Forecasting of Inflation Based on Univariate and Multivariate Time Series Models: An Empirical Application

Maintaining stable prices is one of the goals of monetary policy makers. Since its formation, inflation has been a key issue and priority for every Pakistani government; it is a fundamental macroeconomic variable that plays a significant role in a nation’s economic progress and development. This research investigates the predictive capabilities of different univariate and multivariate models. The study considers autoregressive models, autoregressive neural networks, autoregressive moving average models, and other nonparametric autoregressive models within the univariate category. In contrast, the multivariate models include factor models that utilize Minimax Concave Penalty, Elastic-Smoothly Clipped Absolute Deviation, Principal Component Analysis, and Partial Least Squares. We conducted an empirical analysis using a well-established macroeconomic dataset from Pakistan. This dataset covers the period from January 2013 to December 2020 and consists of 79 variables recorded at that frequency. To evaluate the forecasting accuracy of the models for multiple steps ahead in the post-sample period, an analysis was performed using data extracted from January 2013 to February 2019 for model estimation and then another set from March 2019 to December 2020. The predictability of the univariate models following the sample period is compared with that of the multivariate models using statistical accuracy measurements, specifically root mean square error and mean absolute error. Additionally, the Diebold–Mariano test has been employed to evaluate the accuracy of the average errors statistically. The results indicated that the factor approach based on Partial Least Squares delivers significantly more effective outcomes than its competing methods.