Working Papers
By Year:
Paper #  Author  Title  

16022 
Laura Liu Hyungsik Roger Moon Frank Schorfheide 
Forecasting with Dynamic Panel Data Models  
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 crosssectional information to transform the unitspecific (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 nonparametric estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlatedrandomeffects distribution as known (ratiooptimality). 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. Download Paper


01016 
Hyungsik Roger Moon Frank Schorfheide 
"Minimum Distance Estimation of Nonstationary Time Series Models"  
This paper establishes the consistency and limit distribution of minimum distance (MD) estimators for time series models with deterministic or stochastic trends. We consider models that are linear in the variables, but involve nonlinear restrictions across parameters. Two complications arise. First, the unrestricted and restricted parameter space have to be rotated to separate fast converging components of the MD estimator from slowly converging components. Second, if the model includes stochastic trends it is desirable to use a random matrix to weigh the discrepancy between the unrestricted and restricted parameter estimates. In this case, the objective function of the MD estimator has a stochastic limit. We provide regularity conditions for the nonlinear restriction function that are easier to verify than the stochastic equicontinuity conditions that typically arise from direct estimation of the restricted parameters. We derive the optimal weight matrix when the limit distribution of the unrestricted estimator is mixed normal and propose a goodnessof t test based on overidentifying restrictions. As applications, we investigate cointegration regression models, presentvalue models, and a permanentincome model based on a linearquadratic dynamic programming problem. Download Paper
