Browsing by Subject "VECM"

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.

This thesis is aimed to investigate the long-run and short-run relationship between renewable, nuclear energy consumption, economic growth and CO2 emissions in Finland. The historic development and current situations of the energy consumption, economic growth and CO2 emissions are discussed. Then Johansen cointegration test and Vector Error Correction Model (VECM) are applied in the empirical analysis to further exploit the causal relationships between the variables. There are four variable employed in this thesis: renewable energy consumption (REC), nuclear energy consumption (NUC), Gross Domestic Product (GDP) and CO2 emissions (COE). The data of the variables span from 1977 to 2020 and mainly come from official databases and reports from the Finnish government and international organizations, such as Statistics Finland, Our World in Data, World Bank, etc. The results indicate that there is the positive bidirectional long-run relationships between NUC, REC and GDP. Also, the unidirectional long-run relationships of GDP, REC, NUC with COE are estimated. The increase in GDP and NUC are estimated to increase COE in the long run, while REC is estimated to decrease COE. Furthermore, the negative short-run relationships between REC, NUC and COE are found, which shows that, in the short run, renewable and nuclear energy consumption in Finland is estimated to reduce the CO2 emissions. The findings from the thesis indicate renewable energy consumption is both economic and environmental-friendly. Nuclear energy is also good for economic growth in the long run and environment in the short run, but needs to be cautiously treated with regard to environmental protection in the long-run strategies. Also, there has been the trend of impact decoupling in Finland in the short term. However, further research is needed to test the EKC hypothesis and its turning point.