Does time-frequency scale analysis predict inflation? Evidence from Tunisia

Bilel Ammouri, Fakhri Issaoui and Habib Zitouna

International Journal of Computational Economics and Econometrics, 2021, vol. 11, issue 2, 161-188

Abstract: Forecasting macroeconomic indicators has always been an issue for economic policymakers. Different models are available in the literature; for example univariate and/or multivariate models, linear and/or nonlinear models. This diversity requires a multiplicity of the used techniques. They can be classified as pre and post-time series. However, this multiplicity allows the improvement of a better forecast of the macroeconomic indicators during unrest (be it political, economic, and/or social). In this paper, we deal with the problem of the performance of the macroeconomic models for predicting Tunisia's inflation during instability following the 2011 revolution. To achieve this goal, the time-frequency-scale analysis (Fourier transform, wavelet transform, and Stockwell transform) is used. In fact, we are interested in the ability of these techniques to improve predictor performances. We suggest the performance of the adopted approach (time-frequency-scale analysis). This performance is not quite absolute because their performance is less than the multivariate model (dynamic factor model) during economic instability.

Keywords: inflation forecast; univariate model; multivariate model; time-frequency-scale analysis; Fourier transform; wavelet transform; Stockwell transform; Tunisia. (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:11:y:2021:i:2:p:161-188

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