Working Papers
By Year:
Paper #  Author  Title  

17006 
Laura Liu 
Density Forecasts in Panel Models: A semiparametric Bayesian Perspective*  
This paper constructs individualspecific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coeficients and crosssectional heteroskedasticity. The panel considered in this paper features large crosssectional dimension (N) but short time series (T). Due to short T, traditional methods have difficulty in disentanglingthe 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 individualspecific regressors, and then estimate this distribution by pooling the information from the whole crosssection together. I develop a simulationbased posterior sampling algorithm specifically addressing the nonparametric density estimation of unobserved heterogeneous parameters. 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, an (infeasible) benchmark that is defined as the individualspecific posterior predictive distribution under the assumption that the common parameters and the distribution of the heterogeneous parameters are known. Monte Carlo simulations demonstrate improvements in density forecasts relative to alternative approaches. An application to young firm dynamics also shows that the proposed predictor provides more accurate density predictions. Download Paper


17003 
Francis X. Diebold Laura Liu Kamil Yilmaz 
Commodity Connectedness  
We use variance decompositions from highdimensional vector autoregressions to characterize connectedness in 19 key commodity return volatilities, 20112016. We study both static (fullsample) and dynamic (rollingsample) connectedness. We summarize and visualize the results using tools from network analysis. The results reveal clear clustering of commodities into groups that match traditional industry groupings, but with some notable differences. The energy sector is most important in terms of sending shocks to others, and energy, industrial metals, and precious metals are themselves tightly connected. Download Paper


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


15025 
Mert Demirer Francis X. Diebold Laura Liu Kamil Yilmaz 
“Estimating Global Bank Network Connectedness”  
We use lasso methods to shrink, select and estimate the network linking the publiclytraded subset of the world’s top 150 banks, 20032014. We characterize static network connectedness using fullsample estimation and dynamic network connectedness using rollingwindow estimation. Statistically, we find that global banking connectedness is clearly linked to bank location, not bank assets. Dynamically, we find that global banking connectedness displays both secular and cyclical variation. The secular variation corresponds to gradual increases/decreases during episodes of gradual increases/decreases in global market integration. The cyclical variation corresponds to sharp increases during crises, involving mostly crosscountry, as opposed to withincountry, bank linkages. Download Paper
