Project Work for ECON734, Term I 2024-25

General Instruction A Sample Project

Presentation Time: Friday, Week 14, 12:00 - 15:15, Nov 22, 2024

Presentation Venue: SOE, SR 4.1.

Presentation Sequence:

a) LI Jinwei and Du Xinyi:

Spatial Linear Regression with Matlab and Python (Key Ref: [3], Related: [1], [2], [4], [5])

b) XU Yunning and ZHAI Hao:

Spatial Panel Data Analyses with Matlab and Python, Part I (Key Ref: [6], Related: [7], [8])

c) CAO Lu, HAO Neng, and LIU Zhihao:

Spatial Panel Data Analyses with Matlab and Python, Part II (Key Ref: [6], Related: [7], [8])

d) MIN Pengjin and WANG Haolin:

Hypothesis Testing in Spatial Panel Data Models with Matlab and Python (Key Ref: [10], Related: [9], [11])

e) GAO Qi and WEI Jiezheng:

Hypothesis Testing in Dynamic Spatial Panels with Matlab and Python (Key Ref: [14], Related: [12], [13], [15], [16])

Key References:

[1] Lee, L. F. (2004). Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models (Online Appendix). Econometrica 72, 1899-1925.

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

[3] Liu, S. F. and Yang, Z. L. (2015b). Modified QML estimation of spatial autoregressive models with unknown heteroskedasticity and normality (Matlab Code). Regional Science and Urban Economics, 52, 50-70.

[4] Yang, Z. L. (2015a). A general method for third-order bias and variance corrections on a nonlinear estimator (Matlab Code). Journal of Econometrics 186, 178-200.

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

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

[7] 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.

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

[9] Baltagi, B. H. and Yang, Z. L. (2013a). Standardized LM tests for spatial error dependence in linear or panel regressions (Matlab Code). Econometrics Journal 16, 103-134.

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

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

[12] 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, 118-134.

[13] Yang, Z. L. (2018). Unified M-estimation of fixed-effects spatial dynamic panel data models with short panels (Supplementary Material, Matlab Code). Journal of Econometrics 205, 423-447.

[14] Yang, Z. L. (2021). Joint tests for dynamic and spatial effects in short dynamic panel data models with fixed effects and heteroskedasticity (Matlab Code). Empirical Economics 60, 51-92.

[15] Li, L. Y. and Yang, Z. L. (2020). Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity. Regional Science and Urban Economics 81, 103520.

[16] Baltagi, B. H., Pirotte, A. and Yang, Z. L. (2021). Diagnostic tests for homoscedasticity in spatial cross-sectional or panel models (Supplementary Material, Matlab Code). Journal of Econometrics 224, 245-270.