2 Step Error Correction Model
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variable is I(0) while the other is I(1)? I am currently attempting to construct an error-correction model based Engle-Granger's two-step method. Looking at the first step, which is
Error Correction Model Stata
to determine as to whether the residuals are integrated of order 0 vector error correction model (stationary), I have non-stationary residuals. However, taking into account that one variable is integrated of order one, would
Error Correction Model Eviews
it be sufficient to difference this variable to make it stationary? The variable that is I(0) is the US CPI, surprisingly. Where no the other hand, the price of gold is error correction model interpretation I(1). Please advice. Topics Time Series Econometrics × 203 Questions 3,128 Followers Follow Time Series Analysis × 437 Questions 4,290 Followers Follow Cointegration × 208 Questions 143 Followers Follow Error Control × 11 Questions 18 Followers Follow Feb 26, 2015 Share Facebook Twitter LinkedIn Google+ 1 / 0 Popular Answers Christian Dreger · German Institute for Economic Research If one variable is vector error correction model tutorial I(1) and the other is I(0), cointegration cannot exist, and consequently, you could not proceed with an ECM, according to the Granger representation theorem. There may be a relationship between the I(0) and the differenced I(1) variable. But this is built on stationary variables, and could not be interpreted in terms of an ECM. I suuggest that you re-check your unit root results on US-CPI, whether they are robust over alternative unit root tests. Otherwise, you should think of a different strategy to test for cointegration. It is known that cointegration tests based on the residuals from a static relationship (like Engle Granger) are inferior. Therefore, you should test for cointegration in a dynamic environment (ARDL or Stock-Watson). This makes sense, however, only if you solve your "I(0) problem". Mar 1, 2015 Ghazi Al-Assaf · University of Jordan In your case I recommend you to use Autoregressive Distributed Lag Model (ARDL), and check the F-Bound test and then you proceed to estimate the ECM based on the ARDL. Feb 27, 2015 All Answers (12) Ghazi Al-Assaf · University of Jordan In your case I recommend you to
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