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Asymptotic Inference for Dynamic Panel Estimators of In nite Order Autoregressive Processes

Author

Listed:
  • Yoon-Jin Lee

    (Department of Economics, Indiana University)

  • Ryo Okui

    (Institute of Economic Research, Kyoto University)

  • Mototsugu Shintani

    (Department of Economics, Vanderbilt University)

Abstract

In this paper we consider the estimation of a dynamic panel autoregressive (AR) process of possibly in nite order in the presence of individual effects. We utilize the sieve AR approximation with its lag order increasing with the sample size. We establish the consistency and asymptotic normality of the standard dynamic panel data estimators, including the xed effects estimator, the gen- eralized methods of moments estimator and Hayakawa's instrumental variables estimator, using double asymptotics under which both the cross-sectional sam- ple size and the length of time series tend to in nity. We also propose a bias- corrected xed effects estimator based on the asymptotic result. Monte Carlo simulations demonstrate that the estimators perform well and the asymptotic approximation is useful. As an illustration, proposed methods are applied to dynamic panel estimation of the law of one price deviations among US cities.

Suggested Citation

  • Yoon-Jin Lee & Ryo Okui & Mototsugu Shintani, 2013. "Asymptotic Inference for Dynamic Panel Estimators of In nite Order Autoregressive Processes," KIER Working Papers 879, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:879
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    References listed on IDEAS

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    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
    2. Crucini, Mario J. & Shintani, Mototsugu & Tsuruga, Takayuki, 2015. "Noisy information, distance and law of one price dynamics across US cities," Journal of Monetary Economics, Elsevier, vol. 74(C), pages 52-66.
    3. Lütkepohl, Helmut & Poskitt, D.S., 1991. "Estimating Orthogonal Impulse Responses via Vector Autoregressive Models," Econometric Theory, Cambridge University Press, vol. 7(4), pages 487-496, December.
    4. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, July.
    5. Jinyong Hahn & Guido Kuersteiner, 2002. "Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both "n" and "T" Are Large," Econometrica, Econometric Society, vol. 70(4), pages 1639-1657, July.
    6. Allen Head & Huw Lloyd-Ellis & Hongfei Sun, 2014. "Search, Liquidity, and the Dynamics of House Prices and Construction," American Economic Review, American Economic Association, vol. 104(4), pages 1172-1210, April.
    7. Han, Chirok & Phillips, Peter C. B. & Sul, Donggyu, 2014. "X-Differencing And Dynamic Panel Model Estimation," Econometric Theory, Cambridge University Press, vol. 30(1), pages 201-251, February.
    8. Francesco Franco & Thomas Philippon, 2007. "Firms and Aggregate Dynamics," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 587-600, November.
    9. Lutkepohl, Helmut & Saikkonen, Pentti, 1997. "Impulse response analysis in infinite order cointegrated vector autoregressive processes," Journal of Econometrics, Elsevier, vol. 81(1), pages 127-157, November.
    10. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    11. Lee, Yoonseok, 2012. "Bias in dynamic panel models under time series misspecification," Journal of Econometrics, Elsevier, vol. 169(1), pages 54-60.
    12. Lütkepohl, Helmut & POSKITT, D.S., 1996. "Testing for Causation Using Infinite Order Vector Autoregressive Processes," Econometric Theory, Cambridge University Press, vol. 12(1), pages 61-87, March.
    13. Peter C. B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Econometrica, Econometric Society, vol. 67(5), pages 1057-1112, September.
    14. Silvia Goncalves & Lutz Kilian, 2007. "Asymptotic and Bootstrap Inference for AR(∞) Processes with Conditional Heteroskedasticity," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 609-641.
    15. Jinyong Hahn & Whitney Newey, 2004. "Jackknife and Analytical Bias Reduction for Nonlinear Panel Models," Econometrica, Econometric Society, vol. 72(4), pages 1295-1319, July.
    16. Lee, Yoonseok & Phillips, Peter C.B., 2015. "Model selection in the presence of incidental parameters," Journal of Econometrics, Elsevier, vol. 188(2), pages 474-489.
    17. David C. Parsley & Shang-Jin Wei, 1996. "Convergence to the Law of One Price Without Trade Barriers or Currency Fluctuations," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(4), pages 1211-1236.
    18. Lewis, Richard & Reinsel, Gregory C., 1985. "Prediction of multivariate time series by autoregressive model fitting," Journal of Multivariate Analysis, Elsevier, vol. 16(3), pages 393-411, June.
    19. Kuersteiner, Guido M., 2005. "Automatic Inference For Infinite Order Vector Autoregressions," Econometric Theory, Cambridge University Press, vol. 21(1), pages 85-115, February.
    20. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    21. Okui, Ryo, 2010. "Asymptotically Unbiased Estimation Of Autocovariances And Autocorrelations With Long Panel Data," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1263-1304, October.
    22. Pao-Li Chang & Shinichi Sakata, 2007. "Estimation of impulse response functions using long autoregression," Econometrics Journal, Royal Economic Society, vol. 10(2), pages 453-469, July.
    23. Hayakawa, Kazuhiko, 2009. "A SIMPLE EFFICIENT INSTRUMENTAL VARIABLE ESTIMATOR FOR PANEL AR(p) MODELS WHEN BOTH N AND T ARE LARGE," Econometric Theory, Cambridge University Press, vol. 25(3), pages 873-890, June.
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    Cited by:

    1. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    2. Ryo Okui & Takahide Yanagi, 2020. "Kernel estimation for panel data with heterogeneous dynamics [Econometric tools for analyzing market outcomes]," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 156-175.
    3. Abhimanyu Gupta & Myung Hwan Seo, 2023. "Robust Inference on Infinite and Growing Dimensional Time‐Series Regression," Econometrica, Econometric Society, vol. 91(4), pages 1333-1361, July.
    4. Greenaway-McGrevy, Ryan, 2022. "Forecast combination for VARs in large N and T panels," International Journal of Forecasting, Elsevier, vol. 38(1), pages 142-164.
    5. Juan Sebastian Cubillos-Rocha & Luis Fernando Melo-Velandia, 2018. "Asymptotically unbiased inference for a panel VAR model with p lags," Borradores de Economia 1059, Banco de la Republica de Colombia.
    6. Lu, Xun & Su, Liangjun, 2020. "Determining individual or time effects in panel data models," Journal of Econometrics, Elsevier, vol. 215(1), pages 60-83.

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    More about this item

    Keywords

    Autoregressive Sieve Estimation; Bias Correction; Double Asymptotics; Fixed Effects Estimator; GMM; Instrumental Variables Estimator.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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