A method for estimating the parameters of stochastic differential equations (SDEs) by simulated maximum likelihood is presented. This method is feasible whenever the underlying SDE is a Markov process. Estimates are compared to those generated by indirect inference, discrete and exact maximum...

It is well-known that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size (T) and large cross section sample size (N) asymptotics. The estimation bias is particularly relevant in...

We find ourselves very much in agreement with the thrust of HL’s message concerning the complexity induced by microstructure noise. In particular, we agree that noise is time dependent and correlated with the efficient price - features that in our view are a necessary consequence of the...

Our comments on Fan’s paper will concentrate on two issues that relate in important ways to the paper’s focus on misspecification and discretization bias and the role of nonparametric methods in empirical finance.

This paper proposes a Gaussian estimator for nonlinear continuous time models of the short-term interest rate. The approach is based on a stopping time argument that produces a normalizing transformation facilitating the use of a Gaussian likelihood. A Monte Carlo study shows that the...

A recursive test procedure is suggested that provides a mechanism for testing explosive behavior, date-stamping the origination and collapse of economic exuberance, and providing valid confidence intervals for explosive growth rates. The method involves the recursive implementation of a...

It is well-known that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with .xed eects is inconsistent under .xed time series sample size (T) and large cross section sample size (N) asymptotics. The estimation bias is particularly relevant in...

With the availability of ultra high frequency financial data, the task of finding an appropriate econometric model to describe the movement of financial variables at the tick-by-tick level has become an important goal in financial econometric research. The task has both theoretical and empirical...

In recent years there has been increased interest in using nonparametric methods to deal with various aspects of financial data. The paper by Fan overviews some nonparametric techniques that have been used in the financial econometric literature, focusing on estimation and inference for...

A recursive regression methodology is used to analyze the bubble characteristics of various financial time series during the subprime crisis. The methods provide a technology for identifying bubble behavior and consistent dating of their origination and collapse. Seven relevant financial series...