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Lag Length Specification in Engle-Granger Cointegration Test: A Modified Koyck Mean Lag Approach Based on Partial Correlation

100%
Agunloye O. K., Shangodoyin D. K.
Statistics in Transition new series
|
2014
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vol. 15
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issue 4
559–572
EN
The Engle-Granger cointegration test is highly sensitive to the choice of lag length and the poor performance of conventional lag selection criteria such as standard information criteria in selecting appropriate optimal lag length for the implementation of the Engle-Granger cointegration test is well-established in the statistical literature. Testing for cointegration within the framework of the residual-based Engle-Granger cointegration methodology is the same as testing for the stationarity of the residual series via the augmented Dickey-Fuller test which is well known to be sensitive to the choice of lag length. Given an array of candidate optimal lag lengths that may be suggested by different standard information criteria, the applied researchers are faced with the problem of deciding the best optimal lag among the candidate optimal lag lengths suggested by different standard information criteria, which are often either underestimated or overestimated. In an attempt to address this well-known major pitfall of standard information criteria, this paper introduces a new lag selection criterion called a modified Koyck mean lag approach based on partial correlation criterion for the selection of optimal lag length for the residual-based Engle-Granger cointegration test. Based on empirical findings, it was observed that in some instances over-specification of lag length can bias the Engle-Granger cointegration test towards the rejection of a true cointegration relationship and non-rejection of a spurious cointegration relationship. Using real-life data, we present an empirical illustration which demonstrates that our proposed criterion outperformed the standard information criteria in selecting appropriate optimal truncation lag for the implementation of the Engle-Granger cointegration test using both augmented Dickey-Fuller and generalized least squares Dickey-Fuller tests.
2
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A Bayesian analysis of complete multiple breaks in a panel autoregressive (CMB-PAR(1)) time series model

81%
Agiwal V., Kumar J., Shangodoyin D. K.
Statistics in Transition new series
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2020
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vol. 21
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issue 5
133-149
EN
Most economic time series, such as GDP, real exchange rate and banking series are irregular by nature as they may be affected by a variety of discrepancies, including political changes, policy reforms, import-export market instability, etc. When such changes entail serious consequences for time series modelling, various researchers manage this problem by applying a structural break. Thus, the aim of this paper is to develop a generalised structural break time series model. The paper discusses a panel autoregressive model with multiple breaks present in all parameters, i.e. in the autoregressive coefficient and mean and error variance, which is a generalisation of various sub-models. The Bayesian approach is applied to estimate the model parameters and to obtain the highest posterior density interval. Strong evidence is observed to support the Bayes estimator and then it is compared with the maximum likelihood estimator. A simulation experiment is conducted and an empirical application on the SARRC association’s GDP per capita time series is used to illustrate the performance of the proposed model. This model is also extended to a temporary shift model. Key words: panel autoregressive model, structural break, MCMC, posterior probability.
3
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Nonrandomized response model for complex survey designs

81%
Arnab R., Shangodoyin D. K., Arcos A.
Statistics in Transition new series
|
2019
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vol. 20
|
issue 1
67-86
EN
Warner’s randomized response (RR) model is used to collect sensitive information for a broad range of surveys, but it possesses several limitations such as lack of reproducibility, higher costs and it is not feasible for mail questionnaires. To overcome such difficulties, nonrandomized response (NRR) surveys have been proposed. The proposed NRR surveys are limited to simple random sampling with replacement (SRSWR) design. In this paper, NRR procedures are extended to complex survey designs in a unified setup, which is applicable to any sampling design and wider classes of estimators. Existing results for NRR can be derived from the proposed method as special cases.
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