Cointegration and control
Assessing the impact of events using time series data
Bibliographic Data
| ID | 21653571 |
|---|---|
| Authors | Andrew Harvey (0000-0003-3659-4704, Faculty of Economics Cambridge University Sidgwick Avenue Cambridge UK), Stephen Thiele (0000-0002-5529-2210, School of Economics and Finance Queensland University of Technology Brisbane Queensland Australia) |
| Year | 2021 |
| Volume | 36 |
| Issue | 1 |
| Pages | 71-85 |
| Publication date | 2021-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Applied Econometrics (JOURNAL) |
| Journal identifiers | ISSN: 1099-1255 • E-ISSN: 0883-7252 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jae.2802 |
| OpenAlex | W3122907041 |
| Language | EN |
| Citations received | 2 |
| References cited | 16 |
Control groups can provide counterfactual evidence for assessing the impact of an event or policy change on a target variable. We argue that fitting a multivariate time series model offers potential gains over a direct comparison between the target and a weighted average of controls. More importantly, it highlights the assumptions underlying methods such as difference in differences and synthetic control, suggesting ways to test these assumptions. Gains from simple and transparent time series models are analysed using examples from the literature, including the California smoking law of 1989 and German reunification. We argue that selecting controls using a time series strategy is preferable to existing data‐driven regression methods
Cointegration · Control variable · Counterfactual thinking · Econometrics · Economics · German · Machine learning · Multivariate statistics · Regression · Statistics · Time series · Advanced Causal Inference Techniques · Computer Science · Mathematics · Psychology · Statistical Methods and Inference · Artificial Intelligence
Time Series Analysis by State Space Methods
Forecasting, Structural Time Series Models and the Kalman Filter
Inferring causal impact using Bayesian structural time-series models
Synthetic Control Methods for Comparative Case Studies
How Much Should We Trust Differences-In-Differences Estimates?
Testing the null hypothesis of stationarity against the alternative of a unit root
Comparative Politics and the Synthetic Control Method
Big Data
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,67 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |