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Author:Clark, Todd E. 

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

Small-scale VARs have come to be widely used in macroeconomics, for purposes ranging from forecasting output, prices, and interest rates to modeling expectations formation in theoretical models. However, a body of recent work suggests such VAR models may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. These methods include using different approaches to lag selection, observation windows for estimation, (over-) differencing, intercept correction, stochastically ...
Research Working Paper , Paper RWP 06-09

Working Paper
Forecasting US Inflation Using Bayesian Nonparametric Models

The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error may be subject to large, asymmetric shocks. Inspired by these concerns, we develop a model for inflation forecasting that is nonparametric both in the conditional mean and in the error using Gaussian and Dirichlet processes, respectively. We discuss how both these features may be important in producing accurate forecasts of inflation. In a forecasting exercise involving CPI inflation, we find that our approach has substantial ...
Working Papers , Paper 22-05

Working Paper
Advances in forecast evaluation

This paper surveys recent developments in the evaluation of point forecasts. Taking West's (2006) survey as a starting point, we briefly cover the state of the literature as of the time of West's writing. We then focus on recent developments, including advancements in the evaluation of forecasts at the population level (based on true, unknown model coefficients), the evaluation of forecasts in the finite sample (based on estimated model coefficients), and the evaluation of conditional versus unconditional forecasts. We present original results in a few subject areas: the optimization of power ...
Working Papers , Paper 2011-025

Working Paper
Estimating equilibrium real interest rates in real time

We use a range of simple models and 22 years of real-time data vintages for the U.S. to assess the difficulties of estimating the equilibrium real interest rate in real time. Model specifications differ according to whether the time-varying equilibrium real rate is linked to trend growth, and whether potential output and growth are defined by the CBO's estimates or treated as unobserved variables. Our results reveal a high degree of specification uncertainty, an important one-sided filtering problem, and considerable imprecision due to data uncertainty. Also, the link between trend growth and ...
Research Working Paper , Paper RWP 04-08

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
Real-time density forecasts from VARs with stochastic volatility

Central banks and other forecasters have become increasingly interested in various aspects of density forecasts. However, recent sharp changes in macroeconomic volatility such as the Great Moderation and the more recent sharp rise in volatility associated with greater variation in energy prices and the deep global recession pose significant challenges to density forecasting. Accordingly, this paper examines, with real-time data, density forecasts of U.S. GDP growth, unemployment, inflation, and the federal funds rate from VAR models with stochastic volatility. The model of interest extends ...
Research Working Paper , Paper RWP 09-08

Working Paper
Using out-of-sample mean squared prediction errors to test the Martingale difference hypothesis

We consider using out of sample mean squared prediction errors (MSPEs) to evaluate the null that a given series follows a zero mean martingale difference against the alternative that it is linearly predictable. Under the null of zero predictability, the population MSPE of the null ?no change? model equals that of the linear alternative. We show analytically and via simulations that despite this equality, the alternative model?s sample MSPE is expected to be greater than the null?s. We propose and evaluate an asymptotically normal test that properly accounts for the upward shift of the sample ...
Research Working Paper , Paper RWP 04-03

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 , Paper 2014-25

Working Paper
Large Vector Autoregressions with Stochastic Volatility and Flexible Priors

Recent research has shown that a reliable vector autoregressive model (VAR) for forecasting and structural analysis of macroeconomic data requires a large set of variables and modeling time variation in their volatilities. Yet, there are no papers jointly allowing for stochastic volatilities and large datasets, due to computational complexity. Moreover, homoskedastic VAR models for large datasets so far restrict substantially the allowed prior distributions on the parameters. In this paper we propose a new Bayesian estimation procedure for (possibly very large) VARs featuring time varying ...
Working Papers (Old Series) , Paper 1617

Working Paper
Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

Incoming data in 2020 posed sizable challenges for the use of VARs in economic analysis: Enormous movements in a number of series have had strong effects on parameters and forecasts constructed with standard VAR methods. We propose the use of VAR models with time-varying volatility that include a treatment of the COVID extremes as outlier observations. Typical VARs with time-varying volatility assume changes in uncertainty to be highly persistent. Instead, we adopt an outlier-adjusted stochastic volatility (SV) model for VAR residuals that combines transitory and persistent changes in ...
Working Papers , Paper 21-02


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