138 JOURNAL OF GLOBAL BUSINESS AND ECONOMICS JULY 2011. VOLUME 3. NUMBER 1 APPLICATION OF GARMA (1, 1; 1, ) MODEL TO GDP IN MALAYSIA: AN ILLUSTRATIVE EXAMPLE Thulasyammal Ramiah Pillai -Laboratory of Computational Statistics and Operations Research, Institute for Mathematical Research, Universiti Putra Malaysia -Faculty of Engineering and Computer Technology, AIMST University, Malaysia thulasyram@hotmail.com Mahendran Shitan -Laboratory of Computational Statistics and Operations Research, Institute for Mathematical Research, Universiti Putra Malaysia. - Department of Mathematics, Faculty of Science, Universiti Putra Malaysia ABSTRACT Gross Domestic Product (GDP) per capita is often used as an indicator of standard of living in an economy. GDP per capita observed over the years can be modelled using time series models. A new class of GARMA has been introduced in the time series literature to reveal some hidden features in time series data. In this paper, we illustrate the fitting of GARMA (1, 1; 1,) model to the GDP growth data of Malaysia which has been observed from 1955 to 2009. The estimation of the model was done using Hannan-Rissanen Algorithm. Field of Research: Gross Domestic Product, Time series, GARMA (1, 1; 1, ), Hannan-Rissanen Algorithm ---------------------------------------------------------------------------------------------------------------------------------- 1.0 INTRODUCTION Gross Domestic Product (GDP), Gross National Product (GNP) and Net National Income (NNI), all are indicators of country’s economic power. Nevertheless, in almost all countries, GDP per capita is used as a benchmark for measuring nation’s economic progress. GDP is the measure of the market value of all goods and services produced within a country during a specified period. GDP per capita is the share of individual members of the population to the annual GDP. It is calculated by dividing real or nominal GDP by the total number of population per year. GDP per capita is an indicator of the average standard of living of individual members of the population. An increase in GDP per capita signifies national economic growth (Cathie Madsen (2006)). The GDP per capita observed over years can be modelled using time series models. In the United Arab Emirates, the forecast of the non-oil GDP was done by using ARIMA Models in 2007. In China, ARMA model was used to forecast the GDP in 2006. Jarita Duasa had examined the causal relationship between foreign direct investment (FDI) and economic growth (GDP) in Malaysia using Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model in 2007.