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Do you know your biases? A Monte Carlo analysis of dynamic panel data estimators

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  • Kufenko, Vadim

  • Prettner, Klaus

Abstract

We assess the performance of widely-used dynamic panel data estimators based on Monte Carlo simulations of a dynamic economic process. Knowing the true underlying coefficient of the autoregressive term, we show that most estimators exhibit a severe bias even in the absence of measurement errors, omitted variables, and endogeneity issues. We analyze how the bias changes with the sample size, the autoregressive coefficient, and the estimation options. Based on our insights, we recommend i) carefully choosing appropriate estimators given the underlying structure of the data and ii) scrutinizing the estimation results based on the insights of simulation studies.

Suggested Citation

  • Kufenko, Vadim & Prettner, Klaus, 2021. "Do you know your biases? A Monte Carlo analysis of dynamic panel data estimators," Department of Economics Working Paper Series 316, WU Vienna University of Economics and Business.
    • Handle: RePEc:wiw:wus005:8285
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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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