Simon Fraser University

8888 University Drive

V5A 1S6 Burnaby, British Columbia, Canada

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The name is Thomas Vigié, I come from Bayonne, in the Basque country, France.

I am currently a PhD student at Simon Fraser University, Burnaby, BC, Canada.

I will be on the American and Canadian job market in the Fall 2019. My fields of interest are theoretical econometrics and applied econometrics.

Job market paper:

Improving 2SLS – Polynomial-Augmented 2SLS, Thomas Vigié, working paper

I consider the Two Stage Least Estimator (2SLS) in an environment where the first stage is nonparametrically specified. Given a conditional moment restriction holds for the error term conditional on some valid instrument, an infinite amount of moment conditions can be used to estimate the parameter of interest. However, the number of instruments and their strength can affect the properties of the estimator. Following Bun & Windmeijer (2011), I derive a similar finite sample bias approximation of the 2SLS estimator, and use it to build an easy-to-implement criterion function for the optimal choice of instruments that is asymptotically optimal. The procedure that estimates the parameter of interest after selection of the instruments yields a lower Mean Squared Error than the 2SLS estimator for a broad range of designs, and its use over it is recommended in empirical applications.

Work in progress:

Euler equations: Estimations and Computational Considerations, Thomas Vigié, working paper, submitted.

This paper proposes to solve a canonical stochastic growth model, simulate its solutions and estimate the discount factor and risk aversion parameters through the corresponding Euler equations. In particular, we use the generalized method of moments (GMM) along with two recent refinements from Antoine, Bonnal & Renault (2007) and Guay & Pelgrin (2016) and the generalized empirical likelihood estimators (GEL). The model is calibrated to reflect features encountered in empirical data by matching the variance and autocovariance of interest rates. It reveals the presence of weak identification, so inference procedures robust to weak identification are also considered. We find that the continuously updated-GMM estimator behaves well in small samples, as well as the three-step GMM improvement from Antoine, Bonnal & Renault (2007). In addition, the GEL estimators are highly unstable in the presence of weak identification, and require significantly more time to numerically converge than its competitors.

A kernel based first stage in linear instrumental variable models, Thomas Vigié, working paper

In a linear model with an endogenous variable, I propose to use a kernel-based regression in the first stage to build predictions of the endogenous variable. The second-stage estimator using these predictions is consistent, and asymptotically normal. It is also efficient and includes the Two-stage Least Squares estimator as a special case. I study the effect of the bandwidth on the mean squared error of my estimator in a simulation exercise, and compare it to some existing estimators.