|Keywords:||Social Sciences; Economics and Business; Economics; Samhällsvetenskap; Ekonomi och näringsliv; Nationalekonomi; Social and Behavioural Science, Law; samhälle/juridik; Economics; Nationalekonomi|
|Full text PDF:||http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-30543|
One of the conclusions made in the aftermath of the last financial crisis was that forecasting was failing in the context of predicting the current economic activity, which meant there were few efficient instruments to monitor the economy and thereby no early stage intervention which could have mitigated the severity of the crises. Due to these facts presented nowcasting, forecasting in short horizon, and the use of models that could combine different data frequencies like the Mixed Data Sampling (MIDAS) model gained a lot of attention. This study investigates if the MIDAS model improves nowcasting and if the Baltic Dry Index (BDI) is a good indicator for the US GDP. Before making any conclusions from the result, the characteristic of the BDI is covered and explained as to why it could reflect general growth. The intention of using BDI as an indicator for US GDP was to find an indicator that may explain economic activity in a more accurate way due to the characteristics of the BDI. The result was in line with previous empirical work and proved that the MIDAS model is superior in nowcasting in comparison to the least square model with flat aggregation defined as a benchmark model. The rejection of the cointegration test for BDI may question the use of it as an indicator for US GDP at present time, this due to the extreme circumstances that currently affect the bulk dry market.