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Nonparametric Estimation and Testing for Time-Varying VAR Models

Author

Listed:
  • Jiti Gao
  • Bin Peng
  • Yayi Yan

Abstract

Vector autoregressive (VAR) models are widely used in practical studies, e.g., forecasting, modelling policy transmission mechanism, and measuring connection of economic agents. To better capture the dynamics, this paper introduces a new class of time-varying VAR models in which the coefficients and covariance matrix of the error innovations are allowed to change smoothly over time. Accordingly, we establish a set of asymptotic properties including the impulse response analyses subject to structural VAR identification conditions, an information criterion to select the optimal lag, and a Wald-type test to determine the constant coefficients. Simulation studies are conducted to evaluate the theoretical findings. Finally, we demonstrate the empirical relevance and usefulness of the proposed methods through an application on US government spending multipliers.

Suggested Citation

  • Jiti Gao & Bin Peng & Yayi Yan, 2022. "Nonparametric Estimation and Testing for Time-Varying VAR Models," Monash Econometrics and Business Statistics Working Papers 3/22, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2022-3
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    1. Dani Rodrik & Arvind Subramanian & Francesco Trebbi, 2004. "Institutions Rule: The Primacy of Institutions Over Geography and Integration in Economic Development," Journal of Economic Growth, Springer, vol. 9(2), pages 131-165, June.
    2. Susanto Basu & David N. Weil, 1998. "Appropriate Technology and Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1025-1054.
    3. Alexander Chudik & Kamiar Mohaddes & M. Hashem Pesaran & Mehdi Raissi, 2017. "Is There a Debt-Threshold Effect on Output Growth?," The Review of Economics and Statistics, MIT Press, vol. 99(1), pages 135-150, March.
    4. Bernard, Andrew B & Jones, Charles I, 1996. "Productivity across Industries and Countries: Time Series Theory and Evidence," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 135-146, February.
    5. Hyungsik Roger Moon & Martin Weidner, 2015. "Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects," Econometrica, Econometric Society, vol. 83(4), pages 1543-1579, July.
    6. Borensztein, E. & De Gregorio, J. & Lee, J-W., 1998. "How does foreign direct investment affect economic growth?1," Journal of International Economics, Elsevier, vol. 45(1), pages 115-135, June.
    7. David H. Romer & Jeffrey A. Frankel, 1999. "Does Trade Cause Growth?," American Economic Review, American Economic Association, vol. 89(3), pages 379-399, June.
    8. Dowrick, Steve, 1992. "Technological Catch Up and Diverging Incomes: Patterns of Economic Growth 1960-88," Economic Journal, Royal Economic Society, vol. 102(412), pages 600-610, May.
    9. J. Bradford DeLong & Lawrence H. Summers, 1992. "Equipment Investment and Economic Growth: How Strong Is the Nexus?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 23(2), pages 157-212.
    10. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    11. Laszlo Matyas (ed.), 2017. "The Econometrics of Multi-dimensional Panels," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-319-60783-2, July-Dece.
    12. Orazio P. Attanasio & Lucio Picci & Antonello E. Scorcu, 2000. "Saving, Growth, and Investment: A Macroeconomic Analysis Using a Panel of Countries," The Review of Economics and Statistics, MIT Press, vol. 82(2), pages 182-211, May.
    13. N. Gregory Mankiw & David Romer & David N. Weil, 1992. "A Contribution to the Empirics of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(2), pages 407-437.
    14. Easterly, William & Levine, Ross, 2003. "Tropics, germs, and crops: how endowments influence economic development," Journal of Monetary Economics, Elsevier, vol. 50(1), pages 3-39, January.
    15. Rodrik, Dani, 2011. "The Future of Convergence," Scholarly Articles 5131504, Harvard Kennedy School of Government.
    16. Jiang, Bin & Yang, Yanrong & Gao, Jiti & Hsiao, Cheng, 2021. "Recursive estimation in large panel data models: Theory and practice," Journal of Econometrics, Elsevier, vol. 224(2), pages 439-465.
    17. Kapetanios, George & Serlenga, Laura & Shin, Yongcheol, 2021. "Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 504-531.
    18. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    19. Chaohua Dong & Jiti Gao & Bin Peng, 2021. "Varying-Coefficient Panel Data Models With Nonstationarity and Partially Observed Factor Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 700-711, July.
    20. Dani Rodrik, 2011. "The future of economic convergence," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 13-52.
    21. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    22. Huang, Wenxin & Jin, Sainan & Phillips, Peter C.B. & Su, Liangjun, 2021. "Nonstationary panel models with latent group structures and cross-section dependence," Journal of Econometrics, Elsevier, vol. 221(1), pages 198-222.
    23. Peter J. Klenow & Mark Bils, 2000. "Does Schooling Cause Growth?," American Economic Review, American Economic Association, vol. 90(5), pages 1160-1183, December.
    24. Baumol, William J, 1986. "Productivity Growth, Convergence, and Welfare: What the Long-run Data Show," American Economic Review, American Economic Association, vol. 76(5), pages 1072-1085, December.
    25. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    26. Xu Han, 2021. "Shrinkage Estimation of Factor Models With Global and Group-Specific Factors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 1-17, January.
    27. J. V. Henderson & J. F. Thisse (ed.), 2004. "Handbook of Regional and Urban Economics," Handbook of Regional and Urban Economics, Elsevier, edition 1, volume 4, number 4.
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    More about this item

    Keywords

    Time-varying impulse response; parameter stability; instrumental variable approach;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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