This paper considers estimation of nonparametric moment conditions models with weakly dependent data. The estimator is based on a local linear version the generalized empirical likelihood approach, and an alternative to popular method estimator. derives uniform convergence rates pointwise asymptotic normality resulting also develops second order stochastic expansions (under standard undersmooth...

In this paper we present two nonlinear estimation algorithms that combine generalized polynomial chaos theory with higher moment updates and Bayesian framework. Polynomial chaos theory is used to predict the evolution of uncertainty of the nonlinear random process. In the first estimation algorithm, higher order moment updates are used to estimate the posterior non Gaussian probability density ...

Tests for model specification based on the generalized method of moments have been widely used in the literature. Most of the popular tests typically require estimators which are efficient under the hypotheses that models chosen for estimation are correctly specified. This paper develops alternative tests which can be obtained using any consistent estimator. In particular, the test statistics a...

Starting from an agent-based interpretation of the well-known Bass innovation diffusion model, we perform a Montecarlo analysis of the performance of a method of simulated moment (MSM) estimator. We show that nonlinearities of the moments lead to a small bias in the estimates in small populations, although our estimates are consistent and converge to the true values as population size increases...

A novel approach to the combination of volatility forecasts is discussed. The proposed procedure makes use of the generalized method of moments (GMM) for estimating the combination weights. The asymptotic properties of the GMM estimator are derived while its finite sample properties are assessed by means of a simulation study. The results of an application to a time series of daily returns on t...

We develop a generalized method of moments (GMM) estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underly...

To obtain consistency and asymptotic normality, a generalized method of moments (GMM) estimator typically is defined to be an approximate global minimizer of a GMM criterion function. To compute such an estimator, however, can be problematic because of the difficulty of global optimization. In consequence, practitioners usually ignore the problem and take the GMM estimator to be the result of a...

In this paper, we consider a spatial-autoregressive model with autoregressive disturbances, where we allow for endogenous regressors in addition to a spatial lag of the dependent variable. We suggest a two-step generalized method of moments (GMM) and instrumental variable (IV) estimation approach extending earlier work by, e.g., Kelejian and Prucha (1998, 1999). In contrast to those papers, we ...