Economic 

Research

Report

Number 80

September 2009

United States 

Department 

of Agriculture

Economic 

Research 

Service

Olga Isengildina-Massa

Stephen MacDonald

U.S. Cotton Prices 

and the World Cotton Market

Forecasting and Structural Change
www.ers.usda.gov

Cotton

 http://ers.usda.gov/Briefing/Cotton/

Visit Our Website To Learn More!

National Agricultural Library

Cataloging Record:

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its 

programs and activities on the basis of race, color, national origin, age, 

disability, and, where applicable, sex, marital status, familial status, parental 

status, religion, sexual orientation, genetic information, political beliefs, reprisal, 

or because all or a part of an individual’s income is derived from any public 

assistance program. (Not all prohibited bases apply to all programs.) Persons 

with disabilities who require alternative means for communication of program 

information (Braille, large print, audiotape, etc.) should contact USDA’s 

TARGET Center at (202) 720-2600 (voice and TDD). 

To file a complaint of discrimination write to USDA, Director, Office of Civil 

Rights, 1400 Independence Avenue, S.W., Washington, D.C. 20250-9410 or 

call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA is an equal 

opportunity provider and employer.

Isengildina-Massa, Olga

 U.S. cotton prices and the world cotton market: forecasting and 

structural change.

 (Economic research report (United States. Dept. of Agriculture. 

Economic Research Service); no. 80)

 1. Cotton trade—Econometric models.

 2. Cotton—Prices—United States—Forecasting.

 I. MacDonald, Stephen A.

 II. United States. Dept. of Agriculture. Economic Research Service

 III. Title.

HD9875

Photo credit: Shutterstock
United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

This report analyzes recent structural changes in the world cotton industry and develops 

a statistical model that refl ects current drivers of U.S. cotton prices. Legislative changes 

in 2008 authorized USDA to resume publishing cotton price forecasts for the fi rst time 

in nearly 80 years. Systematic problems have become apparent in the forecasting models used by USDA and elsewhere, highlighting the need for an updated review of price 

relationships. A structural break in the U.S. cotton industry occurred in 1999, and world 

cotton supply has become an important determinant of U.S. cotton prices, along with 

China’s trade and production policy. The model developed here forecasts changes in the 

U.S. upland cotton farm price based on changes in U.S. cotton supply, the U.S. stocks-touse ratio (S/U), China’s net imports as a share of world consumption, the foreign supply 

of cotton, and selected farm policy parameters. 

Keywords: forecasting, cotton, price, demand, trade, structural change, farm programs.

Olga Isengildina-Massa is an Assistant Professor in the Department of Applied Economics 

and Statistics at Clemson University. Stephen MacDonald is a Senior Economist with the 

Economic Research Service’s Market and Trade Economics Division.

Acknowledgments

The authors appreciate the support for this project from the Small Grants Program of 

the Market and Trade Economics Division of the Economic Research Service, and the 

assistance of Carol Skelly and Hunter Colby of the World Agricultural Outlook Board in 

acquiring valuable data and insight. The authors thank the reviewers Linwood Hoffman, 

Alejandro Plastina, John Robinson, Carol Skelly, and Russell Tronstad for their comments, which helped to improve the paper substantially. Finally we gratefully acknowledge 

Dale Simms for editorial assistance and Wynnice Pointer-Napper for layout and design.

Olga Isengildina-Massa

Stephen MacDonald, stephenm@ers.usda.gov

U.S. Cotton Prices and 

the World Cotton Market

Forecasting and Structural Change

Economic

Research 

Report

Number 80

September 2009
ii

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Contents

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Cotton Price Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Theoretical Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Structural Change Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Estimation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Conclusions and Implications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Recommended citation format for this publication:

Isengildina-Massa, Olga, and Stephen MacDonald. Cotton Prices and the 

World Cotton Market: Forecasting and Structural Change. Economic 

Research Report 80, U.S. Dept. of Agriculture, Economic Research Service. 

Sept. 2009.
iii

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Summary

In 1929, Congress passed legislation forbidding the U.S. Department of 

Agriculture from publishing cotton price forecasts. That ban was removed in 

the Food, Conservation, and Energy Act of 2008. Recent changes in world 

cotton markets required that a model to forecast cotton prices be developed. 

This report develops a reduced-form specifi cation for a U.S. cotton price 

forecasting model based on expected changes in U.S. and global supply and 

demand factors.

What Is the Issue?

Although cotton price forecasts were not published by USDA between 1929 

and 2008, USDA’s Interagency Commodity Estimates Committee for cotton calculated unpublished price forecasts each month. USDA’s models 

have tended to overestimate cotton prices in recent years, however, because 

of rapid ongoing structural changes in the cotton industry. Other agencies 

making cotton price forecasts have encountered similar problems. Given the 

poor predictive capability of existing cotton price forecasting models and the 

renewed authority of USDA to publish cotton price forecasts, it is important 

to review and improve the existing models. An updated and improved cotton price forecasting model will more accurately account for the factors now 

determining U.S. upland cotton prices and illuminate the interaction of commodity markets with U.S. and global supply and demand, macroeconomic 

developments, and policy shifts.

What Did the Study Find? 

Structural change has altered the market for U.S. cotton since the 1990s. 

Shifts in textile trade policy, combined with signifi cant liberalization of 

China’s cotton production policies, have overturned longstanding global 

consumption and trade patterns. The result has been to shift the United States 

into a nearly unprecedented dependence on global markets. While about 60 

percent of U.S. cotton was consumed domestically for the last 60 years of the 

20th century, exports have signifi cantly surpassed the use of cotton within 

the United States since 2001/02. As a result, U.S. cotton prices are no longer 

determined solely by domestic supplies and stocks.

To refl ect these recent structural changes, the model draws on a number of 

variables to forecast changes in the U.S. cotton price: U.S. cotton supply, U.S. 

stocks-to-use ratio, China’s net imports as a share of world consumption, the 

proportion of U.S. cotton in the loan program, and the world supply of cotton. 

The model explains 68 percent of the variation in the U.S. upland cotton price 

from 1974/75 through 2006/07. The results suggest that a 1-percent increase in 

U.S. supply from the previous year will cause U.S. cotton prices to drop about 

0.9 percent, in real terms. Changes in foreign supply affect U.S. prices on a 

nearly one-to-one basis: prices fall as foreign supply rises.

U.S. commodity policy helps support U.S. cotton prices: a 1-percent increase 

in the end-of-season stocks covered by the loan program (with stocks measured as a proportion of U.S. cotton use) raises prices by 0.4 percent. Import 

demand by China continues to play an important role in determining prices: 

a 1-million-bale increase in China’s net imports raises prices by 3.1 percent. 
iv

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Given China’s recent imports, this is equivalent to a 1-percent increase in 

China’s cotton imports, as a share of world consumption.

How Was the Study Conducted?

A review of the theoretical framework for commodity price forecasting 

revealed that forecasting price as a function of a stocks-to-use ratio and 

demand shifters, which is the standard methodology, is suffi cient only when 

changes in supply are very small or when changes in stocks-to-use are much 

greater than changes in supply. Since these conditions were not satisfi ed in 

the cotton markets because of the rapid growth in supply due to the spread of 

genetically modifi ed varieties and other technologies, changes in supply were 

included in a proposed model. Several demand shifters were also included. 

China’s net trade as a proportion of world consumption was included to 

account for changes in export demand associated with China’s commodity and 

trade policies. The impacts of U.S. farm policy were accounted for by including a variable representing the amount of cotton in the marketing loan program 

as a share of domestic consumption and by adjusting the dependent variable to 

refl ect the impact of the User Marketing Certifi cate (Step 2) program.

The Quandt-Likelihood Ratio test indicated signifi cant structural change in 

the 1999/2000 marketing year. This structural break was likely caused by 

a combination of factors, including the increased export orientation of the 

U.S. cotton industry following the U.S. textile industry’s contraction as the 

Multifi ber Arrangement was phased out. Thus, the proposed model was modifi ed to include the foreign supply of cotton to refl ect the increased export 

orientation and to correct for the observed structural change. The fi nal model 

was subjected to extensive out-of-sample testing to ensure its appropriateness 

for forecasting.
1

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Introduction

Agricultural prices are notoriously diffi cult to forecast due to shocks from 

weather events around the world, the infl uence of government policy in 

the marketplace, and changing tastes and technology. In addition, agricultural prices are affected by macroeconomic shocks and shifts in energy 

markets. Forecasts of agricultural prices are important to both private and 

public policymakers, as well as producers and consumers of agricultural 

products. Therefore, agricultural price forecasting is a widespread activity. 

USDA alone publishes updates of price forecasts for 24 commodities every 

month in its World Agricultural Supply and Demand Estimates. However, 

until recently USDA was legally prohibited from forecasting cotton prices.1

Cotton price forecasts were available each month from the International 

Cotton Advisory Committee (ICAC) and—less frequently—from the Food 

and Agricultural Policy Research Institute (FAPRI), the Australian Bureau 

of Agricultural and Resource Economics (ABARE), and the World Bank. 

In addition, although USDA did not publish cotton price forecasts, its 

Interagency Commodity Estimates Committee (ICEC) for cotton calculated 

unpublished estimates of world and domestic cotton prices each month. In 

recent years, USDA and other agencies have observed systematic errors in 

their cotton price models, highlighting the need for a thorough review of 

price relationships. The removal of the ban on cotton price forecasting by 

USDA in the 2008 Farm Bill heightened the need to review existing cotton 

price forecasting procedures and to develop a new forecasting model.

This report develops a theoretically based reduced-form specifi cation for a 

cotton price forecasting model. Like earlier models for cotton (Meyer, 1998; 

Valderrama, 1993; Goreux et al., 2007), wheat (Westcott and Hoffman, 

1999), corn (Van Meir, 1983; Westcott and Hoffman, 1999), and soybeans 

(Plato and Chambers, 2004; Goodwin et al., 2005), the U.S. season-average 

farm price is a function of U.S. stocks/use. Other variables include U.S. and 

world cotton supply and a set of shift variables accommodating circumstances particular to cotton markets. The empirical version of the model 

includes a demand shifter for China’s trade and another shifter for the impact 

of U.S. commodity policy. Testing indicates evidence of structural change, 

which is likely the result of the U.S. shift from domestic cotton consumption 

to exporting, and the model is adjusted to include impacts of foreign supply.2

1In 1929, Congress passed legislation forbidding USDA from publishing 

cotton price forecasts (see Townsend 

(1989) for a discussion of the circumstances surrounding this legislation). 

2 Foreign supply is an important 

factor for U.S. cotton prices because 

the United States, as one of the largest 

producers and exporters of cotton in 

the world, directly competes with cotton coming from other countries. 
2

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Data

This study concentrates on the marketing-year average U.S. farm price of 

upland cotton, over 1973-2007. Historical price data are from the USDA’s 

National Agricultural Statistics Service (NASS) database and publications 

(USDA-NASS, 2008). One-year-ahead forecasts for U.S., world, and China 

supply and use variables are published in monthly World Agricultural 

Supply and Demand Estimates (WASDE) reports from the USDA’s World 

Agricultural Outlook Board (USDA-WAOB, selected issues). These forecasts will provide the out-of-sample values for the independent variables 

in future years. Historical cotton supply and demand data are drawn from 

the “Production, Supply and Distribution Online” database maintained by 

USDA’s Foreign Agricultural Service (USDA-FAS, 2008). Unlike wheat 

and feed grains, USDA does not publish forecasts and historical estimates 

of Commodity Credit Corporation (CCC) end-of-year stocks for cotton in 

the monthly WASDE. CCC cotton stocks are forecast twice a year in order 

to project budgetary outlays, and historical data for CCC cotton stocks 

were provided by USDA’s Farm Service Agency (FSA). Data on expenditures for the U.S. User Market Certifi cate Program (“Step 2”) were also 

provided by FSA.
3

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Cotton Price Model

Theoretical Model

The general framework for the cotton price model is based on the theory of 

competitive markets, in which the market price results from allocating available supplies to alternative product uses (e.g., Tomek and Robinson, 2003, p. 

406). For the U.S. cotton market, the identity between supply and demand can 

be written as:

(1)

where I

t

 = ending inventory,

I

t-1 = beginning inventory,

Qt = domestic consumption,

Xt = exports,

At

 = domestic production, and 

Mt = imports.

At the beginning of the marketing year denoted by t, the variables on the 

right-hand side can be treated as predetermined.3 Therefore, the above identity results in the demand for domestic uses, the demand for exports, and the 

demand for inventories at a given level of supply. To simplify, the demand 

for domestic uses and the demand for exports may be summed to represent 

current demand (Dt

). Similarly, the sum of beginning inventory, domestic 

production, and imports refl ects current supply (St

). This allows the identity 

to be expressed as:

 (2)

Each variable in the identity is a function of a set of explanatory variables:

where pt

 is the infl ation-adjusted price, Et-1(pt) is the period t-1 expectation 

of pt

, and zt, yt, and wt are exogenous variables affecting supply, demand, and 

stocks, respectively. All other variables are as defi ned previously. Supply is 

positively related to expected price while demand and stocks are negatively 

related to price. Assuming that supply is predetermined at the beginning of 

the marketing year, equation 2 can be expressed as:

 (3)

3Imports are not predetermined, 

but are trivially small compared with 

domestic production. 

It t ++ = ++ Q X I AM tt t t −1

St − −= D I t t 0

t tt t S −− = g h py p wt (,) (, ) . 0

S bE

g

h

t t-1 t t

t t t

t t t

p z

D p y

I p w

=

=

=

( ), ) (

(,)

(, )
4

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Traditionally, in forecasting models price is specifi ed as a function of the 

stocks-to-use ratio (e.g., Westcott and Hoffman, 1999). Stocks-to-use ratio 

can be introduced in equation 3 by dividing through by g(pt

, yt):

t

t t

t t

t t

t t t

S

p y

p w

p y

p w y

g

h

g

r

(,)

(, )

(,) −= = 1 ( , , ), (4)

where r denotes the ratio of stocks to use. Equation 4 is the implicit price 

equation. To fi nd an explicit equation for price, we differentiate equation 4:4

dS dr g g

p

dp g

y

dy g

p

dp g

y

= + dy r ∂

∂

+

∂

∂

+

∂

∂

+

∂

∂

⎛

⎝

⎜

⎞

⎠

⎟ () . (5)

Solving dS for dp, we obtain the following equation for a change in price:

dp r g

p

dS g r g

p

dr g

p y y

= +

∂

∂

⎛

⎝

⎜

⎞

⎠

⎟ − +

∂

∂

⎛

⎝

⎜

⎞

⎠

⎟ − ∂

∂

⎛

⎝

⎜

⎞

⎠

⎟

− − −

() () 1 1

1 1 1

∂

∂

g

y

dy (6)

Equation 6 shows that change in price can be accurately approximated as 

a function of stocks-to-use ratio and demand shifters only when change in 

supply (dS) is very small or when change in stocks-to-use is much greater 

than change in supply (dr>>dS). Thus, equation 6 provides a more complete 

model of price changes when neither of these two conditions is satisfi ed. The 

result is a model that differs from the traditionally specifi ed models (e.g., 

Meyer, 1998) since supply is now recognized as a variable distinct from 

stocks. Given the problems with forecasting cotton prices in recent years, 

pursuing alternatives to the traditional specifi cation seems appropriate.

This specifi cation models cotton price in fi rst-difference terms, which has 

implications with respect to its time-series properties. For price levels, the 

hypothesis of a unit root cannot be rejected (with an Augmented DickeyFuller test statistic of -1.5). However, for the price series in percentage 

change form, the hypothesis that a unit root is present can be rejected at the 

1-percent signifi cance level (ADF = -8.4), and ordinary least squares estimation of the model will be effi cient and unbiased.

Price enters this model in real rather than nominal terms. This bears discussion since commodity price forecasting models commonly omit any discussion of infl ation, and specify their models in nominal terms. Van Meir (1983) 

specifi es his model in real terms (defl ating with U.S. the gross national product implicit defl ator), but does not discuss the model’s derivation. Goodwin 

et al. (2005) consider the role of infl ation, and test specifi cations with infl ation as an independent variable in their model, which forecasts nominal 

prices. Both including infl ation as a variable and forecasting a defl ated price 

when the ultimate goal is a nominal price forecast put the forecaster’s results 

at the mercy of the available forecasts of infl ation. However, if infl ation 

should be accounted for, a model that completely omits it will see its usefulness diminished in other ways.

4Since the time (t) indicator is identical for all variables, it is omitted from 

the following mathematical derivations. 
5

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Given that this model’s reduced form is based on a theoretical model with 

predetermined supply but demand as a function of price, real rather than 

nominal price is the appropriate dependent variable. Demand is almost 

invariably modeled as a function of real rather than nominal prices (Ferris, 

2005), and a broad measure of infl ation was chosen since cotton products 

will be competing with a broad range of products for consumer demand. 

Furthermore, given that the nominal loan rate is not an independent variable 

in this model, the use of real prices does not adversely affect the role of the 

independent variables as it would for some of the earlier models.

Empirical Analysis

The U.S. cotton price has been highly variable over time under the pressure of various economic and political factors (fi g. 1). Equation 6 provides 

a reduced-form model for evaluating percent changes in U.S. average farm 

price (dp) based on changes in U.S. supply (dS), U.S. stocks-to-use ratio (dr), 

and a set of demand shifters. A signifi cant demand shifter in U.S. and world 

cotton markets is export demand changes associated with China’s trade policy. The strong correlation between world cotton prices and China’s net trade 

was noted as early as 1988 by the ICAC, and the level of China’s net trade 

was included in the International Cotton Advisory Committee’s world price 

forecasting model for the 1974/75-1986/87 period (ICAC, 1988). Similarly, 

MacDonald (1997) adjusted the world (minus China) stocks-to-use variable 

by the amount of China’s net trade in another world price model, estimated 

using 1971/72–1995/96 data. In 2001, researchers at USDA’s Economic 

Research Service highlighted how China’s domestic cotton policy drove its 

cotton trade, with signifi cant impacts on world markets:

Figure 1

U.S. season-average farm price for upland cotton, 1960-2007

Cents/pound (adjusted to 2000 dollars)

Source: ERS calcuations based on data from NASS and the U.S. Department of Commerce.

Food &

Agriculture Act

(1965)

Agriculture & Consumer

Protection Act (1973) Food Security Act 

(1985)

Federal Agriculture

Improvement & 

Reform Act (1996)

Loan rate

(inflation adjusted)

Farm price

1960 63 66 69 72 75 78 81 84 87 90 93 96 99 2002 05

0

20

40

60

80

100

120

140

160

180
6

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

 “Stocks rose after 1994/95 as China raised its farm prices while maintaining an open trade regime. China’s government-mandated farm 

prices proved diffi cult to reduce as world prices fell, and restricting 

imports seemed inconsistent with ensuring the profi tability of its huge 

textile industry. Also, government policy locked older cotton in stocks 

in order to prevent bookkeeping losses as the market value of procured 

cotton tumbled below the cost of purchasing, processing, and storage. 

Stocks reached a staggering 106 percent of use in 1998/99, and China 

accounted for 47 percent of the entire world’s cotton ending stocks. 

Then, starting in 1998/99, the government began applying quantitative 

restrictions to cotton imports and subsidizing exports. In 1999/2000 

the government effectively cut farm prices by refusing to guarantee 

procurement, and in 2000/01 a program to allow the central government to absorb the cost of marketing losses for stockpiled cotton 

went into high gear, opening the fl oodgates for enormous government 

stocks to fl ow into the market. By 2000/01, China had cut its ending 

stocks by nearly 10 million bales, mostly from government stocks 

(USDA/ERS, 2001).”

This demand shifter is measured in this report as an absolute change5 from the 

previous 2-year average of China’s net imports as a share of world consumption:

CN

M t

t

China

t

China

t

World

X

Q = − . (7)

Thus, the empirical price model is specifi ed as:

( ) ( )( ) t t

t

t t

t

t t

t

p p

p

S S

S

r r

r

− = + − + − −

−

−

−

−

−

1

1

0 1

1

1

2

1

1

αα α

 + − α3 CN average t ( ) ( , ), CN CN t t − − 1 2

 (8)

where all variables refer to U.S. values unless otherwise stated.6 Since supply 

is predetermined, changes in supply have an inverse effect on price. Changes 

in stocks-to-use ratio are also negatively related to price. Increases in China’s 

net cotton imports represent a greater export demand for U.S. cotton and thus 

have a positive relationship with price changes.

Another factor affecting the relationship between U.S. ending stocks and 

prices is government policy. The two most relevant policies to cotton prices 

are the loan program and the User Marketing Certifi cate Program (generally referred to as “Step 2” of the marketing loan program) (see Meyer et al., 

2007, for a summary of U.S. farm programs affecting cotton). Since the relationship between how the loan program affects prices and how stocks affect 

prices in this model is relatively straightforward, a simple demand shifter can 

be created to account for the loan program. Step 2’s effects were accounted 

for by adjusting the dependent variable for its impacts.

Before 1986, U.S. commodity programs sometimes served to establish a price 

fl oor for U.S. crops. USDA’s Commodity Credit Corporation (CCC) acquired 

large stocks of cotton (and other commodities) during the early 1980s as market 

prices in the United States fell toward U.S. loan rates (table 1). Stocks owned by 

CCC were not available to the market, and prices were higher than if the stocks 

5Absolute changes from the previous 

2-year average are used instead of 

percent change relative to the previous 

year because of the sporadic changes 

in this variable, which cause small 

absolute changes to appear very large 

in percentage form. 

6The choice of percentage change as 

the functional form followed from the 

theoretical model’s derivation in differences. Alternatives—such as using the 

variables in levels or logs—also resulted 

in less accurate out-of-sample forecasts. 
7

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

could have been drawn upon to satisfy demand. Furthermore, even cotton that 

had not yet been acquired by CCC, but was still being used as collateral in the 

loan program, was also not freely available for spinning, export, or private stockholding. The shift of U.S. cotton policy to a marketing loan program meant that 

CCC acquisition of cotton was signifi cantly reduced, and in 2006 CCC instituted 

a policy of immediately selling any forfeited cotton, ensuring negligible CCC 

stocks at the end of the marketing year. However, the ability of producers to 

place their cotton in the loan program affected prices after 1986, and the volume 

of cotton remaining as collateral in the loan program at the end of the marketing 

year was often signifi cant, even in recent years. While current legislation dictates 

that the maximum duration of a loan is 9 months, before 1996 cotton was permitted to remain under loan as long as 18 months. Given that cotton continues to 

enter the loan in the beginning months of each calendar year, cotton can remain 

under loan for several months after the end of the marketing year (July 31), even 

under current rules. Storage costs are, unlike with grains, covered by the CCC 

when the redemption price applicable to the loan is below the loan rate. This further encourages producers to delay marketing their cotton when the loan program 

is a sound alternative.

Table 1

Season-ending U.S. commodity program cotton stocks, 1974/75-2007/08

 Collateral on Inventory as

Marketing year CCC inventory outstanding loans share of use

1,000 bales 1,000 bales Percent

1974/75 0 901 9

1975/76 0 110 1

1976/77 0 309 3

1977/78 0 1,209 10

1978/79 1 614 5

1979/80 0 501 3

1980/81 0 626 5

1981/82 1 3,643 31

1982/83 396 4,267 43

1983/84 158 444 5

1984/85 124 1,597 15

1985/86 775 5,965 80

1986/87 69 2,914 21

1987/88 5 3,164 22

1988/89 92 4,119 30

1989/90 27 430 3

1990/91 1 215 1

1991/92 3 297 2

1992/93 13 558 4

1993/94 13 179 1

1994/95 0 165 1

1995/96 0 312 2

1996/97 0 311 2

1997/98 0 61 0

1998/99 6 326 2

1999/2000 2 68 0

2000/01 5 1,460 9

2001/02 108 665 4

2002/03 97 668 4

2003/04 0 1,371 7

2004/05 2 301 1

2005/06 5 1,185 5

2006/07 51 857 5

2007/08 0 3,819 21

Sources: Stultz et al., Farm Service Agency (FSA), and ERS calcuations based on data from 

FSA and World Agricultural Supply and Demand Estimates..
8

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Therefore, a variable was created representing the sum of both cotton owned 

by CCC and of cotton with CCC loans still outstanding at the end of the marketing year. This was divided by cotton use for that year to create an additional demand shifter:

CCCt

t

CCC

t

loan

t t

h h

Q X = +

+

 (9)

where ht

CCC is the cotton owned by CCC at the end of the marketing year 

and ht

loan is the volume of cotton remaining as collateral in the loan program 

at that time. By capturing all of the cotton involved in the loan program 

instead of just the cotton owned by CCC, the variable more accurately captures how the loan program supports prices. The loan rate appears to have 

functioned as a price fl oor in 1981, 1982, and 1984, but stocks from cotton 

produced in those marketing years were not acquired by CCC until the next 

marketing year. To correctly attribute the impact of the loan program to those 

years rather than to the subsequent years, the actual loan rate or another variable would have to be added (as in Westcott and Hoffman, 1999). Equation 9 

allows the impact of the loan program to be accounted for with one variable. 

Another government policy that affected cotton markets is the U.S. Step 2 

program, which was introduced in 1990 and continued until 2006. The Step 

2 program offered payments to U.S. textile mills and U.S. exporters when 

the price of U.S. cotton in Northern Europe exceeded the world price of cotton, as measured in Northern Europe. A World Trade Organization (WTO) 

panel in 2005 found the program in violation of the General Agreement 

on Tariffs and Trade (GATT), in large part because the payments to U.S. 

mills were exclusively for the consumption of U.S. cotton rather than either 

U.S. or imported cotton (see Schnepf, 2007, for a summary of the dispute). 

In the program’s early years, the seasonality of the price spread that determined Step 2 payments and the seasonality of U.S. export sales coincided. 

Therefore, exports accounted for a disproportionate share of the payments 

in those years. The ability of exporters to lock in payments for much of the 

year’s exports within a relatively small window of time was also a factor. 

As a result, Step 2 was often perceived to be primarily an export subsidy. 

However, regulatory changes in the program were frequent, and domestic 

U.S. payments exceeded payments to exporters in later years.

During much of the program’s tenure, payments to exporters were made at the 

time of shipment rather than sale. Sales for exports typically occur 9-10 weeks 

before shipment, and sometimes much further in advance. Since the magnitude 

of Step 2 payments fl uctuated weekly, this added uncertainty to the relationship 

between the price of export sales and the subsidy associated with the shipment. 

This, and the fact that payments were made to the fi rms exporting the cotton 

rather than those actually purchasing it, made the link between Step 2 payment 

and subsidization of export demand indirect. However, the subsidies averaged 

5 percent of the value of U.S. cotton use during 1991-2006. Since U.S. cotton accounted for about 20 percent of global cotton use during this time, the 

program likely had an impact on the world price as well as the U.S. price. The 

Step 2 program was terminated in August 2006 as part of the United States’ 

efforts to comply with the WTO panel’s fi ndings. Step 2 therefore is no longer 

a factor in the determination of prices, but must be accounted for when analyzing historical price data.
9

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

The simplest way to understand the impact of the Step 2 program is to abstract 

from the differing effects on U.S. export and domestic demand and simply consider it as a subsidy for consumption of U.S. cotton anywhere in the world (fi gures 2 and 3).7 The introduction of the subsidy would shift the demand for U.S. 

cotton upward from DUS to D′

US (fi g. 2), and the demand for the rest of the 

world’s (ROW) cotton downward from DROW to D′

ROW (fi g. 3). The new equilibrium would have production and consumption of U.S. cotton slightly higher 

than in the absence of a subsidy, and slightly lower for ROW. Similarly, the 

price of cotton in the United States would be higher, and would rise to a greater 

degree than the decline in the ROW’s price. Simulations by FAPRI (2005) and 

Mohanty et al. (2005) found similar impacts, with the removal of Step 2 leading to a U.S. price that was 2.9 percent lower, on average, and a world price 

that was slightly higher (less than 1 percent). 

7While the core of Step 2 was to 

convey payments to consumers of U.S. 

cotton either directly or indirectly, the 

details and history of the program are 

complex (Meyer and MacDonald, 2001). 

Figure 2

Impact of U.S. consumption subsidy on U.S. cotton

Cents/pound (U.S. price)

40

45

50

55

60

65

20 21 22 23 24 25 26 27 28 29 30

Million bales

SUS D'

US

DUS

Source: Simulation of model with linear supply and demand for U.S. and rest of world, substitution 

between U.S. and ROW cotton, and calibrated to approximate recent realizations of the variables.

Simulated 5-percent subsidy 

raises U.S. price 3 percent

Figure 3

Impact of U.S. consumption subsidy on ROW (rest of world) cotton

Cents/pound (ROW price)

SROW

D'

ROW

DROW

40

45

50

55

60

65

95 96 97 98 99 100 101 102 103 104 105

Million bales

Source: Simulation of model with linear supply and demand for U.S. and rest of world, substitution 

between U.S. and ROW cotton, and calibrated to approximate recent realizations of the variables.

Simulated 5-percent subsidy 

lowers ROW price 1 percent
10

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Since Step 2 will no longer be a factor in U.S. prices, and since it infl uenced 

past prices, the forecasting model was estimated with data for the dependent 

variable adjusted to remove the past impact of Step 2. Data on spending for 

Step 2 payments in each year were divided by the value of U.S. cotton use to 

determine the relative subsidy provided each year (table 2). An adjustment 

variable (λt

) was constructed so that each year’s price adjustment was proportional to that year’s subsidy; the average for the adjustment variable over 

1991-2006 is 2.9 percent. Thus, if St

 equals a given year’s subsidy, then λt

= 0.029*St

 / (ΣSt / T), where T = number of years between 1991 and 2006. 

This variable was used to adjust the U.S. season-average upland farm price to 

remove the impact of Step 2:

p

p

GDPDEF and p pGDPDEF t

t

NASS

t

t

t t

NASS

t

= = − * ( ) . 1 λ (10)

Here we defi ne pt

 more explicitly as the season-average price reported 

by USDA’s National Agricultural Statistics Service (pt

NASS), defl ated by 

the U.S. Department of Commerce’s gross domestic product price index 

(GDPDEFt

).8

Thus, the cotton price model adjusted for the impact of the government 

programs is:

( ) ( )( ) * *

*

t t

t

t t

t

t t

t

p p

p

S S

S

r r

r

− = + − + − −

−

−

−

−

−

1

1

0 1

1

1

2

1

1

αα α

 + − α α 3 1 24 ( ) CN average CN CN CC t tt (, ) − − + Ct .

 (11)

Table 2

Step 2 expenditures and price adjustment variable

Marketing year Payments1 Payments/cotton use Subsidy (St) Adjustment (λ t)2

$ Million $/pound Percent Percent

1991/92 140 0.02 2.9 1.6

1992/93 114 0.02 2.7 1.5

1993/94 149 0.02 2.5 1.5

1994/95 88 0.01 1.0 0.6

1995/96 34 0.00 0.5 0.3

1996/97 6 0.00 0.1 0.1

1997/98 416 0.05 6.4 3.7

1998/99 280 0.04 6.7 3.9

1999/2000 445 0.05 10.4 6.0

2000/01 236 0.03 5.5 3.2

2001/02 182 0.02 4.9 2.8

2002/03 455 0.05 8.9 5.1

2003/04 363 0.04 5.5 3.1

2004/05 582 0.06 10.7 6.2

2005/06 397 0.04 6.2 3.6

Average 259 0.03 5.0 2.9

1Fiscal year.

2Derived from annual subsidy so that the 1991-2005 average adjustment is 2.9 percent, and 

each year is proportional to that year’s subsidy: λt = 0.029*St /(ΣSt /T).

Sources: USDA’s Farm Service Agency (FSA), and ERS calculations based on data from the 

FSA, World Agricultural Supply and Demand Estimates, and Cotlook.

8An alternative to this procedure 

would be to continue to defi ne price as 

pt rather than pt*, and instead include 

Step 2 payments or subsidy levels as 

an independent variable. We chose 

to adjust the dependent variable, as 

described, due to higher out-of-sample 

forecasting accuracy of the model using this approach. 
11

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Another factor that may have an important effect on cotton prices is energy 

prices. Previous work (e.g., Barsky and Kilian, 2002) has indicated how oil 

price shocks can affect prices in general. More recently, policy changes—

like those regarding ethanol—have linked energy and grain prices (Westcott, 

2007). Energy market shocks occurred in the 1970s and again after 2004. In 

an effort to develop a model that is robust to both high and low energy prices, 

and to a variety of policy environments, this study concentrates on cotton 

price movements starting from the 1974/75 marketing year and extending to 

2007/08. Since the proposed model is estimated in reduced form, the impact 

of energy prices is included implicitly through the supply variable. A similar 

argument can be made about other supply-inducing variables, such as the 

price of cotton seed. 
12

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Structural Change Test

Following Wang and Tomek (2007), it is important to ensure that a correctly specifi ed model is used to test for structural change. Therefore, equation 11 is used to test for structural change. A traditional approach would 

be to pick an arbitrary sample breakpoint, often the midpoint of the sample, 

and use a Chow test for structural change. This could be further refi ned by 

associating breakpoints with major events relevant to the data series. Either 

of these approaches suffers from the arbitrary nature of the selected breakpoints. Recent literature suggests that the Quandt-Likelihood Ratio (QLR) 

test is superior for detecting structural change of unknown timing (e.g., 

Hansen, 2001). The QLR test consists of calculating Chow breakpoint tests 

at every observation, while ensuring that subsample points are not too near 

the end points of the sample. The QLR test was applied to the pooled data 

in this study with 20-percent trimming. The highest value of the QLR statistic was 5.0 (fi g. 4). The probabilities for these statistics were calculated 

using Hansen’s (1997) method. The critical value of the QLR statistic at the 

99-percent signifi cance level with fi ve restrictions is 4.53 (Stock and Watson, 

2003, p. 471), which indicates that the null hypothesis of no structural change 

is rejected. The maximum statistic of 5.0 was observed in 1999/00, which 

indicates the breakpoint location.

This structural break was likely caused by a combination of factors. 

Besides signifi cant changes in international trade, which have been transitory, this period coincided with some permanent regime changes in China’s 

supply due to the end of guaranteed procurement prices. Some permanent 

changes also took place in China’s consumption sector due to its growing 

textile industry. These regime changes in China’s cotton sector are likely 

associated with China’s accession to the WTO at the end of 2001. China 

joined the WTO just as the textile trade liberalization provisions of the 

Uruguay Round Agreement were having an impact. The phasing out of 

developed country textile trade protection (commonly referred to as the 

Multifi ber Arrangement, or MFA) was an important factor behind the rapid 

Figure 4

Quandt Likelihood Ratio test results for cotton price model, 

1974/75-2006/07

QLR statistic

1The Quandt Likelihood Ratio (QLR) test excludes subsamples too close to the end-points of 

the overall sample. QLR statistics for the 1974-2006 sample are only available for 1981-1999.

90 percent critical value = 3.26

95 percent critical value = 3.66

99 percent critical value = 4.53

1981 83 85 87 89 91 93 95 97 99

0

1

2

3

4

5

6

QLR

1
13

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

increased export orientation of the U.S. cotton industry. As the export share 

of U.S. cotton use surpassed domestic use in the early 2000s (fi g. 5), the 

importance of world supply and demand to U.S. cotton prices increased. In 

addition to policy changes in China, 1999 marked the fi rst year that foreign 

cotton supplies (excluding China) surpassed 75 million bales. As a liberalizing global economy began an accelerated expansion in 1999, foreign cotton supplies began rising to meet this demand.

To correct for the structural change detected in the estimated model (equation 

11), an additional shift variable was added to refl ect the increased export orientation of the U.S. cotton industry. World market signals are assumed to be 

transmitted to the U.S. market through foreign supply, which was constructed 

excluding China’s supply but including China’s net contribution to the global 

availability of cotton (net exports):9

 S S SXM t

Foreign

t

Foreign

t

China

t

China

t

China = −+ − . (12)

Foreign supply is an important factor for U.S. cotton prices. The United 

States is one of the largest producers and exporters of cotton and directly 

competes with cotton coming from other countries. With this variable 

included, no structural break was detected and the specifi cation of the model 

was complete. Thus, the fi nal model specifi cation is:

( ) ( )( ) * *

*

t t

t

t t

t

t t

t

p p

p

S S

S

r r

r

− = + − + − −

−

−

−

−

−

1

1

0 1

1

1

2

1

1

αα α

 + − α α 3 1 24 ( ) CN average CN CN CC t tt (, ) − − + C

S S

S

t

t

Foreign

t

Foreign

+ − − α5

1 ( )

t

Foreign

−1

.

 (13)

9China’s stocks and production were 

excluded from this shift variable since 

the cotton stocks data are particularly 

unreliable (MacDonald, 2007). Stocks 

were regarded as a state secret in 

China for many years, and although 

the degree of secrecy has diminished 

profoundly, even current stock estimates for China are highly conjectural. 

Production data in China are also 

considered less reliable than elsewhere, 

so China’s impact on world supply 

comes through its net trade position. 

With such problems in the data for the 

world’s largest cotton consumer and 

stockholder, neither a world stocks-touse (rw) nor foreign stocks-to-use (rf

) 

ratio would be an appropriate variable. 

Figure 5

U.S. cotton exports and consumption, 1950-2008

Million bales

Source: World Agricultural Supply and Demand Estimates (WASDE), various issues.

1950 55 60 65 70 75 80 85 90 95 2000 05

0

5

10

15

20

Consumption

Export

QLR test indicates structural break (1999)
14

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

All data used for the empirical estimation of this equation are presented in 

table 3. The model is estimated using the most recently available revisions of 

supply and demand categories.

Table 3

Model data, 1974/75 through 2006/07

Marketing China Foreign

year Price Supply S/U net imports CCC supply

 Percent

1974/75 -12.0 -10.8 114.1 -2.1 9.2 8.5

1975/76 11.6 -8.5 -41.6 -0.8 1.0 -4.3

1976/77 17.6 1.6 -26.4 -0.2 2.7 -6.3

1977/78 -23.5 21.2 82.2 1.6 10.1 2.6

1978/79 3.5 -6.5 -34.8 1.7 4.9 0.9

1979/80 -1.4 14.9 -37.6 3.3 3.2 -2.6

1980/81 9.3 -24.0 7.9 0.7 5.3 -0.1

1981/82 -32.4 29.9 179.9 -2.4 30.8 -0.6

1982/83 5.1 1.3 34.0 -3.0 43.5 2.1

1983/84 5.7 -15.8 -68.3 -2.6 4.7 -4.3

1984/85 -13.0 0.2 46.5 -1.9 14.6 18.8

1985/86 -5.6 11.4 227.0 -3.0 80.5 13.6

1986/87 -11.6 8.6 -68.7 -1.4 21.1 -1.3

1987/88 19.9 3.2 13.7 1.1 22.3 0.3

1988/89 -15.8 7.1 33.8 3.0 30.2 0.1

1989/90 10.2 -10.9 -69.2 2.6 2.8 -1.9

1990/91 1.8 -3.1 -24.2 1.0 1.3 0.5

1991/92 -19.0 8.6 82.5 -0.1 1.8 2.6

1992/93 -7.5 -1.0 24.3 -1.9 3.7 -3.4

1993/94 5.9 4.8 -30.0 -0.3 1.1 -5.1

1994/95 22.5 12.0 -35.3 4.8 0.8 -4.6

1995/96 3.0 -9.4 14.3 1.0 1.7 14.2

1996/97 -9.5 4.1 55.4 0.0 1.7 -0.6

1997/98 -10.5 3.8 -6.1 -1.7 0.3 3.3

1998/99 -9.1 -20.1 14.3 -3.4 2.3 2.3

1999/2000 -28.2 13.9 -3.6 -2.5 0.4 5.8

2000/01 11.4 1.4 54.4 0.8 9.4 0.4

2001/02 -41.1 24.5 11.0 1.1 4.1 0.5

2002/03 43.0 -7.2 -29.3 2.5 4.0 -3.8

2003/04 38.2 -2.9 -35.6 7.6 6.8 -5.2

2004/05 -36.7 12.8 52.1 0.2 1.4 23.3

2005/06 14.1 11.0 -6.9 9.2 5.1 -6.2

2006/07 -1.7 -6.7 115.4 -2.7 5.1 7.4

Note: Price is percent change in the real U.S. season-average upland cotton farm price from 

year t-1 to year t. Supply is percent change in U.S. supply from year t-1 to year t. S/U is percent 

change in U.S. stocks-use-ratio from year t-1 to year t. China net imports is the absolute change 

in China’s net imports as a proportion of world demand from their average over the preceding 

2 years. CCC is end-of-season stocks for year t of cotton either owned by USDA’s Commodity 

Credit Corporation or remaining as collateral for the cotton loan program as proportion of 

demand for U.S. cotton that year. Foreign supply is the percent change in global cotton supply 

(minus China’s supply and plus China’s net exports) from year t-1 to year t.

Source: USDA National Agricultural Statistics Service, and World Agricultural Supply and 

Demand Estimates (various issues).
15

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Estimation Results

Table 4 presents the results of cotton price model estimation (equation 13) 

over the 1974/75-2006/07 period. The estimated model explains over 68 percent of the variation in U.S. upland cotton price. All coeffi cients except that 

for the stocks/use variable are signifi cant at the conventional levels and have 

the expected signs. Since most variables are measured in percent changes, 

their coeffi cients are interpreted as elasticities. Thus, a 1-percent increase in 

U.S. supply from the previous year will cause prices to drop by about 0.9 percent. The impact of the stocks/use variable is not statistically different from 

zero at the 10-percent level. An increase of 1 million bales in China NI (net 

imports) from the average of the previous 2 years will cause the U.S. average 

farm price of upland cotton to increase by 3.1 percent relative to the previous 

year’s level. An increase in CCC stocks equal to 1 percent of U.S. use would 

raise price by 0.4 percent. Foreign supply changes have approximately a oneto-one inverse effect on price.

According to Pearson correlation coeffi cients (table 5), signifi cant correlation exists between stocks/use and several other variables in the model. 

Multicollinearity caused by this variable may infl ate standard errors and the 

R-squared statistic of the model. This issue was investigated by dropping the 

stocks/use variable, which resulted in very minor changes (in the second decimal) in the standard errors and the R-squared and no changes in the signs of 

the coeffi cients. Thus, it was determined that multicollinearity did not cause 

signifi cant problems in our model.

The low signifi cance of the stocks/use variable highlights some differences 

of this model from past models, and the changes in world cotton markets. 

Table 4

Estimation results for cotton price model, 1974/75-2006/07

Variable or statistic Coeffi cient Std. error t-Statistic Prob. 

Constant -0.026 0.026 -1.022 0.316

Supply -0.949 0.190 -4.989 0.000

Stocks/use -0.028 0.046 -0.597 0.556

China NI (net imports) 3.060 0.828 3.697 0.001

CCC 0.372 0.162 2.299 0.030

Foreign supply -0.867 0.356 -2.436 0.022

R-squared 0.688 -- -- -- 

Adjusted R-squared 0.630 -- -- -- 

Regression -- 0.118 -- -- 

Sum squared residual 0.376 -- -- -- 

Log likelihood 26.991 -- -- -- 

F-statistic 11.916 -- -- 0.000

Mean dependent variable -0.017 0.194 -- -- 

Akaike info criterion -1.272 -- -- -- 

Schwarz criterion -1.000 -- -- -- 

Hannan-Quinn criterion -1.181 -- -- -- 

Durbin-Watson statistic 2.362 -- -- -- 

Note: Price is percent change in the real U.S. season-average upland cotton farm price from 

year t–1 to year t. Supply is percent change in U.S. supply from year t–1 to year t. S/U is percent 

change in U.S. stocks-use-ratio from year t–1 to year t. China net imports is the absolute change 

in China’s net imports as a proportion of world demand from their average over the preceding 

2 years. CCC is end-of-season stocks for year t of cotton either owned by USDA’s Commodity 

Credit Corporation or remaining as collateral for the cotton loan program as proportion of demand 

for U.S. cotton that year. Foreign supply is the percent change in world minus U.S. cotton supply 

(minus China’s supply and plus China’s net exports) from year t–1 to year t.
16

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Before adjusting for the structural change (i.e., estimating equation 11), the 

parameter for stocks/use was signifi cant at the 12-percent level with the full 

sample, but is signifi cant at the 3-percent level if equation 11 is estimated 

with data through 1999 only. This is despite the presence of signifi cant collinearity with the CCC variable in this truncated sample (65-percent correlation). In the full data set, the stocks/use variable is not statistically signifi cant, 

possibly because the United States now accounts for its smallest share of 

world production since the early 1800s and prices are increasingly set by supply and demand forces outside the United States.

The goodness of fi t of the model in nominal prices is illustrated in fi gure 6. 

Nominal prices are calculated by removing the infl ation adjustment from the 

real prices predicted by the model and adjusting them for Step 2 payments. 

Converting real prices into nominal terms makes it easier to compare model 

predictions against observed prices. The largest in-sample forecast error of 

17.1 cents/pound occurred in 1988 (fi g. 6). The average forecast error for 

the entire sample is 0.2 cent/pound, suggesting that the model is unbiased. 

However, the importance of in-sample properties diminishes if the model 

does not forecast well. 

Table 5

Pearson correlation coeffi cients for cotton price model, 

1974/75 - 2006/07

 Foreign 

 Supply Stocks/use China NI CCC supply

Supply 1.00 0.33 0.13 0.25 0.05

Stocks/use 0.33 1.00 -0.39* 0.60** 0.51**

China net imports 0.13 -0.39* 1.00 -0.26 -0.39*

CCC 0.25 0.60** -0.26 1.00 0.24

Foreign supply 0.05 0.51** -0.39* 0.24 1.00

Note: Supply is percent change in U.S. supply from year t–1 to year t. S/U is percent change in 

U.S. stocks-use-ratio from year t–1 to year t. China net imports is the absolute change in China’s 

net imports as a proportion of world demand from their average over the preceding 2 years. 

CCC is end-of-season stocks for year t of cotton either owned by USDA’s Commodity Credit 

Corporation or remaining as collateral for the cotton loan program as proportion of demand for 

U.S. cotton that year. Foreign supply is the percent change in world minus U.S. cotton supply 

(minus China’s supply and plus China’s net exports) from year t–1 to year t. Number of observations is 33. One asterisk indicates signifi cance at the 5% level (two-tailed), two asterisks indicate 

signifi cance at the 1% level (two-tailed).

Figure 6

Actual and estimated U.S. upland cotton farm price, 1974-2007

Cents/pound, current

Source: World Agricultural Supply and Demand Estimates (WASDE), various issues, 

and authors’ calculations.

1974 77 80 83 86 89 92 95 98 2001 04 07

0

20

40

60

80

100

Actual

Estimated
17

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Granger (2005) highlighted that the construction of a model’s evaluation 

should be motivated by the model’s purpose. While one purpose of this 

model is to discern the impact of supply/demand and policy variables on cotton prices to improve the understanding of these processes, the primary purpose of the model is to assist forecasting. Jumah and Kunst (2008) recently 

demonstrated with a set of grain price forecasting models that statistics 

assessing in-sample fi t and those evaluating out-of-sample performance can 

give distinctly different rankings of model preference.

For this cotton model, a set of out-of-sample forecasts was calculated by 

reestimating the model with a truncated historical sample (ending in 2002/03) 

and using the parameters from this truncated sample to estimate subsequent 

out-of-sample forecasts. Four years of price forecasts (2003/04 to 2006/07) 

were calculated using data available in August 2008. Forecast performance 

was assessed relative to alternative forecasts.

The fi rst alternative is the cotton forecasting model developed by Meyer in 

1998. Meyer’s specifi cation is:

ln(P) = f (ln(S/U), CHFSTKS, Index, DUMSU, ln(LDP)

* DUMSU, ln(1+CCC/Use)), (14)

where CHFSTKS = change in foreign (excluding China) stocks, Index = 

product of the September average of the price of the December futures contract and AMS’s September estimate of the share of expected planted area 

already forward contracted, DUMSU = dummy valued at 1 when stocks/use is 

less than or equal to 22.5 percent, LDP = the difference between the loan rate 

and effective loan repayment rate, and CCC = CCC inventory.

Thus, the main difference between the proposed model and Meyer’s model 

is that the latter model does not take into account changes in domestic and 

world supply (which was less relevant during the time that model was developed) but accounts for the impact of additional information through the index 

variable connected to futures prices. 

The second alternative is the reduced-form model developed by USDA’s 

World Agricultural Outlook Board (WAOB) in 2006 in an attempt to refl ect 

the increased export orientation of the U.S. cotton industry (U.S. Department 

of Agriculture, WAOB, 2006). This model’s specifi cation is:

Pt

 = f (WxC S/Ut, WxC S/Ut-1, China net exportst), (15)

where WxC S/U = world, excluding China, stocks/use.

The forecast from this model was used as one of the inputs in the USDA’s cotton ICEC forecast. The difference between the proposed model and the WAOB 

model is that the WAOB model focuses on international forces, while the proposed model includes both international and domestic components. 

Alternative models were estimated with samples ending in 2002/03. For each 

model, 2008 data for independent variables were used to estimate parameters. Estimated parameters were used to construct out-of-sample forecasts 

for 2003/04-2006/07. Alternative forecasts have been compared based on 
18

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

their individual mean error to test for bias, root mean squared error (RMSE), 

and mean absolute percent error to evaluate the size of the error, as well as 

Theil’s U, with comparisons between forecasts based on the Morgan-GrangerNewbold (MGN) and Diebold-Mariano (DM) statistics (e.g., Diron, 2008).

Table 6 summarizes the proposed and alternative models’ out-of-sample performance over 2003/04-2006/07. The fi rst accuracy statistic presented for 

all forecasts is mean error, which measures forecast bias. This statistic demonstrates the tendency of the WAOB model forecasts to overestimate cotton 

prices in recent years, which was one of the motivations for developing the 

new model. The mean error for the proposed model is one of the smallest, suggesting that this model has been successful in reducing the bias in cotton price 

forecasts. The next two statistics, root mean squared error and mean absolute 

percent error, evaluate the variance of the alternative forecasts. The proposed 

model’s RMSE of 4.1 cents/pound and MAPE of 7 percent are both lower than 

those of the alternative models. Theil’s U statistics indicate that all three forecasts are distinctly better than those of the naive model, but the proposed model 

has the lowest (best) Theil’s U of 0.31. Finally, the alternative forecasts were 

compared to a benchmark of the proposed model, using a the DM and MGN 

tests. The negative sign of the GNM statistic indicates lower accuracy of the 

alternatives relative to the benchmark. This test indicates that even with as little 

as four observations, the proposed model is signifi cantly more accurate than the 

WAOB model (at the 10-percent signifi cance level).10

Additional detail on out-of-sample performance is shown in fi gure 7, which 

plots specifi c errors of the alternative forecasts over 2003/04-2006/07. 

The proposed model had the smallest error in 2003/04, the largest error 

in 2006/07, and an about average performance in 2004/05. Unfortunately, 

Table 6

Evaluation of price forecasting models, 2003/04-2006/071

Model Isengildina and MacDonald Meyer2 WAOB3

Information set4 2008 2008 2008

Sample 1974-2002 1978-2002 1989-2002

Cents/lb

Mean error (bias)5 2.1 2.0 -7.8

Root mean squared error (RMSE)6 4.2 6.2 12.4

Percent

Mean absolute percent error (MAPE) 8 9 16

Theil’s U statistic 0.33 0.39 0.97

Morgan-Granger-Newbold statistic7 -- -0.97 -2.79

Diebold-Mariano statistic8 -- 1.01 0.86

1 Model parameters estimated with samples concluding in 2002/03.

2 Meyer, 1998. 3 Unpublished model developed by the World Agricultural Outlook Board.

4 Information set used to estimate parameters. For each model, 2008 information is used to 

determine the values of the independent variables.

5 For each year, et

 = Yt – Ft, where Yt is the actual realization of the price and Ft is the forecast. 

Therefore, et

 < 0 is an indication of upward bias. None of the models evaluated here had average 

forecast means that were signifi cantly different from zero at either the 1-percent, 5-percent, or 

10-percent level.

6 The RMSE shown here are calculated only for the out-of-sample forecasts over 2003/04-2006/07.

7 GNM statistic testing difference between forecast accuracy of Isengildina and MacDonald forecast. None of the differences were signifi cant at either the 1-percent or 5-percent levels. WAOB 

was signifi cant at the 10-percent level.

8 DM statistic testing difference between forecast accuracy of Isengildina and MacDonald forecast. None of the differences were signifi cant at the 1-percent, 5-percent, or 10-percent level.

10Accuracy of this model deteriorates signifi cantly if the dependent 

variable is switched from real to 

nominal prices. MAPE doubles in 

the out-of-sample test, while RMSE 

and bias also grow. Theil’s U-statistic 

rises to above Meyer’s and the GNM 

statistic falls so that this model is no 

longer more accurate than WAOB. 

Thus, adjusting for infl ation improves 

the accuracy of the model. 
19

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

the necessary tradeoff between the need to estimate models relevant to a 

dynamic economic environment and the already limited universe of annual 

observations available limits the number of observations available for evaluating annual forecasting models, but these tests indicate that the model is an 

improvement over earlier efforts.

Another important characteristic for a forecasting model is parameter stability. 

If estimated parameters change signifi cantly as new observations are added, 

the out-of-sample forecasts may become highly volatile and less accurate, and 

the model may be misspecifi ed. Parameter estimates are relatively unchanged 

when estimated using a 1974/75-2002/03 sample versus a 1974/75-2006/07 

sample (table 7). Furthermore, the out-of-sample forecasts of the model estimated with the 1974/75-2002/03 subsample are only slightly less accurate than 

the in-sample estimated prices of the model using the full dataset. This stability 

bodes well for the model’s usefulness in future forecasting.

Figure 7

Out-of-sample performance of proposed model relative 

to alternative models

Cents/pound, current

Source: Authors’ calculations.

2003/04 2004/05 2005/06 2006/07

-30

-25

-20

-15

-10

-5

0

5

10

15

Meyer WAOB Isengildina & MacDonald

Marketing year

Table 7

Parameter stability between samples and out-of-sample performance

 1974/75-2006/07 1974/75-2002/03 Percent 

Variable or statistic Coeffi cient Coeffi cient difference

Constant -0.026 -0.032 20

Supply -0.949 -0.893 -6

Stocks/use -0.028 -0.041 48

China NI (net imports) 3.060 3.407 11

CCC 0.372 0.408 10

Foreign supply -0.867 -0.830 -4

Accuracy: 2003/04-2007/08

RMSE 3.492 4.173 21

MAPE 0.066 0.079 -- 

Theil’s U statistic 0.333 0.398 -- 

Note: Price is percent change in the real U.S. season-average upland cotton farm price from 

year t–1 to year t. Supply is percent change in U.S. supply from year t–1 to year t. S/U is percent 

in U.S. stocks-use ratio from year t–1 to year t. China net imports is the absolute change in 

China’s net imports as a proportion of world demand from their average over the preceding 2 

years. CCC is end-of-season stocks for year t of cotton either owned by USDA’s Commodity 

Credit Corporation or remaining as collateral for the cotton loan program as proportion of 

demand for U.S. cotton that year. Foreign supply is the percent change in global cotton supply 

(minus China’s supply and plus China’s next exports) from year t–1 to year t.
20

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Conclusions and Implications

This report developed a statistical model that refl ects current drivers of U.S. 

upland cotton prices in response to renewed authority for USDA to publish 

cotton prices and the growing challenges to accurate cotton price forecasting 

in recent years. A review of the theoretical framework for commodity price 

forecasting suggested that changes in supply should be included in a cotton 

price model because of the rapid growth in supply due to the spread of genetically modifi ed varieties and other technologies. Several demand shifters 

were also included in the model. China’s net trade as a proportion of world 

consumption was included to account for changes in export demand associated with China’s commodity and trade policies. The impacts of U.S. farm 

policy were accounted for by including a variable representing the amount 

of cotton in the marketing loan program as a share of domestic consumption 

and by adjusting the dependent variable to refl ect the historical impact of the 

User Marketing Certifi cate (Step 2) program, now discontinued. 

Analysis of the cotton price forecasting model identifi ed a structural break 

that occurred in the U.S. cotton industry in 1999. This structural break was 

likely caused by a combination of factors, including an increased export 

orientation of the U.S. cotton industry as the domestic textile industry contracted following the phasing out of the Multifi ber Arrangement (for more 

information on the end of the MFA, see MacDonald and Vollrath, 2005). 

Thus, the proposed model was modifi ed to include the world supply of cotton, to refl ect the increased export orientation, and to correct for the observed 

structural change. The fi nal model was subjected to extensive out-of-sample 

testing to ensure its appropriateness for forecasting.

The out-of-sample performance measures of the proposed cotton price 

model suggest that it is a considerable improvement over the naive forecast. 

Parameter estimates and forecast errors do not change much between a full 

sample and reduced sample used for out-of-sample forecasting, indicating the 

stability of the model. This stability is an improvement over past forecasting 

models that have been challenged by changing market conditions. Specifi cally, 

the out-of-sample forecasts from the proposed model are characterized by a 

lack of bias and relatively low variance. However, the in-sample root mean 

squared error of the nominal price predictions projected by this model is 6.0 

cents/pound, which is about 10 percent of the 1974/75–2006/07 average for 

U.S. upland cotton farm prices. These errors suggest that there may be some 

variables omitted from the model that can be pursued in future research. 

Omitted variables could include cotton quality characteristics, the role of 

polyester (cotton’s primary substitute in textile spinning), and lower transmission of grain price shocks to signifi cant non-U.S. cotton producers. For 

example, Olmstead and Rhode (2003) demonstrate that the average staple 

length of U.S. cotton rose during the historical sample used to estimate this 

model. The U.S. season-average price is equivalent to the value of the crop of 

upland cotton divided by its volume, and staple length is a key determinant 

of the price of a particular bale, or lot, of cotton. Olmstead and Rhode’s data 

started in 1957, when the U.S. upland crop averaged 32.75 sixteenths of an 

inch long. In 2006/07 and 2007/08, it averaged 35.3 sixteenths, an increase 

since 1957 that in 2007/08 would be worth about 2 cents per pound (USDA/
21

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

AMS, 2008). However, the impact of these quality characteristics on the 

price of cotton was not included in this study as it is very diffi cult to quantify.

The proposed model is particularly informative in an environment of volatile 

commodity prices and the increased role of new players in futures markets. 

These developments and changing market institutions, such as the rise of 

electronic trading, have raised questions about the relationships among cash 

prices, futures prices, and supply/demand fundamentals. Specifi cally, there 

has been growing concern about changes in the relationship between futures 

prices and cash prices in the United States (Irwin et al., 2007). In March 

2008, U.S. cotton futures demonstrated nearly unprecedented volatility. The 

basis between nearby futures and spot prices remained historically wide for 

several months afterward (fi g. 8).

As a result, a cotton price forecast based on futures prices in 2008 would 

have been biased substantially upward. This is illustrated by the surge in 

the futures-based forecasts of farm prices for corn, wheat, and soybeans 

published by USDA’s Economic Research Service during 2008 (U.S. 

Department of Agriculture, Economic Research Service, 2008). Therefore, 

while it would be irrational to ignore the information provided by futures 

markets when forecasting the U.S. farm price of cotton, it is also important to 

have forecasts that are independent of that information.

The out-of-sample performance of the proposed model was superior to that of 

alternative models. This comparison, however, does not include the consensusbased forecasts of USDA’s Interagency Commodity Estimates Committee 

(ICEC) for cotton that became publicly available in the WASDE reports as of 

June 2008. The consensus forecasts were about as accurate as the proposed 

model, despite the handicap of preliminary supply and demand estimates. The 

advantage that the ICEC had in making its forecasts was the ability to incorporate additional information in its forecasting procedure that is hard to quantify 

within the framework of a statistical model. However, consensus forecasts are 

very specifi c to current events and diffi cult to replicate or adjust to changing 

circumstances. As such, they are of limited use when presenting policymakers 

Figure 8

December ICE cotton contracts' basis, 2005-08

Cents/pound

Source: Thompson Reuters Datastream.

Days until expiration

200 180 160 140 120 100 80 60 40 20 0

-25

-20

-15

-10

-5

0

5

Dec. 2005

Dec. 2006

Dec. 2007

Dec. 2008
22

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

with alternative scenarios. A further advantage of this report’s model is the 

opportunity for checking consistency. USDA’s ICEC sometimes adjusts its 

supply and demand outlook in response to prices, and this model provides an 

additional tool to aid in that process.

Future avenues for research relate to both world cotton markets and to the 

characteristics of USDA’s supply and demand forecasts. While this study 

correctly identifi ed some aspects of the structural change that has occurred 

in U.S. cotton markets since 1999, further examination of the sources of 

structural change and the channels through which it affects cotton prices is 

warranted. In addition, forecasts based on this model will depend not only on 

the parameters of the model, but will also be conditional on the forecasts of 

supply and demand used to derive any particular forecast of price. Intuitively, 

early-season forecasts are less reliable than late-season forecasts, but further 

research can inform these intuitions, and identify key points in the season 

with respect to dynamics of forecast performance within the forecasting season. Furthermore, the accuracy of specifi c supply and demand variables and 

their potential contribution to forecast errors should be examined with the 

goal of correcting for systematic errors in the cotton price forecasts.
23

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

References

Barsky, R.B., and L. Kilian. 2002. “Do We Really Know that Oil 

Caused the Great Stagfl ation? A Monetary Alternative.” in NBER 

Macroeconomics Annual 2001, 16, B.S. Bernanke and K. Rogoff, 

eds., Cambridge, MA: MIT Press, pp. 137-183.

Diron, M. 2008. “Short-term forecasts of Euro area real GDP growth: an 

assessment of real-time performance based on vintage data,” Journal 

of Forecasting 27: 371-390.

Ferris, J.N. 2005. Agricultural Prices and Commodity Market Analysis, 

Michigan State University Press.

Food and Agricultural Policy Research Institute. 2005. Impacts of 

Commodity and Conservation Reserve Program Provisions in House 

and Senate Reconciliation Bills, FAPRI-UMC Report #15-05.

Goodwin, B.K., R. Schnepf, and E. Dohlman. 2005. “Modelling soybean prices in a changing policy environment,” Applied Economics, 

37: 253-263.

Goreux, L., A. Plastina, A. Gruere, and Y. Cao. 2007. “The New ICAC 

Cotton Price Forecasting Model.” Cotton: Review of the World 

Situation, ICAC, (July-August 2007).

Granger, C.W.J. 2005. “Modeling, evaluation, and methodology in the 

new century,” Economic Inquiry, 43(1): 1-12.

Hansen, B.E. 2001. “The New Econometrics of Structural Change: 

Dating Breaks in U.S. Labor Productivity,” Journal of Economic 

Perspectives 15:117-28.

Hansen, B.E. 1997. “Approximate Asymptotic P Values for Structural 

Change Tests.” Journal of Business and Economic Statistics 15(1):60-61

International Cotton Advisory Committee. 1998. “Where will cotton prices 

go?” Cotton Review of the International Situation 41(3): 3-5.

Irwin, S.H., H.H. Garcia, and D.L. Good. 2007. “The Performance of 

Chicago Board of Trade Corn, Soybean, and Wheat Futures Contracts 

after Recent Changes in Speculative Limits,” American Agricultural 

Economics Association, Annual Meeting, July 29-Aug. 1, 2007, 

Portland, OR.

Jumah, A., and R.M. Kunst. 2008. “Seasonal production of European 

cereal prices: good forecasts using bad models?” Journal of 

Forecasting 27: 391-406.
24

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

MacDonald, Stephen. 1997. “Forecasting World Cotton Prices.” The Ninth 

Federal Forecasters Conference—Papers and Proceedings,1997. 

National Center for Educational Statistics, U.S. Department of Education. 

MacDonald, Stephen. 2006.“Cotton Price Forecasting and Structural 

Change,” The 15th Federal Forecasters Conference—Papers and 

Proceedings, 2006, U.S. Department of Veterans Affairs, Veterans Health 

Administration.

MacDonald, S., and T. Vollrath. 2005. The Forces Shaping World Cotton 

Consumption After the Multifi ber Arrangement, U.S. Department of 

Agriculture, Economic Research Service, CWS-05C-01. http://www.ers.

usda.gov/publications/cws/apr05/cws05c01/cws05c01.pdf

Meyer, L., and S. MacDonald. 2001. Cotton: Background and Issues for 

Farm Legislation, Outlook Report No. CWS-0601-01, U.S. Department 

of Agriculture, Economic Research Service. http://www.ers.usda.gov/

publications/CWS-0601-01/cotton-policy.pdf

Meyer, L., S. MacDonald, and R. Skinner. 2008. “Cotton and Wool 

Outlook,” Outlook Report No. CWS-08a, U.S. Department of 

Agriculture, Economic Research Service. http://usda.mannlib.cornell.edu/usda/ers/CWS//2000s/2008/CWS-03-12-2008.pdf

Meyer, L., S. MacDonald, and L. Foreman. 2007. Cotton Backgrounder, 

Outlook Report No. CWS-07B01, U.S. Department of Agriculture, 

Economic Research Service. http://www.ers.usda.gov/publications/

CWS/2007/03Mar/CWS07B01/cws07B01.pdf

Meyer, L.A. 1998. “Factors Affecting the U.S. Farm Price of Upland 

Cotton,” Cotton and Wool Situation and Outlook, CWS-1998, U.S. 

Department of Agriculture, Economic Research Service. http://www.ers.

usda.gov/briefi ng/cotton/pdf/cws1998CottonFarmPrice.pdf

Mohanty, S., S. Pan, M. Welch, and D. Ethridge, 2005. “The Impacts of 

Eliminating the Step 2 Program on the U.S. and World Cotton Market,” 

Briefi ng Paper CER-BR05-01, Cotton Economics Research Institute, 

Texas Tech University.

Olmstead, A.L., and P.W. Rhode. 2003. “Hog Round Marketing, Seed 

Quality, and Government Policy: Institutional Change in U.S. Cotton 

Production, 1920-1960,” Working Paper w9612, National Bureau of 

Economic Research.

Plato, G., and W. Chambers. 2004. How Does Structural Change in the 

Global Soybean Market Affect the U.S. Price? OCS-04D-01, U.S. 

Department of Agriculture, Economic Research Service.http://www.ers.

usda.gov/publications/OCS/APR04/OCS04D01/OCS04D01.pdf

Skelly, Carol, ICEC Chair, World Agricultural Outlook Board, private communication, 03/30/2007.
25

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Schnepf, R. 2007. “Brazil’s WTO Case Against the U.S. Cotton Program,” 

CRS Report for Congress, No. RL32571. http://ncseonline.org/NLE/

CRSreports/07Oct/RL32571.pdf

Stock, J.H., and M.W. Watson. 2003. Introduction to Econometrics. Pearson 

Education, Inc., New York, 2003.

Stultz, H., E.H. Glade, S. Sanford, J.V. Lawler, and R.A. Skinner. 1990. 

Fibers: Background for 1990 Farm Legislation, Agriculture Information 

Bulletin No. 591, U.S. Department of Agriculture, Economic Research 

Service. http://www.ers.usda.gov/Publications/aib591/aib591fm.pdf

Tomek, W.G., and K.L. Robinson. 2003. Agricultural Product Prices. 4th 

edition. Cornell University Press.

Townsend, Terry P. 1989. “This Was a High-Powered Instrument That 

Sent Its Projectile to the Vitals of the Industry—Why USDA Does Not 

Forecast Cotton Prices,” Proceedings of the Cotton Beltwide Conferences. 

National Cotton Council.

U.S. Department of Agriculture Agricultural Marketing Service, Cotton 

Program. 2008. Cotton Quality Annual Report, Vol. 81, No. 8.

U.S. Department of Agriculture, Agricultural Marketing Service, Cotton 

Program. 2008. Cotton Price Statistics Annual Report, Vol. 89, No. 13.

U.S. Department of Agriculture, Economic Research Service. 2008. SeasonAverage Price Forecasts. http://www.ers.usda.gov/Data/PriceForecast/

U.S. Department of Agriculture, Economic Research Service. 2001. Cotton 

and Wool Situation and Outlook, CWS-2001, Nov.

U.S. Department of Agriculture, Foreign Agricultural Service, Production, 

Supply, and Distribution Online. http://www.fas.usda.gov/psdonline/

U.S. Department of Agriculture, National Agricultural Statistics Service. 

2008. Agricultural Prices, Sept. 29. 

U.S. Department of Agriculture, World Agricultural Outlook Board. 

Various years. World Agricultural Supply and Demand Estimates. 

http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.

do?documentID=1194

U.S. Department of Agriculture, World Agricultural Outlook Board. 2006. 

Unpublished price model in ICEC current supply and demand summary.

Valderrama, C. 2000. “A Revision of the ICAC Price Model,” Cotton: 

Review of the World Situation 53-6 (July-Aug.), International Cotton 

Advisory Committee.

Valderrama, C. 1993. “Modeling International Cotton Prices,” Proceedings 

of the Cotton Beltwide Conferences. National Cotton Council.
26

U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change / ERR-80

Economic Research Service / USDA

Van Meir, L.W. 1983. Relationships among Ending Stocks, Prices, and 

Loan Rates for Corn. Feed Outlook and Situation Report, FdS-290, U.S. 

Department of Agriculture, Economic Research Service.

Wang, D., and W.G. Tomek. 2007. “Commodity Prices and Unit Root Tests,” 

American Journal of Agricultural Economics 89(4): 873-889.

Westcott, P.C. 2007. Ethanol Expansion in the United States: How Will 

the Agricultural Sector Adjust? Outlook Report No. FDS-07D-01, U.S. 

Department of Agriculture, Economic Research Service. http://www.ers.

usda.gov/Publications/FDS/2007/05May/FDS07D01/fds07D01.pdf

Westcott, P.C., and L.A. Hoffman. 1999. Price Determination for Corn and 

Wheat: The Role of Market Factors and Government Programs. Technical 

Bulletin No. 1878. U.S. Department of Agriculture, Economic Research 

Service. http://www.ers.usda.gov/publications/tb1878/tb1878.pdf

Westcott, P.C., and J.M. Price. 2001. Analysis of the U.S. Commodity Loan 

Program with Marketing Loan Provisions, Agricultural Economic Report 

No. 801, U.S. Department of Agriculture, Economic Research Service. 

http://www.ers.usda.gov/publications/aer801/aer801.pdf