An Alternative Methodology for Combining Different Forecasting Models

Many economic and financial time series exhibit heteroskedasticity, where the variability changes are often based on recent past shocks, which cause large or small fluctuations to cluster together. Classical ways of modelling the changing variance include the use of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and Neural Networks models. The paper starts with a comparative study of these two models, both in terms of capturing the non-linear or heteroskedastic structure and forecasting performance. Monthly and daily exchange rates for three different countries are implemented. The paper continues with different methods for combining forecasts of the volatility from the competing models, in order to improve forecasting accuracy. Traditional methods for combining the predicted values from different models, using various weighting schemes are considered, such as the simple average or methods that find the best weights in terms of minimizing the squared forecast error. The main purpose of the paper is, however, to propose an alternative methodology for combining forecasts effectively. The new, hereby-proposed non-linear, non-parametric, kernel-based method, is shown to have the basic advantage of not being affected by outliers, structural breaks or shocks to the system and it does not require a specific functional form for the combination.     

Macroeconomic and financing determinants of out of pocket payments in health care: Evidence from selected OECD countries

In this paper, we examine the impact of macroeconomic, as well public and private health insurance financing (PHI) factors on out of pocket (OOP) healthcare expenditures, by using fixed/random effects and dynamic panel data methodology to a dataset of 26 EU and OECD countries for a period lasting from 1995 to 2013. The existing empirical literature has focused on testing the hypothesis that several macroeconomic and health financing determinants have an effect on OOP healthcare expenditures. Nevertheless, the related articles have not well tested the hypothesis concerning the potential impact of PHI financing on OOP spending. We find that public and PHI financing have a significant countervailing effect on OOP spending. Moreover, we show that unemployment rate has a significant positive impact on OOP expenditures. Sensitivity tests with variation of specifications and samples show that our findings are robust. We argue that policy-makers should give serious consideration to PHI institution; our results indicate that there is an inverse effect on OOP spending. We suggest that our examined countries have to provide financial risk protection to their citizens against OOP payments, rather than only attending health budgetary retrenchments in order to adjust public finances.