In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. We calibrate autoregression (AR) models, including specifications with a fundamental (exogenous) variable - system load, to California Power Exchange (CalPX) system spot...

One of the most pressing problems in the Latvian economy is related to the energy sector. The most characteristic feature is coupled with the low efficiency of thermal energy consumption of households as a result of poor insulation of existing buildings in Latvia. Solving energy sector problems...

One of the most pressing problems in the Latvian economy is related to the energy sector. The most characteristic feature is coupled with the low efficiency of thermal energy consumption of households as a result of poor insulation of existing buildings in Latvia. Solving energy sector problems...

This paper uses monthly data from 1984:M10 to 2012:M8 to show that oil-sensitive stock price indices, particularly those in the energy sector, have strong power in predicting nominal and real crude oil prices at short horizons (one-month-ahead predictions), using both in- and out-of-sample...

I evaluate the out-of-sample forecasting performance of five models of Chinese and Indian energy consumption. The results are mixed, but in general the auto-regressive distributed lag and unobserved components models perform the best over multiple evaluation criteria. I then use these two models...

Insights about electricity demand dynamics is fundamental for investment capacity, optimal energy policies, and a balanced electricity system. This paper presents an empirical analysis of the monthly Italian electricity demand since January 2001 to June 2012. In the first section we conduct the...

One of the most pressing problems in the Latvian economy is related to the energy sector. The most characteristic feature is coupled with the low efficiency of thermal energy consumption of households as a result of poor insulation of existing buildings in Latvia. Solving energy sector problems...

Over the last four decades, bankruptcy prediction has given rise to an extensive body of literature, the aim of which was to assess the conditions under which forecasting models perform effectively. Of all the parameters that may influence model accuracy, one has rarely been discussed: the...

Of the methods used to build bankruptcy prediction models in the last twenty years, neural networks are among the most challenging. Despite the characteristics of neural networks, most of the research done until now has not taken them into consideration for building financial failure models, nor...