Todd E Clark
Biographic Data
| ID | 8878722 |
|---|---|
| NAME | Todd E Clark |
| GIVEN NAMES | Todd E |
| FAMILY NAME | Clark |
| SIGNATURE | CLARK T E |
| AFFILIATIONS | Federal Reserve Bank of Cleveland |
| ORCID | 0000-0002-4985-1709 |
| VERIFIED | Yes |
| TOTAL WORKS | 17 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 17 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1996 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Specification Choices in Quantile Regression for Empirical Macroeconomics
Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks. This paper examines various choices in the specification of quantile regressions for macro applications, including how and to what extent to include shrinkage and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, measured with quantile scores and quantile‐weighted continu…
Constructing Fan Charts from the Ragged Edge of SPF Forecasts
We develop models that take point forecasts from the Survey of Professional Forecasters (SPF) as inputs and produce estimates of survey-consistent term structures of expectations and uncertainty at arbitrary forecast horizons. Our models combine fixed-horizon and fixed-event forecasts, accommodating time-varying horizons and availability of survey data, as well as potential inefficiencies in survey forecasts. The estimated term structures of SPF-…
Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model
We develop a nonparametric quantile panel regression model. Within each quantile, the quantile function is a combination of linear and nonlinear parts, which we approximate using Bayesian Additive Regression Trees (BART). Cross-sectional information is captured through a conditionally heteroscedastic latent factor. The nonparametric feature enhances flexibility, while the panel feature increases the number of observations in the tails. We develop…
Addressing Covid-19 Outliers in BVARs with Stochastic Volatility
The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To address these issues, we propose BVAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting density forecasts are much less sensitive to outliers in the data than standard BVARs. Predictive Bayes factors indica…
Macroeconomic forecasting in a multi‐country context
In this paper, we propose a hierarchical shrinkage approach for multi‐country VAR models. In implementation, we consider three different scale mixtures Normals priors and provide new theoretical results. Empirically, we examine how model specifications and prior choices affect the forecasting performance for GDP growth, inflation, and a short‐term interest rate for the G7 economies. We find that hierarchical shrinkage, particularly as implemented…
Nowcasting tail risk to economic activity at a weekly frequency
This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information used to produce nowcasts on a weekly basis. We consider Bayesian mixed frequency regressions with stochastic volatility and Bayesian quantile regressions. Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward wit…
No‐arbitrage priors, drifting volatilities, and the term structure of interest rates
We use a Bayesian vector autoregression with stochastic volatility to forecast government bond yields. We form the conjugate prior from a no‐arbitrage affine term structure model. The model improves on the accuracy of point and density forecasts from a no‐change random walk and an affine term structure model with stochastic volatility. Our proposed approach may succeed by relaxing the no‐arbitrage affine term structure model's requirements that y…
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 a…
Measuring Uncertainty and Its Impact on the Economy
We propose a new model for measuring uncertainty and its effects on the economy, based on a large vector autoregression with stochastic volatility driven by common factors representing macroeconomic and financial uncertainty. The uncertainty measures reflect changes in both the conditional mean and volatility of the variables, and their impact on the economy can be assessed within the same framework. Estimates with U.S. data show substantial comm…
Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts
This article 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 now…
Common Drifting Volatility in Large Bayesian VARs
The general pattern of estimated volatilities of macroeconomic and financial variables is often broadly similar. We propose two models in which conditional volatilities feature comovement and study them using U.S. macroeconomic data. The first model specifies the conditional volatilities as driven by a single common unobserved factor, plus an idiosyncratic component. We label this model BVAR with general factor stochastic volatility (BVAR-GFSV) a…
Reality Checks and Comparisons of Nested Predictive Models
This article develops a simple bootstrap method for simulating asymptotic critical values for tests of equal forecast accuracy and encompassing among many nested models. Our method combines elements of fixed regressor and wild bootstraps. We first derive the asymptotic distributions of tests of equal forecast accuracy and encompassing applied to forecasts from multiple models that nest the benchmark model—that is, reality check tests. We then pro…
Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility
Central banks and other forecasters are increasingly interested in various aspects of density forecasts. However, recent sharp changes in macroeconomic volatility, including the Great Moderation and the more recent sharp rise in volatility associated with increased 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…
Tests of Equal Predictive Ability With Real-Time Data
This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy applied to direct, multistep predictions from both nonnested and nested linear regression models. In contrast to earlier work in the literature, our asymptotics take account of the real-time, revised nature of the data. Monte Carlo simulations indicate that our asymptotic approximations yield reasonable size and power properties in most circumstanc…
Approximately normal tests for equal predictive accuracy in nested models
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 monetary policy shocks. In aggregate price analysis, the VAR of Christiano et al. (1996a, 1996b) is used to identify the policy shock as the federal funds rate innovation and trace out the responses of prices. In disaggregate price analysis, the adjustment of prices is examined by comparing inflation before and after a recent policy tightening identified by Romer and…
Small-Sample Properties of Estimators of Nonlinear Models of Covariance Structure
This study examines the small-sample properties of generalized method of moments (GMM) and maximum likelihood estimators of nonlinear models of covariance structure. It considers the properties of estimates for a simple factor model, the Hall and Mishkin model of consumption and income, and a simple structural vector autoregression-type error model. This analysis establishes three basic results. First, optimally weighted GMM estimation yields som…
No prominent works on this page.
Small-Sample Properties of Estimators of Nonlinear Models of Covariance Structure
This study examines the small-sample properties of generalized method of moments (GMM) and maximum likelihood estimators of nonlinear models of covariance structure. It considers the properties of estimates for a simple factor model, the Hall and Mishkin model of consumption and income, and a simple structural vector autoregression-type error model. This analysis establishes three basic results. First, optimally weighted GMM estimation yields som…
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 monetary policy shocks. In aggregate price analysis, the VAR of Christiano et al. (1996a, 1996b) is used to identify the policy shock as the federal funds rate innovation and trace out the responses of prices. In disaggregate price analysis, the adjustment of prices is examined by comparing inflation before and after a recent policy tightening identified by Romer and…
Approximately normal tests for equal predictive accuracy in nested models
Tests of Equal Predictive Ability With Real-Time Data
This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy applied to direct, multistep predictions from both nonnested and nested linear regression models. In contrast to earlier work in the literature, our asymptotics take account of the real-time, revised nature of the data. Monte Carlo simulations indicate that our asymptotic approximations yield reasonable size and power properties in most circumstanc…
Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility
Central banks and other forecasters are increasingly interested in various aspects of density forecasts. However, recent sharp changes in macroeconomic volatility, including the Great Moderation and the more recent sharp rise in volatility associated with increased 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…
Reality Checks and Comparisons of Nested Predictive Models
This article develops a simple bootstrap method for simulating asymptotic critical values for tests of equal forecast accuracy and encompassing among many nested models. Our method combines elements of fixed regressor and wild bootstraps. We first derive the asymptotic distributions of tests of equal forecast accuracy and encompassing applied to forecasts from multiple models that nest the benchmark model—that is, reality check tests. We then pro…
Common Drifting Volatility in Large Bayesian VARs
The general pattern of estimated volatilities of macroeconomic and financial variables is often broadly similar. We propose two models in which conditional volatilities feature comovement and study them using U.S. macroeconomic data. The first model specifies the conditional volatilities as driven by a single common unobserved factor, plus an idiosyncratic component. We label this model BVAR with general factor stochastic volatility (BVAR-GFSV) a…
Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts
This article 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 now…
Measuring Uncertainty and Its Impact on the Economy
We propose a new model for measuring uncertainty and its effects on the economy, based on a large vector autoregression with stochastic volatility driven by common factors representing macroeconomic and financial uncertainty. The uncertainty measures reflect changes in both the conditional mean and volatility of the variables, and their impact on the economy can be assessed within the same framework. Estimates with U.S. data show substantial comm…
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 a…
No‐arbitrage priors, drifting volatilities, and the term structure of interest rates
We use a Bayesian vector autoregression with stochastic volatility to forecast government bond yields. We form the conjugate prior from a no‐arbitrage affine term structure model. The model improves on the accuracy of point and density forecasts from a no‐change random walk and an affine term structure model with stochastic volatility. Our proposed approach may succeed by relaxing the no‐arbitrage affine term structure model's requirements that y…
Macroeconomic forecasting in a multi‐country context
In this paper, we propose a hierarchical shrinkage approach for multi‐country VAR models. In implementation, we consider three different scale mixtures Normals priors and provide new theoretical results. Empirically, we examine how model specifications and prior choices affect the forecasting performance for GDP growth, inflation, and a short‐term interest rate for the G7 economies. We find that hierarchical shrinkage, particularly as implemented…
Nowcasting tail risk to economic activity at a weekly frequency
This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information used to produce nowcasts on a weekly basis. We consider Bayesian mixed frequency regressions with stochastic volatility and Bayesian quantile regressions. Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward wit…
Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model
We develop a nonparametric quantile panel regression model. Within each quantile, the quantile function is a combination of linear and nonlinear parts, which we approximate using Bayesian Additive Regression Trees (BART). Cross-sectional information is captured through a conditionally heteroscedastic latent factor. The nonparametric feature enhances flexibility, while the panel feature increases the number of observations in the tails. We develop…
Addressing Covid-19 Outliers in BVARs with Stochastic Volatility
The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To address these issues, we propose BVAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting density forecasts are much less sensitive to outliers in the data than standard BVARs. Predictive Bayes factors indica…
Specification Choices in Quantile Regression for Empirical Macroeconomics
Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks. This paper examines various choices in the specification of quantile regressions for macro applications, including how and to what extent to include shrinkage and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, measured with quantile scores and quantile‐weighted continu…
Constructing Fan Charts from the Ragged Edge of SPF Forecasts
We develop models that take point forecasts from the Survey of Professional Forecasters (SPF) as inputs and produce estimates of survey-consistent term structures of expectations and uncertainty at arbitrary forecast horizons. Our models combine fixed-horizon and fixed-event forecasts, accommodating time-varying horizons and availability of survey data, as well as potential inefficiencies in survey forecasts. The estimated term structures of SPF-…
Econometrics (15 works) · Monetary Policy and Economic Impact (15 works) · Mathematics (12 works) · Market Dynamics and Volatility (11 works) · Statistics (11 works) · Economics (10 works) · Computer Science (9 works) · Bayesian probability (7 works) · Forecasting Techniques and Applications (6 works) · Stochastic volatility (6 works)