Double - length regressions for the Box – Cox difference model with heteroskedasticity or autocorrelation
نویسنده
چکیده
This paper derives Lagrange multiplier tests based on artificial double length regressions (DLR) to jointly test for differenced linear or loglinear models with no heteroskedasticity or autocorrelation against a more general differenced Box–Cox model with heteroskedasticity or autocorrelation. These tests are easy to implement and are illustrated using an empirical example. 2000 Elsevier Science S.A. All rights reserved.
منابع مشابه
The Varying Risk Market Model: A Reexamination Based On Heteroskedastic Conditions and Other Statistical Robustness Tests
Bhardwaj and Brooks (1993) estimates of a varying risk market model indicate that betas of market value ranked stock portfolios are larger in bull market months for small market value portfolios and smaller for large market value portfolios. This paper investigates the statistical robustness of these and other Bhardwaj and Brooks findings by examining multicollinearity, autocorrelation, and het...
متن کاملTopological Relationship Between One-Dimensional Box Model and Randić Indices in Linear Simple Conjugated Polyenes
The alternative double bonds and conjugation in the polyene compounds are one of the main properties in these compounds. Each carbon-carbon bonds in a polyene compound along the chain has appreciable double-bond character. The p-electrons are therefore not localized but are relatively free to move throughout the entire carbon skeleton as an one-dimensional box. The skeleton be considered as a r...
متن کاملSpatial analysis of annual precipitation of Khuzestan province; An approach of spatial regressions analysis
Knowing of precipitation values in different regions is always of main and strategic issues of human which has important role in short- term and long-term decisions. In order to determine of precipitation model and forecasting it, there are different models, but given that the precipitation data have a spatial autocorrelation, the spatial statistic is a powerful tool to recognition of spatial b...
متن کاملHeteroskedasticity as a leading indicator of desertification in spatially explicit data
Regime shifts are abrupt transitions between alternate ecosystem states including desertification in arid regions due to drought or overgrazing. Regime shifts may be preceded by statistical anomalies such as increased autocorrelation, indicating declining resilience and warning of an impending shift. Tests for conditional heteroskedasticity, a type of clustered variance, have proven powerful le...
متن کاملSpatial Stochastic Volatility for Lattice Data
Spatial heteroskedasticity may arise jointly with spatial autocorrelation in lattice data collected from agricultural trials and environmental studies. This leads to spatial clustering not only in the level but also in the variation of the data, the latter of which may be very important, for example, in constructing prediction intervals. This article introduces a spatial stochastic volatility (...
متن کامل