Empirical asset pricing studies evaluate and select risk factors solely based on their historical aggregate performance, implicitly assuming a time-invariant model specification, and overlooking potential time variations of specification in the stochastic discount factor (SDF) model. This paper...

We develop a novel machine learning method to estimate large dimensional time-varying GMM models via our newly designed ridge fusion regularization scheme. Our method is a one-step procedure and allows for abrupt, smooth and dual type time variation with a fast rate of convergence. It...

Numerous studies have been devoted to estimating and testing of moment condition models. Most of the current literature assumes that structural parameters are either fixed or changed abruptly. This paper considers the estimating and testing for smooth structural changes in moment condition...

Structural change is a long-standing problem in time series econometrics and macroeconomics, and financial time series are likely to be affected by structural instability due to changes in preferences, technologies, policies, etc. Most of the existing literature on moment condition models...

In an exchange economy with recursive preferences (Epstein and Zin, 1989), we propose a novel nonparametric generalized method of moment (GMM) series approach to estimate unknown policy functions which are recursively specified in a system of nonlinear conditional expectation models...