This paper proposes a variational Bayes algorithm for computationally efficient posterior and predictive inference in time-varying parameter (TVP) models. Within this context we specify a new dynamic variable/model selection strategy for TVP dynamic regression models in the presence of a large...

Discount rates affect stock prices directly via the discount-rate channel or indirectly via the cash-flow channel because expected future cash-flow growth varies with the discount rates. The traditional Macaulay duration captures the effect from the discount-rate channel. I propose a novel...

We propose the use of indirect inference estimation to conduct inference in complex locally stationary models. We develop a local indirect inference algorithm and establish the asymptotic properties of the proposed estimator. Due to the nonparametric nature of locally stationary models, the...

We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of large-scale regressions with LASSO is applied to...

This paper develops estimators of the transition density, filters, and parameters of multivariate jump-diffusions with latent components. The drift, volatility, jump intensity, and jump magnitude are allowed to be general functions of the state. Our density and filter estimators converge at the...

This paper studies how people make inference about a state of the world when the information structure includes additional, payoff-irrelevant states. For example, learning about effort from observed performance may require accounting for the otherwise irrelevant role of luck. This creates an...

In this paper, we conduct simultaneous inference of the non-parametric part of a partially linear model when the non-parametric component is a multivariate unknown function. Based on semi-parametric estimates of the model, we construct a simultaneous confidence region of the multivariate...

This paper aims at a critical assessment of the DSGE asset pricing approach. By employing partial indirect inference, we acknowledge that parts of a model are misspecified, while others retain the claim to capture economic reality, namely the ability to price assets traded in real markets....

Time series regression analysis relies on the heteroskedasticity- and auto-correlation-consistent (HAC) estimation of the asymptotic variance to conduct proper inference. This paper develops such inferential methods for high-dimensional time series regressions. To recognize the time series data...

Propensity score matching is a popular way to make causal inferences about a binary treatment in observational data. The validity of these methods depends on which variables are used to predict the propensity score. We ask: quot;Absent strong ignorability, what would be the effect of including...