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Todd Clark |

Vice President

Todd Clark, Vice President

Todd Clark is a vice president at the Federal Reserve Bank of Cleveland. He leads the Research Department’s Money, Financial Markets, and Monetary Policy Group.

Dr. Clark joined the Federal Reserve Bank of Kansas City in 1992 as an economist. He was appointed senior economist in 1996, promoted to assistant vice president in 1999, named vice president in 2003, and appointed the head of the Kansas City Reserve Bank’s macroeconomics group in 2007. In 2010, he joined the Cleveland Reserve Bank and assumed his current position.

Dr. Clark specializes in research related to monetary policy and macroeconomics. He has published research on a variety of topics, including the relationship between producer and consumer prices, the measurement of inflation, forecasting methods, and the evaluation of forecasts. He serves as an associate editor with the Journal of Money, Credit, and Banking.

A native of Indiana, Dr. Clark holds a bachelor’s degree in economics and mathematics from Wabash College and an MA and a PhD in economics from the University of Michigan.

  • Fed Publications
  • Other Publications
Title Date Publication Author(s) Type

 

 October, 2012 ; Economic Commentary
Abstract: This Commentary describes how some of the Cleveland Fed's macroeconomic forecasting models have been modified to use a Taylor rule for monetary policy. After briefly describing the Taylor rule implementation, the article shows that the Taylor rule included in one of our models successfully captures the course of monetary policy in the most recent episode of policy tightening.
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 September, 2012 Vol. 3, No. 2 ; Forefront
Abstract: What academic experts see in the housing market's near future
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 September, 2012 Federal Reserve Bank of Cleveland, working paper no. 12-18 ; Francesco Ravazzolo; Working Papers
Abstract: 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 coefficients), 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 accuracy of forecasts of key macroeconomic time series for real-time post?War-II data both for the United States and United Kingdom. The results show that the AR and VAR specifications with widely used stochastic volatility dominate models with alternative volatility specifications, in terms of point forecasting to some degree and density forecasting to a greater degree.
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 March, 2012 Federal Reserve Bank of Cleveland, working paper no. 12-06 ; Andrea Carriero; Massimiliano Marcellino; Working Papers
Abstract: 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 resulting BVAR with common stochastic volatility (BVAR-CSV). Under the chosen prior, the conditional posterior of the VAR coefficients features a Kroneker structure that allows for fast estimation, even in a large system. Using US and UK data, we show that, compared to a model with constant volatilities, our proposed common volatility model significantly improves model fit and forecast accuracy. The gains are comparable to or as great as the gains achieved with a conventional stochastic volatility specification that allows independent volatility processes for each variable. But our common volatility specification greatly speeds computations.
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 December, 2011 Federal Reserve Bank of Cleveland, working paper no. 11-34 ; Taeyoung Doh; Working Papers
Abstract: With the concept of trend inflation now widely understood as to be important as a measure of the public?s perception of the inflation goal of the central bank and important to the accuracy of longer-term inflation forecasts, this paper uses Bayesian methods to assess alternative models of trend inflation. Reflecting models common in reduced-form inflation modeling and forecasting, we specify a range of models of inflation, including: AR with constant trend; AR with trend equal to last period?s inflation rate; local level model; AR with random walk trend; AR with trend equal to the long-run expectation from the Survey of Professional Forecasters; and AR with time-varying parameters. We consider versions of the models with constant shock variances and with stochastic volatility. We first use Bayesian metrics to compare the fits of the alternative models. We then use Bayesian methods of model averaging to account for uncertainty surrounding the model of trend inflation, to obtain an alternative estimate of trend inflation in the U.S. and to generate medium-term, model-average forecasts of inflation. Our analysis yields two broad results. First, in model fit and density forecast accuracy, models with stochastic volatility consistently dominate those with constant volatility. Second, for the specification of trend inflation, it is difficult to say that one model of trend inflation is the best. Among alternative models of the trend in core PCE inflation, the local level specification of Stock and Watson (2007) and the survey-based trend specification are about equally good. Among competing models of trend GDP inflation, several trend specifications seem to be about equally good.
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 September, 2011 Federal Reserve Bank of Cleveland, working paper no. 11-21 ; Michael W McCracken; Working Papers
Abstract: This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy when the models being compared are overlapping in the sense of Vuong (1989). Two models are overlapping when the true model contains just a subset of variables common to the larger sets of variables included in the competing forecasting models. We consider an out-of-sample version of the two-step testing procedure recommended by Vuong but also show that an exact one-step procedure is sometimes applicable. When the models are overlapping, we provide a simple-to-use fixed regressor wild bootstrap that can be used to conduct valid inference. Monte Carlo simulations generally support the theoretical results: the two-step procedure is conservative while the one-step procedure can be accurately sized when appropriate. We conclude with an empirical application comparing the predictive content of credit spreads to growth in real stock prices for forecasting U.S. real GDP growth.
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 September, 2011 Federal Reserve Bank of Cleveland, working paper no. 11-20 ; Michael W McCracken; Working Papers
Abstract: 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 in determining the split of a sample into in-sample and out-of-sample portions; whether the accuracy of inference in evaluation of multistep forecasts can be improved with the judicious choice of HAC estimator (it can); and the extension of West's (1996) theory results for population-level, unconditional forecast evaluation to the case of conditional forecast evaluation.
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 August, 2011 ; Saeed Zaman; Economic Commentary
Abstract: Sharp rises in energy and other commodity prices have recently ignited concerns about inflation. Will these price increases spill over to other prices more generally? We study the typical responses of different price shocks and assess whether the recent behavior of producer and consumer prices is consistent with historical norms. Our analysis shows that the behavior of various producer and consumer prices since late 2009 has generally matched up with historical patterns. Overall, our findings suggest that effects of the recent energy and commodity price shocks on core consumer prices will be modest going forward.
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 May, 2011 Federal Reserve Bank of Cleveland, working paper no. 11-12 ; Andrea Carriero; Massimiliano Marcellino; Working Papers
Abstract: In this paper we examine how the forecasting performance of Bayesian VARs is affected by a number of specification choices. In the baseline case, we use a Normal-Inverted Wishart prior that, when combined with a (pseudo-) iterated approach, makes the analytical computation of multi-step forecasts feasible and simple, in particular when using standard and fixed values for the tightness and the lag length. We then assess the role of the optimal choice of the tightness, of the lag length and of both; compare alternative approaches to multi-step forecasting (direct, iterated, and pseudo-iterated); discuss the treatment of the error variance and of cross-variable shrinkage; and address a set of additional issues, including the size of the VAR, modeling in levels or growth rates, and the extent of forecast bias induced by shrinkage. We obtain a large set of empirical results, but we can summarize them by saying that we find very small losses (and sometimes even gains) from the adoption of specification choices that make BVAR modeling quick and easy. This finding could therefore further enhance the diffusion of the BVAR as an econometric tool for a vast range of applications.
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Is the Great Moderation Over? An Empirical Analysis

 

 November, 2009 Federal Reserve Bank of Kansas City, Economic Review, Fourth Quarter 2009 ; Economic Review
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Has the Behavior of Inflation and Long-Term Inflation Expectations Changed?

 

 February, 2008 Federal Reserve Bank of Kansas City, Economic Review, First Quarter 2008 ; Taisuke Nakata; Economic Review
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The Trend Growth Rate of Employment: Past, Present, and Future

 

 February, 2006 Federal Reserve Bank of Kansas City, Economic Review, First Quarter 2006 ; Taisuke Nakata; Economic Review
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An Evaluation of the Decline in Goods Inflation

 

 May, 2004 Federal Reserve Bank of Kansas City, Economic Review, Second Quarter 2004 ; Economic Review
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Comparing Measures of Core Inflation

 

 May, 2001 Federal Reserve Bank of Kansas City, Economic Review, Second Quarter 2001 ; Economic Review
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A Comparison of the CPI and the PCE Price Index

 

 August, 1999 Federal Reserve Bank of Kansas City, Economic Review, Third Quarter 1999 ; Economic Review
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Progress Toward Price Stability: A 1997 Inflation Report

 

 February, 1998 Federal Reserve Bank of Kansas City, Economic Review, First Quarter 1998 ; Economic Review
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Sources of New York Employment Fluctuations: Commentary

 

 February, 1997 Federal Reserve Bank of New York, Economic Policy Review, February 1997 ; Economic Review
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U.S. Inflation Developments in 1996

 

 February, 1997 Federal Reserve Bank of Kansas City, Economic Review, First Quarter 1997 ; Economic Review
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U.S. Inflation Developments in 1995

 

 February, 1996 Federal Reserve Bank of Kansas City, Economic Review, First Quarter 1996 ; Economic Review
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Do Producer Prices Lead Consumer Prices?

 

 August, 1995 Federal Reserve Bank of Kansas City, Economic Review, Third Quarter 1995 ; Economic Review
Abstract:
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Nominal GDP Targeting Rules: Can They Stabilize the Economy??

 

 August, 1994 Federal Reserve Bank of Kansas City, Economic Review, Third Quarter 1994 ; Economic Review
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Title Date Publication Author(s) Type
Testing for Unconditional Predictive Ability

 

 July, 2011 In Oxford Handbook on Economic Forecasting, D. Hendry and M. Clements, eds., Oxford University Press, forthcoming ; Michael W McCracken; Article in Book
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In-Sample Tests of Predictive Ability: A New Approach

 

 July, 2011 Journal of Econometrics, forthcoming ; Michael W McCracken; Journal Article
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Decomposing the Declining Volatility of Long-Term Inflation Expectations

 

 July, 2011 Journal of Economic Dynamics and Control, forthcoming ; Troy Davig; Journal Article
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Real-Time Density Forecasts from VARs with Stochastic Volatility

 

 June, 2011 Journal of Business and Economic Statistics, forthcoming ; Journal Article
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Time Variation in the Inflation Passthrough of Energy Prices

 

 October, 2010 Journal of Money, Credit, and Banking, October 2010, pp. 1419-1433 ; Stephen J Terry; Journal Article
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Averaging Forecasts from VARs with Uncertain Instabilities

 

 January, 2010 Journal of Applied Econometrics, January-February 2010, pp. 5-29 ; Michael W McCracken; Journal Article
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Tests of Equal Predictive Ability with Real-Time Data

 

 October, 2009 Journal of Business and Economic Statistics, October 2009, pp. 441-454 ; Michael W McCracken; Journal Article
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Combining Forecasts from Nested Models

 

 June, 2009 Oxford Bulletin of Economics and Statistics, June 2009, pp. 303-329 ; Michael W McCracken; Journal Article
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Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts

 

 May, 2009 International Economic Review, May 2009, pp. 363-395 ; Michael W McCracken; Journal Article
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Forecasting with Small Macroeconomic VARs in the Presence of Instabilities

 

 June, 2008 In Forecasting in the Presence of Structural Breaks and Model Uncertainty, D. Rapach and M. Wohar, eds., Emerald Publishing, 2008, pp. 93-147 ; Michael W McCracken; Article in Book
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Approximately Normal Tests for Equal Predictive Accuracy in Nested Models

 

 May, 2007 Journal of Econometrics, May 2007, pp. 291-311 ; Kenneth D West; Journal Article
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Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference Hypothesis

 

 November, 2006 Journal of Econometrics, November 2006, pp. 155-186 ; Kenneth D West; Journal Article
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The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence

 

 August, 2006 Journal of Money, Credit, and Banking, August 2006, pp. 1127-1148 ; Michael W McCracken; Journal Article
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Disaggregate Evidence on the Persistence of Consumer Price Inflation

 

 July, 2006 Journal of Applied Econometrics, July/August 2006, pp. 563-587 ; Journal Article
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Estimating Equilibrium Real Interest Rates in Real Time

 

 December, 2005 North American Journal of Economics and Finance, December 2005, pp. 395-413 ; Sharon Kozicki; Journal Article
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Evaluating Direct Multistep Forecasts

 

 October, 2005 Econometric Reviews, October 2005, pp. 369-404 ; Michael W McCracken; Journal Article
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The Power of Tests of Predictive Ability in the Presence of Structural Breaks

 

 January, 2005 Journal of Econometrics, January 2005, pp. 1-31 ; Michael W McCracken; Journal Article
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Can Out-of-Sample Forecast Comparisons Help Prevent Overfitting?

 

 March, 2004 Journal of Forecasting, March 2004, pp. 115-139 ; Journal Article
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Tests of Equal Forecast Accuracy and Encompassing for Nested Models

 

 November, 2001 Journal of Econometrics, November 2001, pp. 85-110 ; Michael W McCracken; Journal Article
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Borders and Business Cycles

 

 October, 2001 Journal of International Economics, October 2001, pp. 59-85 ; Eric van Wincoop; Journal Article
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The Sources of Fluctuations Within and Across Countries

 

 June, 2000 In Intranational Macroeconomics, G. Hess and E. van Wincoop, eds., Cambridge University Press, 2000, pp. 189-217 ; Kwanho Shin; Article in Book
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Forecasting an Aggregate of Cointegrated Disaggregates

 

 January, 2000 Journal of Forecasting, January 2000, pp. 1-21 ; Journal Article
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Finite-Sample Properties of Tests of Equal Forecast Accuracy

 

 December, 1999 Journal of Forecasting, December 1999, pp. 489-504 ; Journal Article
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The Responses of Prices at Different Stages of Production to Monetary Policy Shocks

 

 August, 1999 The Review of Economics and Statistics, August 1999, pp. 420-433 ; Journal Article
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Employment Fluctuations in U.S. Regions and Industries: The Roles of National, Region Specific, and Industry Specific Shocks

 

 January, 1998 Journal of Labor Economics, January 1998, pp. 202-229 ; Journal Article
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Cross Country Evidence on Long-Run Growth and Inflation

 

 January, 1997 Economic Inquiry, January 1997, pp. 70-81 ; Journal Article
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Small Sample Properties of Estimators of Nonlinear Models of Covariance Structure

 

 July, 1996 Journal of Business and Economic Statistics, July 1996, pp. 367-373 ; Journal Article
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Rents and Prices of Housing Across Areas of the U.S.: A Cross Section Examination of the Present Value Model

 

 April, 1995 Regional Science and Urban Economics, April 1995, pp. 237-247 ; Journal Article
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