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Author:Clark, Todd E. 

Working Paper
Common drifting volatility in large Bayesian VARs

The estimation of large vector autoregressions with stochastic volatility using standard methods is computationally very demanding. In this paper we propose to model conditional volatilities as driven by a single common unobserved factor.> This is justified by the observation that the pattern of estimated volatilities in empirical analyses is often very similar across variables. Using a combination of a standard natural conjugate prior for the VAR coefficients and an independent prior on a common stochastic volatility factor, we derive the posterior densities for the parameters of the ...
Working Papers (Old Series) , Paper 1206

Working Paper
Assessing International Commonality in Macroeconomic Uncertainty and Its Effects

This paper uses a large vector autoregression (VAR) to measure international macroeconomic uncertainty and its effects on major economies, using two datasets, one with GDP growth rates for 19 industrialized countries and the other with a larger set of macroeconomic indicators for the U.S., euro area, and U.K. Using basic factor model diagnostics, we first provide evidence of significant commonality in international macroeconomic volatility, with one common factor accounting for strong comovement across economies and variables. We then turn to measuring uncertainty and its effects with a large ...
Working Papers (Old Series) , Paper 1803

Working Paper
Assessing International Commonality in Macroeconomic Uncertainty and Its Effects

This paper uses a large vector autoregression to measure international macroeconomic uncertainty and its effects on major economies. We provide evidence of significant commonality in macroeconomic volatility, with one common factor driving strong comovement across economies and variables. We measure uncertainty and its effects with a large model in which the error volatilities feature a factor structure containing time-varying global components and idiosyncratic components. Global uncertainty contemporaneously affects both the levels and volatilities of the included variables. Our new ...
Working Papers , Paper 201803R

Borders and business cycles

We document that business cycles of U.S. Census regions are substantially more synchronized than those of European Union countries, both over the past four decades and the past two decades. Data from regions within the four largest European countries confirm the presence of a European border effect ? within-country correlations are substantially larger than cross-country correlations. These results continue to hold after controlling for exogenous factors such as distance and size. We consider the role of four factors that have received a lot of attention in the debate about EMU: sectoral ...
Staff Reports , Paper 91

Working Paper
The responses of prices at different stages of production to monetary policy shocks

This paper examines the responses of prices at different stages of production to an explicitly identified demand shock: a monetary policy shock. The frameworks of Christiano, Eichenbaum, and Evans (1994, 1996) and Sims and Zha (1995b) are used to identify the policy shock as the innovation to the federal funds rate in a VAR. The adjustment of prices at different stages of production is examined by adding three different sets of prices to the basic VAR model: (a) the PPIs for crude materials, intermediate goods, and finished goods; (b) the newer industry-based PPIs of input and output prices ...
Research Working Paper , Paper 96-12

Working Paper
Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts

This paper shows entropic tilting to be a flexible and powerful tool for combining medium-term forecasts from BVARs with short-term forecasts from other sources (nowcasts from either surveys or other models). Tilting systematically improves the accuracy of both point and density forecasts, and tilting the BVAR forecasts based on nowcast means and variances yields slightly greater gains in density accuracy than does just tilting based on the nowcast means. Hence entropic tilting can offer?more so for persistent variables than not-persistent variables?some benefits for accurately estimating the ...
Working Papers (Old Series) , Paper 1439

Working Paper
The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility

This paper compares alternative models of time-varying macroeconomic volatility on the basis of the accuracy of point and density forecasts of macroeconomic variables. In this analysis, we consider both Bayesian autoregressive and Bayesian vector autoregressive models that incorporate some form of time-varying volatility, precisely stochastic volatility (both with constant and time-varying autoregressive coeffi cients), stochastic volatility following a stationary AR process, stochastic volatility coupled with fat tails, GARCH, and mixture-of-innovation models. The comparison is based on the ...
Working Papers (Old Series) , Paper 1218

Working Paper
Forecasting with small macroeconomic VARs in the presence of instabilities

Small-scale VARs are widely used in macroeconomics for forecasting U.S. output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real time forecasting. We use forecasts from univariate time series models, the Survey of ...
Finance and Economics Discussion Series , Paper 2007-41

Working Paper
Evaluating Conditional Forecasts from Vector Autoregressions

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper provides analytical,Monte Carlo, and empirical evidence on tests of predictive ability for conditional forecasts from estimated models. In the empirical analysis, we consider forecasts of growth, unemployment, and inflation from a VAR, based on conditions on the short-term interest rate. Throughout ...
Working Papers (Old Series) , Paper 1413

Working Paper
Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors

We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. We apply our method to forecasts for various macroeconomic variables from the Survey of Professional Forecasters. Compared to constant variance approaches, our stochastic volatility model improves the accuracy of uncertainty measures for survey forecasts. Our method can also be applied to ...
Working Papers , Paper 201715R


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