We introduce a general formulation for dimension reduction and coefficient estimation in the multivariate linear model. We argue that many of the existing methods that are commonly used in practice can be formulated in this framework and have various restrictions. We continue to propose a new method that is more flexible and more generally applicable. The method proposed can be formulated as a ...

Understanding and quantifying dependence is at the core of all modelling efforts in financial econometrics. The linear correlation coefficient, which is the far most used measure to test dependence in the financial community and also elsewhere, is only a measure of linear dependence. This means that it is a meaningful measure of dependence if asset returns are well represented by an elliptical ...

Regularization and Bayesian methods for system identification have been repopularized in the recent years, and proved to be competitive w.r.t. classical parametric approaches. In this paper we shall make an attempt to illustrate how the use of regularization in system identification has evolved over the years, starting from the early contributions both in the Automatic Control as well as Econom...

Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be esti...

one of the most important goals for researchers in the clinic is to try to find newer and more effective ways to diagnose and cure the diseases. these clinical advances can create new points of view in other sciences and their combination with basic sciences such as statistics can improve these researches. in statistical genetics, linkage analysis is a way of finding the exact locus of a diseas...

In this chapter we discuss conceptually high dimensional sparse econometric models as well as estimation of these models using l1-penalization and postl1-penalization methods. Focusing on linear and nonparametric regression frameworks, we discuss various econometric examples, present basic theoretical results, and illustrate the concepts and methods with Monte Carlo simulations and an empirical...

Long memory analysis is one of the most active areas in econometrics and time series where various methods have been introduced to identify estimate long parameter partially integrated series. One common models used represent that a ARFIMA (Auto Regressive Fractional Integration Moving Average Model) which diffs are fractional number called parameter. To analyze determine model, fractal must be...

The article is aimed at reconsidering the question if project of econometrics can be read in line with scientific realism. Previously, methodological literature focused on philosophy econometrics, voices criticizing realist interpretations were raised. criticism was showing that econometric models lack robustness. use slightly different methods leads to obtaining and often contrary what suppose...