This paper considers the selection of valid and relevant moments for the generalized method of moments (GMM) estimation. For applications with many candidate moments, our asymptotic analysis ccommodates a diverging number of moments as the sample size increases. The proposed procedure achieves...

This paper studies the selection of valid and relevant moments for the generalized method of moments (GMM) estimation. For applications with many candidate moments, our asymptotic analysis accommodates a diverging number of moments as the sample size increases. The proposed procedure achieves...

This paper considers forecast combination with factor-augmented regression. In this framework, a large number of forecasting models are available, varying by the choice of factors and the number of lags. We investigate forecast combination across models using weights that minimize the Mallows...

This supplemental appendix contains additional technical details of Cheng, Dou, and Liao (2020). Section SA provides the proofs of several lemmas on the asymptotic convergence of the random components in the test statistic T and the conditional critical value. Section SB verifies the bounded...

This paper shows that robust inference under weak identification is important to the evaluation of many influential macro asset pricing models, including long-run risk models, disaster risk models, and multi-factor linear asset pricing models. Building on recent developments in the conditional...

Reduced rank regression (RRR) models with time varying heterogeneity are considered. Standard information criteria for selecting cointegrating rank are shown to be weakly consistent in semiparametric RRR models in which the errors have general nonparametric short memory components and shifting...

This paper considers the estimation of dynamic causal effects using external instruments and a structural vector-autoregressive model with possibly non-stationary regressors. We provide general conditions under which the asymptotic normal approximation remains valid. In this case, the asymptotic...

This paper considers the selection of valid and relevant moments for the generalized method of moments (GMM) estimation. For applications with many candidate moments, our asymptotic analysis accommodates a diverging number of moments as the sample size increases. The proposed procedure achieves...

The paper studies inference in nonlinear models where identification loss presents in multiple parts of the parameter space. For uniform inference, we develop a local limit theory that models mixed identification strength. Building on this non-standard asymptotic approximation, we suggest robust...

In high-dimensional factor models, both the factor loadings and the number of factors may change over time. This paper proposes a shrinkage estimator that detects and disentangles these instabilities. The new method simultaneously and consistently estimates the number of pre- and post-break...