Quasi-maximum likelihood estimations and applications for spatial dynamic autoregressive panel model with fixed effects

ZHOU Shaofu, ZHANG Jiajun

Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (1) : 45-57.

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Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (1) : 45-57. DOI: 10.12011/SETP2019-0631

Quasi-maximum likelihood estimations and applications for spatial dynamic autoregressive panel model with fixed effects

  • ZHOU Shaofu, ZHANG Jiajun
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Abstract

The paper investigates the asymptotic properties of quasi-maximum likelihood estimators of the DSAC panel model with fixed effects when the space system is stable and both n and T are large. The paper shows that when using the transformation approach, the quasi-maximum likelihood estimators yield a bias of O(1/T) order in the general case. When (n-1)/T→0, the estimators converge consistently to the true value with the rate of √(n-1)T. When (n-1)/T→∞, the estimators converge to a degenerate distribution at the rate of T. The estimators obtained by the direct approach yield a bias of max(O(1/T),O(1/n)) order in the general case. When n/T→0 and n/T→∞, the estimators are converge to different degenerate distributions at the rate of n and T respectively. The bias corrected estimators have better finite sample properties than the quasi-maximum likelihood estimators. When n/T3→0, the bias corrected estimators obtained by the transformation approach are √(n-1)T consistently converge to the true value. When n/T3 and n3/T both tend to 0, the bias corrected estimators obtained by the direct approach converge to the true value consistently with the rate of √nT. The direct approach can consistently estimate the individual effects and time effects while transformation approach cannot. The finite sample property of the DASC panel model with fixed effects is better than that of DSAR panel model when the error term has the spatial correlation structure. Finally, an empirical research example shows the application value of the DSAC model.

Key words

dynamic spatial autoregressive confused (DSAC) panel model / two components fixed effects / quasi-maximum likelihood estimation / bias correction / population growth rate

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ZHOU Shaofu , ZHANG Jiajun. Quasi-maximum likelihood estimations and applications for spatial dynamic autoregressive panel model with fixed effects. Systems Engineering - Theory & Practice, 2021, 41(1): 45-57 https://doi.org/10.12011/SETP2019-0631

References

[1] Anselin L. Spatial econometrics:Methods and models[M]. Kluwer Academic Press, 1988.
[2] Anselin L, Baltagi B. Spatial econometrics:A companion to theoretical econometrics[M]. Blackwell Press, 2001.
[3] Baltagi B H, Song S H, Kon W. Testing panel data regression models with spatial error correlation[J]. Journal of Econometrics, 2003, 117(1):123-150.
[4] Elhorst J P. Specification and estimation of spatial panel data models[J]. International Regional Science Review, 2003, 26(3):244-268.
[5] Yu J, De Jong R, Lee L F. Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large[J]. Journal of Econometrics, 2008, 146(1):118-134.
[6] Lee L F, Yu J. A spatial dynamic panel data model with both time and individual fixed effects[J]. Econometric Theory, 2010, 26(2):564-597.
[7] Lee L F, Yu J. Estimation of spatial autoregressive panel data models with fixed effects[J]. Journal of Econometrics, 2010, 154(2):165-185.
[8] Cliff A D, Ord J K. Spatial autocorrelation[M]. London:Pion, 1973.
[9] Kapoor M, Kelejian H H, Prucha I R. Panel data models with spatially correlated error components[J]. Journal of Econometrics, 2007, 140(1):97-130.
[10] Baltagi B H, Egger P, Pfaffermayr M. A generalized spatial panel data model with random effects[J]. Econometric Reviews, 2013, 32(5-6):650-685.
[11] Baltagi B H, Fingleton B, Pirotte A. Estimating and forecasting with a dynamic spatial panel data model[J]. Oxford Bulletin of Economics and Statistics, 2014, 76(1):112-138.
[12] Elhorst J P. Dynamic panels with endogenous interaction effects when T is small[J]. Regional Science and Urban Economics, 2010, 40(5):272-282.
[13] Han F, Xie R, Lai M. Traffic density, congestion externalities, and urbanization in China[J]. Spatial Economic Analysis, 2018, 13(4):1-22.
[14] Baltagi B H, Li D. Prediction in the panel data model with spatial correlation:The case of liquor[J]. Spatial Economic Analysis, 2006, 1(2):175-185.
[15] 熊湘辉,徐璋勇.中国新型城镇化水平及动力因素测度研究[J].数量经济技术经济研究, 2018, 35(2):44-63.Xiong X H, Xu Z Y. Research on level and mechanical machine under the guidance of new urbanization[J]. The Journal of Quantitative and Technical Economics, 2018, 35(2):44-63.
[16] Silva D F C D, Elhorst J P, Neto R D M S. Urban and rural population growth in a spatial panel of municipalities[J]. Regional Studies, 2017, 51:1-15.
[17] 邵帅,李欣,曹建华,等.中国雾霾污染治理的经济政策选择-基于空间溢出效应的视角[J].经济研究, 2016, 51(9):73-88.Shao S, Li X, Cao J H, et al. China's economic policy choices for governing smog pollution based on spatial spillover effects[J]. Economic Research Journal, 2016, 51(9):73-88.
[18] Ertur C, Koch W. Growth, technological interdependence and spatial externalities:Theory and evidence[J]. Journal of Applied Econometrics, 2007, 22(6):1033-1062.
[19] 朱国忠,乔坤元,虞吉海.中国各省经济增长是否收敛?[J].经济学(季刊), 2014, 13(3):1171-1194.Zhu G G, Qiao K Y, Yu J H. Is provincial economic growth convergent in China?[J]. China Economic Quarterly, 2014, 13(3):1171-1194.
[20] 陈丰龙,王美昌,徐康宁.中国区域经济协调发展的演变特征:空间收敛的视角[J].财贸经济, 2018, 39(7):128-143.Chen F L, Wang M C, Xu K N. The evolution trend of China's coordinated regional development:A spatial convergence analysis[J]. Finance and Trade Economics, 2018, 39(7):128-143.
[21] Yang Z. Quasi-maximum likelihood estimation for spatial panel data regressions[R]. Working paper, Research Collection School of Economics, 2013.
[22] LeSage J, Pace R K. Introduction to spatial econometrics[M]. CRC Press, 2009.
[23] Yu J, De Jong R, Lee L F. Estimation for spatial dynamic panel data with fixed effects:The case of spatial cointegration[J]. Journal of Econometrics, 2012, 167(1):16-37.
[24] Su L, Yang Z. QML estimation of dynamic panel data models with spatial errors[J]. Journal of Econometrics, 2015, 185(1):230-258.
[25] 陶长琪,周璇.含空间自回归误差项的空间动态面板模型的有效估计[J].数量经济技术经济研究, 2016, 33(4):126-144.Tao C Q, Zhou X. Efficient estimation of spatial dynamic panel data model with spatial errors[J]. The Journal of Quantitative and Technical Economics, 2016, 33(4):126-144.
[26] 徐敏,姜勇.中国产业结构升级能缩小城乡消费差距吗?[J].数量经济技术经济研究, 2015, 32(3):3-21.Xu M, Jiang Y. Can the China's industrial structure upgrading narrow the gap between urban and rural consumption?[J]. The Journal of Quantitative and Technical Economics, 2015, 32(3):3-21.
[27] 肖磊,鲍张蓬,田毕飞.我国服务业发展指数测度与空间收敛性分析[J].数量经济技术经济研究, 2018, 35(11):111-127.Xiao L, Bao Z P, Tian B F. Study on the development index and spatial convergence of service industry in China[J]. The Journal of Quantitative and Technical Economics, 2018, 35(11):111-127.
[28] 郭鹏辉,钱争鸣,刘立虎.初始值外生给定下动态空间面板数据模型的拟极大似然估计[J].数理统计与管理, 2015, 34(1):38-46.Guo P H, Qian Z M, Liu L H. Quasi maximum likelihood estimation of a dynamic spatial panel data model based on exogenous initial value[J]. Journal of Applied Statistics and Management, 2015, 34(1):38-46.
[29] 李欣先.固定效应一般动态空间面板模型及冲击响应研究[D].北京:首都经济贸易大学, 2018.Li X X. Fixed-effects generalised dynamic spatial panel model and impulse response study[D]. Beijing:Capital University of Economics and Business, 2018.
[30] Nickell S. Biases in dynamic models with fixed effects[J]. Econometrica, 1981, 49(6):1417-1426.
[31] Hsiao C. Analysis of panel data[M]. Cambridge:Cambridge University Press, 2003.
[32] Neyman J. Consistent estimates based on partially consistent observations[J]. Econometrica, 1948, 16(1):1-32.
[33] Yu J. Essays on spatial dynamic panel data model:Theories and applications[D]. Columbus:The Ohio State University, 2007.
[34] Hahn J, Kuersteiner G. Asymptotically unbiased inference for a dynamic panel model with fixed effects when both n and t are large[J]. Econometrica, 2002, 70(4):1639-1657.
[35] Ciccarelli C, Elhorst J P. A dynamic spatial econometric diffusion model with common factors:The rise and spread of cigarette consumption in Italy[J]. Regional Science and Urban Economics, 2018, 72:131-142.
[36] Hausman J A. Specification tests in econometrics[J]. Econometrica, 1978, 46(6):1251-1271.
[37] Lee L F. Asymtotic distribution of quasi-maximum likelihood estimators for spatial autoregressive models[J]. Econometrica, 2004, 72(6):1899-1925.
[38] Kelejian H H, Prucha I R. A generalized moments estimator for the autoregressive parameter in a spatial model[J]. International Economic Review, 1999, 40(2):509-533.
[39] Lee L F, Yu J. Some recent developments in spatial panel data models[J]. Regional Science and Urban Economics, 2010, 40(5):255-271.
[40] Glaeser E L. Cities, agglomeration and spatial equilibrium[M]. New York:Oxford University Press, 2008:47-74.

Funding

Fundamental Research Funds for the Central Universities (2019WKYXZX020)
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