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Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging

Author

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  • Joshua Gallin

    () (Board of Governors of the Federal Reserve System)

  • Randal Verbrugge

    () (U.S. Bureau of Labor Statistics)

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 mis-specification. 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%.

Suggested Citation

  • Joshua Gallin & Randal Verbrugge, 2007. "Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging," Working Papers 411, U.S. Bureau of Labor Statistics.
  • Handle: RePEc:bls:wpaper:ec070100
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    References listed on IDEAS

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    1. repec:bla:reesec:v:45:y:2017:i:3:p:591-627 is not listed on IDEAS
    2. Robert Poole & Randal Verbrugge, 2007. "Explaining the Rent-OER Inflation Divergence, 1999-2006," Working Papers 410, U.S. Bureau of Labor Statistics.
    3. Randal Verbrugge & Alan Dorfman & William Johnson & Fred Marsh III & Robert Poole & Owen Shoemaker, 2017. "Determinants of Differential Rent Changes: Mean Reversion versus the Usual Suspects," Real Estate Economics, American Real Estate and Urban Economics Association, pages 591-627.
    4. Ambrose, Brent W. & Coulson, N. Edward & Yoshida, Jiro, 2017. "Inflation Rates Are Very Different When Housing Rents Are Accurately Measured," HIT-REFINED Working Paper Series 71, Institute of Economic Research, Hitotsubashi University.

    More about this item

    Keywords

    Depreciation; Hedonics; Model Averaging; Inflation; CPI Bias;

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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