Have Your Cake and Eat It Too? Cointegration and Dynamic Inference from Autoregressive Distributed Lag Models

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...

Full description

Saved in:
Bibliographic Details
Published in:American journal of political science 2018-01, Vol.62 (1), p.230-244
Main Author: Philips, Andrew Q.
Format: Article
Language:English
Subjects:
AJPS WORKSHOP
Cointegration analysis
Experiments
Inference
Monte Carlo simulation
Political science
Reproducibility
Software
Time series
Unit roots
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0092-5853
1540-5907
DOI:10.1111/ajps.12318