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| ARIMA-Modell (Autoregressive Integrated Moving Average)× | Methode der kleinsten Quadrate (OLS)× | Poisson- und Negativ-Binomial-Regression× | |
|---|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model | Regression model |
| Entstehungsjahr≠ | 2015 | 2019 | 1998 |
| Urheber≠ | Box & Jenkins (Box-Jenkins methodology) | Wooldridge (textbook treatment); classical least squares | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Typ≠ | Univariate time-series model | Linear regression | Generalized linear model for count data |
| Wegweisende Quelle≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Aliasnamen≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Verwandt≠ | 5 | 5 | 4 |
| Zusammenfassung≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | 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). | Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred. |
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