«HETEROSCEDASTICITY»に関連する英語の本
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1
Regression Analysis: Modeling & Forecasting
Thus, it is important to know what causes heteroscedasticity; what kinds of
problems it creates; how to test for it; and, if it is present, how to handle it.
CAUSES OF HETEROSCEDASTICITY There are many causes of
heteroscedasticity.
George C. S. Wang, Chaman L. Jain, 2003
2
Introductory Econometrics for Finance
Box 4.2 'Solutions' for heteroscedasticity (1) Transforming the variables into logs
or reducing by some other measure of 'size'. This has the effect of re-scaling the
data to 'pull in' extreme observations. The regression would then be conducted ...
3
Econometrics: A Practical Approach
The failure of this assumption is called heteroscedasticity. In practice, hetero-
scedasticity could occur due to one, or combinations of, the following factors: (a)
In cross-sectional work, it is often the case that the values of the independent ...
Hamid Seddighi, K. A. Lawler, Anastasios V. Katos, 2000
4
Longitudinal and Panel Data: Analysis and Applications in ...
2.4.5 Heteroscedasticity When lilting regression models to data, an important
assumption is that the variability is common among all observations. This
assumption of common variability is called homoscedasticity, meaning "same
scatter.
5
Econometrics and Data Analysis for Developing Countries
possible to perform the t-tests and F-tests under heteroscedasticity. Thus, in order
to make reliable inferences from the heteroscedastic data on the basis of the
linear regression model, we seek to eliminate heteroscedasticity by means of a ...
Chandan Mukherjee, Howard White, Marc Wuyts, 2013
6
Essays in Financial Econometrics: GMM and Conditional ...
Mike Aguilar. such that the ARCH LM test can detect the presence of conditional
heteroscedasticity within the factor. 4) The loading vector Λ must be chosen such
that the ARCH LM test will also be able to detect the presence of conditional ...
7
Linear Regression Analysis: Theory and Computing
6.1 Test Heteroscedasticity In classical regression, the equal variance
assumption simply states that, given X, the conditional variance of the error terms
are constant for all observations. In other words, whenever X varies, the
corresponding ...
8
Principles of Forecasting: A Handbook for Researchers and ...
With time-series data, heteroscedasticity can be viewed as dynamic and can be
modeled as a function of time. Financial analysts have devoted much study to
changes in variance, or volatility, in stock prices and in series, such as the daily ...
9
Regression Analysis: Theory, Methods, and Applications
Violation of this condition is often called heteroscedasticity, while compliance is
referred to as homoscedasticity. Recall that heteroscedasticity does not bias the
least squares estimates of /?j's, but it causes variances of parameter estimates to
...
Ashish Sen, M. S. Srivastava, 1990
10
ANOVA and MANOVA Under
Heteroscedasticity
CHAPTER 3 A PARAMETRIC BOOTSTRAP SOLUTION TO MANOVA UNDER
HETEROSCEDASTICITY 3.1. Introduction In this chapter, we consider the
problem of comparing several multivariate normal mean vectors when the
covariance ...
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Aggregate blood pressure responses to serial dietary sodium and …
Age effects in the residual variance (heteroscedasticity) were also examined. A given analysis phenotype was the standardized residual from ... «BMC Blogs Network, 6月 15»
SGLT-2 Inhibitor/GLP-1 Agonist Doubles Weight Loss in Diabetes
But there was some heteroscedasticity, whereby a section of patients lost a lot of weight, Dr McGovern explained. Among these responders ... «Medscape, 4月 15»
A Regression Analysis Of Wheat And Oil Prices
The regression model tests negative for serial correlation and heteroscedasticity. However, our regression shows a very low R-Squared, ... «Seeking Alpha, 3月 15»
Why the Time Is Now to Predict (Loudly) a Democratic Victory
Then throw in a few obscure-sounding terms like “heteroscedasticity,” “anti-fragility” or “photosynthesis,” because people tend to really trust ... «New York Times, 11月 14»
Why lobsters turn red when cooked
... unforgiving industry – it's a business where one is expected to deal with terms like interpolation, heteroscedasticity, tolerance invariance, etc. «The Star Online, 10月 14»
Nobel Laureate Robert Engle on VaR, Systemic Risk, and Liquidity
... found for ARCH (autoregressive conditional heteroscedasticity) models and related research. Engle is a professor at the NYU Stern School of ... «CFA Institute Enterprising Investor, 7月 14»
Association of Electronic Health Records With Cost Savings in a …
... logtransformed dependent variables do not have heavy tails or excessive heteroscedasticity such as was found to be true in these data.19. «AJMC.com Managed Markets Network, 6月 14»
Latest Issue of The Journal of Risk: Volume 16/Number 4, 2014
The generalized autoregressive conditional heteroscedasticity (GARCH) model has become one of the most popular specification tools for ... «Risk.net, 4月 14»
Latest Issue of The Journal of Energy Markets: Volume 7/Number 1 …
Particular attention is paid to studying the impacts of the conditional heteroscedasticity and the conditional nonnormality of the GARCH ... «Risk.net, 3月 14»
A review of CPD's critique of the economy in FY 2014
... this regression model the auto correlation (as using lagged variable and time series) and heteroscedasticity etc. have been addressed or not. «Financial Express Bangladesh, 11月 13»
