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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 ...
Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement
We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single- factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state space approach of Kim and Nelson ...
Deconstructing the yield curve
We introduce a novel nonparametric bootstrap for assets with a finite maturity structure such as the nominal yield curve. We analyze the properties of our resampling procedure for inference on bond return predictability. Our method is asymptotically valid and robust to general forms of time and cross-sectional dependence; moreover, it exhibits excellent finite-sample properties. We demonstrate the applicability of our results in two empirical exercises: first, we show that a proxy for equity market tail risk predicts bond returns beyond yield curve factors; second, we provide a bootstrap bias ...