IDEAS home Printed from https://ideas.repec.org/p/cte/werepe/27981.html
   My bibliography  Save this paper

Testing Constancy in Varying Coefficient Models

Author

Listed:
  • Arteaga-Molina, Luis A.

Abstract

This article proposes tests for constancy of coefficients in semi-varying coefficients models. The testing procedure resembles in spirit the union-intersection parameter stability tests in time series, where observations are sorted according to the explanatory variable responsible for the coefficients varying. The test can be applied to model specification checks of interactive effects in linear regression models. Because test statistics are not asymptotically pivotal, critical values and p-values are estimated using a bootstrap technique. The finite sample properties of the test are investigated by means of Monte Carlo experiments, where the new proposal is compared to existing tests based on smooth estimates of the unrestricted model. We also report an application to returns of education modeling

Suggested Citation

  • Arteaga-Molina, Luis A., 2019. "Testing Constancy in Varying Coefficient Models," UC3M Working papers. Economics 27981, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:27981
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/rest/api/core/bitstreams/2c913359-4918-4bea-9019-9d00fb07dde1/content
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Blackburn, McKinley L & Neumark, David, 1995. "Are OLS Estimates of the Return to Schooling Biased Downward? Another Look," The Review of Economics and Statistics, MIT Press, vol. 77(2), pages 217-230, May.
    2. Yoonseok Lee & Andrey Stoyanov & Nikolay Zubanov, 2019. "Olley and Pakes‐style Production Function Estimators with Firm Fixed Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(1), pages 79-97, February.
    3. Li, Qi, et al, 2002. "Semiparametric Smooth Coefficient Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 412-422, July.
    4. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    5. Donald W. K. Andrews, 1997. "A Conditional Kolmogorov Test," Econometrica, Econometric Society, vol. 65(5), pages 1097-1128, September.
    6. Wang, Hansheng & Xia, Yingcun, 2009. "Shrinkage Estimation of the Varying Coefficient Model," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 747-757.
    7. Galen R. Shorack, 1979. "The weighted empirical process of row independent random variables with arbitrary distribution functions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 33(4), pages 169-189, December.
    8. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    9. Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
    10. Sowell, Fallaw, 1996. "Optimal Tests for Parameter Instability in the Generalized Method of Moments Framework," Econometrica, Econometric Society, vol. 64(5), pages 1085-1107, September.
    11. Delgado, Miguel A. & Hidalgo, Javier & Velasco, Carlos, 2005. "Distribution free goodness-of-fit tests for linear processes," LSE Research Online Documents on Economics 6840, London School of Economics and Political Science, LSE Library.
    12. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    13. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    14. Jianqing Fan & Wenyang Zhang, 2000. "Simultaneous Confidence Bands and Hypothesis Testing in Varying‐coefficient Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 715-731, December.
    15. Wooldridge, Jeffrey M., 2009. "On estimating firm-level production functions using proxy variables to control for unobservables," Economics Letters, Elsevier, vol. 104(3), pages 112-114, September.
    16. Shin-Yi Chou & Jin-Tan Liu & Cliff J. Huang, 2004. "Health insurance and savings over the life cycle-a semiparametric smooth coefficient estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(3), pages 295-322.
    17. Delgado, Miguel A. & Stute, Winfried, 2008. "Distribution-free specification tests of conditional models," Journal of Econometrics, Elsevier, vol. 143(1), pages 37-55, March.
    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. Ding, Sai & Jiang, Wei & Li, Shengyu & Wei, Shang-Jin, 2024. "Fiscal policy volatility and capital misallocation: Evidence from China," European Economic Review, Elsevier, vol. 167(C).
    2. Giulio Cainelli & Andrea Fracasso & Giuseppe Vittucci Marzetti, 2015. "Spatial agglomeration and productivity in Italy: A panel smooth transition regression approach," Papers in Regional Science, Wiley Blackwell, vol. 94, pages 39-67, November.
    3. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    4. Chrysovalantis Amountzias, 2019. "Pricing Decisions and Competitive Conduct Across Manufacturing Sectors: Evidence from 19 European Union Manufacturing Industries," Journal of Industry, Competition and Trade, Springer, vol. 19(3), pages 413-440, September.
    5. Ioannis Bournakis & Mike Tsionas, 2024. "A Non‐parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(3), pages 641-671, June.
    6. Khanna, Rupika & Sharma, Chandan, 2022. "Impact of information technology on firm performance: New evidence from Indian manufacturing," Information Economics and Policy, Elsevier, vol. 60(C).
    7. Filippini, Massimo & Geissmann, Thomas & Karplus, Valerie J. & Zhang, Da, 2020. "The productivity impacts of energy efficiency programs in developing countries: Evidence from iron and steel firms in China," China Economic Review, Elsevier, vol. 59(C).
    8. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    9. Roberto Martino & Phu Nguyen-Van, 2014. "Labour market regulation and fiscal parameters: A structural model for European regions," Working Papers of BETA 2014-19, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    10. Zhu, Ling & Liu, Shasha & Kong, Dongmin, 2023. "Governments' fiscal stress and firm decentralization," International Review of Financial Analysis, Elsevier, vol. 90(C).
    11. KONISHI Yoko & NISHIMURA Yoshihiko, 2013. "A Note on the Identification of Demand and Supply Shocks in Production: Decomposition of TFP," Discussion papers 13099, Research Institute of Economy, Trade and Industry (RIETI).
    12. Moisă ALTĂR & Ana-Maria CAZACU, 2016. "Testing Self-Selection And Learning By Exporting Hypotheses. The Case Of Romania," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 5-22.
    13. Javier Papa & Luke Rehill & Brendan O'Connor, 2021. "Patterns of Firm-Level Productivity in Ireland," The Economic and Social Review, Economic and Social Studies, vol. 52(3), pages 241-268.
    14. Békés, Gábor & Hornok, Cecília & Muraközy, Balázs, 2016. "Globalization and the markups of European firms," Kiel Working Papers 2044, Kiel Institute for the World Economy (IfW Kiel).
    15. Kancs, d’Artis & Siliverstovs, Boriss, 2016. "R&D and non-linear productivity growth," Research Policy, Elsevier, vol. 45(3), pages 634-646.
    16. Axel Demenet & Quynh Hoang, 2018. "How important are management practices for the productivity of small and medium enterprises?," WIDER Working Paper Series 69, World Institute for Development Economic Research (UNU-WIDER).
    17. Themann, Michael & Koch, Nicolas, 2021. "Catching up and falling behind: Cross-country evidence on the impact of the EU ETS on firm productivity," Ruhr Economic Papers 904, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    18. Li, Lei & Luo, Changtuo, 2023. "Does administrative decentralization promote outward foreign direct investment and productivity? Evidence from China," Economic Modelling, Elsevier, vol. 124(C).
    19. Filippo Biondi & Sergio Inferrera & Matthias Mertens & Javier Miranda, 2023. "Declining Business Dynamism in Europe: The Role of Shocks, Market Power, and Technology," Jena Economics Research Papers 2023-011, Friedrich-Schiller-University Jena.
    20. Doerr, Sebastian & Raissi, Mehdi & Weber, Anke, 2018. "Credit-supply shocks and firm productivity in Italy," Journal of International Money and Finance, Elsevier, vol. 87(C), pages 155-171.

    More about this item

    Keywords

    Varying coefficient models;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:cte:werepe:27981. 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: Ana Poveda (email available below). General contact details of provider: http://www.eco.uc3m.es/ .

    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.