Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Ljung-Box Q-test voor Autocorrelatie× | Durbin-Watson-test voor Autocorrelatie× | |
|---|---|---|
| Vakgebied | Econometrie | Econometrie |
| Familie≠ | Hypothesis test | Regression model |
| Jaar van ontstaan≠ | 1978 | 1950 |
| Grondlegger≠ | Greta Ljung & George Box | James Durbin & Geoffrey Watson |
| Type≠ | Portmanteau goodness-of-fit test | Test for first-order residual autocorrelation |
| Oorspronkelijke bron≠ | Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297–303. DOI ↗ | Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428. DOI ↗ |
| Aliassen≠ | Ljung-Box Q Test, Modified Box-Pierce Test, Portmanteau Test for Autocorrelation, Otokorelasyon Portmanteau Testi | DW test, Durbin-Watson statistic, Durbin-Watson otokorelasyon testi |
| Verwant≠ | 3 | 4 |
| Samenvatting≠ | The Ljung-Box Q test is a diagnostic portmanteau test proposed by Ljung and Box (1978) to assess whether a group of autocorrelations in a time series residual sequence is jointly zero. It is widely used to evaluate the adequacy of fitted time series models — especially ARIMA models — by testing whether remaining residuals exhibit any systematic pattern. The test is applicable in econometrics, finance, and any field that relies on temporal data modeling. | The Durbin-Watson test, developed by James Durbin and Geoffrey Watson in 1950–1951, detects first-order serial correlation in the residuals of a linear regression. Its statistic ranges from 0 to 4, with a value near 2 indicating no autocorrelation, values toward 0 indicating positive autocorrelation, and values toward 4 indicating negative autocorrelation. It remains one of the most reported regression diagnostics despite well-known limitations. |
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