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Incidental parameters, initial conditions and sample size in statistical inference for dynamic panel data models

Hsiao, Cheng; Zhou, Qiankun - 2017 - This version August 27, 2017

Persistent link: https://www.econbiz.de/10011788727

Estimating and predicting the general random effects model

Kouassi, Eugène; Kamdem, Alain Constant; Mougoué, Mbodja - In: Journal of forecasting 33 (2014) 4, pp. 270-283

Persistent link: https://www.econbiz.de/10010425747

Maximum Likelihood Estimation and Inference for High Dimensional Nonlinear Factor Models with Application to Factor-Augmented Regressions

Wang, Fa - 2020

This paper reestablishes the main results in Bai (2003) and Bai and Ng (2006) for high dimensional nonlinear factor models, with slightly stronger conditions on the relative magnitude of N(number of subjects) and T(number of time periods). Factors and loadings are estimated by maximum...

Persistent link: https://www.econbiz.de/10012849457

Non linear correlated random effects models with endogeneity and unbalanced panels

Bates, Michael; Papke, Leslie E.; Wooldridge, Jeffrey M. - In: Econometric reviews 43 (2024) 9, pp. 713-732

Persistent link: https://www.econbiz.de/10015050637

Maximum likelihood estimation of dynamic panel data models with interactive effects : quasi-differencing over time or across ndividuals?

Hsiao, Cheng; Zhou, Qiankun - In: Essays in honor of Joon Y. Park : econometric …, (pp. 353-384). 2023

Persistent link: https://www.econbiz.de/10014315463

Chapter 8 Robust Estimation and Inference for Importance Sampling Estimators with Infinite Variance

Chan, Joshua C. C.; Hou, Chenghan; Yang, Thomas Tao - In: Essays in honor of Cheng Hsiao, (pp. 255-285). 2020

Importance sampling is a popular Monte Carlo method used in a variety of areas in econometrics. When the variance of … the importance sampling estimator is infinite, the central limit theorem does not apply and estimates tend to be erratic …

Persistent link: https://www.econbiz.de/10015088870

Parametric inference for discretely sampled stochastic differential equations

Sørensen, Michael - In: Handbook of financial time series, (pp. 531-553). 2009

Persistent link: https://www.econbiz.de/10003834179

Empirical likelihood in missing data problems

Qin, Jing; Zhang, Biao; Leung, Denis H. Y. - In: Journal of the American Statistical Association : JASA 104 (2009) 488, pp. 1492-1503

Persistent link: https://www.econbiz.de/10003993009

Parametric Inference for Discretely Sampled Stochastic Differential Equations

Sorensen, Michael - 2008

A review is given of parametric estimation methods for discretely sampled multivariate diffusion processes. The main … focus is on estimating functions and asymptotic results. Maximum likelihood estimation is briefly considered, but the …

Persistent link: https://www.econbiz.de/10012723991

Robust estimation and inference for importance sampling estimators with infinite variance

Chan, Joshua; Hou, Chenghan; Yang, Thomas Tao - In: Essays in honor of Cheng Hsiao, (pp. 255-285). 2020

Persistent link: https://www.econbiz.de/10012249406