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Full-Text Articles in Public Policy
The Mundlak Spatial Estimator, Badi H. Baltagi
The Mundlak Spatial Estimator, Badi H. Baltagi
Center for Policy Research
The spatial Mundlak model first considered by Debarsy (2012) is an alternative to fixed effects and random effects estimation for spatial panel data models. Mundlak modelled the correlated random individual effects as a linear combination of the averaged regressors over time plus a random time-invariant error. This paper shows that if spatial correlation is present whether spatial lag or spatial error or both, the standard Mundlak result in panel data does not hold and random effects does not reduce to its fixed effects counterpart. However, using maximum likelihood one can still estimate these spatial Mundlak models and test the correlated …
The Two-Way Mundlak Estimator, Badi H. Baltagi
The Two-Way Mundlak Estimator, Badi H. Baltagi
Center for Policy Research
Mundlak (1978) shows that the fixed effects estimator is equivalent to the random effects estimator in the one-way error component model once the random individual effects are modeled as a linear function of all the averaged regressors over time. In the spirit of Mundlak, this paper shows that this result also holds for the two-way error component model once this individual and time effects are modeled as linear functions of all the averaged regressors across time and across individuals. Woolridge (2021) also shows that the two-way fixed effects estimator can be obtained as a pooled OLS with the regressors augmented …
Robust Dynamic Space-Time Panel Data Models Using Ε- Contamination: An Application To Crop Yields And Climate Change, Badi H. Baltagi, Georges Bresson, Anoop Chaturvedi, Guy Lacroix
Robust Dynamic Space-Time Panel Data Models Using Ε- Contamination: An Application To Crop Yields And Climate Change, Badi H. Baltagi, Georges Bresson, Anoop Chaturvedi, Guy Lacroix
Center for Policy Research
This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach de-parts from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the …
Lasso For Stochastic Frontier Models With Many Efficient Firms, William C. Horrace, Hyunseok Jung, Yoonseok Lee
Lasso For Stochastic Frontier Models With Many Efficient Firms, William C. Horrace, Hyunseok Jung, Yoonseok Lee
Center for Policy Research
We apply the adaptive LASSO (Zou, 2006) to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted L1 penalty with sign restrictions for firm-level inefficiencies allows simultaneous estimation of the maximal efficiency and firm-level inefficiency parameters, which results in a faster rate of convergence of the corresponding estimators than the least-squares dummy variable approach. We show that the estimator possesses the oracle property and selection consistency still holds with our proposed tuning parameter selection criterion. We also propose an efficient optimization algorithm based on coordinate descent. We apply the method to estimate …