Open AccessForecasting Albanian Time Series with Linear and Nonlinear Univariate Models
Author(s) -
Blerina Vika,
Ilir Vika
Publication year - 2021
Publication title -
academic journal of interdisciplinary studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.148
H-Index - 5
eISSN - 2281-3993
pISSN - 2281-4612
DOI - 10.36941/ajis-2021-0140
Subject(s) - univariate , econometrics , autoregressive model , nonlinear system , artificial neural network , autoregressive integrated moving average , benchmark (surveying) , series (stratigraphy) , linear model , rank (graph theory) , nonparametric statistics , nonlinear autoregressive exogenous model , time series , computer science , statistics , mathematics , economics , multivariate statistics , artificial intelligence , paleontology , physics , geodesy , quantum mechanics , combinatorics , biology , geography
Albanian economic time series show irregular patterns since the 1990s that may affect economic analyses with linear methods. The purpose of this study is to assess the ability of nonlinear methods in producing forecasts that could improve upon univariate linear models. The latter are represented by the classic autoregressive (AR) technique, which is regularly used as a benchmark in forecasting. The nonlinear family is represented by two methods, i) the logistic smooth transition autoregressive (LSTAR) model as a special form of the time-varying parameter method, and ii) the nonparametric artificial neural networks (ANN) that mimic the brain’s problem solving process. Our analysis focuses on four basic economic indicators – the CPI prices, GDP, the T-bill interest rate and the lek exchange rate – that are commonly used in various macroeconomic models. Comparing the forecast ability of the models in 1, 4 and 8 quarters ahead, we find that nonlinear methods rank on the top for more than 75 percent of the out-of-sample forecasts, led by the feed-forward artificial neural networks. Although the loss differential between linear and nonlinear model forecasts is often found not statistically significant by the Diebold-Mariano test, our results suggest that it can be worth trying various alternatives beyond the linear estimation framework. Received: 19 June 2021 / Accepted: 25 August 2021 / Published: 5 September 2021