IDEAS home Printed from https://ideas.repec.org/p/boj/bojwps/wp21e08.html
   My bibliography  Save this paper

New Hedonic Quality Adjustment Method using Sparse Estimation

Author

Listed:
  • Sahoko Furuta

    (Bank of Japan)

  • Yudai Hatayama

    (Bank of Japan)

  • Atsushi Kawakami

    (Bank of Japan)

  • Yusuke Oh

    (Bank of Japan)

Abstract

In the application of the hedonic quality adjustment method to the price index, multicollinearity and the omitted variable bias arise as practical issues. This study proposes the new hedonic quality adjustment method using esparse estimation f in order to overcome these problems. The new method deals with these problems by ensuring two properties: the egrouped effect f that gives robustness for multicollinearity and the eoracle property f that provides the appropriate variable selection and asymptotically unbiased estimators. We conduct an empirical analysis applying the new method to the producer price index of passenger cars in Japan. In comparison with the conventional standard estimation method, the new method brings the following benefits: 1) a significant increase in the number of variables in the regression model; 2) an improvement in the fit of the regression model to actual prices; and 3) reduced overestimation of the product quality improvements due to the omitted variable bias. These results suggest the possible improvement in the accuracy of the price index while enhancing the usefulness of the hedonic quality adjustment method.

Suggested Citation

  • Sahoko Furuta & Yudai Hatayama & Atsushi Kawakami & Yusuke Oh, 2021. "New Hedonic Quality Adjustment Method using Sparse Estimation," Bank of Japan Working Paper Series 21-E-8, Bank of Japan.
  • Handle: RePEc:boj:bojwps:wp21e08
    as

    Download full text from publisher

    File URL: https://www.boj.or.jp/en/research/wps_rev/wps_2021/data/wp21e08.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ariel Pakes, 2003. "A Reconsideration of Hedonic Price Indexes with an Application to PC's," American Economic Review, American Economic Association, vol. 93(5), pages 1578-1596, December.
    2. Halvorsen, Robert & Pollakowski, Henry O., 1981. "Choice of functional form for hedonic price equations," Journal of Urban Economics, Elsevier, vol. 10(1), pages 37-49, July.
    3. Efthymiou, D. & Antoniou, C., 2013. "How do transport infrastructure and policies affect house prices and rents? Evidence from Athens, Greece," Transportation Research Part A: Policy and Practice, Elsevier, vol. 52(C), pages 1-22.
    4. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    5. Phil Graves & James C. Murdoch & Mark A. Thayer & Don Waldman, 1988. "The Robustness of Hedonic Price Estimation: Urban Air Quality," Land Economics, University of Wisconsin Press, vol. 64(3), pages 220-233.
    6. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    7. Shigenori Shiratsuka, 1995. "Automobile Prices and Quality Changes : A Hedonic Price Analysis of Japanese Automobile Market," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 13(2), pages 1-44, December.
    8. Cropper, Maureen L & Deck, Leland B & McConnell, Kenneth E, 1988. "On the Choice of Functional Form for Hedonic Price Functions," The Review of Economics and Statistics, MIT Press, vol. 70(4), pages 668-675, November.
    9. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    10. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sahoko Furuta & Yudai Hatayama & Atsushi Kawakami & Yusuke Oh, 2021. "New Hedonic Quality Adjustment Method using Sparse Estimation," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 39, pages 109-142, November.
    2. Kenneth Y. Chay & Michael Greenstone, 2005. "Does Air Quality Matter? Evidence from the Housing Market," Journal of Political Economy, University of Chicago Press, vol. 113(2), pages 376-424, April.
    3. David Prentice & Xiangkang Yin, 2004. "Constructing a Quality‐Adjusted Price Index for a Heterogeneous Oligopoly," Manchester School, University of Manchester, vol. 72(4), pages 423-442, July.
    4. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    5. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
    6. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    7. Yize Zhao & Matthias Chung & Brent A. Johnson & Carlos S. Moreno & Qi Long, 2016. "Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1427-1439, October.
    8. Wesley Nimon & John Beghin, 1999. "Are Eco-Labels Valuable? Evidence From the Apparel Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(4), pages 801-811.
    9. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    10. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
    11. Gareth M. James & Peter Radchenko & Jinchi Lv, 2009. "DASSO: connections between the Dantzig selector and lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 127-142, January.
    12. Mostafa Rezaei & Ivor Cribben & Michele Samorani, 2021. "A clustering-based feature selection method for automatically generated relational attributes," Annals of Operations Research, Springer, vol. 303(1), pages 233-263, August.
    13. Mai, Nhat Chi, 2018. "도이모이 이후 베트남의 주거 이동, 선택, 가격 결정요인 연구: 호치민시 사례 중심으로," OSF Preprints 6kdfy, Center for Open Science.
    14. Theodore M. Crone, 2006. "Capitalization of the quality of local public schools: what do home buyers value?," Working Papers 06-15, Federal Reserve Bank of Philadelphia.
    15. Ramírez Muñoz de Toro, Gonzalo R. & Uriarte, Juan I. & Delbianco, Fernando & Larrosa, Juan M.C., 2017. "Un modelo hedónico de precios en línea de automóviles usados en Argentina || A Hedonic Model of Online Prices of Used Cars in Argentina," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 24(1), pages 25-53, Diciembre.
    16. Umberto Amato & Anestis Antoniadis & Italia De Feis & Irene Gijbels, 2021. "Penalised robust estimators for sparse and high-dimensional linear models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 1-48, March.
    17. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Post-Print halshs-00917797, HAL.
    18. Wang Zhu & Wang C.Y., 2010. "Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-33, June.
    19. Wang, Christina Dan & Chen, Zhao & Lian, Yimin & Chen, Min, 2022. "Asset selection based on high frequency Sharpe ratio," Journal of Econometrics, Elsevier, vol. 227(1), pages 168-188.
    20. repec:jss:jstsof:33:i01 is not listed on IDEAS
    21. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.

    More about this item

    Keywords

    Price Index; Quality Adjustment; Hedonic Regression Model; Multicollinearity; Omitted Variable Bias; Sparse Estimation; Adaptive Elastic Net;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    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:boj:bojwps:wp21e08. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Bank of Japan (email available below). General contact details of provider: https://edirc.repec.org/data/bojgvjp.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.