Dynamic panel data modelling using maximum likelihood: an alternative to Arellano-Bond
By Enrique Moral-Benito, P. Allison, Richard Williams. Applied Economics, 2018
155 citations recorded by the source corpus.
ABSTRACT The Arellano-Bond estimator is widely used among applied researchers when estimating dynamic panels with fixed effects and predetermined regressors. This estimator might behave poorly in finite samples when the cross-section dimension of the data is small (i.e. small ), especially if the variables under analysis are persistent over time. This paper discusses a maximum likelihood estimator that is asymptotically equivalent to Arellano and Bond (1991) but presents better finite sample behaviour. The estimator is based on an alternative parametrization of the likelihood function introduced in Moral-Benito (2013). Moreover, it is easy to implement in Stata using the xtdpdml command as described in a companion paper published in the Stata Journal, which also discusses further advantages of the proposed estimator for practitioners.
Subfields: Economics and Econometrics, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/745ce76757f606846d3fcce44e40ee18b6948bd5
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Citations in the corpus
- How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study (2019, methodology): “Although Moral-Benito et al. (2018) and Williams, Allison, and Moral-Benito (2018) highlight that MLSEM can use full information maximum likelihood to deal with missing data and also give recommendations on how to deal with nonnormality, it remains open to future research whether our simulation…”
- Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects (2017, unknown): “For example, if the predictors in the model consist of nine 9For examples of the use of FIML to handle missing data, see Williams et al. (2016) and Moral-Benito et al. (2017). time-varying variables and T = 11, then the covariance matrix will be 101 × 101.”