Several features may be present in rainfall data, and sophisticated time series procedures are needed for the analysis. These features are that of seasonality, long range dependency of observations and time trend as observed in the climatological series. This paper therefore considered the...

Brand distribution and promotional decisions require store-level forecasts for Brand sales. This may be accomplished by developing individual store-level models using store-level sales and promotional data. The disadvantage in this is that the number of models could run into hundreds. Also,...

This paper describes how the frequency domain analysis provides an alternative approach to time domain analysis for a given time series. Spectral and periodogram analysis for a given time series are performed to detect trends and seasonalities in the data. A cross-spectral analysis is used to...

The purpose of this paper is to investigate the asymptotic null distribution of stationarity and nonstationarity tests when the distribution of the error term belongs to the normal domain of attraction of a stable law in any finite sample but the error term is an i.i.d. process with finite...

This paper is concerned with simulation based inference in generalized models of stochastic volatility defined by heavy-tailed student-t distributions (with unknown degrees of freedom) and covariate effects in the observation and volatility equations and a jump component in the observation...

This paper develops a systematic Markov Chain Monte Carlo (MCMC) framework based upon Efficient Importance Sampling (EIS) which can be used for the analysis of a wide range of econometric models involving integrals without an analytical solution. EIS is a simple, generic and yet accurate...

Lawsuits brought pursuant to section 10(b) of the Securities and Exchange Act depend on the reliability of a statistical tool called an event study to adjudicate issues of reliance, materiality, loss causation, and damages. Although judicial acceptance of the event study technique is pervasive,...

We introduce advanced idiosyncratic risk (“AI-Risk”), a parsimonious correlated residual correction to a predictive stress CAPM-like factor model, aimed to get more accurate stock-stock correlations. We find that AI-Risk can be significant for stock portfolios. Inclusion of AI-Risk gives a...

The issue of long memory, though has important theoretical and practical implications, has not received much attention in India. This article examines the issue of long memory in mean of the stock returns by employing a set of sophisticated time-series tests including a bias reduced log...