2021
DOI: 10.3390/math9222839
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ARDL as an Elixir Approach to Cure for Spurious Regression in Nonstationary Time Series

Abstract: In conventional Econometrics, the unit root and cointegration analysis are the only ways to circumvent the spurious regression which may arise from missing variable (lag values) rather than the nonstationarity process in time series data. We propose the Ghouse equation solution of autoregressive distributed lag mechanism which does not require additional work in unit root testing and bound testing. This advantage makes the proposed methodology more efficient compared to the existing cointegration procedures. T… Show more

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Cited by 25 publications
(26 citation statements)
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“…However, the methods (e.g., Johansen cointegration tests) face some significant constraints, such as strict pre-determined lag structures and stationarity properties of the data (in which variables have to be stationary at levels or at their first difference), the presence of multiple cointegrating vectors, etc. In these instances, the inferences drawn from the methods might not be reliable (Ghouse et al, 2021; Nkoro & Uko, 2016).…”
Section: Methodsmentioning
confidence: 99%
“…However, the methods (e.g., Johansen cointegration tests) face some significant constraints, such as strict pre-determined lag structures and stationarity properties of the data (in which variables have to be stationary at levels or at their first difference), the presence of multiple cointegrating vectors, etc. In these instances, the inferences drawn from the methods might not be reliable (Ghouse et al, 2021; Nkoro & Uko, 2016).…”
Section: Methodsmentioning
confidence: 99%
“…But studies show that there is no spurious regression problem in ARDL models if all variables are stationary at level I(1) level. Ghouse et al 71 showed in a study that the ARDL model can be used as an alternative tool to avoid the spurious regression problem.…”
Section: The Stationary Test Of the Variablesmentioning
confidence: 99%
“…26 Gonzalo and Lee (1998) recommend using various techniques in order to avoid empirical pitfalls in the case of fractional integration of the time series. 27 See Ghouse et al (2018) for further insights on the ARDL method in the context of spurious regression.…”
Section: The General Settingmentioning
confidence: 99%