Lecture Materials for ECON747: Spatial Econometric Models and Methods, Term I 2024-25

Lecture Notes: (Download lecture notes and the linked files into the same folder)

Lecture 1: Introduction and Preliminaries (neighborhood crime, readme)

(Fischer & Getis 2010; Baltagi 2005; Anderson 2003, Appendix A; Greene 2022, Appendix A)

Lecture 2: Spatial Linear Regression (SLR) Model (Supplement: IV Estimation)

Lecture 3: Tests of Hypotheses for SLR Model (Updated on 24/10/2024)

Lecture 4: Bias-Corrected Estimation of SLR Model (deferred to after Lecture 12)

Lecture 5: Bootstrap LM Tests for SLR Model (deferred to after Lecture 12)

Lecture 6: SLR Model with Heteroskedasticity (deferred to after Lecture 10)

Lecture 7: Spatial Panel Data Models (for regular panel data models: see Baltagi Book, Greene Book)

Lecture 8: SPD Model with Heteroskedasticity (a discussion based on Liu and Yang 2020)

Lecture 9: Tests of Hypothesis for SPD Model (a discussion based on Baltagi and Yang 2013b)

Lecture 10: Dynamic Spatial Panel Data Models (for regular dynamic panel data models: see Baltagi Book, Greene Book)

Lecture 11: DSPD Model with Heteroskedasticity (a discussion based on Li and Yang 2020)

Lecture 12: Tests of Hypothesis for DSPD Model (based on Yang 2021a and Yang 2021b)

Midterm Test: 8:30 AM – 11:30 PM, Saturday Week 10, October 26, 2024 (Instructions)

Reference Books:

1. Anselin, Luc (1988). Spatial Econometrics: Methods and Models, Dordrecht: Kluwer.

2. Lung-Fei Lee (2023). Spatial Econometrics: Spatial Autoregressive Models, World Scientific.

3. Roger S. Bivand, Edzer Pebesma, and Virgilio Gómez-Rubio (2013). Applied Spatial Data Analysis with R, 2^nd ed (pdf), Springer.

4. Elhorst P.J. (2014). Spatial econometrics: from Cross-Sectional Data to Spatial Panels (pdf), Heidelberg: Springer.

5. LeSage, J.P. and R..K. Pace (2009). Introduction to Spatial Econometrics (pdf), Boca Raton: Taylor and Francis.

Recommended Papers:

(Published versions of some papers are for teaching use only. Please DO NOT circulate.)

1. Anselin, L., Bera, A. K., 1998. Spatial dependence in linear regression models with an introduction to spatial econometrics. In: Handbook of Applied Economic Statistics, edited by Aman Ullah and David E. A. Giles}. New York: Marcel Dekker

2. Anselin, L., 2001. Spatial Econometrics. In: A Companion to Theoretical Econometrics, edited by Badi H. Baltagi. Blackwell Publishing.

3. Lee, L. F., 2004a. Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models. Econometrica 72, 1899-1925.

4. Kelejian H. H. and Prucha, I. R. (2001). On the asymptotic distribution of the Moran I test statistic with applications. Journal of Econometrics 104, 219-257.

5. Liu, S. F. and Yang, Z. L. (2015a). Asymptotic distribution and finite-sample bias correction of QML estimators for spatial error dependence Model. Econometrics, 3, 376-411.

6. Baltagi, B. and Yang, Z. L. (2013a). Standardized LM tests for spatial error dependence in linear or panel regressions. The Econometrics Journal 16, 103-134.

7. Baltagi, B. and Yang, Z. L. (2013b). Heteroskedasticity and non-normality robust LM tests of spatial dependence. Regional Science and Urban Economics 43, 725-739.

8. Yang, Z. L. (2015a). A general method for third-order bias and variance correction on a nonlinear estimator. Journal of Econometrics, 186, 178-200.

9. Yang, Z. L. (2015b). LM tests of spatial dependence based on bootstrap critical values. Journal of Econometrics, 185, 33-39.

10. Liu, S. F. and Yang, Z. L. (2015b). Improved Inferences for Spatial Regression Models. Regional Science and Urban Economics, 55, 55-67.

11. Liu, S. F. and Yang, Z. L. (2015c). Modified QML estimation of spatial autoregressive models with unknown heteroskedasticity and normality. Regional Science and Urban Economics, 52, 50-70.

12. Kelejian H. H. and Prucha, I. R. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model. International Economic Review 40, 509-533.

13. Lee, L. F. (2007a). GMM and 2SLS estimation of mixed regressive, spatial autoregressive models. Journal of Econometrics 137, 489-514.

14. Lee, L. F. (2007b). The method of elimination and substitution in the GMM estimation of mixed regressive, spatial autoregressive models. Journal of Econometrics 140, 155-189.

15. Lee, L. F. and Liu, X. (2010). Efficient GMM estimation of high order spatial autoregressive models with autoregressive disturbances. Econometric Theory 26, 187-230.

16. Kelejian H. H. and Prucha, I. R. (2010) Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics 157, 53-67.

17. Lin, X. and Lee, L. F. (2010). GMM estimation of spatial autoregressive models with unknown heteroskedasticity. Journal of Econometrics 157, 34-52.

18. Lee, L. F., Yu, J. H. (2010). Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics 154, 165-185.

19. Lee, L.-F. and Ju, J. H. (2012). Spatial panels: random components versus fixed effects. International Economic Review 53, 1369-1412.

20. Yang, Z. L., Yu, J. H, and Liu, S. F. (2016). Bias correction and refined inferences for fixed effects spatial panel data models. Regional Science and Urban Economics, 61, 52-72.

21. Liu, S. F. and Yang, Z. L. (2020). Robust estimation and inference of spatial panel data models with fixed effects. Japanese Journal of Statistics and Data Science 3, 257–311.

22. Yu, J. H., de Jong, R., 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, 118-134.

23. Su, L. J. and Yang, Z. L. (2015). QML estimation of dynamic panel models with spatial errors. Journal of Econometrics, 185, 230-258.

24. Yang, Z. L. (2018a). Unified M-estimation of fixed-effects spatial dynamic panel data models with short panels. Journal of Econometrics, 423-447

25. Yang, Z. L. (2018b). Supplement to “Unified M-estimation of fixed-effects spatial dynamic panel data models with short panels”.