IDEAS home Printed from
   My bibliography  Save this paper

Hedonic Price Indexes with Unobserved Product Characteristics, and Application to PC's


  • C. Lanier Benkard
  • Patrick Bajari


We show that hedonic price indexes may be biased when not all product characteristics are observed. We derive two primary sources of bias. The first is a classical selection problem that arises due to changes over time in the values of unobserved characteristics. The second comes from changes in the implicit prices of unobserved characteristics. Next, we show that the bias can be corrected for under fairly general assumptions using extensions of factor analysis methods. We test our methods empirically using a new comprehensive monthly data set for desktop personal computer systems. For this data we find that the standard hedonic index has a slight upward bias of approximately 1.4\% per year. We also find that omitting an important characteristic (CPU benchmark) causes a large bias in the index with standard methods, but that this bias is essentially eliminated when the proposed correction is applied.

Suggested Citation

  • C. Lanier Benkard & Patrick Bajari, 2003. "Hedonic Price Indexes with Unobserved Product Characteristics, and Application to PC's," NBER Working Papers 9980, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:9980
    Note: IO PR

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Iain M. Cockburn & Aslam H. Anis, 2001. "Hedonic Analysis of Arthritis Drugs," NBER Chapters,in: Medical Care Output and Productivity, pages 439-462 National Bureau of Economic Research, Inc.
    3. Ohta, Makoto & Griliches, Zvi, 1986. "Automobile Prices and Quality: Did the Gasoline Price Increases Change Consumer Tastes in the U.S.?," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(2), pages 187-198, April.
    4. G. Christian Ehemann & Brent R. Moulton, 2001. "Balancing the GDP Account," BEA Papers 0014, Bureau of Economic Analysis.
    5. Rosa L. Matzkin, 2003. "Nonparametric Estimation of Nonadditive Random Functions," Econometrica, Econometric Society, vol. 71(5), pages 1339-1375, September.
    6. Makoto Ohta, 1983. "Automobile Prices and Quality: Did the Gasoline Price Increase Change Consumer Tastes in the U.S.?," NBER Working Papers 1211, National Bureau of Economic Research, Inc.
    7. Cragg, John G. & Donald, Stephen G., 1997. "Inferring the rank of a matrix," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 223-250.
    8. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Robert C. Feenstra & Christopher R. Knittel, 2009. "Re-Assessing the U.S. Quality Adjustment to Computer Prices: The Role of Durability and Changing Software," NBER Chapters,in: Price Index Concepts and Measurement, pages 129-160 National Bureau of Economic Research, Inc.
    2. Francisco Requena-Silvente & James Walker, 2006. "Calculating Hedonic Price Indices with Unobserved Product Attributes: An Application to the UK Car Market," Economica, London School of Economics and Political Science, vol. 73(291), pages 509-532, August.
    3. C. Lanier Benkard & Patrick Bajari, 2004. "Demand Estimation with Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach," NBER Working Papers 10278, National Bureau of Economic Research, Inc.

    More about this item

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:9980. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.