We use realized volatilities based on after hours high frequency returns to predict next day volatility. We extend GARCH and long-memory forecasting models to include additional information: the whole night, the preopen, the postclose realized variance, and the overnight squared return. For four...

This paper examines the long-term impacts of infrastructural investment. It considers the case of British investment in railway infrastructure in colonial India. Railways had an immediate impact on trade and development in the predominantly agricultural India. In this paper, we show that the...

This paper examines the decision of undergraduate students to engage in paid employment during their academic semester. We present the decision to work as part of the human capital earnings function and examine how term time employment affects academic performance. Using a unique dataset, we...

Objective: To examine the long-term response of respiratory cancer mortality to the changes of real cigarette excise taxes.Design: First, using a panel of state-level data from 1954 to 2005, this study performs unit root co-integration tests. Second, the analysis utilizes the Fully Modified...

This paper examines the out-of-sample forecasting performance of the S&P/Case-Shiller Home Price Index by a simple ARIMA model. We find that the model failed to predict the housing bubble burst. However, the model has successfully predicted declining prices since 2006:6; therefore, the magnitude...

In recent years, a number of empirical studies and energy regulators have applied benchmarking techniques to measuring the efficiency and performance of network utilities. An important issue has been the extent to which the results are influenced by contextual factors. Among these, weather...

Individual attitudes and opinions may visibly impact upon an individual's decisions on how and when to use health care services and associated decisions with respect to medical expenditures. These health care preferences also serve as important inputs in helping to predict health insurance...