In nonlinear state-space models, sequential learning about the hidden state can proceed by particle filtering when the density of the observation conditional on the state is available analytically (e.g. Gordon et al. 1993). This condition need not hold in complex environments, such as the...

Econometric estimation using simulation techniques, such as the efficient method of moments, may betime consuming. The use of ordinary matrix programming languages such as Gauss, Matlab, Ox or S-plus will very often cause extra delay. For the Efficient Method of Moments implemented to...

Gravity type models are widely used in international economics. In these models the inclusion of time-fi0xed regressors like geographical or cultural distance, language and institutional (dummy) variables is often of vital importance e.g. to analyse the impact of trade costs on...

Propensity score matching estimators have two advantages. One is that they overcome the curse of dimensionality of covariate matching, and the other is that they are nonparametric. However, the propensity score is usually unknown and needs to be estimated. If we estimate it nonparametrically, we...

In view of applications to diagnostic tests of ARMA models, the asymptotic behavior of multivariate empirical and copula processes based on residuals of ARMA models is investigated. Multivariate empirical processes based on squared residuals and other functions of the residuals are also...

It is shown that parametric bootstrap can be used for computing P-values of goodness-of-fit tests of multivariate time series parametric models. These models include Markovian models, GARCH models with non-Gaussian innovations, regime-switching models, as well as semi parametric models involving...

This short note describes some statistical tests and experiments for serial correlations of historical stock prices. More precisely, some parameters calculated via empirical characteristics functions are compared with the same parameters for time series with known degree of correlation

Propensity score matching estimators have two advantages. One is that they overcome the curse of dimensionality of covariate matching, and the other is that they are nonparametric. However, the propensity score is usually unknown and needs to be estimated. If we estimate it nonparametrically, we...

Specification tests are developed for the conditional distribution of a dependent process {X_i} in a family of nonlinear time-series models. The family includes several Generalized AutoRegressive Conditional Heteroscedastic [GARCH] models that are widely used in finance and economics. Such tests...

We propose a general test for exogeneity that is robust against distributional misspecification. The test can also be used to identify other types of misspecifications, such as the presence of a random coefficient. The idea is to sort the data with respect to a variable (a sorting score) and...