Search results for "Heteroscedasticity"

Permutation Tests in Linear Regression

2015

Exact permutation tests are available only in rather simple linear models. The problem is that, although standard assumptions allow permuting the errors of the model, we cannot permute them in practice, because they are unobservable. Nevertheless, the residuals of the model can be permuted. A proof is given here which shows that it is possible to approximate the unobservable permutation distribution where the true errors are permuted by permuting the residuals. It is shown that approximation holds asymptotically and almost surely for certain quadratic statistics as well as for statistics which are expressible as the maximum of appropriate linear functions. The result is applied to testing t…

Volatility co-movements: a time scale decomposition analysis

2014

In this paper we are interested in detecting contagion from US to European stock market volatilities in the period immediately after the Lehman Brothers’ collapse. The analysis, based on a factor decomposition of the covariance matrix of implied and realized volatilities, is carried for different sub-samples (identified as normal and crisis periods) and across different (high) frequency bands. In particular, the analysis is split in two stages. In the first stage, we retrieve the time series of wavelet coefficients for each volatility series for high frequency scales, using the Maximal Overlapping Discrete Wavelet transform and, in a second stage, we apply Maximum Likelihood for a factor de…

Conditionally heteroscedastic intensity-dependent marking of log Gaussian Cox processes

2009

Spatial marked point processes are models for systems of points which are randomly distributed in space and provided with measured quantities called marks. This study deals with marking, that is methods of constructing marked point processes from unmarked ones. The focus is density-dependent marking where the local point intensity affects the mark distribution. This study develops new markings for log Gaussian Cox processes. In these markings, both the mean and variance of the mark distribution depend on the local intensity. The mean, variance and mark correlation properties are presented for the new markings, and a Bayesian estimation procedure is suggested for statistical inference. The p…

Olley–Pakes productivity decomposition: computation and inference

2016

Summary We show how a moment-based estimation procedure can be used to compute point estimates and standard errors for the two components of the widely used Olley–Pakes decomposition of aggregate (weighted average) productivity. When applied to business level microdata, the procedure allows for autocovariance and heteroscedasticity robust inference and hypothesis testing about, for example, the coevolution of the productivity components in different groups of firms. We provide an application to Finnish firm level data and find that formal statistical inference casts doubt on the conclusions that one might draw on the basis of a visual inspection of the components of the decomposition.

Change-points detection for variance piecewise constant models

2011

A new approach based on the fit of a generalized linear regression model is introduced for detecting change-points in the variance of heteroscedastic Gaussian variables, with piecewise constant variance function. This approach overcome some limitations of both exact and approximate well-known methods that are based on successive application of search and tend to overestimate the real number of changes in the variance of the series. The proposed method just requires the computation of a gamma GLM with log-link, resulting in a very efficient algorithm even with large sample size and many change points to be estimated.

Robust estimation and inference for bivariate line-fitting in allometry.

2011

In allometry, bivariate techniques related to principal component analysis are often used in place of linear regression, and primary interest is in making inferences about the slope. We demonstrate that the current inferential methods are not robust to bivariate contamination, and consider four robust alternatives to the current methods -- a novel sandwich estimator approach, using robust covariance matrices derived via an influence function approach, Huber's M-estimator and the fast-and-robust bootstrap. Simulations demonstrate that Huber's M-estimators are highly efficient and robust against bivariate contamination, and when combined with the fast-and-robust bootstrap, we can make accurat…

On the convenience of heteroscedasticity in highly multivariate disease mapping

2019

Highly multivariate disease mapping has recently been proposed as an enhancement of traditional multivariate studies, making it possible to perform the joint analysis of a large number of diseases. This line of research has an important potential since it integrates the information of many diseases into a single model yielding richer and more accurate risk maps. In this paper we show how some of the proposals already put forward in this area display some particular problems when applied to small regions of study. Specifically, the homoscedasticity of these proposals may produce evident misfits and distorted risk maps. In this paper we propose two new models to deal with the variance-adaptiv…

2019

In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In this article, we study an ICA method which combines the use of linear and quadratic autocorrelations to enable efficient estimation of various kinds of stationary time series. Statistical properties of the estimator are studied by finding its limiting distribution under general conditions, and the asymptotic variances are derived in the case of ARMA-GARCH model. We use the asymptotic results and a finite sample simulation study to compare different choices of a weight coef…

Hétérogénéité spatiale : principes et méthodes

2004

Spatial Heterogeneity : Principles and Methods This article has a dual purpose . First , it describes the main econometric specifications which can be used to represent spatial heterogeneity , reflected in an instability of parameters in space and / or a heteroscedasticity of error terms . Only the specifications valid in cross-section are examined . Second , it explains the links between spatial heterogeneity and autocorrelation , the other major feature of localised data , defined by the absence of independence between geographical observations . In particular , we look at the extent to which traditional tests of heteroscedasticity or instability need to be amended to take account of spat…