Keynote speaker of the JEF'2024 Conference
Keynote speaker of the JEF'2024 Conference
DIW Berlin and Freie Universität Berlin, Berlin, Germany
Currently Helmut Lütkepohl has a joint professorial position in the field of "Methods of Empirical Economics" at the Freie Universität Berlin and the DIW Berlin. From January 2012 to December 2016, he was Dean of the DIW Berlin Graduate Center and Bundesbank Professor at the Freie Universität Berlin. Before that, he was Professor of Econometrics at the European University Institute in Florence (2002-2011) and the Faculty of Economics at the Humboldt Universität zu Berlin (1992-2001), Professor of Statistics at the Christian-Albrechts-Universität Kiel (1987-1992) and the University of Hamburg (1985-1987) and Visiting Assistant Professor at the University of California, San Diego (1984/85). He has been on the editorial board of several scientific journals like Econometric Theory, Journal of Econometrics, Journal of Applied Econometrics, Macroeconomic Dynamics, Empirical Economics und Econometric Reviews and he has published numerous papers in academic journals. He is the author, co-author and editor of a number of books, like “Handbook of Matrices” (Wiley, 1996), “Applied Time Series Econometrics” (Cambridge University Press, 2004), “New Introduction to Multiple Time Series Analysis” (Springer, 2005) and “Structural Vector Autoregressive Analysis” (Cambridge University Press, 2017).
Title: Avoiding Unintentionally Correlated Shocks in Proxy Vector Autoregressive Analysis
Summary: The shocks in structural vector autoregressive (VAR) analysis are typically assumed to be instantaneously uncorrelated. This condition may easily be violated in proxy VAR models if more than one shock is identified by a proxy variable. Correlated shocks may be obtained even if the proxies are uncorrelated and satisfy the usual relevance and exogeneity conditions individually. Examples from the recent proxy VAR literature are presented. It is shown that assuming uncorrelated proxies that satisfy the usual relevance and exogeneity conditions individually actually over-identifies the shocks of interest and a Generalized Method of Moments (GMM) algorithm is proposed that ensures orthogonal shocks and provides efficient estimators of the structural parameters. It generalizes an earlier GMM proposal that works only if at least K − 1 shocks are identified by proxies in a VAR with K variables.