空间计量经济模型的经验似然研究进展

doi:10.16088/j.issn.1001-6600.2022012002

Abstract

Abstract: Spatial econometric data appear in almost all fields of society and have a wide application prospect. The study of the statistical inference for spatia econometric models (including the estimation and test of model parameters) has important theoretical significance and practical value. In this paper, some common spatial econometric models are briefly introduced, a brief review on the research progress of spatial econometric models except the empirical likelihood method is provided, and the background of the empirical likelihood and the research progress of the empirical likelihood for spatial econometric models are provided in detail.

Key words: spatial econometric model, martingale difference sequence, empirical likelihood, review

CLC Number:

O212.7

QIN Yongsong, LEI Qingzhu. Review on Empirical Likelihood for Spatial Econometric Models[J].Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(5): 138-149.

References

[1]CLIFF A D, ORD J K. Spatial autocorrelation[M]. London: Pion Ltd, 1973.
[2]ANSELIN L. Spatial econometrics: methods and models[M]. The Netherland: Kluwer Academic Publishers, 1988.
[3]LEE L F. Asymptotic distributions of quasi-maximum likelihood estimators for spatial autore-gressive models[J]. Econometrica, 2004, 72: 1899-1925.
[4]LIU X, LEE L F, BOLLINGER C R. An efficient GMM estimator of spatial autoregressive models[J]. Journal of Econometrics, 2010, 159: 303-319.
[5]HAN X Y, LEE L F. Bayesian estimation and model selection for spatial Durbin error model with finite distributed lags[J]. Regional Science and Urban Economics, 2013, 43: 816-837.
[6]LESAGE J, PACE R K. Introduction to spatial econometrics[M]. Boca Raton: CRC Press, 2009.
[7]ELHORST J P. Spatial econometrics from cross-sectional data to spatial panels[M]. New York: Springer, 2014.
[8]KAPOOR M, KELEJIAN H H, PRUCHA I R. Panel data models with spatially correlated error components[J]. Journal of Econometrics, 2007, 140: 97-130.
[9]LEE L F, YU J. Estimation of spatial autoregressive panel data models with fixed effects[J]. Journal of Econometrics, 2010, 154: 165-185.
[10]LEE L F, YU J. Some recent developments in spatial panel data models[J]. Regional Science and Urban Economics, 2010, 40: 255-271.
[11]LEE L F, YU J. Spatial panel data models, In: BALTAGI B H (Eds), Oxford Handbook of Panel Data[M]. Oxford: Oxford University Press, 2015.
[12]ANSELIN L. Spatial econometrics[M]// BALTAGI B H. A companion to theoretical econometrics. Massachusetts: Blackwell Publishers Ltd, 2001: 310-330.
[13]ELHORST J P. Unconditional maximum likelihood estimation of linear and log-linear dynamic models for spatial panels[J]. Geographical Analysis, 2005, 37: 85-106.
[14]ELHORST J P. Dynamic panels with endogenous interaction effects when T is small[J]. Regional Science and Urban Economics, 2010, 40: 272-282.
[15]HSIAO C, PESARAN M H, TAHMISCIOGLU A K. Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods[J]. Journal of Econometrics, 2002, 109: 107-150.
[16]BALTAGI B H, EGGER P, PFAFFERMAYR M. A generalized spatial panel model with random effects[J]. Econometric Reviews, 2013, 32: 650-685.
[17]SU L J, YANG Z L. QML estimation of dynamic panel data models with spatial errors[J]. Journal of Econometrics, 2015, 185: 230-258.
[18]QU X, LEE L F, YU J. QML estimation of spatial dynamic panel data models with endogenous time varying spatial weights matrices[J]. Journal of Econometrics, 2017, 197: 173-201.
[19]YANG Z. Unified M-estimation of fixed-effects spatial dynamic models with short panels[J]. Journal of Econometrics, 2018, 205: 423-447.
[20]MARTELLOSIO F, HILLIER G. Adjusted QMLE for the spatial autoregressive parameter[J]. Journal of Econometrics, 2020, 219: 488-506.
[21]WANG H X, LIN J G, WANG J D. Nonparametric spatial regression with spatial autoregressive error structure[J]. Statistics, 2016, 50: 60-75.
[22]MOZAROVSKY P, VOGLER J. Composite marginal likelihood estimation of spatial autore-gressive probit models feasible in very large samples[J]. Economics Letters, 2016, 148: 87-90.
[23]王晓瑞. 空间自回归模型变量选择的理论研究和实证分析[D]. 北京:北京工业大学, 2018.
[24]ZHU J, HUANG H C, REYES P E. On selection of spatial linear models for lattice data[J]. Journal of the Royal Statistical Society B, 2010, 72: 389-402.
[25]WU Y, SUN Y. Shrinkage estimation of the linear model with spatial interaction[J]. Metrika, 2017, 80: 51-68.
[26]戴晓文, 之振, 田茂再. 带固定效应面板数据空间误差模型的分位回归估计[J]. 应用数学学报, 2016, 39: 847-858.
[27]李序颖. 基于空间自回归模型的缺失值插补方法[J]. 数理统计与管理, 2005, 24: 45-50.
[28]MORAN P A P. Notes on continuous stochastic phenomena[J]. Biometrika, 1950, 37: 17-23
[29]ANSELIN L. Local indicators of spatial association-LISA[J]. Geographical Analysis, 1995, 27(2): 93-115.
[30]YU J, JONG R D, 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: 118-134.
[31]SU L, ZHANG Y. Semiparametric estimation of partially linear dynamic panel data models with fixed effects[J]. Advances in Econometrics, 2016, 36: 137-204.
[32]许永洪, 陈剑伟. 部分线性固定效应空问滞后面板模型的估计研究[J]. 统计研究, 2017, 34: 99-109.
[33]LESAGE J P, PACE P K. A matrix exponential spatial specification[J]. Journal of Econometrics, 2017, 140: 190-214.
[34]QIN Y S. Empirical likelihood for spatial autoregressive models with spatial autoregressive disturbances[J]. Sankhy A: The Indian Journal of Statistics, 2021, 83: 1-25.
[35]QIN J, LAWLESS J. Empirical likelihood and general estimating equations[J]. The Annals of Statistics, 1994, 22: 300-325.
[36]LI Y H, LI Y, QIN Y S. Empirical likelihood for panel data models with spatial errors[J]. Communications in Statistics-Theory and Methods, 2022, 51(9): 2838-2857.
[37]THOMAS D R, GRUNKEMEIER G L. Confidence interval estimation of survival probabilits for censored data[J]. Journal of the American Statistical Association, 1975, 70: 865-871.
[38]OWEN A B. Empirical likelihood ratio confidence intervals for a single functional[J]. Biometrika, 1988, 75: 237-249.
[39]OWEN A B. Empirical likelihood ratio confidence regions[J]. The Annals of Statistics, 1990, 18: 90-120.
[40]HALL P. The Bootstrap and edgeworth expansion[M]. New York: Springer-Verlag, 1992.
[41]HALL P, LA SCALA B. Methodology and algorithms of empirical likelihood[J]. International Statistical Review, 1990, 58: 109-127.
[42]OWEN A B. Empirical likelihood for linear models[J]. The Annals of Statistics, 1991, 19: 1725-1747.
[43]KOLACZYK E D. Emprical likelihood for generalized linear models[J]. Statistica Sinica, 1994, 4: 199-218.
[44]CHEN S X, QIN Y S. Empirical likelihood confidence intervals for local linear smoothers[J]. Biometrika, 2000, 87: 946-953.
[45]QIN Y S. Empirical likelihood ratio confidence regions in a partly linear model[J]. Chinese Journal of Applied Probability and Statistics, 1999, 15: 363-369.
[46]SHI J, LAU T S. Empirical likelihood for partially linear models[J]. Journal of Multivariate Analysis, 2000, 72: 132-148.
[47]WANG Q, JING B Y. Empirical likelihood for partially linear models with fixed design[J]. Statistics & Probability Letters, 1999, 41: 425-433.
[48]CUI H J, CHEN S X. Empirical likelihood confidence regions for parameter in the error-in-variable models[J]. Journal of Multivariate Analysis, 2003, 84: 101-115.
[49]XUE L G, ZHU L X. Empirical likelihood for a varying coefficient model with longitudinal data[J]. Journal of the American Statistical Association, 2007, 102: 642-654.
[50]XUE L G.Empirical likelihood confidence intervals for response mean with data missing at random[J]. Scandinavian Journal of Statistics, 2009, 36: 671-685.
[51]TANG C Y, QIN Y S. An efficient empirical likelihood approach for estimating equations with missing data[J]. Biometrika, 2012, 99: 1001-1007.
[52]CHEN S X, KEILEGOM I V. A review on empirical likelihood methods for regression[J]. Test, 2009, 18: 415-447.
[53]NORDMAN D J. An empirical likelihood method for spatial regression[J]. Metrika, 2008, 68: 351-363.
[54]NORDMAN D J, CARAGEA P C. Point and interval estimation of variogram models using spatial empirical likelihood[J]. Journal of the American Statistical Association, 2008, 103: 350-361.
[55]BANDYOPADHYAY S, LAHIRI N, NORDMAN D J. A frequency domain empirical likelihood method for irregularly spaced spatial data [J]. The Annals of Statistics, 2015, 43: 519-545.
[56]JIN F, LEE L F. GEL estimation and tests of spatial autoregressive models[J]. Journal of Econometrics, 2019, 208: 585-612.
[57]QIN Y S, LEI Q Z. Empirical likelihood for mixed regressive, spatial autoregressive model based on GMM[J]. Sankhy A: The Indian Journal of Statistics, 2021, 83: 353-378.
[58]QIN Y S. Empirical likelihood and GMM for spatial models[J]. Communications in Statistics-Theory and Methods, 2021, 50: 4367-4385.
[59]LI Y H, QIN Y S, LI Y. Empirical likelihood for nonparametric regression models with spatial autoregressive errors[J]. Journal of the Korean Statistical Society, 2021, 50: 447-478.
[60]RONG J R, LIU Y, QIN Y S. Empirical likelihood for spatial dynamic panel data model-s with spatial lags and spatial errors[J/OL]. Communications in Statistics-Theory and Methods[2022-03-01]. https://doi.org/10.1080/03610926.2022.2032172.
[61]曾庆樊, 秦永松, 黎玉芳. 时变系数空间面板数据模型的经验似然推断[J]. 广西师范大学学报(自然科学版), 2022, 40(1): 30-42.

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Review on Empirical Likelihood for Spatial Econometric Models

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