Prediction of GDP Based on the Lag Economic Indicators

Abstract

There are several institutions that constantly announce their predictions of general domestic product (GDP) and lots of institutions that use this information. Frequently the forecasts of different institutions vary because they use different methods, but all the institutions for which this indicator is relevant cannot make the predictions themselves because the models are too sophisticated. The main purpose of this research is to create simple enough but also accurate model for prediction of Lithuanian GDP that can be used by all the institutions that need this indicator. The research is based on the economic data that are measured and published quarterly or monthly by Statistics Lithuania. 154 economic indicators were analysed as possible independent variables for regression model creation. The analysis showed that the regression model with twelve lag independent variables can be quite accurate for a short-term prediction of Lithuanian GDP. It can be forecasted by such indexes as the number of immigrants, the turnover of wholesale and retail trade and repair of motor vehicles and motorcycles, the number of overnight stays in the accommodation establishments, an average hourly earnings, the rate of change in the producer prices of all the industry (except construction) of Lithuanian market, the imports and seasonally adjusted imports, the seasonally adjusted exports, the projected number of employees in the trade enterprises for the next 2-3 months, the industrial production (of all the industry except construction).

The Czech Republic’s Economy since the 1990s and Its Development Forecasts: Selected Macroeconomic Indicators

“…Kubiszewski, Costanza, Franco, Lawn, Talberth, Jackson and Aylmer (2013) synthesized estimates of genuine progress indicator for 17 countries over the 1950-2003 period, comparing GPI with GDP human development index, ecological footprint, biocapacity, Gini coefficient and life satisfaction scores, revealing a significant variation between the countries studied. Stundziene (2015) seeks to create a practical, simple but accurate model for predicting Lithuanian GDP, a regression model with twelve lag independent variables meeting these criteria for short-term GDP forecasting. Varjan, Rovnanikova and Gnap (2017) carry out a long-term analysis of GDP per capita in Slovakia, recommending measures for the development of transport infrastructure in terms of sustainable growth.…”