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Have Your Cake and Eat It Too? Cointegration and Dynamic Inference from Autoregressive Distributed Lag Models
Author(s) -
Philips Andrew Q.
Publication year - 2018
Publication title -
american journal of political science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.347
H-Index - 170
eISSN - 1540-5907
pISSN - 0092-5853
DOI - 10.1111/ajps.12318
Subject(s) - cointegration , autoregressive model , distributed lag , econometrics , computer science , monte carlo method , inference , series (stratigraphy) , variety (cybernetics) , time series , software , lag , statistics , economics , mathematics , machine learning , artificial intelligence , programming language , paleontology , computer network , biology
Although recent articles have stressed the importance of testing for unit roots and cointegration in time‐series analysis, practitioners have been left without a straightforward procedure to implement this advice. I propose using the autoregressive distributed lag model and bounds cointegration test as an approach to dealing with some of the most commonly encountered issues in time‐series analysis. Through Monte Carlo experiments, I show that this procedure performs better than existing cointegration tests under a variety of situations. I illustrate how to implement this strategy with two step‐by‐step replication examples. To further aid users, I have designed software programs in order to test and dynamically model the results from this approach.