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Autoregressive Lagged Ordered Model: A Long-Run Relationship Model with Short-Run Adjustment

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  • Chibuzo Gabriel Amaefula

    (Department of Mathematics and Statistics, Federal University Otuoke, Bayelsa, Nigeria.)

Abstract

An autoregressive lagged ordered model (ARLOM) is a modelling approach proposed for examining long-run relationship between a dependent variable and one or more explanatory variables where short-run effect is required. ARLOM infused some pre-tests such as order of integration test, co-integration test and bound test for optimal lag inclusion as regularity conditions. The application of ARLOM is demonstrated using empirical data sets such as Consumers price index(CPI(Y)), Naira/1 Chinese Yuan exchange rate (X1) and Naira/1 US Dollar (X2) covering the period of 2008m1-2020m12. Least squares method is used in estimating the parameters of the model. The results showed that the two fitted simple ARLOM for {Y, X1} and {Y, X2} respectively, are in line with their pooled single equation ARLOM for {Y, X1, X2}. The empirical results revealed that, at the long–run, both X1 and X2 at lag zero and one have significant relationship with Y in Nigeria. The result also indicated that short-run effect terms (SRET1 and SRET2) are significantly positive at 1% level, as expected both in the bivariate and multivariate frameworks. The theoretical model assumptions are subjected to tests and satisfied, and the diagnostic tests are found adequate. Hence, ARLOM is recommended for modelling long-run relationship where adjustment to short-run equilibrium effect is also vital.

Suggested Citation

  • Chibuzo Gabriel Amaefula, 2022. "Autoregressive Lagged Ordered Model: A Long-Run Relationship Model with Short-Run Adjustment," Post-Print hal-05149894, HAL.
    • Handle: RePEc:hal:journl:hal-05149894
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