Todd E. Clark
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View article: A Nonparametric Approach to Augmenting a Bayesian VAR with Nonlinear Factors
A Nonparametric Approach to Augmenting a Bayesian VAR with Nonlinear Factors Open
This paper proposes a Vector Autoregression augmented with nonlinear factors that are modeled nonparametrically using regression trees. There are four main advantages of our model. First, modeling potential nonlinearities nonparametrically…
View article: Specification Choices in Quantile Regression for Empirical Macroeconomics
Specification Choices in Quantile Regression for Empirical Macroeconomics Open
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, inclu…
View article: Constructing fan charts from the ragged edge of SPF forecasts
Constructing fan charts from the ragged edge of SPF forecasts Open
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 c…
View article: Constructing Fan Charts from the Ragged Edge of SPF Forecasts
Constructing Fan Charts from the Ragged Edge of SPF Forecasts Open
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 c…
View article: Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model
Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model Open
We develop a Bayesian non-parametric quantile panel regression model. Within each quantile, the response function is a convex combination of a linear model and a non-linear function, which we approximate using Bayesian Additive Regression …
View article: Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model
Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model Open
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 i…
View article: Forecasting Core Inflation and Its Goods, Housing, and Supercore Components
Forecasting Core Inflation and Its Goods, Housing, and Supercore Components Open
This paper examines the forecasting efficacy and implications of the recently popular breakdown of core inflation into three components: goods excluding food and energy, services excluding energy and housing, and housing. A comprehensive h…
View article: The Impacts of Supply Chain Disruptions on Inflation
The Impacts of Supply Chain Disruptions on Inflation Open
Since early 2021, inflation has consistently exceeded the Federal Reserve’s target of 2 percent. Using a combination of data, economic theory, and narrative information around historical events, we empirically assess what has caused persis…
View article: TAIL FORECASTING WITH MULTIVARIATE BAYESIAN ADDITIVE REGRESSION TREES
TAIL FORECASTING WITH MULTIVARIATE BAYESIAN ADDITIVE REGRESSION TREES Open
We develop multivariate time‐series models using Bayesian additive regression trees that posit nonlinearities among macroeconomic variables, their lags, and possibly their lagged errors. The error variances can be stable, feature stochasti…
View article: What is the predictive value of SPF point and density forecasts?
What is the predictive value of SPF point and density forecasts? Open
This paper presents a new approach to combining the information in point and density forecasts from the Survey of Professional Forecasters (SPF) and assesses the incremental value of the density forecasts. Our starting point is a model, de…
View article: Constructing fan charts from the ragged edge of SPF forecasts
Constructing fan charts from the ragged edge of SPF forecasts Open
We develop a model that permits the estimation of a term structure of both expectations and forecast uncertainty for application to professional forecasts such as the Survey of Professional Forecasters (SPF). Our approach exactly replicate…
View article: The anatomy of out-of-sample forecasting accuracy
The anatomy of out-of-sample forecasting accuracy Open
We develop metrics based on Shapley values for interpreting time-series forecasting models, including "black-box" models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, …
View article: Tail Forecasting with Multivariate Bayesian Additive Regression Trees
Tail Forecasting with Multivariate Bayesian Additive Regression Trees Open
We develop multivariate time series models using Bayesian additive regression trees that posit nonlinearities among macroeconomic variables, their lags, and possibly their lagged errors. The error variances can be stable, feature stochasti…
View article: Macroeconomic forecasting in a multi‐country context
Macroeconomic forecasting in a multi‐country context Open
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 s…
View article: Addressing COVID-19 Outliers in BVARs with Stochastic Volatility
Addressing COVID-19 Outliers in BVARs with Stochastic Volatility Open
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…
View article: Nowcasting tail risk to economic activity at a weekly frequency
Nowcasting tail risk to economic activity at a weekly frequency Open
Summary 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…
View article: Corrigendum: Measuring Uncertainty and Its Impact on the Economy
Corrigendum: Measuring Uncertainty and Its Impact on the Economy Open
Carriero, Clark, and Marcellino (2018, CCM2018) used a large BVAR model with a factor structure to stochastic volatility to produce an estimate of time-varying macroeconomic and financial uncertainty and assess the effects of uncertainty o…
View article: Replication data for: Addressing COVID-19 Outliers in BVARs with Stochastic Volatility
Replication data for: Addressing COVID-19 Outliers in BVARs with Stochastic Volatility Open
Carriero, Andrea, Clark, Todd E., Marcellino, Massimiliano, and Mertens, Elmar, (2024) “Addressing COVID-19 Outliers in BVARs with Stochastic Volatility.” Review of Economics and Statistics 106:5, 1403–1417.
View article: Forecasting US Inflation Using Bayesian Nonparametric Models
Forecasting US Inflation Using Bayesian Nonparametric Models Open
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 d…
View article: Forecasting US Inflation Using Bayesian Nonparametric Models
Forecasting US Inflation Using Bayesian Nonparametric Models Open
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 d…
View article: Macroeconomic Forecasting in a Multi-country Context
Macroeconomic Forecasting in a Multi-country Context Open
In this paper we propose a hierarchical shrinkage approach for multi-country VAR models. In implementation, we consider three different scale mixtures of Normals priors — specifically, Horseshoe, Normal- Gamma, and Normal-Gamma-Gamma prior…