Indirect Inference (I-I) estimation of structural parameters θ requires matching observed and simulated statistics, which are most often generated using an auxiliary model that depends on instrumental parameters Ø. The estimators of the instrumental parameters will encapsulate the...

Indirect Inference (I‐I) estimation of structural parameters θ requires matching observed and simulated statistics, which are most often generated using an auxiliary model that depends on instrumental parameters β. The estimators of the instrumental parameters will encapsulate the...

We derive indirect estimators of multivariate conditionally heteroskedastic factor models in which the volatilities of the latent factors depend on their past values. Specifically, we calibrate the analytical score of a Kalman-filter approximation, taking into account the inequality constraints...

We derive indirect estimators of conditionally heteroskedastic factor models in which the volatilities of common and idiosyncratic factors depend on their past unobserved values by calibrating the score of a Kalman-filter approximation with inequality constraints on the auxiliary model...

This paper determines coverage probability errors of both delta method and parametric bootstrap confidence intervals (CIs) for the covariance parameters of stationary long-memory Gaussian time series. CIs for the long-memory parameter d_0 are included. The results establish that the bootstrap...

Theoretical constraints on economic model parameters often are in the form of inequality restrictions. For example, many theoretical results are in the form of monotonicity or nonnegativity restrictions. Inequality constraints can truncate sampling distributions of parameter estimators, so that...

In a recent paper, Hughes (1999) showed that the power of tests of linear regression parameters could be improved by utilizing one-sided information regarding the nuisance parameters in the testing problem. In this paper, we extend this principle to the problem of diagnosing departures from the...

In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model....