Fractional dependent variables and models with state dependence arise in many economic applications. However, estimating models with fractional dependent variables is complicated by the presence of two corner solution outcomes. When coupled with a dynamic panel data setting, estimating...

This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov Chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the...

Range-based volatility estimators are analyzed in both daily and intraday sampling frequency and are also compared to the realized volatility estimator. The family of realized range-based estimators is extended as three range-based estimators are introduced. These three realized Parkinson...

We characterize the asymptotic bias that arises in ordinary least squares regression when control variables have nonlinear effects on an outcome variable, but are assumed to enter the regression equation linearly. We show that this bias can be larger than that from omitting such variables...

At its core, portfolio and risk management is about gathering and processing market-related data in order to make effective investment decisions. To this end, risk and return statistics are estimated from relevant financial data and used as inputs within the investment process. It is this...

The correlation in time series has received considerable attention in literature. Its use has attained an important role in the social sciences and finance. For example, pair trading in finance is concerned with the correlation between stock prices, returns, etc. In general, Pearson's...

This paper develops a wavelet (spectral) approach to estimate the parameters of a linear regression model where the regressand and the regressors are persistent processes and contain a measurement error. We propose a wavelet filtering approach which does not require instruments and yields...

Classical regression analysis uses partial coefficients to measure the influences of some variables (regressors) on another variable (regressand). However, a descriptive point of view shows that these coefficients are very bad measures of influence. Their interpretation as an average change of...