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2021•Journal of Economic SurveysRequires access

Recent developments of the autoregressive distributed lag modelling framework

Jin Seo Cho, Matthew Greenwood‐Nimmo, Yongcheol Shin

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Abstract

Abstract We review the literature on the autoregressive distributed lag (ARDL) model, from its origins in the analysis of autocorrelated trend stationary processes to its subsequent applications in the analysis of cointegrated non‐stationary time series. We then survey several recent extensions of the ARDL model, including asymmetric and non‐linear generalisations of the ARDL model, the quantile ARDL model, the pooled mean group dynamic panel data model and the spatio‐temporal ARDL model.

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What this paper is about

Abstract We review the literature on the autoregressive distributed lag (ARDL) model, from its origins in the analysis of autocorrelated trend stationary processes to its subsequent applications in the analysis of cointegrated non‐stationary time series. We then survey several recent extensions of the ARDL model, including asymmetric and non‐linear generalisations of the ARDL model, the quantile ARDL model, the pooled mean group dynamic panel data model and the spatio‐temporal ARDL model.

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Available abstract

Abstract We review the literature on the autoregressive distributed lag (ARDL) model, from its origins in the analysis of autocorrelated trend stationary processes to its subsequent applications in the analysis of cointegrated non‐stationary time series. We then survey several recent extensions of the ARDL model, including asymmetric and non‐linear generalisations of the ARDL model, the quantile ARDL model, the pooled mean group dynamic panel data model and the spatio‐temporal ARDL model.

Key concepts: Distributed lag, Autoregressive model, Econometrics, Autocorrelation, Quantile, Lag, STAR model, Time series

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