Have Your Cake and Eat It Too? Cointegration and Dynamic Inference from Autoregressive Distributed Lag Models
I would like to thank Lorena Barberia, Allyson Benton, Harold Clarke, Peter Enns, Nathan Favero, Eric Guntermann, Mark Pickup, Joe Ura, B. Dan Wood, and participants of the Texas A&M methodology brownbag lunches. Special thanks go to Soren Jordan, Paul Kellstedt, and Guy D. Whitten. Despite this helpful advice, any errors and omissions remain my own.
Abstract
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.
Citing Literature
Number of times cited according to CrossRef: 13
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