In this paper we consider a discretely sampled Jacobi process appropriate to specify the dynamics of a process with range [0,1], such as a discount coefficient, a regime probability, or a state price. The discrete time transition of the Jacobi process does not admit a closed form expression and therefore the exact maximum likelihood is unfeasable. We first review different characterizations of ...

The contribution of generalized method of moments (Hansen and Singleton, 1982) was to allow frequentist inference regarding the parameters of a nonlinear structural model without having to solve the model. Provided there were no latent variables. The contribution of this paper is the same. With latent variables.

In this paper we propose a class of efficient Generalized Method-of-Moments (GMM) algorithms for computing parameters of the Plackett-Luce model, where the data consists of full rankings over alternatives. Our technique is based on breaking the full rankings into pairwise comparisons, and then computing parameters that satisfy a set of generalized moment conditions. We identify conditions for t...

Part A reviews the basic estimation theory of the generalized method of moments (GMM) and Part B deals with optimal instrumental variables. 1 For the most part, we restrict attention to iid observations. Linear Regression Economists often use linear regression to quantify a relationship between economic variables. A linear regression between y and x is a relationship of the form y = x 0 β + ε (...

11 THE GMM ESTIMATION 2 11.1 Consistency and Asymptotic Normality . . . . . . . . . . . . . . . . . . . . . 3 11.2 Regularity Conditions and Identification . . . . . . . . . . . . . . . . . . . . . 4 11.3 The GMM Interpretation of the OLS Estimation . . . . . . . . . . . . . . . . . 5 11.4 The GMM Interpretation of the MLE . . . . . . . . . . . . . . . . . . . . . . . 6 11.5 The GMM Estimation ...

The Robust Robust Generalized Methods of Moments (RGMM) and the Indirect Robust GMM (IRGMM) are algorithms for estimating parameter values in statistical models, such as diffusion models for interest rates, in a robust way. The long computation time is one of the main challenge facing these methods. In this paper, we introduce accelerated variants of RGMM and IRGMM. The fixed point iteration in...

We develop a GMM estimator for stationary heavy tailed data by trimming an asymptotically vanishing sample portion of the estimating equations. Trimming ensures the estimator is asymptotically normal, and self-normalization implies we do not need to know the rate of convergence. Tail -trimming, however, ensures asymmetric models are covered under rudimentary assumptions about the thresholds, an...

In this paper we propose a class of efficient Generalized Method-of-Moments (GMM) algorithms for computing parameters of the Plackett-Luce model, where the data consists of full rankings over alternatives. Our technique is based on breaking the full rankings into pairwise comparisons, and then computing parameters that satisfy a set of generalized moment conditions. We identify conditions for t...