השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מבחן קואינטגרציה מבוסס שיריים של פיליפס-אוליאריס× | מבחן שורש יחידה פיליפס-פררון (PP)× | |
|---|---|---|
| תחום | אקונומטריקה | אקונומטריקה |
| משפחה≠ | Hypothesis test | Regression model |
| שנת המקור≠ | 1990 | 1988 |
| הוגה השיטה≠ | Peter Phillips & Sam Ouliaris | Peter C. B. Phillips & Pierre Perron |
| סוג≠ | Residual-based nonparametric cointegration test | Unit-root test for stationarity |
| מקור מכונן≠ | Phillips, P. C. B., & Ouliaris, S. (1990). Asymptotic properties of residual based tests for cointegration. Econometrica, 58(1), 165–193. DOI ↗ | Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. DOI ↗ |
| כינויים≠ | Phillips-Ouliaris Cointegration Test, PO Residual-Based Test, Residual-Based Cointegration Test, Phillips-Ouliaris Eşbütünleşme Testi | PP test, Phillips-Perron unit root test, Phillips-Perron birim kök testi |
| קשורות≠ | 2 | 4 |
| תקציר≠ | The Phillips-Ouliaris test, introduced by Phillips and Ouliaris in their 1990 Econometrica article, is a residual-based nonparametric procedure for testing the null hypothesis of no cointegration among a set of integrated I(1) time series. It corrects OLS residuals from a cointegrating regression for serial correlation and endogeneity using kernel-based long-run variance estimators, yielding two statistics—Z_alpha (variance-ratio) and Z_t (normalized coefficient)—whose asymptotic distributions are tabulated specifically for systems with multiple stochastic regressors. | The Phillips-Perron test, proposed by Peter Phillips and Pierre Perron in 1988, tests for a unit root in a time series, like the Augmented Dickey-Fuller test, but corrects for autocorrelation and heteroskedasticity in the errors non-parametrically rather than by adding lagged differences. It runs a simple Dickey-Fuller regression and then adjusts the test statistic using a long-run variance estimate, so the practitioner need not choose a lag length for the regression itself. |
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