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| Difference-in-Differences (Diff-in-Diff)× | Regression with Ordinary Least Squares (OLS)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1994 | 2019 |
| Ideatore≠ | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Causal inference / panel regression | Linear regression |
| Fonte seminale≠ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Correlati | 5 | 5 |
| Sintesi≠ | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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