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Stochastic Expansions and Moment Approximations for Three Indirect Estimators

Demos, Antonis; Arvanitis, Stelios - Department of International and European Economic … - 2010

), we are concerned with valid moment approximations and employ them to characterize the second order bias structure of the …

Persistent link: https://www.econbiz.de/10008552086

Biases in CAPM Beta Estimation

Chen, Linda H. - 2018

In this paper, we show that conditions derived under the CAPM ensure only weak exogeneity in a linear regression setting. Since strong exogeneity is not guaranteed, the OLS estimator of CAPM beta is only consistent but not necessarily unbiased. We provide empirical evidence that individual daily...

Persistent link: https://www.econbiz.de/10012935615

A Penalty Function Approach to Bias Reduction in Non-Linear Panel Models with Fixed Effects

Bester, Alan; Hansen, Christian Bailey - 2005

nonlinearity of the problem. We present a penalty for the objective function that reduces the bias in the resulting point estimates … practice. The form of the penalty also provides interesting intuition into how the bias reduction is working. We present … simulation results that suggest that the penalized optimization approach may substantially reduce the bias in nonlinear fixed …

Persistent link: https://www.econbiz.de/10014027743

Robust Detection of Multivariate Outliers in Asset Returns and Risk Factors Data

Green, Christopher G - 2017

It is well-known that outliers exist in the type of multivariate data used by financial practitioners for portfolio construction and risk management. Typically, outliers are addressed prior to model fitting by applying some combination of trimming and/or Winsorization to each individual...

Persistent link: https://www.econbiz.de/10012946531

Robust Estimation of Some Nonregular Parameters

Song, Kyungchul - 2010

This paper develops optimal estimation of a potentially non-differentiable functional Γβ of a regular parameter β, when Γ satisfies certain conditions. Primary examples are min or max functionals that frequently appear in the analysis of partially identified models. This paper investigates...

Persistent link: https://www.econbiz.de/10013142864

Factor Profiling for Ultra High Dimensional Variable Selection

Wang, Hansheng - 2010

We propose here a novel method of factor profiling (FP) for ultra high dimensional variable selection. The new method assumes that the correlation structure of the high dimensional data can be well represented by a set of low-dimensional latent factors (Fan et al., 2008). The latent factors can...

Persistent link: https://www.econbiz.de/10013143110

On a Principal Varying Coefficient Model

Jiang, Qian - 2012

We propose a novel varying coefficient model, called principal varying coefficient model (PVCM), by characterizing the varying coefficients through linear combinations of a few principal functions. Compared with the conventional varying coefficient model (VCM; Chen and Tsay, 1993; Hastie and...

Persistent link: https://www.econbiz.de/10013099854

Understanding Limit Theorems for Semimartingales : A Short Survey

Podolskij, Mark - 2009

This paper presents a short survey on limit theorems for certain functionals of semimartingales, which are observed at high frequency. Our aim is to explain the main ideas of the theory to a broader audience. We introduce the concept of stable convergence, which is crucial for our purpose. We...

Persistent link: https://www.econbiz.de/10013155852

Building Blocks of Linear Time Series Modeling

Louangrath, Paul - 2013

Three models are presented: AR (autoregressive), MA (moving average) and ARMA (autoregressive moving average) are common models used in time series forecasting. These three models are the various definition of each element of the General Linear Model: Y = a + b + c. For the study of linear...

Persistent link: https://www.econbiz.de/10013076067

Heteroscedastic Analysis in Nonlinear Modeling

Louangrath, Paul - 2013

In order to discuss nonlinear, it is necessary to know linear regressive as a priori. Without simple regression as the starting point, it would be difficult to understand nonlinear regression. In words, in order to understand the curve and the behavior of a curve, it is necessary to known a...

Persistent link: https://www.econbiz.de/10013076070