Li, Kunpeng (2018): Spatial panel data models with structural change.
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Abstract
Spatial panel data models are widely used in empirical studies. The existing theories of spatial models so far have largely confine the analysis under the assumption of parameters stabilities. This is unduely restrictive, since a large number of studies have well documented the presence of structural changes in the relationship of economic variables. This paper proposes and studies spatial panel data models with structural change. We consider using the quasi maximum likelihood method to estimate the model. Static and dynamic models are both considered. Large-$T$ and fixed-$T$ setups are both considered. We provide a relatively complete asymptotic theory for the maximum likelihood estimators, including consistency, convergence rates and limiting distributions of the regression coefficients, the timing of structural change and variance of errors. We study the hypothesis testing for the presence of structural change. The three super-type statistics are proposed. The Monte Carlo simulation results are consistent with our theoretical results and show that the maximum likelihood estimators have good finite sample performance.
Item Type: | MPRA Paper |
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Original Title: | Spatial panel data models with structural change |
Language: | English |
Keywords: | Spatial panel data models, structural changes, hypothesis testing, asymptotic theory. |
Subjects: | C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C31 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions ; Social Interaction Models C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models |
Item ID: | 85388 |
Depositing User: | Kunpeng Li |
Date Deposited: | 22 Mar 2018 17:32 |
Last Modified: | 01 Oct 2019 16:33 |
References: | Anderson T W, Hsiao C. Estimation of dynamic models with error components. Journal of the American statistical Association, 1981, 76(375): 598-606. Andrews, D. W. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 821-856. Andrews, D. W. (2003). Tests for parameter instability and structural change with unknown change point: A corrigendum. Econometrica, 395-397. Anselin, L. (1988). {Spatial econometrics: methods and models. The Netherlands: Kluwer Academic Publishers. Bai, J. (1997). Estimation of a change point in multiple regression models. Review of Economics and Statistics, 79(4), 551-563. Bai, J., and Carrion-I-Silvestre, J. L. (2009). Structural changes, common stochastic trends, and unit roots in panel data. The Review of Economic Studies, 76(2), 471-501. Bai, J., Lumsdaine, R. L., and Stock, J. H. (1998). Testing for and dating common breaks in multivariate time series. The Review of Economic Studies, 65(3), 395-432. Bai, J., and Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 47-78. Billingsley, P. (1968). Convergence of probability measures. John Wiley & Sons. Bode, E., Nunnenkamp, P., and Waldkirch, A. (2012). Spatial effects of foreign direct investment in US states. Canadian Journal of Economics, 45(1), 16-40. Bramoull\'{e, Y., Djebbari, H., and Fortin, B. (2009). Identification of peer effects through social networks. Journal of econometrics, 150(1), 41-55. Calv\'{o-Armengol, A., Patacchini, E., and Zenou, Y. (2009). Peer effects and social networks in education. The Review of Economic Studies, 76(4), 1239-1267. Chirinko, R. S., and Wilson, D. J. (2017). Tax competition among US states: Racing to the bottom or riding on a seesaw?. Journal of Public Economics, 155, 147-163. Cliff, A. D., and Ord, J. K. (1973) {\em Spatial autocorrelation, {London: Pion Ltd. Cressie, N. (1993): Statistics for Spatial Data. New York: John Wiley & Sons. Dhaene, G., and Jochmans, K. (2015). Split-panel jackknife estimation of fixed-effect models. The Review of Economic Studies, 82(3), 991-1030. Estrella, A. (2003). Critical values and p values of bessel process distributions: computation and application to structural break tests. Econometric Theory, 19(6), 1128-1143. Hall, P., and Heyde, C. C. (1980). Martingale Limit Theory and Its Applications. Academic Press. Holly, S., Pesaran, M. H., and Yamagata, T. (2011). The spatial and temporal diffusion of house prices in the UK. Journal of Urban Economics, 69(1), 2-23. Jennrich, R. I. (1969). Asymptotic properties of non-linear least squares estimators. The Annals of Mathematical Statistics, 633-643. Kapoor, M., Kelejian, H. H., and Prucha, I. R. (2007). Panel data models with spatially correlated error components. Journal of Econometrics, 140(1), 97-130. Kelejian, H. H., and Prucha, I. R. (1998). A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. The Journal of Real Estate Finance and Economics, 17(1), 99-121. Kelejian, H. H., and Prucha, I. R. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model. International economic review, 40(2), 509-533. Kou, S., Peng, X., and Zhong, H. (2017). Asset pricing with spatial interaction. Forthcoming in Management Science. Lee, L. F. (2004). Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models. Econometrica, 1899-1925. Lee, L. F., and Yu, J. (2010). Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics, 154(2), 165-185. LeSage, J. P., and Pace, R. K. (2009). Introduction to Spatial Econometrics (Statistics, textbooks and monographs). CRC Press. Li, K. (2017). Fixed-effects dynamic spatial panel data models and impulse response analysis. Journal of Econometrics, 198(1), 102-121. Lin, X. (2010). Identifying peer effects in student academic achievement by spatial autoregressive models with group unobservables. Journal of Labor Economics, 28(4), 825-860. Lyytik\"{ainen, T. (2012). Tax competition among local governments: Evidence from a property tax reform in Finland. Journal of Public Economics, 96(7-8), 584-595. Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. The review of economic studies, 60(3), 531-542. Ord, K. (1975). Estimation methods for models of spatial interaction. Journal of the American Statistical Association, 70(349), 120-126. Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6) 1361-1401. Picard, D. (1985). Testing and estimating change-points in time series. Advances in applied probability, 17(4), 841-867. Sengupta, A. (2017). Testing for a structural break in a spatial panel model. Econometrics, 5(1), 12. Qu, Z., and Perron, P. (2007). Estimating and testing structural changes in multivariate regressions. Econometrica, 75(2), 459-502. Van Der Vaart A. W., and Wellner J. A. (1996). Weak Convergence and Empirical Processes with Application to Statistics. Springer. Yao, Y. C. (1987). Approximating the distribution of the maximum likelihood estimate of the change-point in a sequence of independent random variables. The Annals of Statistics, 1321-1328. Yu, J., de Jong, R., and Lee, L. F. (2008). Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large. Journal of Econometrics, 146(1), 118-134. Zivot, E., and Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of business & economic statistics, 10(1), 251-270. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/85388 |