (Grade-AA Butter Wholesale Price Index 1.7636 0.1922 9.18 <.0001

/ (Order 7 Class II Pooled Milk / Order 7 Class IV Price at Test Index)

+ Order 7 Class II Non-Pool Milk)) log (Non-Fat Dry Milk Wholesale Price Index 1.3263 0.1889 7.02 <.0001

/ Order 7 Class IV Price at Test Index)

Dummy for 2010 0.4679 0.1209 3.87 0.0022

Table 35: Federal Order 30 Non-Fluid Milk Use

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log ((Order 30 Class III Pooled Milk Intercept 2.8821 0.0395 72.90 <.0001 0.7120

+ Order 30 Class III Non-Pool Milk) log (Cheddar Cheese Wholesale Price Index 1.3792 0.2781 4.96 0.0003

/ (Order 30 Class II Pooled Milk / Order 30 Class III Price at Test Index)

+ Order 30 Class II Non-Pool Milk)) Dummy for years before 2007 -0.1596 0.0333 -4.79 0.0004

log ((Order 30 Class IV Pooled Milk Intercept -5.6125 1.5386 -3.65 0.0029 0.7795

+ Order 30 Class IV Non-Pool Milk) log (Non-Fat Dry Milk Wholesale Price Index 1.4246 0.1859 7.66 <.0001

/ (Order 30 Class II Pooled Milk / Order 30 Class IV Price at Test Index)

+ Order 30 Class II Non-Pool Milk)) log (Grade-AA Butter Wholesale Price Index / CPI All) 1.0152 0.3527 2.88 0.0129
30

Table 36: Federal Order 32 Non-Fluid Milk Use

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log ((Order 32 Class III Pooled Milk Intercept -2.0744 1.4807 -1.40 0.1865 0.8223

+ Order 32 Class III Non-Pool Milk) log (Cheddar Cheese Wholesale Price Index 0.9515 0.3571 2.66 0.0206

/ (Order 32 Class II Pooled Milk / CPI All)

+ Order 32 Class II Non-Pool Milk)) Dummy for years after 2003 * log(Trend from 2000) -0.2071 0.0385 -5.38 0.0002

Dummy from 2007 to 2008 -0.6659 0.1302 -5.11 0.0003

log ((Order 32 Class IV Pooled Milk Intercept -2.6627 1.0707 -2.49 0.0286 0.7200

+ Order 32 Class IV Non-Pool Milk) log (Non-Fat Dry Milk Wholesale Price Index 1.1367 0.2308 4.92 0.0004

/ (Order 32 Class II Pooled Milk / Order 32 Class IV Price at Test Index)

+ Order 32 Class II Non-Pool Milk)) log(Grade-AA Butter Wholesale Price Index -0.2071 0.0385 -5.38 0.0002

/ CPI All)

Dummy for 2007 -0.6659 0.1302 -5.11 0.0003

Table 37: Federal Order 33 Non-Fluid Milk Use

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log ((Order 33 Class III Pooled Milk Intercept 1.1634 0.0744 15.65 <.0001 0.7643

+ Order 33 Class III Non-Pool Milk) log (Cheddar Cheese Wholesale Price Index 3.4121 0.5667 6.02 <.0001

/ (Order 33 Class II Pooled Milk / Order 33 Class III Price at Test Index)

+ Order 33 Class II Non-Pool Milk)) Dummy for 2000 -0.5986 0.1498 -4.00 0.0018

Dummy form 2008 to 2009 -0.3587 0.1051 -3.41 0.0051

log ((Order 33 Class IV Pooled Milk Intercept -1.0257 0.0488 -21.00 <.0001 0.8778

+ Order 33 Class IV Non-Pool Milk) log (Grade-AA Butter Wholesale Price Index 1.0889 0.1857 5.86 <.0001

/ (Order 33 Class II Pooled Milk / Order 33 Class IV Price at Test Index)

+ Order 33 Class II Non-Pool Milk)) log(Non-Fat Dry Milk Wholesale Price Index 0.7577 0.1577 4.81 0.0003

/ Order 33 Class IV Price at Test Index)

Table 38: Federal Order 124 Non-Fluid Milk Use

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log ((Order 124 Class III Pooled Milk Intercept 1.6734 0.0352 47.49 <.0001 0.8546

+ Order 124 Class III Non-Pool Milk) log (Cheddar Cheese Wholesale Price Index 0.8540 0.2993 2.85 0.0145

/ (Order 124 Class II Pooled Milk / Order 124 Class III Price at Test Index)

+ Order 124 Class II Non-Pool Milk)) Dummy for 2002 0.3151 0.0645 4.89 0.0004

Dummy for years after 2008*Trend from 2000 0.0267 0.0030 8.81 <.0001

log ((Order 124 Class IV Pooled Milk Intercept 1.5390 0.0212 72.76 <.0001 0.7159

+ Order 124 Class IV Non-Pool Milk) log(Grade-AA Butter Wholesale Price Index 1.2012 0.1797 6.68 <.0001

/ (Order 124 Class II Pooled Milk / Order 124 Class IV Price at Test Index)

+ Order 124 Class II Non-Pool Milk)) log (Non-Fat Dry Milk Wholesale Price Index 1.5237 0.2816 5.41 0.0002

/ Order 124 Class IV Price at Test Index)

Dummy for 2009 -0.2707 0.0672 -4.03 0.0017
31

Table 39: Federal Order 126 Non-Fluid Milk Use

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log ((Order 126 Class III Pooled Milk Intercept 1.2330 0.1162 10.61 <.0001 0.8547

+ Order 126 Class III Non-Pool Milk) log (Cheddar Cheese Wholesale Price Index 4.4087 1.3979 3.15 0.0092

/ (Order 126 Class II Pooled Milk / Order 126 Class III Price at Test Index)

+ Order 126 Class II Non-Pool Milk)) log (Dry Whey Wholesale Price Index 0.6015 0.2302 2.61 0.0242

/ Order 126 Class III Price at Test Index)

Dummy for years after 2003*Trend from 2000 0.0614 0.0149 4.11 0.0017

log (Weighted Class 2 CPI / Order 126 Class 2 Price at Test Index) -1.1149 0.3361 -3.32 0.0069

log ((Order 126 Class IV Pooled Milk Intercept 0.0215 0.0369 0.58 0.5728 0.8600

+ Order 126 Class IV Non-Pool Milk) log(Grade-AA Butter Wholesale Price Index 0.3503 0.0919 3.81 0.0029

/ (Order 126 Class II Pooled Milk / Order 126 Class IV Price at Test Index)

+ Order 126 Class II Non-Pool Milk)) log(Non-Fat Dry Milk Wholesale Price Index 1.1626 0.1546 7.52 <.0001

/ Order 126 Class IV Price at Test Index)

log (Weighted Class II CPI / Order 126 Class II Price at Test Index) -0.4428 0.1125 -3.94 0.0023

Dummy for 2011 0.2176 0.0869 2.50 0.0292

Table 40: Federal Order 131 Non-Fluid Milk Use

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log ((Order 131 Class III Pooled Milk Intercept 1.4182 0.0440 32.24 <.0001 0.9560

+ Order 131 Class III Non-Pool Milk) log (Cheddar Cheese Wholesale Price Index 2.1186 0.3449 6.14 <.0001

/ (Order 131 Class II Pooled Milk / Order 131 Class III Price at Test Index)

+ Order 131 Class II Non-Pool Milk)) Dummy for years before 2003 0.7417 0.0796 9.32 <.0001

log ((Order 131 Class IV Pooled Milk Intercept 0.2637 0.6439 0.41 0.6894 0.8468

+ Order 131 Class IV Non-Pool Milk) log (Non-Fat Dry Milk Wholesale Price Index 0.3684 0.1599 2.30 0.0399

/ (Order 131 Class II Pooled Milk / CPI All)

+ Order 131 Class II Non-Pool Milk)) Dummy from 2004 to 2007 -0.4755 0.0831 -5.72 <.0001

Dummy for years after 2002 -0.4137 0.1001 -4.13 0.0014

Table 41: California "Order" Non-Fluid Milk Use

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| R-Square

log (California Class 3 Total Solids Intercept 0.0265 0.0367 0.72 0.4837 0.8737

/ California Class 2 Total Solids) log (Frozen Dairy Products CPI 2.2621 0.7405 3.05 0.0100

/ Other Dairy Products CPI (2000 Base Year))

Dummy for years after 2008 -0.3870 0.0496 -7.81 <.0001

log (California Class 4a Total Solids Intercept 1.3544 0.2661 5.09 0.0003 0.6993

/ California Class 2 Total Solids) lag (log (Grade-AA Butter Wholesale Price Index 0.1349 0.0643 2.10 0.0599

/ CPI All)

Dummy for 2015 -0.1660 0.0430 -3.86 0.0026

Dummy for 2008 0.1351 0.0408 3.31 0.0069
32

log (California Class 4b Total Solids Intercept 1.0313 0.3317 3.11 0.0099 0.9286

/ California Class 2 Total Solids) log (Cheddar Cheese Wholesale Price Index 0.1816 0.0814 2.23 0.0476

/ CPI All)

Dummy for years after 2007 * log(Trend from 2000) -0.1213 0.0105 -11.58 <.0001

lag (log (Dry Whey Wholesale Price Index 0.1393 0.0358 3.89 0.0025

/ CPI All))

Table 42: National Domestic Production Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Percentage of Class II Solids Used in Intercept 0.0001 0.0186 0.01 0.9958 0.9824

Frozen Production log (Frozen Products CPI 1.7953 0.3542 5.07 0.0003 1.795285

/ (1 - Percentange of Class II Solids / Other Dairy Products CPI (2000 Base Year))

Used in Frozen Production)) Dummy for 2008 0.1270 0.0454 2.80 0.0160

Trend from 2000 -0.0443 0.0020 -21.78 <.0001

log (Condensed Skim Milk Intercept -3.0049 3.4674 -0.87 0.3938 0.8863

Used in Cheese Production) log (American Cheese Production + Other Cheese Production) 0.7814 0.3921 1.99 0.0565

Dummy for years after 2005 0.9526 0.2108 4.52 0.0001

Dummy for 1993 0.7833 0.3468 2.26 0.0322

log (American Cheese Production Intercept 0.0707 0.0522 1.35 0.1857 0.9580

Percentage lag (log (Cheddar Cheese Wholesale Price Index 0.3575 0.1344 2.66 0.0124 0.3575

/ 1- American Cheese Production / Mozzarella Price Index))

Percentage) Dummy for years after 2007 0.0925 0.0370 2.50 0.0182

Trend from 1980 -0.0187 0.0020 -9.42 <.0001

Dummy for years before 1985 0.2849 0.0389 7.31 <.0001

log (Dry Whey Production) Intercept -6.2459 3.6715 -1.70 0.1044 0.8928

log (Dry Whey Wholesale Price / CPI Food) 0.0333 0.0243 1.37 0.1849 0.0333

log (Other Cheese Production + American Cheese Production) 1.5461 0.4236 3.65 0.0016

Trend from 1990 -0.0508 0.0115 -4.41 0.0003

Dummy for 2001 -0.0751 0.0304 -2.47 0.0227

Dummy for 2014 -0.1304 0.0329 -3.97 0.0008

log (Canned Milk Production) Intercept 7.0077 0.1630 43.00 <.0001 0.7349

log (Dry Whole Milk Production) -0.0566 0.0301 -1.88 0.0690 -0.0566

log (Trend from 1980) -0.1610 0.0191 -8.43 <.0001

log (Non-fat Dry Milk Ratio) Intercept -3.4049 0.0886 -38.42 <.0001 0.6360

lag (log (Grade-AA Butter Wholesale Price -0.7912 0.5030 -1.57 0.1440 -0.7912

/ Cheddar Cheese Wholesale Price))

Dummy for years after 2007 -0.3657 0.1216 -3.01 0.0119

Dummy for 2005 0.4470 0.2412 1.85 0.0908
33

CPI Food Intercept 0.2352 0.0446 5.27 <.0001 0.9983

log (CPI All) 0.9506 0.0090 105.71 <.0001

Dummy for years after 2008 0.0527 0.0069 7.68 <.0001

Table 43: National Product Domestic Consumption Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Other Class II Per Capita Intercept 3.2377 1.7844 1.81 0.0911 0.8382

Domestic Consumption) log (Other Dairy Products CPI (2000 Base Year) -2.2617 0.5459 -4.14 0.0010 -2.2617

/ CPI All)

log (Personal Disposable Income Per Capita / CPI All) 2.9832 0.6881 4.34 0.0007 2.9832

Trend from 1996 -0.0507 0.0106 -4.77 0.0003

Trend from 1996 * Dummy for years after 2003 0.0235 0.0061 3.85 0.0018

Dummy for 2012 -0.1746 0.0570 -3.06 0.0085

log (Frozen Product Per Capita Intercept 6.1928 0.6148 10.07 <.0001 0.8535

Domestic Consumption) log (Frozen Products CPI / CPI All) -0.7295 0.1509 -4.83 <.0001 -0.7295

Dummy for years after 2003 -0.1813 0.0139 -13.06 <.0001

log (American Cheese Per Capita Intercept 0.7725 0.3920 1.97 0.0574 0.9667

Domestic Consumption) log (Cheddar Cheese Wholesale Price / CPI Food) -0.1064 0.0438 -2.43 0.0211 -0.1064

log (Personal Disposable Income Per Capita / CPI All) 0.8045 0.0765 10.52 <.0001 0.8045

Trend from 1980 * Dummy for years after 2000 0.0027 0.0008 3.44 0.0016

log (Other Cheese Per Capita Intercept -0.6017 0.5061 -1.19 0.2430 0.9594

Domestic Consumption) log (Mozzarella Price / CPI Food) -0.5582 0.1527 -3.66 0.0009 -0.5582

log (Personal Disposable Income Per Capita / CPI All) 1.2886 0.1787 7.21 <.0001 1.2886

log (Dry Whey Per Capita Intercept 2.0482 0.1956 10.47 <.0001 0.9215

Domestic Consumption) log (Dry Whey Wholesale Price / CPI All) -0.2554 0.0797 -3.20 0.0038 -0.2554

Trend from 1989 -0.0392 0.0031 -12.60 <.0001

log (Butter Per Capita Intercept 0.6467 0.2740 2.36 0.0250 0.9651

Domestic Consumption) log (Grade-AA Butter Wholesale Price / CPI Food) -0.0426 0.0223 -1.91 0.0660 -0.0426

log (Personal Disposable Income Per Capita / CPI All) 0.3678 0.0747 4.92 <.0001 0.3678

Trend from 2000 * Dummy for years after 2003 0.0080 0.0022 3.62 0.0011

Dummy from 1989 to 1992 -0.1311 0.0172 -7.61 <.0001

Dummy for years after 2010 0.0839 0.0247 3.40 0.0019

log (Non-Fat Dry Milk Per Capita Intercept 0.5922 1.0354 0.57 0.5715 0.7615

Domestic Consumption) log (Non-Fat Dry Milk Wholesale Price / CPI Food) -0.3808 0.1250 -3.05 0.0047 -0.3808

log (Personal Disposable Income Per Capita / CPI All) 0.7492 0.2165 3.46 0.0016 0.7492

Dummy from 1994 to 1997 0.3125 0.0630 4.96 <.0001

Dummy from 1985 to 1987 -0.2727 0.0748 -3.65 0.0010
34

Table 44: National Average Stock Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (Fourth Quarter Average Intercept 5.8992 0.0342 172.60 <.0001 0.7364

American Cheese Stocks) log (Cheddar Cheese Wholesale Price / CPI Food) -0.4645 0.1276 -3.64 0.0009 -0.4645

Dummy for years after 2006 0.3773 0.0739 5.10 <.0001

log (Fourth Quarter Average Intercept -1.2465 1.0932 -1.14 0.2624 0.8906

Other Cheese Stocks) log (Mozzarella Price / CPI All) -1.3968 0.2511 -5.56 <.0001 -1.3968

Dummy for years after 2006 0.8339 0.1041 8.01 <.0001

log (Second Half of the Year Intercept 2.8698 0.1710 16.78 <.0001 0.8444

Dry Whey Average Stocks) log (Dry Whey Wholesale Price / CPI Food) -0.2496 0.0841 -2.97 0.0057 -0.2496

Trend from 1980 0.0124 0.0023 5.32 <.0001

Dummy from 2007 to 2008 0.4365 0.0947 4.61 <.0001

Dummy for years after 2012 0.4388 0.0907 4.84 <.0001

log (Second Half of the Year Intercept 4.3312 0.0841 51.52 <.0001 0.8918

Butter Average Stocks) log (Grade-AA Butter Wholesale Price / CPI All) -1.4978 0.2182 -6.87 <.0001 -1.4978

Dummy from 1990 to 1997 -1.6869 0.1881 -8.97 <.0001

Dummy from 1999 to 2000 -0.6255 0.2947 -2.12 0.0419

Dummy for years after 2010 0.4450 0.1975 2.25 0.0315

log (Second Half of the Year Intercept 4.3193 0.1026 42.12 <.0001 0.6502

Non-Fat Dry Milk Average Stocks) log (Non-Fat Dry Milk Wholesale Price / CPI All) -0.3176 0.2298 -1.38 0.1772 -0.3176

Dummy for 2006 -0.5483 0.3227 -1.70 0.0996

Dummy for years after 2006 0.5096 0.1313 3.88 0.0005

Dummy for 2014 0.4561 0.3242 1.41 0.1698

Table 45: National Ending Stock Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (American Cheese Ending Stocks) Intercept -0.0565 0.0752 -0.75 0.4579 0.9963

log (Fourth Quarter Average American Cheese Stocks) 1.0096 0.0124 81.13 <.0001

log (Other Cheese Ending Stocks) Intercept -0.0871 0.0466 -1.87 0.0700 0.9979

log (Fourth Quarter Average Other Cheese Stocks) 1.0178 0.0091 112.19 <.0001

log (Dry Whey Ending Stocks) Intercept 0.4746 0.3152 1.51 0.1414 0.8047

log (Second Half of the Year Dry Whey Average Stocks) 0.8860 0.0866 10.23 <.0001
35

log (Butter Ending Stocks) Intercept 0.1379 0.2620 0.53 0.6020 0.8710

log (Second Half of the Year Butter Average Stocks) 0.8867 0.0609 14.55 <.0001

log (Non-Fat Dry Milk Ending Stocks) Intercept 0.2424 0.2836 0.85 0.3987 0.8740

log (Second Half of the Year Non-Fat Dry Milk Average Stocks) 0.9513 0.0616 15.44 <.0001

Table 46: National Product Import and Export Equations

Dependent Variable Parameter Estimate Std. Error t-Value Pr>|t| Elasticity R-Square

log (American Cheese Imports over Intercept 0.2059 0.0959 2.15 0.0465 0.7655

Tariff Rate Quota)1(Cheddar Cheese Wholesale Price 1.4643 0.8246 1.78 0.0937 0.0733

- Oceania Cheddar Cheese Price)^3

Dummy for 2002 0.5266 0.2906 1.81 0.0877

Dummy for years after 2009 -0.6979 0.1514 -4.61 0.0002

log (Other Cheese Imports over Intercept -33.3135 14.1668 -2.35 0.0328 0.8532

Tariff Rate Quota)1(Mozzarella Price - Oceania Cheddar Cheese Price) 63.4321 14.7941 4.29 0.0006 50.1694

The equation is not updated lag (Other Cheese Imports over 0.8083 0.0892 9.06 <.0001

Tariff Rate Quota)

Dummy for 1997 -36.5867 16.5049 -2.22 0.0425

Dummy for 2015 58.1624 16.3752 3.55 0.0029

log (Milk Fat Imports) Intercept 5.0406 0.9009 5.60 <.0001 0.6958

log (Major Imports of Fat) 1.4434 0.1847 7.81 <.0001 1.4434

Trend from 1996 0.0104 0.0058 1.79 0.0917

log (Solids Non Fat Imports) Intercept 8.7425 0.6024 14.51 <.0001 0.7119

log (Major Imports of Fat) 0.8656 0.1207 7.17 <.0001 0.8656

Trend from 1996 0.0108 0.0033 3.26 0.0046

log (Other Class 4 Imports) Intercept 2.8159 0.8872 3.17 0.0059 0.5777

lag (log (Other Class 4 Imports)) 0.5020 0.1583 3.17 0.0059 0.5020

Dummy for 2008 0.2161 0.0861 2.51 0.0232

Dummy for 2009 -0.2744 0.0964 -2.85 0.0116

log (American Cheese Exports) Intercept 3.8001 0.1657 22.93 <.0001 0.8257

(Cheddar Cheese Wholesale Price -1.9143 0.4167 -4.59 0.0002 -0.3597

- Oceania Cheddar Cheese Price)

Dummy for years after 2010 1.2974 0.2847 4.56 0.0002
36

log (Other Cheese Exports) Intercept 5.3142 0.3332 15.95 <.0001 0.9197

(Mozzarella Price - Oceania Cheddar Cheese Price) -1.1227 0.3713 -3.02 0.0073 -0.8880

Dummy for years after 2008 0.8999 0.2521 3.57 0.0022

Dummy for years after 2011 0.8286 0.2850 2.91 0.0094

log (Dry Whey Exports) Intercept 5.5874 0.0584 95.65 <.0001 0.7788

(Dry Whey Wholesale Price - EU Dry Whey Price) -1.6583 0.5675 -2.92 0.0087 0.0417

Dummy for years after 2004 0.5291 0.0885 5.98 <.0001

log (Butter Imports over Intercept -0.2397 0.1446 -1.66 0.1138

Tariff Rate Quota)1(Grade AA Butter Wholesale Price - Oceania Butter Price)^3 1.3769 0.3587 3.84 0.0011 0.2982 0.6019

log (Butter Exports) Intercept 4.1661 0.1235 33.73 <.0001 0.8705

(Grade AA Butter Wholesale Price - Oceania Butter Price) -3.8569 0.5782 -6.67 <.0001 -1.3993

log (Non-Fat Dry Milk Exports) Intercept 3.6237 0.3458 10.48 <.0001 0.9710

(Non-Fat Dry Milk Wholesale Price/Oceania Skim Milk -0.8397 0.3810 -2.20 0.0408 -0.9061

Powder Price)

Trend from 1985 * Dummy form 2004 to 2013 0.1127 0.0137 8.20 <.0001

Dummy for years after 2013 3.4681 0.3502 9.90 <.0001

1

In-quota butter imports are assumed to be filled over the projection period.