Professor Dennis Kristensen

We establish nonparametric identification in a class of so-called index models using a novel approach that relies on general topological results.

We propose to combine smoothing, simulations and sieve approximations to solve for either the integrated or expected value function in a general class of dynamic discrete choice (DDC) models.

New nonparametric methods that identify and estimate counterfactuals for individuals, when each is characterized by a vector of unobserved characteristics, are developed and applied to estimate systems of individual consumer demand and welfare measures.

A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered/estimated volatility process replacing the latent process.

This paper derives sufficient conditions for nonparametric transformation models to be identified and develops estimators of the identified components.

The authors propose a two-step estimation method of stochastic volatility models. The estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise.

The authors 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 to standard likelihood-based versions since they rely on low-dimensional auxiliary statistics and so avoid computation of high-dimensional integrals

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Compu- tation which build likelihoods based on limited information.

This article investigates the asymptotic properties of the Gaussian quasi-maximum-likelihood estimators (QMLEs) of the GARCH model augmented by including an additional explanatory variable—the so-called GARCH-X model.

This paper develops a new approach to the estimation of consumer demand models with unobserved heterogeneity subject to revealed preference inequality restrictions.

Many modern estimation methods in econometrics approximate an objective function, for instance, through simulation or discretisation. These approximations typically affect both bias and variance of the resulting estimator.

This paper investigates the asymptotic properties of the Gaussian quasi-maximum-likelihood estimators (QMLE's) of the GARCH model augmented by including an additional explanatory variable- the so-called GARCH-X model.

We argue that nonadditive models are a key characteristic of simultaneous models of economic behavior with unobserved heterogeneity.

This paper was given as part of a session on 'Revealed Preferences: Modeling and Inference' as part of the Econometric Society North American Winter Meeting 2012.

This paper develops a new technique for the estimation of consumer demand models with unobserved heterogeneity subject to revealed preference inequality restrictions.

This paper studies a shape-invariant Engel curve system with endogenous total expenditure, in which the shape-invariant specification involves a common shift parameter for each demographic group in a pooled system of nonparametric Engel curves.

This paper concerns the identification and estimation of a shape-invariant Engel curve system with endogenous total expenditure.