Seminario Econometrics Field seminar

1) 14.30-15.30

Presenter: Jiti Gao

Title: Semiparametric Instrumental Variable Method

Abstract: Endogeneity can be caused by omitted variables, errors in variables and many other sources. In the existing literature, endogeneity issues are often addressed under an instrumental variables regression setting, assuming the availability and validity of instrumental variables (IVs).  As revealed and experienced in many empirical problems, there are difficulties finding available and valid IVs in practice. In order to avoid such difficulty finding valid IVs and offer a simple alternative to address endogeneity issues, we propose to project the original model under study and subtract possible omitted regressors (covariates) left in the error term of the original model before we construct an exogenous regression model. As the projection and construction procedure itself is semiparametric, we define it as a semiparametric instrumental variable (SIV) method. We then employ the proposed SIV method to fully identify and then estimate the parameters and functions of interest involved in the original model consistently and unbiasedly. One advantage is that the proposed SIV method offers a valid and closed--form IV function based on the data under consideration. Another advantage is that the proposed SIV method is a unified approach to addressing endogeneity issues and also invariant to the degree of endogeneity, covering a wide range of weak endogeneity, involved in many classes of linear, nonlinear, and non--separable models. An additional advantage is that the proposed SIV method is easily computable and implementable. This paper establishes an asymptotic theory for the proposed SIV estimation method, and then propose a simple LASSO approach coupled with a generalized cross--validation method to examine the finite--sample performance of both the proposed method and the established theory by simulated and real data examples.

2) 15.30-16.30

Presenter: Silvia Goncalves

Title: Out-of-sample inference with annual benchmark revisions

Abstract: This paper examines the properties of out-of-sample predictability tests evaluated with real-time data subject to annual benchmark revisions. The presence of both regular and annual revisions can create time heterogeneity in the moments of the real-time forecast evaluation function, which is not compatible with the standard covariance stationarity assumption used to derive the asymptotic theory of these tests. To cover both regular and annual revisions, we replace this standard assumption with a periodic covariance stationarity assumption that allows for periodic patterns of time heterogeneity. Despite the lack of stationarity, we show that the Clark and McCracken (2009) test statistic is robust to the presence of annual benchmark revisions. A similar robustness property is shared by the bootstrap test of Gon¸calves, McCracken, and Yao (2025). Monte Carlo experiments indicate that both tests provide satisfactory finite sample size and power properties even in modest sample sizes. We conclude with an application to U.S. employment forecasting in the presence of real-time data.