TIME SERIES FORECASTING OF GDP IN INDIA USING ARIMA MODEL

Abstract

Gross Domestic Product (GDP) of a country is the money value of all final goods and services produced by all the enterprises within the borders of a country in a year. It represents the aggregate statistic of all economic activity. The performance of economy can be measured with the help of GDP. Forecasting future economic outcomes is a vital component of the decision-making process in central banks for all countries. Monetary policy decisions affect the economy with a delay, so, monetary policy authorities must be forward looking, i.e. must know what is likely to happen in the future.Gross domestic product (GDP) is one of the most important indicators of national economic activities for countries. Scientific prediction of the indicator has important theoretical and practical significance on the development of economic development goals.For the forecasting of time series we use models that are based on a methodology that was first developed in Box and Jenkins (1976), known as ARIMA (Auto-Regressive-Integrated-Moving-Average) methodology. This approach was based on the World representation theorem, which states that every stationary time series has an infinite moving average (MA) representation, which actually means that its evolution can be expressed as a function of its past developments (Jovanovic and Petrovska 2010).The rest of the paper is organized as follows: Section 1 describes introduction while in Section 2 and 3, theoretical background and data interpretation and coding in R is given.In Section 4 the empirical results are presented. Section 5 is the forecasting and finally, conclusions.