Econometrics and Statistics Seminar

June 7, 2018 Thomas Kneib, Univ. of Goettingen

Title: "Bayesian Structured Additive Distributional Regression"

Abstract: We propose a generic Bayesian framework for inference in distributional regression models in which each parameter of a potentially complex response distribution and not only the mean is related to a structured additive predictor. The latter is composed additively of a variety of different functional effect types such as nonlinear effects, spatial effects, random coefficients, interaction surfaces or other (possibly nonstandard) basis function representations. To enforce specific properties of the functional effects such as smoothness, informative multivariate Gaussian priors are assigned to the basis function coefficients. Inference can then be based on computationally efficient Markov chain Monte Carlo simulation techniques where a generic proceduremakes use of distribution-specific iteratively weighted least squares approximations to the full conditionals. We will discuss practical aspects of distributional regression along different applications concerning for example the analysis of income inequality in Germany.

June 14, 2018 Maximilian Kasey, Harvard/HCM

Title: "Identification of and correction for publication bias"

Abstract: Some empirical results are more likely to be published than others. Such selective publication leads to biased estimates and distorted inference. This paper proposes two approaches for identifying the conditional probability of publication as a function of a study's results, the first based on systematic replication studies and the second based on meta-studies. For known conditional publication probabilities, we propose median-unbiased estimators and associated confidence sets that correct for selective publication. We apply our methods to recent large-scale replication studies in experimental economics and psychology, and to meta-studies of the effects of minimum wages and de-worming programs.

June 21, 2018 Yves Robinson Kruse-Becher, Univ. of Cologne

June 26 (Tue.), 2018 (11:00) Jaap Abbring, Tilburg Univ./HCM

Title: ''Identifying the Discount Factor in Dynamic Discrete Choice Models''

Abstract: Empirical applications of dynamic discrete choice models usually either take the discount factor to be known or rely on ad hoc functional form assumptions to identify and estimate it. We give identification results under economically motivated exclusion restrictions on primitive utilities. We prove that, in contrast to common intuition, such exclusion restrictions do not suffice for point identification, but identify the discount factor up to a finite set. This identified set contains the solutions to a single, well-behaved moment condition that can be used directly in estimation. We also show that exclusion restrictions limit the choice and state transition probability data; that is, they give the model nontrivial empirical content.

July 5, 2018 Hans-Georg Müller, UC Davis

July 12, 2018 Marina Khismatullina, Univ. of Bonn

July 19, 2018 Christoph Rothe, Univ. of Mannheim

November 29, 2018 Johan Vikström, Uppsala University

January 17, 2019 Koen Jochmans, Cambridge University