We consider the Generalised Normal Variance-Mean (GNVM) model in which the mixing random variable is Gamma distributed for financial return data. This model generalises the popular Variance-Gamma (VG) distribution. This GNVM model can be interpreted as the addition of noise to a (skew) VG base. In this presentation, we will not only discuss the parameter estimation of the general model, but als...

This article considers unbiased estimation of mean, variance and sensitivity level of a sensitive variable via scrambled response modeling. In particular, we focus on estimation of the mean. The idea of using additive and subtractive scrambling has been suggested under a recent scrambled response model. Whether it is estimation of mean, variance or sensitivity level, the proposed scheme of esti...

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain efficient estimates of...

Variance estimation is central to many questions in finance and economics. Until now ex-post variance estimation has been based on infill asymptotic assumptions that exploit high-frequency data. This paper offers a new exact finite sample approach to estimating ex-post variance using Bayesian nonparametric methods. In contrast to the classical counterpart, the proposed method exploits pooling o...

today, in the rangeland management science and determination of rangecapacity, accurate and true information about range production is crucial. in fact, rangeproduction is considered as a basis for range management. the aim of this study was tocompare different methods for the estimation of forage production with four samplingmethods in the rangelands of kurdistan province, iran. the sampling m...

Estimation by simulation involves drawing random terms for each observation and calculating an element (such as the probability or score) that enters an objective function (e.g. the simulated log-likelihood function) and/or a first-order condition (e.g., the sum of simulated scores or simulated moment conditions). The resulting parameter estimates depend on the particular values of the draws fo...

In industries, how to improve forecasting accuracy such as sales, shipping is an important issue. There are many researches made on this. In this paper, a hybrid method is introduced and plural methods are compared. Focusing that the equation of exponential smoothing method(ESM) is equivalent to (1,1) order ARMA model equation, new method of estimation of smoothing constant in exponential smoot...