Background
SIMS, Christopher Albert was born in 1942 in Washington, District of Columbia, United States of America.
SIMS, Christopher Albert was born in 1942 in Washington, District of Columbia, United States of America.
Bachelor of Arts (Mathematics), Doctor of Philosophy Harvard University, 1963, 1968.
Sims is currently the Harold B. Helms Professor of Economics and Banking at Princeton University.
He has held teaching positions at Harvard, University of Minnesota, Yale University and, since 1999, Princeton.
In 1995 he was the president of the Econometric Society. He will be the President-Elect of the American Economic Association in 2011 and then the President of the American Economic Association in 2012.
I have aimed since the start of my research career at making econometric analysis of data more practically useful by freeing it from conventional assumptions, known to be untrue, whose effects must be allowed for by judgemental adjustment of results. My aim has not been iconoclastic, however, and usually I have proceeded in the spirit of clarifying, modifying, and formalising the methods used by the best people actually analysing data. In early papers I wrote on the roles of the assumptions that time is discrete and that an exact finitedimensional ‘time’ model exists in distributed lag models.
My work on the relation of exogeneity to Grangercaused priority was motivated by the observation that regression models with right-hand-side variables taken as exogenous were pervasive in economics, and that economists regularly justified the choice of right-hand-side variable by vague appeals to notions of causal priority. It turned out that the notion of causal priority required was Granger’s.
In recent years I have been aiming at building a multiple time series methodology as useful as conventional simultaneous equations modelling for macroeconomics, yet with a formal probability model which can be taken seriously. While the components of the methodology I have been using are not in themselves new, the style — elaborate modelling of the predicitive structure of the data preceding a cautious and some
times informal application of a priori knowledge to interpret the results — is different from much previous econometric work.
The methodology is increasingly being applied for forecasting and policy analysis, has provided “styilised facts’ as grist for the mill of ‘pure theory’ and is undergoing rapid development by other economists as well as myself. It does make heavy demands on its user’s tolerance for ambiguity, however, and this may preclude its becoming standard.
Fellow Econometric Society; National Academy of Sciences
American Economic Association
American Statistical Association
Institute Math. Statistics