Latvian GDP: The Optimal Time Series Forecasting Algorithm

Aplimat - Journal of Applied Mathematics 2011
Aleksandrs Bezručko

In this work an algorithm is developed for finding optimal time series model for GDP forecasting. Latvian GDP data with quarterly observation frequency is taken as time series. ARMA Analysis of Latvian GDP time series is performed. The set of model has been constructed. In order to check the accuracy of models, different residual tests are performed: autocorrelation, heteroscedasticity and normality of residual distribution. Models are compared in their forecast quality.

Keywords
time series, Gross Domestic Product, ARMA (Autoregressive Moving Average) Analysis, Residual tests, Serial Correlation, Heteroskedasticity
Hyperlink
http://www.aplimat.com/volume_4_2011/Journal_volume_4/Number_4.pdf

Bezručko, A. Latvian GDP: The Optimal Time Series Forecasting Algorithm. Aplimat - Journal of Applied Mathematics, 2011, No. 4, pp.477-486. ISSN 1337-6365.

Publication language
English (en)