Theory and methodology for dynamic panel data: tested by simulations based on financial data

Savas Papadopoulos

International Journal of Computational Economics and Econometrics, 2010, vol. 1, issue 3/4, 239-253

Abstract: A new method is introduced for panel-data models. Asymptotic robustness is used for a multivariate model with latent variables for a family of estimators. It is shown numerically that in comparison to standard methods we obtain: 1) better predictions in out-of-sample occasions; 2) smaller asymptotic standard errors (a.s.e.s); 3) more accurate a.s.e.s; 4) very small bias. Our methodology handles dynamic models with lag-independent variables, individual and time effects, time heteroscedasticity, non-normality, non-stationarity, fixed variables, non-linear and variant-over-time coefficients, and unbalanced data, by using restrictions on the parameters and the multi-sample technique (m.s.t.). Also, a novel formula for the duplication matrix is provided and a solution for a matrix equation is given.

Keywords: longitudinal data; repeated measures; duplication matrix; maximum likelihood estimator; MLE; generalised method of moments; GMM; MD estimators; dynamic panel data; simulation; financial data; dynamic modelling. (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:1:y:2010:i:3/4:p:239-253

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