"Semiparametric
Estimation of A Nonlinear Error-Correction Model" (with A. Rahbek)
ABSTRACT: We consider a nonlinear cointegration model where the transfer function (or loadings)
of the stationary relationships is unknown and potentially nonlinear. We
propose estimators of the parameters for the linear stationary relationships
and the nonparametric transfer function, and derive their asymptotic
properties.
"Estimation of Stochastic
Volatility Models By Nonparametric Filtering" (with S. Kanaya)
ABSTRACT: A new estimation method of
stochastic volatility models is proposed based on the nonparametric filter of
the instantaneous volatility process of Kristensen
(2006). We propose to use standard estimation methods for fully observed
diffusion processes but with the filtered volatility process replacing the
latent process. The estimator will carry a bias due to the use of the filtered
volatility instead of the actual volatility, but under regularity conditons this vanishes asymptotically and our estimators
inherit the asymptotic properties of the infeasible estimators based on
observations of the volatility process. The estimation strategy is applicable
both for parametric and nonparametric stochastic volatility models, and we give
theoretical results for both. A simulation study examines the estimators finite-sample properties.
"Testing Conditional Factor
Models" (with Andrew Ang)
ABSTRACT: In conditional factor models with
time-varying factor loadings, testing whether an alpha is significantly
different from zero, either unconditionally or at a point in time, depends
crucially on the time-series variation of betas. We estimate time-varying alphas
and betas using nonparametric techniques that allow us to estimate the alpha's
and beta's as functions at any given point of time. Using these estimators, we
derive a methodology that can test whether conditional alphas averaged over the
sample are different from zero and for constancy of the factor loadings.
"Higher Order Improvements for
Approximate Estimators" (with Bernard Salanié)
ABSTRACT: We propose two methods to improve on
the precision of approximate estimators. Both of these methods only carry a
small additional computation burden. The first method is targeted at estimators
based on stochastic approximators, such as
simulation-based estimators. It consists of a general formula that corrects the
objective function and eliminates the leading order of the additional bias of
the approximate estimator. Our second proposed improvement is a two-step method
which applies quite generally. In the first step, we compute the approximate
estimator, using an approximator that may be coarser
than what is usually done; and in the second step we run one or several Newton-Raphson iterations based on the same objective function,
but with a much finer degree of approximation. The second step removes some or
all of the additional bias and variance of the initial approximate estimator.
"Estimation of Non-additive
Engle Curves under Revealed Preference Restrictions" (with R. Blundell and
R.
Matzkin)
ABSTRACT: In the econometric analysis of Engel
curves it is standard to assume an additive relationship between the
expenditure share, the total expenditure and the error term. In this paper, we
relax this assumption and instead consider non-separable relations between the
three variables. Furthermore, we impose revealed preferences restrictions in
the estimation. A nonparametric sieve estimator is developed, and its
asymptotic properties derived. We apply our estimator the UK Family Expenditure
Survey data.
"Estimation of Hidden Markov
Models with Nonparametric Simulated Maximum Likelihood" (with Y. Shin)
ABSTRACT: We extend the nonparametric simulated
maximum-likelihood estimator (NPSMLE) of Kristensen
and Shin (2006) to include built-in nonlinear filtering. By recursively
approximating the unknown conditional densities, our method enables calculation
of simulated version of the MLE of general dynamic models with latent
variables---including time-inhomogeneous and non-stationary processes. We
establish the asymptotic properties of the NPSMLE’s
for hidden Markov models, and then demonstrate the usefulness of our proposed
method with