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| La prova de límits ARDL (Pesaran Bounds Test)× | Model d'ARIMA (Autoregressive Integrated Moving Average)× | Regressió per Mínims Quadrats Ordinàris (MQO)× | |
|---|---|---|---|
| Camp | Econometria | Econometria | Econometria |
| Família | Regression model | Regression model | Regression model |
| Any d'origen≠ | 2001 | 2015 | 2019 |
| Autor original≠ | Pesaran, Shin & Smith | Box & Jenkins (Box-Jenkins methodology) | Wooldridge (textbook treatment); classical least squares |
| Tipus≠ | Cointegration test / Autoregressive distributed lag model | Univariate time-series model | Linear regression |
| Font seminal≠ | Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289–326. DOI ↗ | 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 |
| Àlies≠ | Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionats≠ | 4 | 5 | 5 |
| Resum≠ | The ARDL bounds test is an autoregressive distributed lag method that tests for a cointegrating (long-run level) relationship between time series, introduced by Pesaran, Shin and Smith in 2001. Unlike the Johansen procedure, it remains valid whether the variables are I(0), I(1) or a mix of the two, and it is more reliable than Johansen in small samples of roughly 30 to 80 observations. | 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). |
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