Dennis Kristensen

Learn More
This paper studies a shape-invariant Engel curve system with endogenous total expenditure, in which the shape-invariant speci…cation involves a common shift parameter for each demographic group in a pooled system of nonparametric Engel curves. We focus on the identi…cation and estimation of both the nonparametric shapes of the Engel curves and the(More)
We develop a methodology for estimating time-varying alphas and factor loadings based on nonparametric techniques. We test whether conditional alphas and long-run alphas, which are averages of conditional alphas, are equal to zero and derive test statistics for the constancy of factor loadings. The tests can be performed for a single asset or jointly across(More)
1 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. We focus on the identification and estimation of both the nonparametric shapes of the En-gel curves and the(More)
We propose a simulated maximum likelihood estimator (SMLE) for general stochastic dynamic models based on nonparametric kernel methods. The method requires that, while the actual likelihood function cannot be written down, we can still simulate observations from the model. From the simulated observations, we estimate the unknown density of the model(More)
This paper concerns the identification and estimation of a shape-invariant Engel curve system with endogenous total expenditure. The shape-invariant specification involves a common shift parameter for each demographic group in a pooled system of Engel curves. Our focus is on the identification and estimation of both the nonparametric shape of the Engel(More)
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 likelihood (MIL) estimator. We also propose a computationally(More)
We propose a closed-form estimator for the linear GARCH(1,1) model. The estimator has the advantage over the often used quasi-maximum-likelihood estimator (QMLE) that it can be easily implemented, and does not require the use of any numerical optimisation procedures or the choice of initial values of the conditional variance process. We derive the(More)
Many modern estimation methods in econometrics approximate an objective function, through simulation or discretization for instance. Approximations typically impart additional bias and variance to the resulting estimator. We here propose three methods to improve the properties of such “approximate” estimators at a low computational cost. The first method(More)
Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel(More)