Twenty Years of Time Series Econometrics in Ten Pictures
Author(s) -
James H. Stock,
Mark W. Watson
Publication year - 2017
Publication title -
the journal of economic perspectives
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.614
H-Index - 196
eISSN - 1944-7965
pISSN - 0895-3309
DOI - 10.1257/jep.31.2.59
Subject(s) - econometrics , series (stratigraphy) , inference , econometric model , estimation , time series , causal inference , statistical inference , economics , variation (astronomy) , structural estimation , computer science , mathematics , statistics , artificial intelligence , machine learning , paleontology , physics , management , astrophysics , biology
This review tells the story of the past 20 years of time series econometrics through ten pictures. These pictures illustrate six broad areas of progress in time series econometrics: estimation of dynamic causal effects; estimation of dynamic structural models with optimizing agents (specifically, dynamic stochastic equilibrium models); methods for exploiting information in "big data" that are specialized to economic time series; improved methods for forecasting and for monitoring the economy; tools for modeling time variation in economic relationships; and improved methods for statistical inference. Taken together, the pictures show how 20 years of research have improved our ability to undertake our professional responsibilities. These pictures also remind us of the close connection between econometric theory and the empirical problems that motivate the theory, and of how the best econometric theory tends to arise from practical empirical problems.
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