Volume 3 | Issue 1 | 1 J Curr Trends Comp Sci Res, 2024 University of Monastir, Tunisia. Rue Ibn Sina Hiboun, Mahdia Tunisia. Abstract AutoRegressive Distributed Lag models (ARDL) are dynamic models which involve variables lagged over time unlike static models. The paper aims is present how to apply ARDL models using the R software and show how to use the package dynamac and will make interesting recommendations for estimating models ARDL using R. Then in this paper, i present the benefit of dynamac package for the statistical language R, demonstrating its main functionalities in a step by step guide. Citation: Mestiri, S. (2024). ARDL Modeling Using R Software. J Curr Trends Comp Sci Res, 3(1), 01-05. * Corresponding Author Sami Mestiri, University of Monastir, Tunisia. Rue Ibn Sina Hiboun, Mahdia Tunisia. Submitted: 2023, Dec 12; Accepted: 2024, Jan 09; Published: 2024, Feb 13 ISSN: 2836-8495 ARDL Modeling Using R Software Sami Mestiri* JEL codes: C15, C88 Keywords: R Software, ARDL, Cointegration Test. 1. Introduction Pesaran et al. (2001) introduced the bounds test for cointegration based on the previous work of Pesaran and Shin (1999) using the ARDL model as a platform for the test. Since then, the ARDL framework and the bounds test are used constantly by practitioners who seem to adopt every new advancement of the initial framework. A recent example combining various techniques, is Wu et al. (2022) who applied bootstrap ARDL with a Fourier function. This paper provides a smooth introduction to the dynamac package in R (R Core Team, 2023) and its main features and capabilities. Regarding proprietary software like EViews, although they are generally considered more user-friendly, they lack flexibility compared to programming languages such as R (Mestiri (2019 ) ). Additionally, these software platforms are often slow to adopt the latest advancements in research and can be prohibitively expensive for many users. On the other hand, open-source software do not provide any guarantees regarding the quality of results, and it is the responsibility of the user to verify the code. The problem lies in the fact that not everyone is an expert in the field, making it challenging to technically validate the code’s implementation. Many practitioners simply seek reliable software they can trust. The dynamac package is a suite of programs in R designed to assist users in modeling and visualizing the effects of autoregressive distributed lag models, as well as testing for cointegration. The core program is dynardl, a flexible program designed to dynamically simulate and plot a variety of types of autoregressive distributed lag models, including error-correction models. The research paper is organized as follows: We provide AutoRegressive Distributed Lag models in Section 2. Section 3 presents Cointegration test. In section 4, we apply the model. And finally, we conclude in section 5. 2. Auto Regressive Distributed Lag models AutoRegressive Distributed Lag models (ARDL), are dynamic models which involve variables lagged over time unlike static models. These models have the particularity of taking into account temporal dynamics (adjustment time, expectations, etc.) in the explanation of a variable (time series), thus improving the forecasts and effectiveness of policies (decisions, actions, etc.), unlike the simple (non-dynamic) model whose instantaneous explanation (immediate effect or not spread over time) only restores part of the variation in the variable to explain In ARDL models we find, among the explanatory variables (   ), the lagged dependent variable ( − ) and the past values of the independent variable ( − ). They have the following general form:   = (  , − , − ) Research Article Journal of Current Trends in Computer Science Research