مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| آزمون کیوِ لیانگ-باکس برای خودهمبستگی× | آزمون ضریب لاگرانژ (LM) برِيش-گادفری برای همبستگی سریالی× | |
|---|---|---|
| حوزه | اقتصادسنجی | اقتصادسنجی |
| خانواده≠ | Hypothesis test | Regression model |
| سال پیدایش | 1978 | 1978 |
| پدیدآور≠ | Greta Ljung & George Box | Trevor Breusch & Leslie Godfrey |
| نوع≠ | Portmanteau goodness-of-fit test | Lagrange-multiplier test for serial correlation |
| منبع بنیادین≠ | 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 ↗ | Godfrey, L. G. (1978). Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46(6), 1293–1301. DOI ↗ |
| نامهای دیگر | Ljung-Box Q Test, Modified Box-Pierce Test, Portmanteau Test for Autocorrelation, Otokorelasyon Portmanteau Testi | BG test, LM test for autocorrelation, Breusch-Godfrey serial correlation test, Breusch-Godfrey otokorelasyon testi |
| مرتبط | 3 | 3 |
| خلاصه≠ | 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 Breusch-Godfrey test is a Lagrange-multiplier test for serial correlation in regression residuals, developed independently by Trevor Breusch (1978) and Leslie Godfrey (1978). Unlike the Durbin-Watson test, it detects autocorrelation up to any chosen order p, remains valid when the model includes lagged dependent variables, and produces a definite chi-square p-value rather than an inconclusive region — making it the modern standard for autocorrelation testing. |
| ScholarGateمجموعهداده ↗ |
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