Likelihood-Based Estimation of Dynamic Panels With Predetermined Regressors

By Enrique Moral-Benito. 2013

130 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/d1e1eccbf3894823e6cc4a653d4dc59a60e4242c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. R. Poldermans
2. M. Pleus
3. Mark F. J. Steel
4. Jan F. Kiviet
5. Lars Leszczensky
6. Tobias Wolbring
7. Martin Feldkircher
8. Tomáš Havránek

In Sociology and Political Science

1. R. Poldermans
2. Mark F. J. Steel
3. Jan F. Kiviet
4. Lars Leszczensky
5. Tobias Wolbring
6. M. Pleus

Citations in the corpus

• Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models (2017, background): “Moral-Benito [32] examines stationary and nonstationary regressors in a dynamic model with heteroskedasticity, but the extra regressor is predetermined or strictly exogenous.”
• Dynamic panel data modelling using maximum likelihood: an alternative to Arellano-Bond (2018, unknown): “With respect to the parameter values, we follow the baseline Design 5 in Moral-Benito (2013) and we fix λ ¼ 0:75, β ¼ 0:25, ρ ¼ 0:5, ϕ ¼ 0:17, and π ¼ 0:67.”
• Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects (2017, unknown): “These efforts have culminated in the work of Moral-Benito (2013), who developed an ML method that effectively addresses the key problems of dynamic panel data models.”
• How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study (2019, methodology): “In Monte Carlo simulations, Moral-Benito (2013), Allison et al. (2017), and Moral-Benito et al. (2018) show that applying the ML-SEM method to the cross-lagged panel model with FE seems to keep the promise of offering protection against both time-invariant unobserved heterogeneity and reverse…”
• Model Averaging and Its Use in Economics (2017, methodology): “Thus, Moral-Benito (2016) proposes the use of a suitable likelihood function (derived in Moral-Benito (2013)) for dynamic panel data with fixed effects and weakly exogenous51 regressors, which is argued to be the most relevant form of endogeneity in the growth regression context.”