Replication Data for: Hypothesis Testing with Error Correction Models
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Grant and Lebo (2016) and Keele, Linn, and Webb (2016) clarify the conditions under which the popular general error correction model (GECM) can be used and interpreted easily: In a bivariate GECM the data must be integrated in order to rely on the error correction coeﬃcient, α*, to test cointegration and measure the rate of error correction between a single exogenous x and a dependent variable, y. Here we demonstrate that even if the data are all integrated, the test on α* is misunderstood when there is more than a single independent variable. The null hypothesis is that there is no cointegration between y and any x but the correct alternative hypothesis is that y is cointegrated with at least one—but not necessarily more than one—of the x’s. A signiﬁcant α* can occur when some I(1) regressors are not cointegrated and the equation is no' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Cointegration","Error correction model","Time series"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Grant and Lebo (2016) and Keele, Linn, and Webb (2016) clarify the conditions under which the popular general error correction model (GECM) can be used and interpreted easily: In a bivariate GECM the data must be integrated in order to rely on the error correction coeﬃcient, α*, to test cointegration and measure the rate of error correction between a single exogenous x and a dependent variable, y. Here we demonstrate that even if the data are all integrated, the test on α* is misunderstood when there is more than a single independent variable. The null hypothesis is that there is no cointegration between y and any x but the correct alternative hypothesis is that y is cointegrated with at least one—but not necessarily more than one—of the x’s. A signiﬁcant α* can occur when some I(1) regressors are not cointegrated and the equation is no' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Grant and Lebo (2016) and Keele, Linn, and Webb (2016) clarify the conditions under which the popular general error correction model (GECM) can be used and interpreted easily: In a bivariate GECM the data must be integrated in order to rely on the error correction coeﬃcient, α*, to test cointegration and measure the rate of error correction between a single exogenous x and a dependent variable, y. Here we demonstrate that even if the data are all integrated, the test on α* is misunderstood when there is more than a single independent variable. The null hypothesis is that there is no cointegration between y and any x but the correct alternative hypothesis is that y is cointegrated with at least one—but not necessarily more than one—of the x’s. A signiﬁcant α* can occur when some I(1) regressors are not cointegrated and the equation is no
Cointegration; Error correction model; Time series