Browsing by Subject "taloudelliset ennusteet"

This thesis researches empirically, whether variables that are able to reliably predict Finnish economic activity can be found. The aim of this thesis is to find and combine several variables with predictive ability into a composite leading indicator of the Finnish economy. The target variable it attempts to predict, and thus the measure of the business cycle used, is Finnish industrial production growth. Different economic theories suggest several potential predictor variables in categories, such as consumption data, data on orders in industry, survey data, interest rates and stock price indices. Reviewing a large amount of empirical literature on economic forecasting, it is found that particularly interest rate spreads, such as the term spread on government bonds, have been useful predictors of future economic growth. However, the literature surveyed suggests that the variables found to be good predictors seem to differ depending on the economy being forecast, the model used and the forecast horizon. Based on the literature reviewed, a pool of over a hundred candidate variables is gathered. A procedure, involving both in-sample and pseudo out-of-sample forecast methods, is then developed to find the variables with the best predictive ability from this set. This procedure yields a composite leading indicator of the Finnish economy comprising of seven component series. These series are very much in line with the types of variables found useful in previous empirical research. When using the developed composite leading indicator to forecast in a sample from 2007 to 2009, a time span including the latest recession, its forecasting ability is far poorer. The same occurs when forecasting a real-time data set. It would seem, however, that individual very large forecast errors are the main reason for the poor performance of the composite leading indicator in these forecast exercises. The findings in this thesis suggest several developments to the methods adopted in order to produce more accurate forecasts. Other intriguing topics for further research are also explored.