Econometrics: Forecasting with a Panel Tobit Model

Professor Laura Liu, Indiana University

'Forecasting with a Panel Tobit Model '
February 25, 4.00-5.00pm
Zoom online seminar only

Abstract

We use a dynamic panel Tobit model with heteroskedasticity to generate forecasts for a large cross-section of short time series of censored observations. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of heterogeneous coefficients and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. In addition to density forecasts, we construct set forecasts that explicitly target the average coverage probability for the cross-section. We present a novel application in which we forecast bank-level loan charge-off rates for small banks.

Biography

Laura Liu's research interests encompass econometrics, macroeconomics, and network economics. 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, Journal of Econometrics, and Journal of Applied Econometrics. She currently serves as an Associate Editor for the Journal of Applied Econometrics.

Laura Liu received her Ph.D. in Economics from the University of Pennsylvania in 2017. Her dissertation, "Point and Density Forecasts in Panel Data Models", received the 2018 Arnold Zellner Thesis Award in Econometrics and Statistics from the American Statistical Association (ASA) section of Business and Economic Statistics and the Journal of Business and Economic Statistics.

Further information: business-events@glasgow.ac.uk

First published: 18 February 2022

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Adam Smith Business School