4,827 research outputs found
A selective overview of nonparametric methods in financial econometrics
This paper gives a brief overview on the nonparametric techniques that are
useful for financial econometric problems. The problems include estimation and
inferences of instantaneous returns and volatility functions of
time-homogeneous and time-dependent diffusion processes, and estimation of
transition densities and state price densities. We first briefly describe the
problems and then outline main techniques and main results. Some useful
probabilistic aspects of diffusion processes are also briefly summarized to
facilitate our presentation and applications.Comment: 32 pages include 7 figure
Fixed Effect Estimation of Large T Panel Data Models
This article reviews recent advances in fixed effect estimation of panel data
models for long panels, where the number of time periods is relatively large.
We focus on semiparametric models with unobserved individual and time effects,
where the distribution of the outcome variable conditional on covariates and
unobserved effects is specified parametrically, while the distribution of the
unobserved effects is left unrestricted. Compared to existing reviews on long
panels (Arellano and Hahn 2007; a section in Arellano and Bonhomme 2011) we
discuss models with both individual and time effects, split-panel Jackknife
bias corrections, unbalanced panels, distribution and quantile effects, and
other extensions. Understanding and correcting the incidental parameter bias
caused by the estimation of many fixed effects is our main focus, and the
unifying theme is that the order of this bias is given by the simple formula
p/n for all models discussed, with p the number of estimated parameters and n
the total sample size.Comment: 40 pages, 1 tabl
Analysis of error propagation in particle filters with approximation
This paper examines the impact of approximation steps that become necessary
when particle filters are implemented on resource-constrained platforms. We
consider particle filters that perform intermittent approximation, either by
subsampling the particles or by generating a parametric approximation. For such
algorithms, we derive time-uniform bounds on the weak-sense error and
present associated exponential inequalities. We motivate the theoretical
analysis by considering the leader node particle filter and present numerical
experiments exploring its performance and the relationship to the error bounds.Comment: Published in at http://dx.doi.org/10.1214/11-AAP760 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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