Forecasting with Dynamic Panel Data Models / Laura Liu, Hyungsik Roger Moon, Frank Schorfheide.

Format:

Book

Author/Creator:

Liu, Laura.

Contributor:

National Bureau of Economic Research.

Moon, Hyungsik Roger.

Schorfheide, Frank.

Series:

Working Paper Series (National Bureau of Economic Research) no. w25102.

NBER working paper series no. w25102

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2018.

Summary:

This paper considers the problem of forecasting a collection of short time series using cross sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross-sectional information to transform the unit-specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a non-parametric kernel estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated-random-effects distribution as known (ratio-optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions.

Notes:

Print version record

September 2018.