Cointegration Tests Using Instrumental Variables
This paper proposes new cointegration tests based on instrumental variable (IV) estimation. An important property of our tests is that the asymptotic distribution remains standard normal (or Chi-square) regardless of the number of regressors, differing deterministic terms, structural dummies, and in...
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Veröffentlicht in: | International Journal of Empirical Economics 2022-06, Vol.1 (2), p.1-39 |
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description | This paper proposes new cointegration tests based on instrumental variable (IV) estimation. An important property of our tests is that the asymptotic distribution remains standard normal (or Chi-square) regardless of the number of regressors, differing deterministic terms, structural dummies, and inclusion of stationary covariates. Thus, our IV cointegration tests have the operational advantage that they do not depend on nuisance parameters. As such, we can incorporate stationary covariates into a model to enhance power without affecting the asymptotic distribution of the test. This is important because it alleviates the need to tabulate the critical values for every possible case or to bootstrap the critical values. |
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An important property of our tests is that the asymptotic distribution remains standard normal (or Chi-square) regardless of the number of regressors, differing deterministic terms, structural dummies, and inclusion of stationary covariates. Thus, our IV cointegration tests have the operational advantage that they do not depend on nuisance parameters. As such, we can incorporate stationary covariates into a model to enhance power without affecting the asymptotic distribution of the test. 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An important property of our tests is that the asymptotic distribution remains standard normal (or Chi-square) regardless of the number of regressors, differing deterministic terms, structural dummies, and inclusion of stationary covariates. Thus, our IV cointegration tests have the operational advantage that they do not depend on nuisance parameters. As such, we can incorporate stationary covariates into a model to enhance power without affecting the asymptotic distribution of the test. This is important because it alleviates the need to tabulate the critical values for every possible case or to bootstrap the critical values.</description><subject>Causality</subject><subject>Cointegration analysis</subject><subject>Economic analysis</subject><subject>Economic theory</subject><subject>Economics</subject><subject>Normal distribution</subject><subject>standard normal distribution</subject><subject>Stationary instrumental variables</subject><subject>Variables</subject><issn>2810-9430</issn><issn>2810-9449</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ADCHV</sourceid><sourceid>DOA</sourceid><recordid>eNplkF1LwzAUhoMoOOZ-gHcFr6vJSZqmlzL8GAy8cPM2pKfpyOiamXSI_97MyrzYVcLheZ_kvITcMnrPmICHd1CMVoJTgIJSKtUFmRxHeSVEdXm6c3pNZjFuEwIV8FLAhNC5d_1gN8EMzvfZysYhZuvo-k226OMQDjvbD6bLPkxwpu5svCFXreminf2dU7J-flrNX_Pl28ti_rjMESqucsNZBYYb2dKyhkbUAoHJGmQhLNYNSluCYZQVVJYGC8NsixIsCtkwUC3yKVmM3sabrd4HtzPhW3vj9O_Ah402YXDYWY2NbBGwEGVthSyxsslRIMdaQaPM0XU3uvbBfx7SinrrD6FP39egykIISZVKFBspDD7GYNvTq4zqY8_6rOeUycaMRd-7-J9QhWIsbXxE6Ih8-dA1EV0q1LUOT-y59QeUiYiN</recordid><startdate>202206</startdate><enddate>202206</enddate><creator>Lee, Junsoo</creator><creator>Yucel, Ali</creator><general>World Scientific Publishing Company</general><general>World Scientific Publishing Co. 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An important property of our tests is that the asymptotic distribution remains standard normal (or Chi-square) regardless of the number of regressors, differing deterministic terms, structural dummies, and inclusion of stationary covariates. Thus, our IV cointegration tests have the operational advantage that they do not depend on nuisance parameters. As such, we can incorporate stationary covariates into a model to enhance power without affecting the asymptotic distribution of the test. This is important because it alleviates the need to tabulate the critical values for every possible case or to bootstrap the critical values.</abstract><cop>Singapore</cop><pub>World Scientific Publishing Company</pub><doi>10.1142/S2810943022500068</doi><tpages>39</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Causality Cointegration analysis Economic analysis Economic theory Economics Normal distribution standard normal distribution Stationary instrumental variables Variables |
title | Cointegration Tests Using Instrumental Variables |
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