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Working Paper
Tail Forecasting with Multivariate Bayesian Additive Regression Trees
We develop novel multivariate time series models using Bayesian additive regression trees that posit nonlinear relationships among macroeconomic variables, their lags, and possibly the lags of the errors. The variance of the errors can be stable, driven by stochastic volatility (SV), or follow a novel nonparametric specification. Estimation is carried out using scalable Markov chain Monte Carlo estimation algorithms for each specification. We evaluate the real-time density and tail forecasting performance of the various models for a set of US macroeconomic and financial indicators. Our ...
Report
Revisiting useful approaches to data-rich macroeconomic forecasting
This paper analyzes the properties of a number of data-rich methods that are widely used in macroeconomic forecasting, in particular principal components (PC) and Bayesian regressions, as well as a lesser-known alternative, partial least squares (PLS) regression. In the latter method, linear, orthogonal combinations of a large number of predictor variables are constructed such that the covariance between a target variable and these common components is maximized. Existing studies have focused on modelling the target variable as a function of a finite set of unobserved common factors that ...
Working Paper
Economic Activity by Race
We observe empirical differences between races across various macroeconomic variables for the White, Black, Asian, and Hispanic populations in the U.S. For instance, the Black unemployment rate in the U.S. is more often than not double the White unemployment rate. In this paper, I treat nine macroeconomic variables as noisy indicators of economic activity and estimate an index that measures the economic activity of racial demographic groups in the U.S., called Economic Activity by Race (EAR). The noise of the indicators motivates the use of Kalman filter estimation to extract a common ...
Working Paper
Tail Forecasting with Multivariate Bayesian Additive Regression Trees
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 stochastic volatility, or follow a nonparametric specification. We evaluate density and tail forecast performance for a set of US macroeconomic and financial indicators. Our results suggest that the proposed models improve forecast accuracy both overall and in the tails. Another finding is that when allowing for nonlinearities in the conditional mean, ...