Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Zn. We call this the maximum indirect...

Standard indirect Inference (II) estimators take a given finite-dimensional statistic, Z_{n} , and then estimate the parameters by matching the sample statistic with the model-implied population moment. We here propose a novel estimation method that utilizes all available information contained...

This is a guide that explains how to use software that implements the simulated nonparametric moments (SNM) estimator proposed by Creel and Kristensen (2009). The guide shows how results of that paper may easily be replicated, and explains how to install and use the software for estimation of...

This paper presents a cross validation method for selection of statistics for Approximate Bayesian Computing, and for related estimation methods such as the Method of Simulated Moments. The method uses simulated annealing to minimize the cross validation criterion over a combinatorial search...

Given a sample from a fully specified parametric model, let Z_<em>n</em> be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Z_<em>n</em>. We call this the maximum indirect...

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative...

Given a sample from a fully specified parametric model, let $Z_n$ be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of $Z_n$. We call this the maximum...

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative...

A graphical processing unit (GPU) is a hardware device normally used to manipulate computer memory for the display of images. GPU computing is the practice of using a GPU device for scientific or general purpose computations that are not necessarily related to the display of images. Many...