Working Papers

Specification of FAVAR Models (Job Market Paper)

This paper proposes a novel methodology for determining the specification of factor-augmented vector autoregression (FAVAR) models. Without strong a priori beliefs about the set of possible models, the complexity of the problem renders traditional model selection techniques infeasible. By contrast, my proposed solution only requires the estimation of a single model. This makes the process easy to scale in both the cross-sectional and time series dimensions. An efficient optimization algorithm for model estimation is developed. Monte Carlo studies show the technique to be highly effective in small samples, even in the presence of a low signal-to-noise ratio and missing data. Applications to large datasets of monthly and quarterly U.S. macroeconomic variables identify observed factors not normally considered in the FAVAR literature. The methodology is then used to analyze the asset-pricing model of Fama and French (1993). I find that their constructed factors for firm size and book-to-market equity ratio are likely observed components, but excess market return is not.

A Nonparametric Endogenous Switching Model with an Application to Macroeconomics

This paper proposes a regime-switching linear model with time-varying transition probabilities, endogenous switching, and a nonparametric error distribution. The last two qualities are achieved by letting the conditional mean of the normalized observation errors be a potentially nonlinear function of the errors in the state equation. I demonstrate that this specification permits a very flexible marginal distribution for the observation error. A Markov Chain Monte Carlo algorithm for sampling from the posterior distribution of parameters is developed. A simulation study demonstrates that existing parametric switching models yield biased parameter estimates when the data is generated by a model with nonlinear endogenous switching. The proposed model is shown to fit the data better than parametric switching models when applied to quarterly U.S. output growth.

A Composite Mean Function for Count Data Analysis

Count data models are at the core of a large and diverse empirical literature in the social and natural sciences. A key component in this class of models is the mean function, which defines the relationship between the covariates and the conditional expectation of the count process. Here we consider a general approach for representing the mean function that is adaptable, tractable, and dispenses with problematic facets of count data models such as explosive covariate effects and restrictive time series properties. The methodology is broadly applicable in cross-sectional, longitudinal, and time-series settings, with likelihood-based, generalized linear, copula and other models. We provide theoretical results that distinguish our methodology from existing work and implement it in three examples that demonstrate its relevance and practical appeal.