To Lag or Not to Lag
Re-Evaluating the Use of Lagged Dependent Variables in Regression Analysis
Dados Bibliográficos
| ID | 7972607 |
|---|---|
| Autores | Arjun S Wilkins, Arjun Wilkins (0009-0000-5315-356X, autor correspondente) |
| Ano | 2018 |
| Volume | 6 |
| Fascículo | 2 |
| Páginas | 393-411 |
| Data de publicação | 2018-04-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Political Science Research and Methods (JOURNAL) |
| Identificadores do periódico | ISSN: 2049-8470 • E-ISSN: 2049-8489 |
| Editora | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/psrm.2017.4 |
| OpenAlex | W2610192110 |
| Idioma | EN |
| Citações recebidas | 101 |
| Referências citadas | 28 |
Lagged dependent variables (LDVs) have been used in regression analysis to provide robust estimates of the effects of independent variables, but some research argues that using LDVs in regressions produces negatively biased coefficient estimates, even if the LDV is part of the data-generating process. I demonstrate that these concerns are easily resolved by specifying a regression model that accounts for autocorrelation in the error term. This actually implies that more LDV and lagged independent variables should be included in the specification, not fewer. Including the additional lags yields more accurate parameter estimates, which I demonstrate using the same data-generating process scholars had previously used to argue against including LDVs. I use Monte Carlo simulations to show that this specification returns much more accurate coefficient estimates for independent variables (across a wide range of parameter values) than alternatives considered in earlier research. The simulation results also indicate that improper exclusion of LDVs can lead to severe bias in coefficient estimates. While no panacea, scholars should continue to confidently include LDVs as part of a robust estimation strategy
Autocorrelation · Econometrics · Lag · Monte Carlo method · Regression · Regression analysis · Statistics · Computer Science · Mathematics · Monetary Policy and Economic Impact · Spatial and Panel Data Analysis · Statistical Methods and Inference
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| Obras citantes distintas | 101 |
|---|---|
| Citações por ano | 8,42 |
| Intervalo de citações | 2014 - 2026 (13) |
| Velocidade de citação | current |
| Altamente citado | Sim |
| Tipos de citação | Neutras: 100 |