APPLICATION OF VECTOR AUTOREGRESSION MODEL FOR LITHUANIAN INFLATION
Keywords:inflation, HCPI, vector autoregression model, stationary
Inflation is one of the crucial modern macroeconomic problems. Nowadays the issue of inflation isvery relevant. Inflation is a constant and consistent increase in the general price level in the country, due towhich the purchasing power of a national currency unit decreases. In practice, the measures of inflation arevarious price indices, such as a consumer price index (CPI), producer price index (PPI), or gross domesticproduct deflator. However, inflation is usually defined as a change in the HCPI over a year. Time seriesmodels, linear regression models and a vector autoregression model (VAR) can be used to model and forecastinflation processes. This paper examines Lithuanian consumer price inflation using a modern stationary timeseries and econometric theory. The vector autoregression model is proposed for inflation modelling.Theoretical aspects of model estimation are reviewed: time series stationarity, model identification, parameterestimation, model usage and forecasts. The stationarity of the HCPI index and exogenous variables areanalyzed using the Augmented Dickey–Fuller (ADF) test. A vector autoregression model of Lithuanianinflation processes is investigated and proposed for inflation modelling. The obtained model is used forforecasting purposes and shows a fairly high degree of accuracy of the inflation forecast in the coming 12-month period.