Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective

Start date

Thursday, 3 Oct 2019, 16:00

End date

Thursday, 3 Oct 2019, 17:00

Mandeville Building
Campus Woudestein

Laura Liu (Indiana University)

This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients and cross-sectional heteroskedasticity. The panel considered in this paper features a large cross-sectional dimension N but short time series T. Due to the short T, traditional methods have difficulty in disentangling the heterogeneous parameters from the shocks, which contaminates the estimates of the heterogeneous parameters. To tackle this problem, I assume that there is an underlying distribution of heterogeneous parameters, model this distribution nonparametrically allowing for correlation between heterogeneous parameters and initial conditions as well as individual-specific regressors, and then estimate this distribution by pooling the information from the whole cross-section together. Theoretically, I prove that both the estimated common parameters and the estimated distribution of the heterogeneous parameters achieve posterior consistency, and that the density forecasts asymptotically converge to the oracle forecast. Methodologically, I develop a simulation-based posterior sampling algorithm specifically addressing the nonparametric density estimation of unobserved heterogeneous parameters. Monte Carlo simulations and an application to young firm dynamics demonstrate improvements in density forecasts relative to alternative approaches. 

  • About Laura Liu

    Laura Liu is an Assistant Professor in the Department of Economics at Indiana University Bloomington. She has been developing and implementing methods that facilitate estimation and improve forecasting performance in large-dimensional frameworks, with empirical applications mainly in macroeconomic and network economic setups. Her recent research topics include panel data and forecasting, structural macro models with granular data, and networks analysis from a time-series perspective. Her research has been published in Econometrica and Journal of Applied Econometrics.


dr. (Maria) M Grith
dr. (Mikhail) M Zhelonkin

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