Xuan, Liang and Jiti, Gao and xiaodong, Gong (2021): Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients.
Preview |
PDF
MPRA_paper_108497.pdf Download (3MB) | Preview |
Abstract
This paper develops a time--varying coefficient spatial autoregressive panel data model with individual fixed effects to capture the nonlinear effects of the regressors, which vary over the time. To effectively estimate the model, we propose a method that incorporates local linear estimation and concentrated quasi-maximum likelihood estimation to obtain consistent estimators for the spatial autoregressive coefficient, variance of error term and nonparametric time-varying coefficient function. The asymptotic properties of these estimators are derived as well, showing regular the standard rate of convergence for the parametric parameters and common standard rate of convergence for the time-varying component, respectively. Monte Carlo simulations are conducted to illustrate the finite sample performance of our proposed method. Meanwhile, we apply our method to study the Chinese labor productivity to identify the spatial influences and the time--varying spillover effects among 185 Chinese cities with comparison to the results on a subregion--East China.
Item Type: | MPRA Paper |
---|---|
Original Title: | Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients |
English Title: | Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients |
Language: | English |
Keywords: | Concentrated quasi-maximum likelihood estimation, local linear estimation, time-varying coefficient |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models |
Item ID: | 108497 |
Depositing User: | Jiti Gao |
Date Deposited: | 04 Jul 2021 15:40 |
Last Modified: | 04 Jul 2021 15:40 |
References: | Anselin, L., Florax, R., and Rey, S. J. (2013). Advances in Spatial Econometrics: Methodology, Tools and Applications. Springer Science & Business Media. Arellano, M. (2003). Panel Data Econometrics. Oxford University Press. Baltagi, B. (2008). Econometric Analysis of Panel Data. John Wiley & Sons. Baltagi, B. H., Blien, U., and Wolf, K. (2012). A dynamic spatial panel data approach to the german wage curve. Economic Modelling, 29(1):12–21. Baltagi, B. H., Song, S. H., and Koh, W. (2003). Testing panel data regression models with spatial error correlation. Journal of Econometrics, 117(1):123–150. Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1):289–300. Box, G. E. and Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65(332):1509–1526. Braid, R. M. (2002). The spatial effects of wage or property tax differentials, and local government choice between tax instruments. Journal of Urban Economics, 51(3):429–445. Burkholder, D. L. (1973). Distribution function inequalities for martingales. the Annals of Probability, 1(1):19–42. Cai, Z. (2007). Trending time-varying coefficient time series models with serially correlated errors. Journal of Econometrics, 136(1):163–188. Chen, J., Gao, J., and Li, D. (2012). Semiparametric trending panel data models with cross-sectional dependence. Journal of Econometrics, 171(1):71–85. Chen, J., Li, D., and Linton, O. (2019). A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables. Journal of Econometrics, 212(1):155–176s. Chung, K. L. (2001). A Course in Probability Theory. Academic Press. Cliff, A. D. and Ord, J. K. (1973). Spatial Autocorrelation, Monographs in Spatial Environmental Systems Analysis. London: Pion Limited. Combes, P.-P., D ́emurger, S., and Li, S. (2017). Productivity gains from agglomeration and migration in the people’s republic of china between 2002 and 2013. Asian Development Review, 34(2):184–200. Dou, B., Parrella, M. L., and Yao, Q. (2016). Generalized yule–walker estimation for spatio-temporal models with unknown diagonal coefficients. Journal of Econometrics, 194(2):369–382. Doukhan, P. and Louhichi, S. (1999). A new weak dependence condition and applications to moment inequalities. Stochastic Processes and Their Applications, 84(2):313–342. Fan, J. and Gijbels, I. (1996). Local Polynomial Modelling and its Applications. Chapman & Hall/CRC. Fan, J. and Yao, Q. (2008). Nonlinear Time Series: Nonparametric and Parametric Methods. Springer Science & Business Media. Fingleton, B. (2008). A generalized method of moments estimator for a spatial panel model with an endogenous spatial lag and spatial moving average errors. Spatial Economic Analysis, 3(1):27–44. Gao, J. (2007). Nonlinear Time Series: Semiparametric and Nonparametric Methods. Chapman & Hall/CRC, London. Gao, Y. and Li, K. (2013). Nonparametric estimation of fixed effects panel data models. Journal of Nonparametric Statistics, 25(3):679–693. Hsiao, C. (2014). Analysis of Panel Data. Cambridge University Press. Im, K. S., Pesaran, M. H., and Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of econometrics, 115(1):53–74. 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. 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. Kelejian, H. H. and Prucha, I. R. (2001). On the asymptotic distribution of the moran i test statistic with applications. Journal of Econometrics, 104(2):219–257. Lee, L.-F. (2004). Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models. Econometrica, 72(6):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. Lee, L.-F. and Yu, J. (2014). Efficient GMM estimation of spatial dynamic panel data models with fixed effects. Journal of Econometrics, 180(2):174–197. LeSage, J. and Pace, R. K. (2009). Introduction to Spatial Econometrics. Chapman and Hall/CRC. Li, D., Chen, J., and Gao, J. (2011). Non-parametric time-varying coefficient panel data models with fixed effects. Econometrics Journal, 14(3):387–408. Li, K. (2017). Fixed-effects dynamic spatial panel data models and impulse response analysis. Journal of Econometrics, 198(1):102–121. Li, Q. and Racine, J. S. (2007). Nonparametric Econometrics: Theory and Practice. Princeton University Press. Lin, Z. and Bai, Z. (2011). Probability inequalities. Springer Science & Business Media. Malikov, E. and Sun, Y. (2017). Semiparametric estimation and testing of smooth coefficient spatial autoregressive models. Journal of Econometrics, 199(1):12–34. Miller Jr, R. G. (1966). Simultaneous Statistical Inference. Springer. Robinson, P. M. (2012). Nonparametric trending regression with cross-sectional dependence. Journal of Econometrics, 169(1):4–14. Seber, G. A. F. (2007). A Matrix Handbook for Statisticians. Wiley-Interscience. Silvapulle, P., Smyth, R., Zhang, X., and Fenech, J.-P. (2017). Nonparametric panel data model for crude oil and stock market prices in net oil importing countries. Energy Economics, 67:255–267. Su, L. (2012). Semiparametric gmm estimation of spatial autoregressive models. Journal of Econometrics, 167(2):543– 560. Su, L. and Jin, S. (2010). Profile quasi-maximum likelihood estimation of partially linear spatial autoregressive models. Journal of Econometrics, 157(1):18–33. Su, L. and Ullah, A. (2006). Profile likelihood estimation of partially linear panel data models with fixed effects. Economics Letters, 92(1):75–81. Sun, Y. (2016). Functional-coefficient spatial autoregressive models with nonparametric spatial weights. Journal of Econometrics, 195(1):134–153. Sun, Y. and Malikov, E. (2018). Estimation and inference in functional-coefficient spatial autoregressive panel data models with fixed effects. Journal of Econometrics, 203(2):359–378. Van Biesebroeck, J. (2015). How Tight is the Link Between Wages and Productivity?: A Survey of the Literature. ILO. White, H. (1996). Estimation, Inference and Specification Analysis. Cambridge University Press. 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. Zhang, Y. and Shen, D. (2015). Estimation of semi-parametric varying-coefficient spatial panel data models with random-effects. Journal of Statistical Planning and Inference, 159:64–80. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/108497 |