Browsing by Subject "VAR"

The Finnish Customs collects and maintains the statistics of the Finnish intra-EU trade with the Intrastat system. Companies with significant intra-EU trade are obligated to give monthly Intrastat declarations, and the statistics of the Finnish intra-EU trade are compiled based on the information collected with the declarations. In case of a company not giving the declaration in time, there needs to exist an estimation method for the missing values. In this thesis we propose an automatic multivariate time series forecasting process for the estimation of the missing Intrastat import and export values. The forecasting is done separately for each company with missing values. For forecasting we use two dimensional time series models, where the other component is the import or export value of the company to be forecasted, and the other component is the import or export value of the industrial group of the company. To complement the time series forecasting we use forecast combining. Combined forecasts, for example the averages of the obtained forecasts, have been found to perform well in terms of forecast accuracy compared to the forecasts created by individual methods. In the forecasting process we use two multivariate time series models, the Vector Autoregressive (VAR) model, and a specific VAR model called the Vector Error Correction (VEC) model. The choice of the model is based on the stationary properties of the time series to be modelled. An alternative option for the VEC model is the so-called augmented VAR model, which is an over-fitted VAR model. We use the VEC model and the augmented VAR model together by using the average of the forecasts created with them as the forecast for the missing value. When the usual VAR model is used, only the forecast created by the single model is used. The forecasting process is created as automatic and as fast as possible, therefore the estimation of a time series model for a single company is made as simple as possible. Thus, only statistical tests which can be applied automatically are used in the model building. We compare the forecast accuracy of the forecasts created with the automatic forecasting process to the forecast accuracy of forecasts created with two simple forecasting methods. In the non-stationary-deemed time series the Naïve forecast performs well in terms of forecast accuracy compared to the time series model based forecasts. On the other hand, in the stationary-deemed time series the average over the past 12 months performs well as a forecast in terms of forecast accuracy compared to the time series model based forecasts. We also consider forecast combinations where the forecast combinations are created by calculating the average of the time series model based forecasts and the simple forecasts. In line with the literature, the forecast combinations perform overall better in terms of the forecast accuracy than the forecasts based on the individual models.

House prices have a very important role in the economy. House prices have strong influence to the economy especially in Finland, where around one-half of the value of households' total assets is coming from households' own dwellings. The real estate investment market is large in proportion in Finland when compared internationally to the size of the economy. Surprisingly, there are not many papers discussing the relationship between house prices and output in Finland. This paper intends to enrich the recent literature about this topic. Primary research question in this paper was do house prices affect output in Finland. Secondary interests were transmission mechanisms. The methods used in this thesis are typical in vector autoregression (VAR) analysis in recent literature. First, the time series are analysed visually and with unit root tests. Then, the optimal VAR model was chosen by using different information criterion tests and correlation tests. After selecting the optimal VAR model, Granger causality was tested with Toda-Yamamoto causality test. Other methods utilized in this paper were cointegration tests, forecasting, impulse responses and forecast error variance decomposition. These empirical methods were computed in intention to answer the research question. The most important empirical results of the paper were following. The results of Toda-Yamamoto causality test suggested that there are unidirectional Granger causality going from real house prices to real GDP per capita. This indicates that house prices could have significant explanatory power for GDP. Cointegration tests implied that the series are not cointegrated. This suggests that the series do not share a common stochastic trend for the long-run. The results of forecasting supported the results of Toda-Yamamoto causality test and it seemed that house prices might be a useful predictor when forecasting output. This result implied that the house prices have an effect on output. The analysis of impulse responses suggested that a house price shock have a positive and persistent effect on output. Forecast error variance decomposition intimated that after 15 quarters 63 percent of the output variation can be explained by the house price shock which was suspiciously strong result. The conclusion were made based on the results of the empirical analysis. Answer to the primary research question were house prices seem to have effect on output in Finland. The results of this paper supported the theory behind the wealth effect. If policy makers have a desire to stabilize output in Finland, they might need to consider stabilizing the house prices to further the stabilization of the output. It is necessary to understand the effects of housing prices to the business cycle for an efficient housing policy strategy.