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Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients

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  • Xuan Liang
  • Jiti Gao
  • Xiaodong Gong

Abstract

This paper considers a semiparametric spatial autoregressive panel data model with fixed effects with time-varying coefficients. The time-varying coefficients are allowed to follow an unknown function of time while the other parameters are assumed to be constants. We propose a "local linear concentrated quasi-maximum likelihood estimation" method to obtain consistent estimators for the spatial autoregressive coefficient, the variance of the error term and the nonparametric time-varying coefficients. We show that the estimators of the parametric components converge at the rate of sqrt(NT), and those of the nonparametric time-varying coefficients converge at the rate of sqrt(NTh). Monte Carlo simulations are conducted to illustrate the finite sample performance of our proposed method. We apply our method to study the spatial influences and the time-varying spillover effects in the wage level among 159 Chinese cities.

Suggested Citation

  • Xuan Liang & Jiti Gao & Xiaodong Gong, 2021. "Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients," Monash Econometrics and Business Statistics Working Papers 5/21, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2021-5
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    1. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, September.
    2. Bernard Fingleton, 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, Taylor & Francis Journals, vol. 3(1), pages 27-44.
    3. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, September.
    4. Su, Liangjun, 2012. "Semiparametric GMM estimation of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 167(2), pages 543-560.
    5. Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
    6. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    7. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
    8. Pierre-Philippe Combes & Sylvie Démurger & Shi Li, 2017. "Productivity Gains from Agglomeration and Migration in the People's Republic of China between 2002 and 2013," Asian Development Review, MIT Press, vol. 34(2), pages 184-200, September.
    9. Pierre-Philippe Combes & Sylvie Démurger & Shi Li, 2016. "Productivity gains from agglomeration and migration in Chinese cities over 2002-2013," Post-Print halshs-01420835, HAL.
    10. Chen, Jia & Gao, Jiti & Li, Degui, 2012. "Semiparametric trending panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 171(1), pages 71-85.
    11. Doukhan, Paul & Louhichi, Sana, 1999. "A new weak dependence condition and applications to moment inequalities," Stochastic Processes and their Applications, Elsevier, vol. 84(2), pages 313-342, December.
    12. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    13. Chen, Jia & Li, Degui & Linton, Oliver, 2019. "A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables," Journal of Econometrics, Elsevier, vol. 212(1), pages 155-176.
    14. Malikov, Emir & Sun, Yiguo, 2017. "Semiparametric estimation and testing of smooth coefficient spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 199(1), pages 12-34.
    15. Sun, Yiguo & Malikov, Emir, 2018. "Estimation and inference in functional-coefficient spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 203(2), pages 359-378.
    16. Kelejian, Harry H & Prucha, Ingmar 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, Springer, vol. 17(1), pages 99-121, July.
    17. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    18. Braid, Ralph M., 2002. "The Spatial Effects of Wage or Property Tax Differentials, and Local Government Choice between Tax Instruments," Journal of Urban Economics, Elsevier, vol. 51(3), pages 429-445, May.
    19. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    20. Silvapulle, Param & Smyth, Russell & Zhang, Xibin & Fenech, Jean-Pierre, 2017. "Nonparametric panel data model for crude oil and stock market prices in net oil importing countries," Energy Economics, Elsevier, vol. 67(C), pages 255-267.
    21. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    22. Degui Li & Jia Chen & Jiti Gao, 2011. "Non‐parametric time‐varying coefficient panel data models with fixed effects," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 387-408, October.
    23. Su, Liangjun & Jin, Sainan, 2010. "Profile quasi-maximum likelihood estimation of partially linear spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 157(1), pages 18-33, July.
    24. Lee, Lung-fei & Yu, Jihai, 2014. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 180(2), pages 174-197.
    25. Dou, Baojun & Parrella, Maria Lucia & Yao, Qiwei, 2016. "Generalized Yule–Walker estimation for spatio-temporal models with unknown diagonal coefficients," Journal of Econometrics, Elsevier, vol. 194(2), pages 369-382.
    26. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    27. Yichen Gao & Kunpeng Li, 2013. "Nonparametric estimation of fixed effects panel data models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(3), pages 679-693, September.
    28. Bai, Jushan & Li, Kunpeng, 2010. "Theory and methods of panel data models with interactive effects," MPRA Paper 43441, University Library of Munich, Germany, revised Dec 2012.
    29. Baltagi, Badi H. & Blien, Uwe & Wolf, Katja, 2012. "A dynamic spatial panel data approach to the German wage curve," Economic Modelling, Elsevier, vol. 29(1), pages 12-21.
    30. Sun, Yiguo, 2016. "Functional-coefficient spatial autoregressive models with nonparametric spatial weights," Journal of Econometrics, Elsevier, vol. 195(1), pages 134-153.
    31. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    32. Li, Kunpeng, 2017. "Fixed-effects dynamic spatial panel data models and impulse response analysis," Journal of Econometrics, Elsevier, vol. 198(1), pages 102-121.
    33. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    34. Su, Liangjun & Ullah, Aman, 2006. "Profile likelihood estimation of partially linear panel data models with fixed effects," Economics Letters, Elsevier, vol. 92(1), pages 75-81, July.
    35. Dou, Baojun & Parrella, Maria Lucia & Yao, Qiwei, 2016. "Generalized Yule–Walker estimation for spatio-temporal models with unknown diagonal coefficients," LSE Research Online Documents on Economics 67151, London School of Economics and Political Science, LSE Library.
    36. Robinson, Peter M., 2012. "Nonparametric trending regression with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 169(1), pages 4-14.
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    2. Shosei Sakaguchi & Hayato Tagawa, 2024. "Identification and Inference for Synthetic Control Methods with Spillover Effects: Estimating the Economic Cost of the Sudan Split," Papers 2408.00291, arXiv.org, revised Oct 2024.

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    More about this item

    Keywords

    concentrated quasi-maximum likelihood estimation; local linear estimation; time-varying coefficient;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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