An alternative method for forecasting price volatility by combining models

In this article, we study the volatility in the monthly price series of edible oils in domestic and international markets using the two popular family of nonlinear time-series models, viz, Generalized autoregressive conditional heteroscedastic (GARCH) models and Stochastic volatility (SV) models. To improve the forecasts of the volatility process, we also propose a new method of combining the volatility of these two competing models using the powerful technique of Kalman filter. The individual models as well as the combined models are assessed on their ability to predict the correct directional change (CDC) in future values as well as other goodness-of-fit statistics. Further, forecasting performance are also evaluated by computing various measures to validate the proposed methodology.     

SPC for short-run multivariate autocorrelated processes

This paper discusses the development of a multivariate control charting technique for short-run autocorrelated data manufacturing environment. The proposed approach is a combination of the multivariate residual charts for autocorrelated data and the multivariate transformation technique for i.i.d. process observations of short lengths. The proposed approach consists in fitting adequate multivariate time-series model of various process outputs and computes the residuals, transforming them into standard normal N(0, 1) data and then using standardized data as inputs to plot conventional univariate i.i.d. control charts. The objective for applying multivariate finite horizon techniques for autocorrelated processes is to allow continuous process monitoring, since all process outputs are controlled trough the use of a single control chart with constant control limits. Throughout simulated examples, it is shown that the proposed short-run process monitoring technique provides approximately similar shifts detection properties as VAR residual charts.     

Fertility and women's employment reconsidered: A macro-level time-series analysis for developed countries, 1960–2000

This paper examines causality and parameter instability in the long-run relationship between fertility and women's employment. This is done by a cross-national comparison of macro-level time-series data from 1960 to 2000 for France, West Germany, Italy, Sweden, the UK, and the USA. By applying vector error correction models (a combination of Granger-causality tests with recent econometric time-series techniques) we find causality in both directions. This finding is consistent with simultaneous movements of both variables brought about by common exogenous factors such as social norms, social institutions, financial incentives, and the availability and acceptability of contraception. We find a negative and significant correlation until about the mid-1970s and an insignificant or weaker negative correlation afterwards. This result is consistent with a recent hypothesis in the demographic literature according to which changes in the institutional context, such as changes in childcare availability and attitudes towards working mothers, might have reduced the incompatibility between child-rearing and the employment of women.     

China's Divorce and Remarriage Rates: Trends and Regional Disparities

China's rapid economic growth and significant increase in divorce and remarriage rates since the early 1980s provide an excellent case for studying the divorce and remarriage patterns in economic transition. Following extremely low divorce and remarriage rates in the 1960s and 1970s, China's crude divorce rate increased from 0.33 in 1979 to 1.59 in 2007, and the percentage of remarriages among the people who married each year increased from 3.05% in 1985 to 10.24% in 2007. Our graphical and econometric analyses based on the most recently available data suggest that the variations in divorce rate and remarriage rate across regions and over time were associated with regional factors, per-capita income, and education level. Also, there was a positive trend in both divorce and remarriage rates across all regions in China over the study period.     

An improved fuzzy time-series method of forecasting based on L–R fuzzy sets and its application

Classical time-series theory assumes values of the response variable to be ‘crisp’ or ‘precise’, which is quite often violated in reality. However, forecasting of such data can be carried out through fuzzy time-series analysis. This article presents an improved method of forecasting based on L–R fuzzy sets as membership functions. As an illustration, the methodology is employed for forecasting India's total foodgrain production. For the data under consideration, superiority of proposed method over other competing methods is demonstrated in respect of modelling and forecasting on the basis of mean square error and average relative error criteria. Finally, out-of-sample forecasts are also obtained.