ARDL:An R package for the analysis of level relationships

Autoregressive Distributed Lag (ARDL) and Error Correction Models (ECM) (Pesaran &Shin, 1999) are widely used in various economic, environmental, political science applications etc. These are very flexible because of the autoregressive (AR) and the distributed lag (DL, essentially AR terms of the independent variables) terms in the ARDL model. Also, they are used in the context of cointegration analysis as a platform to test and analyze the levels (long-run) relationship between variables. One of the most popular such tests is the bounds test proposed by Pesaran et al. (2001) which allows testing for cointegration while at the same time estimates the level relationship. ARDL (Natsiopoulos & Tzeremes, 2021; Natsiopoulos & Tzeremes, 2022) is an R package that aims to help users in the modeling process of ARDL and ECM and it also provides the tools towards the bounds test for cointegration

ARDL:An R Package for ARDL Models and Cointegration

This paper presents the ARDL package for the statistical language R, demonstrating its main functionalities in a step by step guide. Some of its main advantages over other related R packages are the intuitive API, and the fact that includes many important features missing from other packages that are essential for an in depth analysis. Additionally, it is designed in such a way that it can be combined with other packages for post regression diagnostics and tests. These characteristics are shown through an example, where we showcase part of the application demonstrated in the seminal work of Pesaran et al. (J Appl Econom 16:289–326, 2001)

ARDL bounds test for cointegration:Replicating the Pesaran et al. (2001) results for the UK earnings equation using R

This paper replicates the UK earnings equation using the autoregressive distributed lag (ARDL) modeling approach and the bounds test for cointegration by Pesaran et al. (Journal of Applied Econometrics, 2001, 16(3), 289–326). The findings from the narrow sense fully replicate the original results using the open-source language R and the ARDL package. In the wide sense replication, augmented data are employed, thus extending the end period from 1997:Q4 to 2019:Q4, using an alternative measure for union power. Adopting the new dataset, this study reinvestigates the UK earnings equation, thereby providing supporting evidence of a long-run relationship and reveals empirical findings about the long-run effects of productivity, unemployment, tax wedge, and union power on wages.</p