In this paper we review parametric and nonparametric models and methods widely used in financial econometrics.

A simple and robust approach is proposed for the parametric estimation of scalar homogeneous stochastic di¤erential equations. We specify a parametric class of di¤usions and estimate the parameters of interest by minimizing criteria based on the integrated squared di¤erence between kernel estimates of the drift and di¤usion functions and their parametric counterparts. The procedure does not req...

In this paper we investigate the joint conditional distribution of health (life expectancy) and income growth, and its evolution over time. The conditional distributions of these two variables are obtained by applying non-parametric methods to a bivariate non-parametric regression system of equations. Analyzing the distributions of the non-parametric fitted values from these models we find stro...

In this paper we consider the forecasting performance of a range of semiand non-parametric methods applied to high frequency electricity price data. Electricity price time-series data tend to be highly seasonal, mean reverting with price jumps/spikes and timeand price-dependent volatility. The typical approach in this area has been to use a range of tools that have proven popular in the financi...

iranian female participation rate in the labor market of iran in the recent decade shows a increasing trend which amounted from 8% in 1999 to 18% in 2005.using parametric and non-parametric econometrics methods, in this research we aim to study the behavior of individual married females labor participation. our dataset consist of the annually budget survey of 2006 which is reported by statistic...

Although it is common to refer to “the bootstrap,” there are actually a great many different bootstrap methods that can be used in econometrics. We emphasize the use of bootstrap methods for inference, particularly hypothesis testing, and we also discuss bootstrap confidence intervals. There are important cases in which bootstrap inference tends to be more accurate than asymptotic inference. Ho...

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given a flexible Dirichlet process prior. The GARCH functional form enters into each of the components of th...