IDEAS home Printed from https://ideas.repec.org/a/cup/macdyn/v4y2000i01p42-72_01.html
   My bibliography  Save this article

Tests Of Nonnested Hypotheses In Nonstationary Regressions With An Application To Modeling Industrial Production

Author

Listed:
  • Chao, John C.
  • Swanson, Norman R.

Abstract

In the context of I(1) time series, we provide some asymptotic results for the Davidson-MacKinnon J-type test. We examine both the case where our regressor sets x1t and x2t are not cointegrated, and the case where they are. In the former case, the OLS estimator of the weighting coefficient from the artificial compound model converges at rate T to a mixed normal distribution, and the associated t-statistic has an asymptotic standard normal distribution. In the latter case, we find that the J-test also has power against violation of weak exogeneity (with respect to the short-run coefficients of the null model), which is caused by correlation between the disturbance of the null model and that of the cointegrating equation linking x1t and x2t. Moreover, unlike the previous case, the OLS estimator of the weighting coefficient from the artificial compound model converges at \sqrt{T} to an asymptotic normal distribution when the null model is specified correctly. In an empirical illustration, we use the tests to examine an industrial production data set for six countries.

Suggested Citation

  • Chao, John C. & Swanson, Norman R., 2000. "Tests Of Nonnested Hypotheses In Nonstationary Regressions With An Application To Modeling Industrial Production," Macroeconomic Dynamics, Cambridge University Press, vol. 4(1), pages 42-72, March.
  • Handle: RePEc:cup:macdyn:v:4:y:2000:i:01:p:42-72_01
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S1365100500014036/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:macdyn:v:4:y:2000:i:01:p:42-72_01. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/mdy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.