BLS WORKING PAPERS 

 

U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Prices and Living Conditions 

Improving the CPI’s Age-Bias Adjustment: 

Leverage, Disaggregation and Model Averaging 

Joshua Gallin, Board of Governors of the Federal Reserve System 

Randal Verbrugge, U.S. Bureau of Labor Statistics 

Working Paper 411

October 2007

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
Improving the CPI’s Age-Bias Adjustment:

Leverage, Disaggregation and Model-Averaging

Joshua Gallin

Board of Governors of the Federal Reserve System

Washington, DC

Email: Joshua.H.Gallin@frb.gov

Randal J. Verbrugge

Division of Price and Index Number Research

Bureau of Labor Statistics

2 Massachusetts Ave. NE

Washington, DC 20212

Email: verbrugge.randal@bls.gov

First Version: December 6, 2004

This version completed on: October 17, 2007

JEL Classification Codes: E31; C81; C82; R31; R21; O47

Keywords: depreciation; hedonics; model averaging; inflation; CPI bias.

Acknowledgement 1 We thank David Johnson, Louise Campbell, Tim Erickson, John Greenlees,

Johannes Hoffmann, Greg Kurtzon, Frank Ptacek and Elliot Williams, and participants at the

DPINR research seminar and the 2006 Western Economics Association Conference. All errors,

misinterpretations and omissions are ours. All the analysis, views, and conclusions expressed in

this paper are those of the authors; they do not reflect the views or policies of the Bureau of Labor

Statistics or the views of other BLS staff members; neither do they indicate concurrence by other

members of the research staff of the Board of Governors, or by the Board of Governors itself.
Improving the CPI’s Age-Bias Adjustment

Abstract

As a rental unit ages, its quality typically falls; a failure to correct for this would result

in downward bias in the CPI. We investigate the BLS age bias imputation and explore two

potential categories of error: approximations related to the construction of the age bias factor,

and model misspecification. We find that, as long as one stays within the context of the current

official regression specification, the approximation errors are innocuous. On the other hand,

we find that the official regression specification — which is more or less of the form commonly

used in the hedonic rent literature — is severely deficient in its ability to match the conditional

log-rent vs. age relationship in the data, and performs poorly in out-of-sample tests. It is

straightforward to improve the specification in order to address these deficiencies.

However, basing estimates upon a single regression model is risky. Age-bias adjustment

inherently suffers from a general problem facing some types of hedonic-based adjustments, which

is related to model uncertainty. In particular, age-bias adjustment relies upon specific coefficient

estimates, but there is no guarantee that the true marginal influence of a regressor is being

estimated in any given model, since one cannot guarantee that the Gauss-Markov conditions

hold. To address this problem, we advocate the use of model averaging, which is a method

that minimizes downside risks related to model misspecification and generates more reliable

coefficient estimates. Thus, after selecting several appropriate models, we estimate age-bias

factors by taking a trimmed average over the factors derived from each model. We argue that

similar methods may be readily implemented by statistical agencies (even very small ones) with

little additional effort.

We find that, in 2004 data, BLS age-bias factors were too small, on average, by nearly 40%.

Since the age bias term itself is rather small, the implied downward-bias of the aggregate indexes

is modest. On the other hand, errors in particular metropolitan areas were much larger, with

annual downward-bias as large as 0.6%.

2
Improving the CPI’s Age-Bias Adjustment

1 Introduction

Housing costs are a substantial part of most American’s monthly outlays. As a result, these costs

account for about one-third of the total weight of the Consumer Price Index (CPI). There are

two major components of these shelter costs. First, there is tenant’s rent, covering the shelter

expenditures of renters. Second, there is owner’s equivalent rent (OER), covering the shelter expenditures of owners. (The rental equivalence method — which abstracts from the highly-volatile,

difficult-to-measure, financial-asset aspect of homeownership — is probably the best of the available

methods for estimating changes in homeowner shelter costs. For details, see Ptacek and Baskin

1996, Diewert 2003, Poole, Ptacek and Verbrugge 2005, and Verbrugge 2007.)

Since shelter expenditures have such an enormous weight in the CPI, accurate measurement of

shelter costs is crucial to obtaining an accurate measurement of the overall inflation experienced

by the average US consumer. The measurement goal for the shelter components of the CPI is

cost changes in constant-quality housing units. To approximate quality-adjusted price changes,

the Bureau of Labor Statistics (BLS) collects rental price data from a sample of housing units

over time, and makes adjustments for changes in observed physical characteristics (such as the

number of rooms), and for aging.1 Failing to adjust for aging would introduce a downward bias

into the CPI, since housing units deteriorate over time; an unchanged rent on unit which has aged

represents a price increase, since the same amount of money is purchasing a lower-quality good.

(Of course, renovation and remodeling can temporarily reverse the deterioration experienced by

a particular unit; the adjustment for aging is based upon the net effect of aging and renovation

within a metropolitan area.)

The BLS adjustments for aging are based on Randolph (1988a), and involve scaling observed

rents by “age-bias” factors that are based on a hedonic model for housing rents.2 For any given

year, this adjustment is fairly modest; still, its impact over many years is nontrivial. In this paper,

we describe several shortcomings in the way the BLS specifies its hedonic model, and show that

these shortcomings can have significant effects on reported CPI housing indexes.

The BLS estimates its hedonic model every year using cross-section data from its housing unit

1See the BLS Handbook of Methods. 2 see also Lane, Randolf, and Berenson (1988)

3
Improving the CPI’s Age-Bias Adjustment

sample merged with data from the decennial Census. The model assumes that the log rent for a

given housing unit depends on the unit’s age, age-squared, age interacted with several housingunit characteristics, and a large set of controls for other physical and neighborhood characteristics.

We use the same data to illustrate two types of shortcomings of the BLS methodology. First,

as described below, the BLS makes two approximations in constructing aging factors, which turn

out to impart a bias to these estimates. Second, the BLS model specification suffers from two

problems: it allows a small number of extremely old units to have a large effect on the estimated

age-bias factors, and it is too restrictive in that it does not take full advantage of the available

geographic information. In particular, the BLS hedonic model has only five age-related terms, and

estimates its hedonic model separately only for each of the four Census regions, thereby ignoring

the diversity across statewide or metropolitan housing markets. (A third possible shortcoming is

a potential bias related to a confounding of the effects of historical depreciation of surviving units

with average annual depreciation of all units, and the inability of the BLS procedure to control

for unit-specific characteristics. We investigate this set of topics in a companion paper, Gallin and

Verbrugge 2007.)

Age-bias adjustment also inherently suffers from a general problem facing some types of hedonicbased adjustments: it relies upon specific coefficient estimates, but there is no guarantee that the

true marginal influence of a regressor is being estimated in any particular model, since one cannot

guarantee that the Gauss-Markov conditions hold. The implied estimated marginal influence of a

given variable can differ dramatically across models which otherwise appear roughly equivalent in

terms of their complexity, their fit to the data, their out-of-sample predictive ability, and so on.

(Coefficient estimates often change when the model changes, which is why empirical work often

includes tables of regression results for different models.) We argue that the potential for such

variability is a compelling argument for a model-averaging approach, which reduces the risk of

choosing a model whose coefficient estimates are far from the true marginal effects. Simple variants

of model averaging can be implemented with very little additional work.

We use the BLS and Census data to show that simple remedies for these shortcomings alter

estimates of age-bias factors. Our estimated age-bias factors are on average almost 40% larger than,

and often quite different from, those produced by the BLS methods. The effects turn out to be

4
Improving the CPI’s Age-Bias Adjustment

relatively small at the national level. In contrast, the resulting estimates of the growth rate of the

CPI for housing can differ importantly from the baseline BLS approach in many metropolitan areas.

In particular, these estimates can be altered by more than 0.6%,3 so that — for example — estimated

rent inflation in a metropolitan area might increase from 1.5% to 2.1%, easily large enough to alter

the overall inflation rate in the metropolitan area. This in turn can be of major local significance;

for example, Colorado’s Amendment 42, which passed in 2006, indexes Colorado’s minimum wage

to Denver’s CPI.

2 Age-Bias Adjustment of the CPI for Housing

2.1 Description

The CPIs for renter- and owner-occupied housing are meant to measure “price” changes (in this

case, rent changes) for the service flow from a constant-quality unit of housing. The BLS uses

three methods to control for changes in quality. First, estimates of rent growth are based on a

panel sample, so the same units are tracked over time.4 Second, the BLS makes adjustments to

account for major changes in a housing unit’s physical characteristics, such as the number of rooms.

Third, the BLS corrects for so-called “age-bias” by scaling observed rents by an age-bias factor that

controls for changes in the quality of a housing unit that owes to aging. The focus of this paper

is to investigate the BLS procedure for adjusting for aging bias. In doing so, we illustrate some

potential shortcomings of typical procedures for specifying and using hedonic models.

The BLS constructs its index for Rent (or OER) for an area k, Ik, using a “rent relative”

approach. In particular,

Ik

t = Ikt−1 ∗ Rkt

where R is the rent relative, and t indexes months. As explained in Section 3 below, the BLS

reprices the housing units in their sample only every six months. Accordingly, the rent relative —

3The metropolitan-area aging bias estimates depend to an appreciable extent upon modeling choices of

the sort we investigate. The estimates we offer here are conservative, and might understate true changes to

the baseline BLS approach. 4Sample attrition over time is unavoidable, as units are demolished, or as units become vacant and then

tenants are replaced by unresponsive tenants. BLS procedures account for such unobserved rent changes;

see Ptacek and Baskin (1996) and Crone, Nakamura and Voith (2006).

5
Improving the CPI’s Age-Bias Adjustment

which is used to move the index in the current month t — is defined as

Rk

t =

Ã Pwirenti,t

Pwirenti,t−6e

Fk

i,t !1

6

(1)

where w is an expenditure weight and Fk

i,t is the age-bias factor; i indexes housing units.5 The

age-bias factor, roughly speaking, adds six months of aging to the t − 6 unit, in order to compute

inflation based upon constant-quality units. The expenditure weights for the Rent relative differ

from those for the OER relative, since — for example — the OER expenditure weights are zero on all

rent-control units. (For further discussion on the OER approach to pricing shelter service inflation

for homeowners, see Poole, Ptacek and Verbrugge, 2005.)

The age-bias factor Fk is based on a hedonic regression for rent. The BLS model is of the

general form

ln renti,t = αt + γ1agei,t + γ2age2

i,t + γe3zei,tagei,t + βeXei,t + ui,t (2)

where Xei,t and zei,t are each vectors. In particular, Xe is a vector that includes over 20 measures of

unit-level characteristics (such as number of rooms, and whether the structure is detached or multiunit), dummy variables indicating the size of the metropolitan area (termed a “Primary Sampling

Unit,” or PSU), and Census neighborhood variables (such as percent of population that is under the

poverty line). The vector zei,t consists of three variables: the number of rooms, a dummy variable

indicating if a unit is a detached unit, and a dummy variable indicating if a unit is aged 85 years

or more. Thus there are five terms related to age.6 While the BLS expends considerable effort

in determining the correct age of each unit, in some cases the age of the unit must be estimated,

based upon (for example) knowledge of the decade in which the structure was built. If there is no

reliable information on the age of a unit at all, such units are excluded from the regression. The

BLS estimates the regression coefficients separately for each Census region (BLS, 2006). The data

used are from July-December of year t.

The CPI’s age-bias factor for area k in the following year is equal to the partial derivative

of equation (2) with respect to age, evaluated at the area-level averages for age, agek and the

interaction terms, ezk. In other words, the common 6-month age-bias factor for all housing units in

5We ignore many technical details, such as nonresponse adjustment and utilities adjustment for OER, the

latter of which is studied in Verbrugge (2007). For more details, see Ptacek and Baskin (1996). 6These terms are called “depreciation terms” in Lane, Randolph, and Berenson (1988)

6
Improving the CPI’s Age-Bias Adjustment

area k is given by

F BLS

k = 1

2

(γb1 + 2γb2agek,t + bγe3ezk,t) (3)

where division by 2 converts the annual aging factor into a semi-annual factor.

2.2 Discussion

Adjusting OER The age-bias factor is derived from a hedonic regression on rental units, but is

applied in the computation of both the Rent and OER indexes. It is sometimes argued that, since

owner units likely depreciate at a different rate than rental units, it is erroneous to use an age-bias

factor which has been estimated from rental-market data. However, this objection is invalid. The

measurement goal for OER is inflation in the shelter service price. Since OER is constructed from

inflation in market rents — which by definition, are from rented units — the aging correction required

to properly remove the effects of depreciation on rented units must also be estimated from rentedunit data. What remains after the correction is constant-quality shelter-service inflation, which is

precisely the measurement goal.

Approximation errors Computing age-bias factors using (3) involves making two approximations, which we discuss and investigate in Section 5 below. These approximations are not

perfect and impart biases into (2); but it turns out that these are of a small magnitude and largely

offsetting, as long as one remains in the context of the BLS model (2).

Coefficient-estimates versus true marginal effects The intention of age-bias adjustment is

to adjust for the marginal effect of age on rent; what is ultimately required is an accurate estimate

of this true marginal effect. In practice, the age-bias factor (3) is constructed using particular

coefficient estimates from a regression model. Hence, age-bias adjustment accuracy requires that

particular coefficient estimates accurately estimate the true marginal effect of age.7 But in any given

model, one cannot guarantee that the Gauss-Markov conditions hold; so the coefficient estimates

γb1, γb2, and bγe3 might imply marginal effects of age that are quite different from reality (despite the

7Unfortunately, unlike many other applications of hedonics, age-bias adjustment cannot make use of the

extremely useful fact that a regression model like (2) can deliver unbiased predictions for missing left-handside variables — given a full set of right-hand-side variables whose joint distribution is the same as the variables

used to estimate the model. See Erickson and Pakes (2007) for an unbiased-prediction application to quality

change in television data.

7
Improving the CPI’s Age-Bias Adjustment

fact that, in the context of the regression model being estimated, the coefficient estimates could well

be accurately capturing the marginal relationship of age to the (conditional) expectation of the

dependent variable).

A hypothetical case, namely quality-adjusting for newly-installed air-conditioning in a Los Angeles apartment, illustrates this point. Adding air-conditioning to a non-air-conditioned apartment

is clearly a quality increase to that apartment. If this unit were in the BLS housing sample, then

a quality adjustment (or a “structural change adjustment” in the parlance of the BLS) would be

required in order to avoid bias in the shelter indexes. A hedonics-based adjustment would rely

upon the estimated coefficient on the relevant air-conditioning dummy variable. (In the BLS specification, there are three air-conditioning dummy variables: “central,” “window,” and “other.”) But

suppose that the window air-conditioning coefficient estimate was negative and statistically significant at conventional levels. This would undoubtedly reflect overall correlations in the data — for

example, window air-conditioning being negatively correlated with an unobserved quality variable.

But the implied quality adjustment is then negative — even though it is clear that this is a quality

improvement, with an upward impact on rent.8

Specification error can lead to biased coefficient estimates; careful attention to specification is

simply good practice. But specification testing does not solve the more general problem. (And unfortunately, applying conventional specification-search procedures can readily yield models which

imply less reliable age-bias factors.) Since any particular regression model may yield unreliable coefficient estimates, this strongly supports the practice of model averaging: selecting several repectable

regression models, and averaging the age-adjustments estimated by each. Below, we discuss the

rationale for model averaging in more detail, and suggest an appropriate and simple averaging

method.

Sign of the age-bias adjustment All structures deteriorate over time. Most receive maintenance that helps offset the deterioration, and some receive major improvements that temporarily

reverse the deterioration. Furthermore, certain age-groups or vintages of units might become more

desirable over time, which could offset or reverse the otherwise downward effect of aging on rent.

8This is not a blanket criticism of the use of hedonics. Indeed, hedonics are an elegant and rigorous

solution to many challenging problems in price indexes. In other cases — such as adjusting for the effect of

aging — it is the only game in town, and far better than doing nothing.

8
Improving the CPI’s Age-Bias Adjustment

However, all housing is eventually torn down or completely renovated, which implies that the

average housing unit depreciates (Lane, Randolph, and Berenson, 1988). Thus, we expect that

PSU-average age-bias factors will be negative.

3 The data

The data used are neighborhood data from the decennial 2000 Census, and confidential BLS rental

housing microdata from July-December 2004. As the Census data is well-known and described

elsewhere, we here describe the BLS data.

Decisions regarding the BLS methodology for rental housing sampling are described in Ptacek

and Baskin (1996). In brief, for each of the metropolitan areas (Primary Sampling Units, or PSU’s)

in the BLS sample, the BLS randomly selects a geographically-diverse set of rental housing units,

via a geographic stratification procedure. In the initial data collection steps, the BLS collects a large

amount of information about each unit, such as its age, structural characteristics (e.g., “located

within a multi-unit building with an elevator,” “detached unit,” etc.), number of bedrooms and

bathrooms, utilities (including whether utilities are included in the rent), and so on. The housing

sample is divided into six panels; that is, each unit is placed into one of six panels. Rent price

data on all the units in a particular panel are collected in the same month, and then — given that a

typical unit experiences a rent price change every twelve months (see Crone, Nakamura and Voith

(2006) — not again until six months later. Each panel is thus priced twice a year; for example, panel

1 is priced in January and July, panel 2 in February and August, and so on.

A typical unit remains in the sample for many years. The BLS data we use are from the second

half of 2004.9 Table 1 lists the BLS microdata variables used, along with some descriptive statistics.

9 2003 data yield results which, if anything, are more striking.

9
Improving the CPI’s Age-Bias Adjustment

Rent sample distribution statistics

1% 25% 50% 75% 99%

log rent 5.4 6.2 6.5 6.8 7.7

age 4 23 35 54 128

bedrooms 0 1 2 2 4

bathrooms 1 1 1 2 3

other rooms 1 2 2 3 4

% of rental sample featuring:

single family detached 21% electric heat 41% central A/C 44%

duplex/townhouse 18% gas heat 50% window A/C 15%

multi-unit w/ elevator 9% other heating fuel 1% other A/C 11%

multi-unit w/out elevator 50% heat included 18%

mobile home 2% electricity included 7%

Table 1

4 Empirical Strategy

We investigate two potential types of shortcomings in BLS methodology. The first relates to the

aforementioned approximation errors in estimating age-bias factors. Below, we demonstrate the

shortcomings of these assumptions: they impart a bias on the estimated aging factors. (This bias

turns out to be fairly small for the BLS hedonic model, though we demonstrate that it becomes a

lot more problematic under other empirical specifications.)

The other set of potential shortcomings relates to model specification issues. The first of these

relates to overly-influential observations. The distribution of age across units is strongly skewed to

the right. This suggests that extremely old units could well have high leverage, i.e. that they have

a inappropriately-large impact on the coefficients related to age, since the quadratic specification

estimated by OLS will heavily penalize large errors on old units. We investigate this issue, examining

in a simple way both the leverage of old units as a group, as well as the extent to which coefficient

10
Improving the CPI’s Age-Bias Adjustment

estimates are altered by inclusion or exclusion of very old units. (A dummy-variable approach is

already in use; we demonstrate that it does not solve the problem. We also investigate the use of

a specification which is based upon ln (age).)

The second specification issue relates to whether or not the BLS model is unduly restrictive.

We investigate two types of restrictions. First, the BLS model restricts coefficient estimates to be

identical within Census regions; is further disaggregation warranted? Second, the BLS model has

only five age-related terms, assumes that age effects are quadratic (aside from linear interactions),

and implicitly imposes a common average log-rent across PSU’s. Are other specifications superior?

As we argue that basing one’s estimates upon a single model is risky, we advocate a simple form

of model-averaging.

We use a straightforward metric for determining whether any particular shortcoming is problematic: to what extent is the rent or OER inflation rate impacted by a particular potential remedy?

This is, after all, the bottom line. However, applying this metric is not entirely straightforward,

since deficiencies can interact; for example, using a piecewise-linear or higher-order polynomial in

age implies that a PSU-average approach can impart an significant bias upon estimated age-bias

factors. Furthermore, although we attempt to replicate BLS methods in estimating age-bias factors,

we do not have access to BLS’s full set of production programs, and therefore cannot exactly replicate the BLS estimates. We perform our own estimation of both these factors and our alternative

factors. In this way, any imperfection in our procedure for estimating aging factors will likely net

out.10

In particular, we compute our metric as follows. We estimate a baseline model in which we

mimic the BLS hedonic model for 2004, which yields baseline age-bias factors, FbBLS

i,2004. We compare

alternative age-bias factors, denoted F ALT

i,t , to this baseline. Using (1), it is straightforward to

deduce that the revision to this relative is given by

RALT

t

RBLS

t

=

Pwiri,t−6e

FeBLS

i,t

Pwiri,t−6e

F ALT

i,t

. (4)

For example, if (4) equals 1.02, this implies that our alternative factors would have generated an

1 0Our estimates of the BLS factors are very similar to the actual BLS factors; the correlation coefficient

across the 87 PSUs is about .95. Thus, we are confident that our strategy yields reliable results.

11
Improving the CPI’s Age-Bias Adjustment

inflation estimate that was 2% larger than the official estimate. As noted above, we approximate

F BLS

i,t with our estimate, FbBLSi,t . Notice that the size of the revision depends upon the weights wi,

which differ across units and across indexes (Rent or OER).

5 Approximation Errors

As noted above, computing age-bias factors as in (3) involves making two approximations. First,

because the age-bias factor is based on the partial derivative of equation (2), it can only precisely

represent the effect of an infinitesimal change in age, rather than the effect due to a discrete change

in age. Second, (3) generates a common age-bias factor across all units within the the same PSU,

via the use of PSU-averages in the formula.

It is straightforward to show that the correct six-month unit-i-specific age-bias term implied by

model (2) is

Fi = γb1

2 + γb2

μ

agei,t +

1

4

¶

+ bγe3

2

zei,t

which implies that for unit i in PSU k, the approximation error is given by

Fi − F BLS

k = γb2

μ1

4 + agei,t − agek,t¶

+ bγe3

2

3

zei,t − ezk,t ́

(5)

The presence of γe2

4 term in (5) is a consequence of the infinitisemal-time approximation, and

— since γb2 is typically positive — implies that F BLS

k is biased downwards. However, in 2004 data

this coefficient is on the order of 10−3 or smaller. To produce a ceteris paribus comparison, we

computed (4) using the unit-by-unit age-bias factors mentioned immediately above as the baseline,

and using unit-by-unit age-bias factors computed using the correct non-infinitesmal formula as the

alternative. Here, the error is quite small, resulting in an downward bias of less than .003% in

almost every PSU, and an overall downward bias of about .001%.

The presence of the γb2

¡

agei,t − agek,t¢and γ

eh3

2

3

zei,t − ezk,t ́terms in (5) is a result of the PSUaverage approximation. Referring back to (1), notice that this approximation introduces a source

of error into the BLS rent-relative computation. In particular, this amounts to a distortion of the

expenditure-based weights wi — i.e., it incorrectly increases the relative importance of some units

12
Improving the CPI’s Age-Bias Adjustment

compared to others — with units whose age or z is different from the PSU average receiving a weight

distortion, the sign and size of which depends upon the signs and sizes of the estimated coefficients

γb2 and γb3. Since this term is exponentiated in (1), it will not cancel out across units, and hence

this approximation will introduce bias. (Furthermore, the bias in Rent might well be different from

the bias in OER, owing to their different aggregation weights.)

How large is the error due to this approximation? We computed (4) for the 87 PSU’s, for the

four Census regions, and for the entire US, computing unit-by-unit factors rather than PSU-average

factors, but continuing to use the infinitesimal-time approximation. The largest rent-relative adjustment for a “published” PSU was 0.99944 for Chicago, implying that this approximation error

(ceterus paribus) caused inflation to be overstated in Chicago by perhaps .06%. (Conversely, inflation was understated in Phoenix by about .05%.) Overall, the bias caused by this approximation

error on the US rent index was upward, but by less than .001%.

Thus, the approximation errors appear to be insignificant, and largely offsetting, if one remains

in the context of (2), the BLS hedonic model. However, we argue below that the BLS specification

has important weaknesses. And as noted above, a PSU-average approach in conjunction with an

alternative specification — such as a piecewise-linear or higher-order polynomial in age — could

impart significant bias upon estimated age-bias factors. Indeed, with a third-order polynomial,

this approximation will readily generate age-bias factors of the incorrect sign. (This is ultimately

because the effect of age is now quadratic, so that the (weighted) average effect of age can differ

substantially from the effect evaluated at the (weighted) average age.) Similarly, higher-order

terms in age make the infinitesimal-time approximation more questionable. Hence, in the sequel

we compute all alternative age-bias factors without making these approximations.

If a common PSU-wide Rent or OER factor is required for production or reporting purposes,

the weighting in (4) implies that this should not be computed as the simple average of the factors

in the PSU. Instead, each of these two factors should be computed for PSU k as

Fek,t = ln

⎛

⎝

P

i∈PSU(k) wiri,t−6e

F ALT

i,t

P

i∈PSU(k) wiri,t−6

⎞

⎠

These factors are easily computed; note the similarity to (1). The Rent and OER factors will differ

in general, since they do not share the same distribution of relative weight across age; they will be

13
Improving the CPI’s Age-Bias Adjustment

identical only for very simple regression specifications.11,12

6 Specification Issues: Leverage, Disaggregation, and Model Selection

6.1 Leverage

Leverage is a key issue in age-bias estimation. Recall from Section 2.2 that obtaining unbiased

estimates of the γb vector itself is crucial. But in (2), when taken as a group, units aged 101 years

or more — which comprise about 5% of the sample — have high leverage, on the order of twice that

of the typical unit. (Units aged 201 years or more, comprising 0.2% of the sample, have about ten

times the leverage of the typical unit.) However, even this statistic understates the influence of old

units: such units form an “outlier group,” in which the presence of other members in the group

masks the importance of any particular individual. (Robust regression techniques such as least

trimmed squares are a potential solution to this type of problem, but are too costly to implement

except in relatively small data sets.)

It is easy to illustrate the negative consequences of aged units. We estimated three models

using all units in the sample. The first is the official BLS specification, which — in addition to other

non-age-related regressors — includes age, age2, and three interaction term, agei · Iage>85

i (where

Iage>85

i = 1 if unit i’s age is greater than 85), age · allrooms and age · detached. The other two

models were an age-bin model, and a three-part-spline model which had second-order terms in the

first two parts, and featured knots at ages 26 and 85. (For comparability, we included age·allrooms

and age · detached in these latter two models as well; estimated coefficients on these terms are very

similar across the three models, and qualitative results are not sensitive to keeping them in or

leaving them out.) Note that the age − age2 specification (without any further age-interaction

1 1 Indeed, for the entire US, the 2004 Rent weights are quite variable across groups of ages, and turn out to

be largest on units aged 70-100 years, followed by units aged 18-34 and over 100. Conversely, the 2004 OER

weights are less variable across groups, but largest on units aged 50-70 and on units aged over 100 years. 1 2Suppose that operational considerations require that a single factor be produced for each PSU. Denote

OER weights by wi, and Rent weights by vi. If we assume that the final criterion is to minimize the weighted

sum of squared errors, with weights φ and (1 − φ) on the squared errors of OER and Rent respectively, then

the PSU factor Fe should be computed as Fe = ln 3φSwiri

SwirieFi+(1−φ)

Sviri

SvirieFi

φ(

Swiri)2+(1−φ)(Sviri)2

 ́

14
Improving the CPI’s Age-Bias Adjustment

terms) is common in the hedonic rent literature.13

As is evident in Figure 1 below, the standard BLS specification does not adequately capture the

effect of age: there is a noticeable understatement of the rent-reducing effect of age for units aged

26 years or less (which will be consequential, since about 30% of the sample has age < 26 years),

and an overstatement of the rent-reducing effect of age for older units. Removing units aged 100

years and older brings a marked improvement to the fit of the BLS model (these results are not

depicted, so as to keep the figure uncluttered). Evidently minimizing least-squared errors with such

an inflexible functional form resulted in an overemphasis upon very aged units, which comprise a

trivial fraction of the sample. Conversely, a three-piece spline does a much better job fitting the

true conditional age-log rent profile.

5.6

5.7

5.8

5.9

6.0

6.1

6.2

1

10192837465564738291

100109118127136145154163172181190199208

age bins

BLS

spline

Figure 1: Leverage with age-age2

The leverage problem may also be diagnosed by comparing coefficient estimates upon restricting

1 3See, e.g., Crone, Nakamura and Voith (2003) and Gordon and vanGoethem (2004).

15
Improving the CPI’s Age-Bias Adjustment

the sample to units aged 200 and less, and then units aged 100 and less: the estimated coefficient

on age triples in size, and the estimated coefficient on age2 increases by an order of magnitude,

despite a 5% change in sample size. Clearly, this will have a substantial impact on estimated

age-bias factors.

It is desirable to avoid undue influence of old units on age-factor estimates, since these comprise

only a small fraction of the sample. Thus, we approached the problem by splitting the sample into

two parts: the 95% of the sample comprised of units aged 100 years or less, and the remainder. We

treat each part of the sample separately, as described in more detail below.

6.2 Disaggregation: location, location, location

A key issue in nearly all empirical work related to real-estate is, not surprisingly, location. The

current official BLS aging-bias regressions are conducted on a Census-region basis, with dummy

variables indicating PSU-size. But should one thus impose equality of coefficients (including the

constant) across all the cities within a Census region?

Both theory and informal evidence suggest that this is not appropriate. The real estate markets

of Honolulu, Anchorage, San Francisco and Denver — all cities in the “West” — do not move in

lockstep; nor is the importance of such features as air-conditioning identical across these cities.

(In keeping with this, it is probably unadvisable to impose common effects of deterioration across

diverse cities.) One can also formally test the non-equality of particular regression coefficients

gracefully in the context of a single regression; in each of the handful of cases we investigated,

formal tests of equality of particular regression coefficients between PSU’s also rejected the null

hypothesis of equality. As we report below, F-tests for the exclusion of PSU dummies in regional

regressions strongly reject the null hypothesis, indicating that these variables should not be omitted

from the regression specification,14 and suggesting that further disaggregation might be appropriate.

But what about the danger of overfitting? This is a valid concern, but one which is partly

addressed using the cross-validation procedure which we describe below; if increasing the level of

disaggregation yields vastly superior out-of-sample predictions to those obtained from a smaller

1 4Previously, the official BLS specification included PSU dummy variables; see Campbell (2006).

16
Improving the CPI’s Age-Bias Adjustment

amount of disaggregation, this does much to alleviate concerns related to overfitting.

However, we again remind the reader that superior predictive ability does not necessarily imply

superior age bias adjustments. Unbiased age bias adjustments rely upon coefficient estimates

which accurately estimate the marginal effect of age; but one simply cannot guarantee this, even

if the correct level of disaggregation were known. Standard error estimates are not informative to

this question. The fact that one can estimate, with great apparent precision, the all-US-average

reduction in rent caused by an extra year of aging, does not imply that this estimate is an accurate

estimate of the true effects of an extra year of aging on a typical unit in Sacramento. In other words,

one simply cannot quantify the benefits or costs relating to the larger-but-less-relevant samples.

Superior out-of-sample predictive ability is surely related to improved overall model accuracy, but

does not necessarily mean improved correspondence of estimated coefficients to true marginal effect.

For this reason, we argue below that a model averaging approach is important.

Given the importance of location, we considered a disaggregation scheme which placed PSU’s

into fourteen groupings, which are listed in the Appendix. In each group — including two “groups”

which consist of a single PSU — there is a minimum of 200 degrees of freedom, and generally an

order of magnitude more. In some of the models we considered, we also investigated the usefulness

of age × PSU interaction terms. Preliminary data analysis using three different models indicated

that the 14-region level of disaggregation was far superior to the four-region level in terms of

out-of-sample prediction.

Having considered leverage and disaggregation, we now turn to the issue of model selection.

6.3 Model selection

6.3.1 Model uncertainty and model averaging

Empirical research typically aims to determine the degree of support for hypotheses about unknown parameters. Usually, researchers will provide information both about point estimates and

about their reliability, or about multivariate analogues such as forecasts or impulse response functions (along with confidence intervals). Hedonic adjustments are often conducted on the basis of

17
Improving the CPI’s Age-Bias Adjustment

coefficient estimates, so it is naturally desirable that these estimates be precise.

However, prior to any of this, an empirical model must be selected.

Theory should guide a regression specification. But theory uncertainty is common; i.e., there

are often competing theories which try to explain economic outcomes. Thus, a researcher may

have little guidance about the empirical specification, and there may be a large list of potential

independent (or “control”) variables.

Why is this a problem? Suppose we are interested in estimating “the marginal influence of

xi on y” — which, loosely speaking, amounts to estimating βi, a coefficient in some appropriate

regression model. But which model? Generally, the researcher does not know for certain which

other variables enter into the regression — and may not have the relevant data in any case. The

researcher may not know for certain the correct specification in xi: should this variable enter as

ln (xi), as xi, as (1 + xi)

1

6 , or in some other manner? There is no way to guarantee that the GaussMarkov conditions hold, and good reason to doubt that they hold when there are important omitted

variables. Hence, even if estimated very precisely, there is no way to guarantee that any particular

coefficient estimate is truly capturing “the marginal influence of xi on y.” Furthermore, coefficient

estimates can vary substantially across models; that is, estimates can be sensitive to changes in

specification, so that inferences can be fragile. (Indeed, this motivates pleas for sensitivity analysis,

which Magnus (2006) defines as “the study of the effect of small changes in model assumptions on

an estimator or test statistic of a parameter of interest.”)

This model-uncertainty problem is typically ignored almost completely. In usual practice, a

researcher begins by choosing a small or large set of potential models, and uses some selection

criterion — such as step-down testing, information criteria, or informal specification searches guided

by t-statistics — to select a single model.15 After this, inference then proceeds as if the model is

correct and as if this model selection had not taken place. But in the context of nonexperimental

data, when there is fundamental uncertainty about the data generating process, presenting the

results of a single preferred regression model can vastly understate the degree of uncertainty about

parameter values. Indeed, this practice induces size distortions and can be dramatically misleading

1 5We further suspect that in many cases, model selection is incomplete until the results are “acceptable,”

i.e., are “statistically significant” and match the priors of the researcher. An advantage of the Bayesian

approach to statistics is that the researcher must reveal his or her priors.

18
Improving the CPI’s Age-Bias Adjustment

(see Freedman 1983 and Raftery 1995): standard theory is based on the assumption that the model

in use was specified without any data-dependent modeling choices, whereas typical practice uses

the data to reject many models before a particular model is chosen.16 (Even the simple “innocuous” practice of omitting “insignificant” variables will typically lead to unjustifiable confidence in

parameter estimates.) But in most studies, model uncertainty only receives a cursory glance (at

best) via the presentation of regression results from several closely-related models.

Different model selection procedures account for some of the divergence in coefficient estimates

that, in turn, incite bitter empirical battles in many literatures. The flip side of this is that typically

several different models may all seem reasonable given the data, yet these models may lead to

very different conclusions regarding particular parameters. Furthermore, the true data generating

process is likely far more complex and subtle than any of the models being entertained by the

researcher. Thus, any particular model must be viewed as being one approximation among many;

and it seems implausible that any one empirical specification truly captures “relevant reality,” or

that any particular model will dominate all others at every point in the domain. Model uncertainty

is thus a key issue facing almost all empirical analysis, and ignoring it does not make it go away.

(See Temple (2000) for a more thorough discussion of model uncertainty.)

A recent approach to the general problem model selection and model uncertainty starts with the

admission that one does not know which model is true (or, when one knows that there are important

omitted variables, with the admission that none of the models is true), and then does what is

sensible: take averages over models. This is an approach deriving from Leamer (1978)17 which

is increasingly gaining wide acceptance. Both theoretical and empirical evidence support model

averaging. In a forecasting context, Makridakis and Winkler (1983) explain one aspect of this as

follows (page 990): “When a single method is used, the risk of not choosing the best method can be

very serious. The risk diminishes rapidly when more methods are considered and their forecasts are

1 6Raftery (1995) is a key reference, and provides a more thorough discussion of many of the points noted

here. Note also that there is a large literature discussing the effects of model selection on inference. For

example, Potscher (1991) shows that AIC selection results in distorted inference; and Kabaila (1995) examines

the impact on confidence regions. Caudill and Holcombe (1999) explore two popular specification search

methods and show that these can readily lead to spurious t-statistics. Danilov and Magnus (2004) show that

ignoring model selection can generate substantial error in the prediction interval. Unit-root inferences are

notoriously susceptible to changes in model-selection methods. 1 7Leamer (1978) argues that there are six distinct reasons for specification searches, which lead to six

different varieties of search procedures.

19
Improving the CPI’s Age-Bias Adjustment

averaged.” There is a large Bayesian literature (see Hoeting, Madigan, Raftery, and Volinsky 1999

for a survey), and a growing frequentist literature (see Buckland, Burnham and Augustin 1997; see

also Magnus and Durbin, 1999). In economics, model averaging has become commonplace in the

cross-country growth literature18 — and is increasingly dominant in the forecasting literature. In

particular, since Bates and Granger (1969), a large body of research in the forecasting literature has

confirmed that combinations of individual forecasts often outperform individual model forecasts, in

the sense that the combined forecast delivers a smaller mean-squared forecast error (MSFE); see,

e.g., Stock & Watson (2004).19

Model averaging is straightforward. To implement this technique in the conventional manner,

initially L models are selected, where the set of models might have already been reduced via a

model selection procedure (to eliminate clearly inferior models).20 Then estimates are formed by

weighted averages. For example, to form the prediction yb, one forms a weighted average over the

predictions of the L models:

yb = X

L

l=1

wlybl.

As long as one is using weights which have been fixed beforehand and which sum to one, then if

each individual model yields predictions which are unbiased, this weighted-average prediction will

also be unbiased.

Estimating a parameter θ (assumed common to all models) is accomplished in the same way;

1 8 In the economics literature, six recent Bayesian-model-averaging studies are Fernandez, Ley and Steel

(2001), Brock and Durlauf (2001), Koop and Potter (2003), Sala-i-Martin, Doppelhoffer and Miller (2004),

Eklund and Karlsson (2005), and Masanjala and Papageorgiou (2007). See also Brock, Durlauf and West

(2003) for insightful comments, and Durlauf, Johnson and Temple (2005) for technical advice. 1 9By combining forecasts from several models, the forecaster implicitly acknowledges that more than

one model could provide good forecasts, and guards against misspecification, poor estimation, or

instabilities/non-stationarities by not putting all the weight on one single model (see Hendry and Clements

2004, and Timmermann 2005). Furthermore, it can be shown (see Timmermann 2005) that even if the

forecasts from one model dominate those from another model (in the sense that they lead to lower expected

loss), it may still be optimal to combine the two forecasts. 2 0Swanson and Teng (2001) propose using a criterion like this to choose which subset of forecasts to

combine. Others use such criteria as the basis of weights; see below.

20
Improving the CPI’s Age-Bias Adjustment

one forms a weighted average of bθ over the L models:21

bθ = X

L

l=1

wl

bθl. (6)

One can also determine the variance of bθ; see Buckland, Burnham and Augustin (1997) and Salai-Martin, Doppelhoffer, and Miller (2004). The argument for model-averaging is even more compelling in this case than in the prediction case. As noted above, the idea is to avoid an error

stemming from the use of an incorrect model. In the case of coefficient estimates, we cannot guarantee that the coefficient estimate from any particular model l, βbl, accurately estimates the true

marginal effects of the variables in question. If these coefficient estimates vary across models which

are otherwise roughly comparable in their ability to approximate the data, this is cause for concern,

since any particular model is but one approximation of reality. When there are multiple reliable

signals, it makes sense to average, even if they are correlated; the average estimate is likely to be

closer to the truth than any one taken individually.

Obviously, a key practical issue is how one should determine the weights wl. There are several

approaches, one being simple averaging (i.e. setting wl = 1

L), which in the forecasting context is

often difficult to beat (see, e.g., Clemen 1989 and Stock and Watson 2001). Weights might also be

estimated by regression, i.e. by choosing weights to minimize the mean squared forecast error of

the averaged model. But estimation errors that contaminate the combination weights are known

to be a serious problem for many combination techniques; see Diebold and Pauly (1990), Elliott

(2004), Hendry and Clements (2004), Yang (2004), and Timmermann (2005).

Alternatively, to construct weights, a common suggestion is to use some weighting criterion

2 1Suppose one is particularly interested in a particular coefficient estimate. Since a particular variable xk

might not appear in every model, the sum of the weights applied to the coefficient βk will not equal unity,

which will “bias” the estimate of that coefficient toward zero. An alternative construction uses rescaled

weights, i.e. sets

wl = P

Cl

i∈N(k) Ci

where N (k) is the set of models which contain xk.

But one must be careful in interpreting averaged coefficients. A coefficient estimate in a particular regression model equals, at best, the marginal influence of its regressor conditional on the presence of all the other

variables in the model. Thus, there is no reason to suspect that the coefficient estimates from two different

specifications should be equal: they are estimates of two conceptually different things.

21
Improving the CPI’s Age-Bias Adjustment

Cl,

22 and apply it in a formula such as

wl = P

Cl

Ci

.

In a Bayesian context, the weighting criterion is the posterior probability. However, a purely

Bayesian approach is rarely used; see Jacobson and Karlsson (2006), and the discussion in Shtatland

et al. (2000) and Yuan and Yang (2005). A commonly-used approximation to the Bayes factor is

the Bayes-Schwarz information criteria; see Raftery (1995).23

An alternative approach is to use out-of-sample cross-validation methods. Cross-validation, due

to Allen (1974), is a commonly used model selection criteria, with various consistency results; see,

e.g., Yang (2005). In general terms, the data is split into two parts: N − k observations, to be

used for fitting each competing model (or procedure), and the remaining k observations, to be

used to measure the performance of the models. A common performance measure is the MSFE

on the k reserved (out-of-sample) observations. In a cross-section context, it is straightforward

to iterate upon this procedure, either via partitioning the sample into n equal-sized parts (with k

observations in each), or by randomly selecting the k observations each iteration. Such methods

base model selection, or weights in model averaging, either upon appropriate ratios of MSFEs, or

upon the fraction of iterations a particular model wins the implicit horse race. (See Pesaran and

Timmerman 2006, who — in a context of forecasting under uncertainty about break dates — compare

2 2The criterion might be the inverse of mean squared error, or an exponentiated information criterion ICl,

i.e., Cl = exp ¡

−1

2 ICl

¢

. 2 3Shtatland et al. (2000) note that the AIC and SIC can emulate the Bayesian approach under two opposite

and situations. Model comparisons based on AIC are asymptotically equivalent (see Kass and Raftery, 1995)

to those based on Bayes factors, under the assumption that the precision of the priors is comparable to

that of the likelihood (in other words, only if the information in the prior increases at the same rate as

the information in the likelihood, so that the prior is as informative as the data). Conversely, exp(−1

2SIC)

provides a surprisingly good approximation to the Bayes factor (see Kass and Wassermam, 1995) when the

amount of information in the prior is equal to that in one observation (at least when comparing nested

models), so that the prior is not informative at all. Shtatland et al. (2000) recommend following a standard

model selection procedure (such as step-down testing), then determining the model favored by AIC, the

model favored by BIC, and all models “in between,” i.e. those which lead (by one’s selection process)

from the larger AIC-favored model to the smaller BIC-favored model. They term this set of models (from

AIC through BIC) the “AIC-BIC window,” and recommend averaging over these models using one of the

criterion. As these researchers point out, this model selection procedure is straightforward to implement

and avoids the conceptual and computational difficulties associated with a purely Bayesian approach. Of

course, their method does not generalize to situations in which models are inherently non-nested. Hansen

(2006) provides evidence that selecting weights by minimizing a Mallow’s criterion, which is an estimate of

the squared error, is superior to using exponentiated-AIC or BIC weights. The focus of the investigation

might determine the appropriate criterion. In the context of obtaining a reliable coefficient estimate (as

opposed to a reliable forecast), the risk associated with including an irrelevant variable is lower than the risk

of excluding a relevant variable, which would favor AIC over BIC.

22
Improving the CPI’s Age-Bias Adjustment

equal weights to weights which are chosen to be proportional to the inverse of the MSFE values.)

One key advantage of the implicit-horse-race method is that the resultant weights are not distorted

by the presence of a large number of similar models.

Granger & Jeon (2004) suggest a thick-modeling approach, based on trimming to eliminate the

k% worst performing forecasts, and then taking a simple average of the remaining forecasts; this

concurs with a conclusion of Hendry and Clements (2004), who state “since otherwise, one really

poor forecast would worsen the combination needlessly.”

In this study, we use a combination of cross-validation and thick-modeling, as described below.

6.3.2 Applying model averaging to age-bias estimation

In the age-bias context, the true data-generating process is almost certainly more complicated

than any estimable model, if only because — in this real-estate context, where the rule of thumb is

“location-location-location” — a large number of neighborhood variables are missing. Furthermore,

coefficient estimates turn out to vary across different specifications. Thus, in the age-bias context,

the argument for model averaging is quite compelling.

How would one implement such averaging? Once weights are chosen, it is straightforward to

apply averaging to age-bias estimates from different models — i.e., for each unit i, to construct

aged-biasi = X

L

l=1

wl · aged-biasi,l (7)

Which criterion should be used in selecting weights? If the goal were to impute missing log-rent

observations, a pure cross-validation approach would be the natural choice. However, the goal in

age-bias estimation is to obtain a reliable estimate of the effect of increased age on log-rent, which

is not necessarily guaranteed by a model which reliably predicts log-rent out-of-sample. There

are two reasons for this. First, as noted above, there is no guarantee that individual coefficient

estimates accurately capture the true marginal effect of age. Second, and somewhat surprisingly,

even cross-validation could lead to overfitting in the present context. This is due to the sampling

procedures underlying our data. The geographic stratification scheme employed by the BLS begins

by dividing a PSU into six regions, and then into geographic “segments” in each region, which

23
Improving the CPI’s Age-Bias Adjustment

are Census blocks or block-groups. Segments from each region are randomly selected proportional

to their “size” or shelter expenditure. Once a segment is selected, the goal is to obtain five or

more rental housing units from each segment. But the rental housing in a Census block is often

similar along many characteristics, including both log-rent and age. If this is the case, even a

cross-validation procedure might lead to “overfitting” the data along dimensions of age, with age

proxying for the missing neighborhood variable (and thereby helping the model to predict missing

observations). This could occur even though a large number of neighborhood variables, such as

percent-renter, are included in the regression.

For these reasons, in this study we selected weights using a combination of cross-validation,

trimming, and simple averaging, as follows. First, we conducted an extensive cross-validation

exercise as an initial “filter,” examining a number of alternative specifications at various levels of

disaggregation, to weed out poorly-performing models. (We report some of the findings of this study

below.) We conducted several iterations of the cross-validation exercise, as some specifications were

refined based upon the results of previous iterations. Upon obtaining seven reasonable models, we

then followed Granger and Jeon (2004) and, on a unit-by-unit basis, trimmed the highest and

lowest estimated age-bias estimates; our age-bias estimate is the simple average of the remaining

estimates. (Our decision to trim estimates and average in this way was partly motivated by the fact

that, as we discuss below, one of our best-predicting models sometimes generated implausible agebias estimates. Note that one could use trimming in combination with weighting; simply include

multiple copies of each estimate in the pool of estimates to take a trimmed mean over, tying the

number of copies of an estimate to the relative weight on that model.)

Is this procedure infeasible? Most other statistical agencies in the world simply lack the manpower to undertake a study similar to this one. Furthermore, many statistical agencies might resist

the idea of model averaging in any case, since it is difficult to motivate and explain to the public,

and thus could end up having a bit of a “black box” character. Our response is threefold. First,

statistical agencies still must perform model selection in any case; the amount of additional effort

required to implement a simple form of model averaging is minimal. In particular, instead of discarding all but one model, analysts could retain several of the top candidates, specify simple-average

weights or information-criterion weights, estimate each model, and form (7) as above. Second, the

24
Improving the CPI’s Age-Bias Adjustment

risk to using the wrong model can be substantial. Third, it is possible to describe this procedure

to the public in a simple manner. Here are two possibilities. First, it could be described as an

estimation technique: "Given our desired large model, we estimate each coefficient estimate in this

large model using (6)." Second, model averaging over the l surviving candidate models could be

described as estimating a single large model as in

ln renti = 1

l

f1

3

age, Xe;αe1

 ́

+

1

l

f2

3

age, Xe; αe2

 ́

+ ... +

1

l

fl

3

age, Xe; αel

 ́

+ ui

where ui := 1

l (u1i + u2i + ... + uli), αej is the coefficient vector corresponding to model j, and each

model specifies age and/or various elements of Xe differently. Of course, estimation must still proceed

via estimating each model j separately. (The system should not be estimated jointly; although this

could be readily accomplished upon dividing each element in each model by 1

l , it could run into

degrees-of-freedom difficulties. Even if it didn’t, joint estimation would still likely lead to overfitting

the data.)

6.3.3 Candidate models

There are obviously an enormous number of alternative model specifications one could consider. In

practice, one must rely upon intuition to help narrow the search to a manageable number. Given

the preponderance of hedonic studies using log-price as the dependent variable, we did not examine

any alternative, and used log-rent as our dependent variable in all cases.24 Given our findings

regarding leverage, we split the sample into two parts: the 95% of the sample comprised of units

aged 100 years or less, and the remainder. We modeled each part of the sample separately, except

as noted below.

For units with age ≤ 100, we considered ten alternative models.25 Preliminary work indicated

the necessity including PSU dummy variables, so these are included except as otherwise noted.

Barring any strong evidence suggesting the contrary, for each model Xe includes the unit-level

characteristics (such as number of rooms, and whether the structure is detached or multi-unit)

and the Census-2000 neighborhood variables (such as % of population that is white, and % of

2 4Since the index is moved by a rent relative, the standard log-bias adjustment term cancels out. 2 5Some key omitted models are those which are estimated using panel methods. There are several advantages to panel estimation; for example, this allows one to control for unit-specific effects. As results are so

starkly different, we explore this issue in Gallin and Verbrugge (2007).

25
Improving the CPI’s Age-Bias Adjustment

population that is under the poverty line) which are in the official BLS aging-bias specification.

(The complete list of variables is given in the appendix.) In principle, information criteria such

as AIC could be used to help determinine whether “marginally significant” regressors should be

included in any particular regional specification;26 however, we do not make systematic use of such

criterion in this paper, except for answering broad questions such as “should PSU dummy variables

be included?” The models derive from the following six specifications:

• Standard BLS model with PSU dummy variables: within group j,

ln renti = α + βeX

fi + γ1agei + γ2age2

i + γe3zeiagei + X

PSU(k)∈j

γ4,kI

PSU(k)

i + ui

where I

PSU(k)

i = 1 if unit i is in PSU (k), and 0 otherwise. We examined this model at the

14-group level, and — for comparison purposes — the standard BLS model, i.e., the model

without PSU dummy variables, estimated on the full sample, at the four-Census-region level

of disaggregation.

• Augmented age − age2 model: within group j,

ln renti = α+βeX

fi+γ1agei+γ2age2

i +γ

e0

3

ez0

iagei+ X

PSU(k)∈j

γ4,kI

PSU(k)

i + X

PSU(k)∈j

γ5,kageiI

PSU(k)

i +ui

This model augments the standard BLS model by including PSU dummy variables and more

age interaction terms, including age × PSU interaction terms. This model was examined

at the the four-Census-region level of disaggregation, as well as at the 14-group level. In

this case and in many of the cases following, we used informal criteria — such as insignificant

t-statistics — to eliminate particular age × PSU or age × z terms.

• Piecewise-linear: within group j,

ln renti = α + βeX

fi + γ1agei + γ2Iage≥s

i (agei − s) + γe3zeiagei + X

PSU(k)∈j

γ4,kI

PSU(k)

i + ui

where s is the knot, i.e. the point at which the first linear piece intersects the second linear

piece. (Note that this specification imposes the restriction that this intersection occurs at s,

i.e. the piecewise-linear fit to the data is continuous.) Here, the knot was chosen to be age

26, i.e., s = 26, based upon AIC.

• Two-piece and three-piece spline: within group j,

ln renti = Iage<s1

i

¡

α1 + δ1agei + θ1age2

i

¢

+ Is1≤age<s2

i

¡

α2 + δ2agei + θ2age2

i

¢

+Iage>s2

i (α3 + δ3agei) + βeX

fi + γe3zeiagei + X

PSU(k)∈j

γ4,kI

PSU(k)

i + ui

2 6AIC is perhaps preferable to BSIC in this context, given the danger of omitted variable bias. See

Shtatland et al. (2000), who suggest combining stepwise regression techniques with information criteria in

order to avoid the intractible problem of searching a combinatorial number of models.

26
Improving the CPI’s Age-Bias Adjustment

where, in the two-piece case, s1 = 0 and s1 = 50, and in the three-piece case, s1 = 26 and

s2 = 80, and where the restrictions α1 +δ1s1 +θ1s2

1 = α2 +δ2s1 +θ2s21 and α2 +δ2s2 +θ2s22 =

α3+δ3s2 must be imposed. These models were estimated on the full sample, rather than only

on units aged 100 years and less. In some cases, higher-order terms were also considered.

• Chebyshev polynomial: within group j,

ln renti = α+βeX

fi +

Xm

r=1

λrzr +γe3zeiagei + X

PSU(k)∈j

γ4,kI

PSU(k)

i + X

PSU(k)∈j

γ5,kageiI

PSU(k)

i +ui

where z := 3agei−100

100  ́

, with higher-order Chebyshev terms zr defined analogously, and where

m is chosen between 3 and 5 (with an attempt to avoid overfitting). We considered two

Chebyshev polynomial models, one a greatly restricted version of the other, with smaller m

and fewer regressors.

• Log-age: within group j,

ln renti = α + βeX

fi + γ1 ln agei + γe3zeiagei + X

PSU(k)∈j

γ4,kI

PSU(k)

i + ui

For units with age > 100 (which comprise about 5% of the sample), we considered the two 14-

region spline models described above (as before, estimated on all units), the standard BLS model

at the four-Census-region level of aggregation (estimated on all units), and two other models at the

all-US level of aggregation. The first of these is very simple, having only one term in age, namely

ln age, 30 other regressors (including a constant, 11 PSU dummy variables, and one region dummy

variable), and is estimated only on units with age > 100. The other model contained only linear

terms in age (including age-interaction terms), and was estimated on all units with age between 40

and 300. Based upon a small cross-validation study, our final age-bias factor for these older units

is the average of the factors from the latter two models and the three-part spline model.

Each model likely suffers from heteroskedasticity; but this is not a problem because we do

not rely on standard error estimates, and heteroskedasticity does not bias coefficient estimates.

Outliers are likely not problematic, for two reasons. First, sample sizes are generally comfortably

large. Second, the effect of outliers on model selection is muted by our procedure: we use a crossvalidation horse-race procedure, which will penalize both overfitting and excessive sensitivity to

outliers. (One might still wish to consider the possible effects of outliers on final model estimation,

once model selection and averaging weights have been determined.)

27
Improving the CPI’s Age-Bias Adjustment

In our cross-validation procedure, for each iteration, we reserve 10% of the sample for testing

forecasts. Each of the models is fit (on each of the 14 groups) on the fitting portion of the data,

and then each model is used to forecast the remaining 10% of the observations in that group. At

the end of the iteration, the overall MSFE — summed across all units — is computed. Then the

procedure is repeated.

6.3.4 Cross-validation results

The results of the cross-validation study for units aged 100 years and less are given below. (We

conducted a similar study on older units, and also examined the performance on a region-by-region

basis, but do not report these results in the interest of brevity.)

Define

devk: =

100

N

X

N

s=1

MSFEk

s − MSFEbests

MSFEbest

s

mdevk : = 100 · Maxs

MSFEk

s − MSFEbests

MSFEbest

s

where MSFEk

s is the MSFE for model k in iteration s, and MSFEbests is the smallest MSFE

(across models) for iteration s. The first measure, devk

, is the average percentage increase in

MSFE corresponding to model k; for example, if devk = 4% , then the use of model k implies

an MSFE that is, on average across the N iterations, 4% higher than the best model. Similarly,

the second measure, mdevk, is the maximum observed percentage loss in MSFE corresponding to

model k. We also report the overall MSFE win percentage for each model k, i.e., the percentage of

iterations for which MSFEbest

s = MSFEks .

28
Improving the CPI’s Age-Bias Adjustment

Based upon 500 iterations, results are as follows:

BLS

BLS

+PSU

Aug.

-4

Aug.

-14

Piec.

Lin.

2-

Spl.

3-

Spl.

Cheb.

Cheb.

(rest.)

Log

Win % 0% 0% 9% 1% 0% 5% 1% 4% 79% 0%

dev 45% 3.6% 2.2% 1.5% 2.7% 1.6% 2.1% 1.4% 0.2% 2.4%

mdev 56% 10% 8% 9% 10% 9% 10% 8% 7% 10%

Table 2

In Table 2, “BLS” refers to the official specification based upon 4-regions, “BLS +PSU” refers

to the official specification plus PSU dummy variables (estimated on age<100), “Aug. -4” and

“Aug. -14” refer to the Augmented age−age2 model (estimated on 4 and 14 regions, respectively),

“Piec. Lin.” refers to the Piecewise Linear specification, “2-Spl.” and “3-Spl.” refer to the spline

models (estimated on all ages), “Cheb.” and “Cheb. (rest.)” refer to the Chebyshev and restrictedChebyshev specification, and “Log” refers to the Log-age specification.

In terms of MSFE, the best model overall is clearly the restricted Chebyshev model. But

surprisingly, a carefully-specified augmented age − age2 model, estimated at the highly-aggregated

four-Census-region level (on age < 100), is actually competitive with more disaggregated models,

despite its multitude of implied coefficient restrictions.27 This suggests that the 14-region level of

disaggregation may be too aggressive. A common feature of these two best-performing models is

the presence of the age · IPSU(k) interaction terms, which underscores the importance of adequate

treatment of location. Notice that the only model which is clearly rejected out-of-hand is the official

BLS specification; even the 14-region age − age2 specification, which adds only PSU dummies to

the standard BLS specification (and is estimated on age < 100) is, on average, a mere 3.6% worse

than the best-predicting model in terms of MSFE (although across regions, there are five regions

in which it is outperformed in the 5-8% range).

2 7This model contains numerous age · IPSU(k) interaction terms; F-tests for their inclusion have p-values

less than 0.0000. Across the fourteen regions, the MSFE evidence is a bit more mixed. This four-region

model outperforms all the other models in three of the fourteen regions, and is within 4% of the best in five

others. But on the other hand, in four of the regions, it is outperformed by 9% or more.

29
Improving the CPI’s Age-Bias Adjustment

These results also suggest that the 2-spline is a strong model. However, this model generated

age-bias estimates which were clearly outside the bounds of possibility for most units in some

regions (despite very good predictions on those regions). In other words, despite its superior outof-sample prediction performance, we concluded that this model was probably overfitting the data —

a possibility which is discussed in Section 6.2.2 — and we eliminated this model from consideration.

6.4 Constructing our final age-bias estimates

For units aged 100 years and less, we constructed age-bias estimates as follows. On the basis of the

cross-validation results, we chose seven models — the four- and fourteen-region augmented age−age2

models, the fourteen-region age−age2 model (with PSU dummies), the restricted Chebyshev model,

the three-part spline, the log-age model, and the piecewise-linear model. Using the unit-by-unit agebias factor estimates from these models, we followed the trimmed-mean approach discussed above,

trimming the highest and lowest of these, and constructing the simple average of the remainder;

this became our age-bias estimate for that unit.

For units aged greater than 100 years, we constructed age-bias estimates (on a unit-by-unit

basis) by taking the simple average of the age-bias estimates derived from the two all-US models

and the three-part spline.

7 Results

Above, we pointed out a number of potential deficiencies in the current BLS approach to age-bias

estimation. How significant are these deficiencies in toto?

Figure 2 below plots our estimated inflation adjustments from (4), across the PSU’s whose

shelter inflation indexes are published by the BLS.

30
Improving the CPI’s Age-Bias Adjustment

0.992

0.994

0.996

0.998

1.000

1.002

1.004

1.006

1.008

all US

NE

Boston

Philadelphia

Pittsburgh

NYC

NE midsize

MW

Chicago

Cincinnatti

Cleveland

Detroit

Kansas City

Milwaukee

Minneapolis

St. Louis

MW midsize

S

Atlanta

Baltimore

Dallas

Houston

Miami

Tampa

Washington, D.C.

S midsize

W

Anchorage

Denver

Honolulu

LA

Phoenix

Portland

San Diego

San Francisco

Seattle

W midsize

Rent

OER

Figure 2: Estimated Adjustments to CPI Shelter Components

The average annual age-bias factor estimated by the BLS method was -0.00201; our estimated

age-bias factor has an average of -0.00278, which is about 38% larger. Due to the presence of

aggregation weights, this does not translate immediately into differences in rent relatives. We

find that over the 2004-2005 period, the aggregate BLS Rent and OER indexes were downwardbiased, but only by a small amount. According to our estimates and using (4), aggregate Rent

inflation between 2004 and 2005 was downward-biased by 0.06%, and aggregate OER inflation

was downward-biased by 0.04%. However, our suggested improvements can make a much bigger

difference to estimates of shelter price inflation experienced by specific PSU’s; the adjustments to

the relative are in the range [−0.2%, +0.6%]; in other words, inflation may be overstated by as

much as 0.2%, or understated by as much as 0.6%. A striking example is the country’s fifth largest

city, Phoenix: between 2004 and 2005, reported Rent inflation in this PSU was 0.6%, but our

estimates imply that Rent inflation was double this, at 1.2%. Since OER inflation would have risen

31
Improving the CPI’s Age-Bias Adjustment

a comparable amount as well, the use of our factors would have raised overall estimated inflation

in Phoenix by about 0.2%.

Averaging over models makes a difference. For example, if one replaces our preferred age-bias

factors with those from the four-region augmented age − age2 model (but retaining the same agebias factor on old units), the range of rent-relative-adjustments across PSU’s is wider, namely

[−0.7%, +0.7%]. These adjustments also differ, on average in absolute terms, from those derived

from our averaged factors by 0.001 (with a maximum divergence as large as 0.005). (Having said

this, their averages are essentially identical, and their correlation across PSU’s is 0.89.)

8 Conclusion

We investigated the BLS age-bias imputation, and discovered a number of potential improvements.

Do they matter? We found that, in 2004 data, BLS age-bias factors were too small, on average, by

almost 40%. Since the age bias term itself is rather small, this bias had a rather modest impact on

overall aggregate indexes. On the other hand, errors in particular metropolitan areas were much

larger, with downward-bias in the area’s shelter inflation as large as 0.6%.

We found that errors from formula approximations underlying official estimates were, in the

context of the BLS model, of little consequence. However, we found more serious deficiencies

related to model misspecification. In particular, the BLS hedonic regression specification — which

is more or less of the form commonly used in the hedonic rent literature — is severely deficient in

its ability to match the conditional log-rent vs. age relationship in the data, and performs poorly

in out-of-sample tests. We found many models which are superior.

A related problem is that aging bias adjustment inherently suffers from a general problem

facing some types of hedonic-based adjustments, namely the inherent impossibility of ensuring that

coefficient estimates accurately estimate the true marginal impact. We advocated the use of model

averaging to address this problem. This is a method that minimizes downside risks related to model

misspecification and generates more reliable coefficient estimates. Simple versions of this method

are easy to implement with very little additional effort.

32
Improving the CPI’s Age-Bias Adjustment

After selecting seven “best” models using a cross-validation approach, we estimated age-bias

factors by taking a trimmed average over the factors derived from each model.

We cannot argue that our estimated age-bias factors are perfect; proving or disproving this

would be impossible. What we argue here is that our estimates are likely to be much better than

those resulting from current BLS methods.

Currently, as a result of this study, the BLS is investigating the use of model averaging and

a richer set of regression specifications. These are based upon a differential treatment of old

units, including higher-order terms in age and additional age interaction terms, and an increased

level of disaggregation, in conjunction with the unit-by-unit age-bias estimation that these changes

necessitate.

33
Improving the CPI’s Age-Bias Adjustment

9 Appendix

9.1 14 PSU groups

1. Northeast and and Suburbs: Boston, Hartford CT, New York City, Philadelphia, Reading

PA

2. Off-Seabord Northeast Mid-Sized: Burlington VT, Johnstown PA, Sharon PA, Springfield

MA

3. Mid-Atlantic Seaboard: Baltimore, Washington DC, Norfolk VA

4. “Midwest” Larger Cities: Buffalo, Chicago, Detroit, Cincinnati, Cleveland, Columbus, Dayton, Kansas City, Milwaukee, Minneapolis, Pittsburgh, Saint Louis, Syracuse

5. Midwest Smaller Cities: Brookings SD, Chanute KS, Decatur IL, Elkhart IN, Evansville IN,

Faribault MN, Lincoln NE, Madison WI, Mt. Vernon IL, Saginaw MI, Wausau WI, Youngstown

OH

6. Texas: Amarillo, Beaumont-Port Arthur, Brownsville-Harlingen-San Benito, Dallas, Midland, Houston, San Antonio

7. Florida: Arcadia, Gainesville, Ft. Myers, Melbourne, Miami, Ocala, Tampa

8. South Big/Medium: Atlanta, Birmingham AL, Raleigh NC, Richmond VA

9. South Small: Albany GA, Baton Rouge, Chattanooga TN, Florence AL, Florence SC,

Greensville SC, Lafayette LA, Morristown TN, Oklahoma City, Picayune MS, Pine Bluff AR,

Statesboro GA

10. Big West Coast: Los Angeles, Portland, San Diego, San Francisco, Seattle

11. Honolulu

12. Anchorage

13. Bigger Mountain/Desert: Denver, Las Vegas, Phoenix

14. Small West: Bend OR, Boise City ID, Chico CA, Modesto CA, Provo UT, Pullman WA,

Yuma AZ

34
Improving the CPI’s Age-Bias Adjustment

9.2 Set of regressors

There are six categories of potential conditioning variables: Census neighborhood characteristics,

Unit-specific, PSU, Services-included-with-rent, Structure type, and Age-related. However, no

model includes all the variables. Almost all models included all Census variables, all unit-specific

variables, and the full set of PSU variables. In some regions, various services-included-with-rent

were not included. Only rarely were more than one or two structure type variables included. Finally,

age-interaction terms were used somewhat sparingly.

• Census neighborhood characteristic variables: % white; % in large buildings; % in mobile

homes; % with 2 or more autos; % of children aged 6-18; % aged 65+; % with some college

education; % lacking plumbing; % under poverty; % renter.

• Unit-specific variables: bathrooms, bathrooms2, bedrooms, bedrooms2, other rooms, other

rooms2, heat (electric, gas, other), air conditioning (central, window, other).

• PSU variables: Large- or Medium-sized city; PSU-dummy variables.

• Services-included-with-rent variables: heat included; electricity included; parking included.

• Structure type: detached, duplex, multi-unit with elevator, multi-unit without elevator, mobile home, other.

• Age-related: age, age2, etc.; and age-interaction terms, of which the most common are:

detached, multi-unit with elevator, Iage>85, rooms, electricity included, % white, PSU.

35
Improving the CPI’s Age-Bias Adjustment

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