Gary Koop
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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: Monthly GDP Growth Estimates for the U.S. States
Monthly GDP Growth Estimates for the U.S. States Open
This paper develops a mixed frequency vector autoregressive (MF-VAR) model to produce nowcasts and historical estimates of monthly real state-level GDP for the 50 U.S. states, plus Washington DC, from 1964 through the present day. The MF-V…
View article: Fast and order‐invariant inference in Bayesian VARs with nonparametric shocks
Fast and order‐invariant inference in Bayesian VARs with nonparametric shocks Open
Summary The shocks that hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non‐Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper that uses a Di…
View article: Decision synthesis in monetary policy
Decision synthesis in monetary policy Open
The macroeconomy is a sophisticated dynamic system involving significant uncertainties that complicate modelling. In response, decision-makers consider multiple models that provide different predictions and policy recommendations which are…
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: Incorporating short data into large mixed-frequency vector autoregressions for regional nowcasting
Incorporating short data into large mixed-frequency vector autoregressions for regional nowcasting Open
Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer f…
View article: Predictive Density Combination Using a Tree-Based Synthesis Function
Predictive Density Combination Using a Tree-Based Synthesis Function Open
Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in…
View article: Predictive Density Combination Using a Tree-Based Synthesis Function
Predictive Density Combination Using a Tree-Based Synthesis Function Open
Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in…
View article: Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods
Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods Open
Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain M…
View article: Estimating the ordering of variables in a VAR using a Plackett–Luce prior
Estimating the ordering of variables in a VAR using a Plackett–Luce prior Open
Estimating Bayesian Vector Autoregressions (VARs) involving the Cholesky decomposition is sensitive to the ordering of variables. We treat the ordering as unknown, develop a prior over variable orderings and Markov Chain Monte Carlo (MCMC)…
View article: Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks
Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks Open
The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper which uses a Dirichle…
View article: Incorporating short data into large mixed-frequency VARs for regional nowcasting
Incorporating short data into large mixed-frequency VARs for regional nowcasting Open
Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer f…
View article: Subspace shrinkage in conjugate Bayesian vector autoregressions
Subspace shrinkage in conjugate Bayesian vector autoregressions Open
Summary Macroeconomists using large datasets often face the choice of working with either a large vector autoregression (VAR) or a factor model. In this paper, we develop a conjugate Bayesian VAR with a subspace shrinkage prior that combin…
View article: Bayesian modeling of time-varying parameters using regression trees
Bayesian modeling of time-varying parameters using regression trees Open
In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART…
View article: Large Order-Invariant Bayesian VARs with Stochastic Volatility
Large Order-Invariant Bayesian VARs with Stochastic Volatility Open
Many popular specifications for Vector Autoregressions (VARs) with multivariate stochastic volatility are not invariant to the way the variables are ordered due to the use of a lower triangular parameterization of the error covariance matr…
View article: BAYESIAN DYNAMIC VARIABLE SELECTION IN HIGH DIMENSIONS
BAYESIAN DYNAMIC VARIABLE SELECTION IN HIGH DIMENSIONS Open
This article addresses the issue of inference in time‐varying parameter regression models in the presence of many predictors and develops a novel dynamic variable selection strategy. The proposed variational Bayes dynamic variable selectio…
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: Bayesian Forecasting in Economics and Finance: A Modern Review
Bayesian Forecasting in Economics and Finance: A Modern Review Open
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quant…
View article: Bayesian Modeling of TVP-VARs Using Regression Trees
Bayesian Modeling of TVP-VARs Using Regression Trees Open
In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART…
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: Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates
Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates Open
Recent decades have seen advances in using econometric methods to produce more timely and higher frequency estimates of economic activity at the national level, enabling better tracking of the economy in real-time. These advances have not …
View article: APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs
APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs Open
Panel vector autoregressions (PVARs) are a popular tool for analyzing multicountry data sets. However, the number of estimated parameters can be enormous, leading to computational and statistical issues. In this article, we develop fast Ba…
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…