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Topic: Heteroskedasticity

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Autoregressive conditional heteroskedasticity

	Heteroskedasticity - Wikipédia
		Heteroskedasticity (aka skewedness, lawan: homoskedasticity) ngalawan asumsi ieu.
		Heteroskedasticity is often studied as part of econometrics, which frequently deals with data exhibiting it.
		Heteroskedasticity often occurs when there is a large difference between the size of observations.
	su.wikipedia.org /wiki/Heteroskedasticity (301 words)

	Heteroskedasticity - Wikipedia, the free encyclopedia
		In statistics, a sequence or a vector of random variables is heteroskedastic if the random variables in the sequence or vector may have different variances.
		When OLS is applied to heteroskedastic models the estimated variance is a biased estimator of the true variance.
		That is, it either overestimates or underestimates the true variance, and, in general it is not possible to determine the nature of the bias.
	en.wikipedia.org /wiki/Heteroscedasticity (481 words)

	Autoregressive conditional heteroskedasticity - Wikipedia, the free encyclopedia
		In econometrics, an autoregressive conditional heteroskedasticity (ARCH, Engle (1982)) model considers the variance of the current error term to be a function of the variances of the previous time period's error terms.
		IGARCH or Integrated Generalized Autoregressive Conditional Heteroskedasticity is a restricted version of the GARCH model, where the sum of the persistent parameters sum up to one.
		Generally, when testing for heteroskedasticity in econometric models, the best test is the White test.
	en.wikipedia.org /wiki/Autoregressive_conditional_heteroskedasticity (274 words)

	Heteroskedasticity (Site not responding. Last check: 2007-10-12)
		That is, the proof that the OLS estimator is unbiased does not use the heteroskedasticity assumption.
		The observed heteroskedasticity in the residuals may be an indication of model misspecification such as incorrect functional form.
		This example uses the Griffiths, Hill and Judge data set on household expenditure that was analyzed in the section on testing for heteroskedasticity.
	shazam.econ.ubc.ca /intro/glshet.htm (473 words)

	Testing for Heteroskedasticity (Site not responding. Last check: 2007-10-12)
		Heteroskedasticity can arise in a variety of ways and a number of tests have been proposed.
		A 'c' is often used instead of a 'k' in the spelling of heteroskedasticity.
		Heteroskedasticity has been found to be a feature of cross-section studies on household expenditure.
	shazam.econ.ubc.ca /intro/testhet.htm (524 words)

	Homoskedasticity and Heteroskedasticity
		Heteroskedasticity is an important concept in finance because asset returns in the capital, commodity and energy markets often exhibit heteroskedasticity.
		For example, stock or bond returns tend to be conditionally heteroskedastic.
		If a process is not unconditionally heteroskedastic or not conditionally heteroskedastic, it is said to be unconditionally homoskedastic or conditionally homoskedastic, respectively.
	www.riskglossary.com /articles/heteroskedasticity.htm (326 words)

	[No title]
		Your answer: True or False Heteroskedasticity biases the ordinary least squares estimators of the slope and intercept parameters.
		Your answer: True or False Heteroskedastic errors means that estimated of the model parameters are biased means that the variance of the error terms differs across observation means that the model is correctly specified means that errors across observations are not independent.
		Heteroskedasticity may be a problem if the plots Your answer: appear completely random.
	userpages.umbc.edu /~tgindlin/q14.doc (176 words)

	Introductory Econometrics Chapter 19: Heteroskedasticity
		If the form of the heteroskedasticity is known, it can be corrected (via appropriate transformation of the data) and the resulting estimator, generalized least squares (GLS), can be shown to be BLUE.
		Although heteroskedasticity can sometimes be identified by eye, Section 19.4 presents a formal hypothesis test to detect heteroskedasticity.
		Section 19.6 discusses a more aggressive method for dealing with heteroskedasticity comparable to the approaches commonly employed in dealing with autocorrelation in which data transformation is applied to obtain the best linear unbiased estimator.
	www.wabash.edu /econometrics/EconometricsBook/chap19.htm (452 words)

	Logit - Test for Heteroskedasticity (Site not responding. Last check: 2007-10-12)
		It is assumed that the heteroskedasiticity is a function of variables Z. The Z variables are typically chosen from the X variables that are included in the logit or probit model.
		The estimation results from a logit or probit model are used to construct an artificial regression designed to test for heteroskedasticity.
		The second test for heteroskedasticity considered the possibility of a different error variance for school teachers and individuals in occupations other than school teaching.
	shazam.econ.ubc.ca /intro/logit3.htm (637 words)

	Neural Tests for Conditional Heteroskedasticity in ARCH-M Models
		This paper deals with tests for detecting conditional heteroskedasticity in ARCH-M models using three kinds of methods: neural networks techniques, bootstrap methods and both combined.
		Lastly, to examine the size and the power properties of the tests in small samples, Monte Carlo simulations are carried out with various standard and non-standard models for conditional heteroskedasticity as to illustrate a variety of situations.
		In addition, the graphical presentation of Davidson and MacKinnon (1998a) is used to show the "true" power of the tests and not only the (nominal) power, as it is often the case, that can be meaningless.
	www.bepress.com /snde/vol8/iss3/art3 (296 words)

	[Gretl-users] Heteroskedasticity test (Site not responding. Last check: 2007-10-12)
		> Then, I suspected that the residuals were heteroskedastic yet, so > I tried to do some testing on them.
		I would day that in this case (regression on only a constant), gretl should make the heteroskedasticity test menu item unavailable.
		I tend to think it is the researcher's responsibility to save the residuals (which are just de-meaned y) and then regress them on whatever variable might be relevant.
	ricardo.ecn.wfu.edu /pipermail/gretl-users/2004-October/000058.html (331 words)

	Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
		This paper is concerned with the estimation of covariance matrices in the presence of heteroskedasticity and autocorrelation of unknown forms.
		Currently available estimators that are designed for this context depend upon the choice of a lag truncation parameter and a weighting scheme.
		"Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation, Yale University, revised Jul 1989.
	ideas.repec.org /a/ecm/emetrp/v59y1991i3p817-58.html (284 words)

	FAQ: Testing for panel-level heteroskedasticity and autocorrelation
		I see how one can correct for potential heteroskedasticity across panels using xtgls, but I am unsure of a simple way to test for it.
		Normally, lrtest infers the number of constraints when we fit nested models by looking at the number of parameters estimated.
		Iterated GLS with autocorrelation does not produce the maximum likehood estimates, so we cannot use the likelihood-ratio test procedure, as with heteroskedasticity.
	www.stata.com /support/faqs/stat/panel.html (303 words)

	ABSTRACT : Seasonal heteroskedasticity in Census Bureau construction series
		Seasonal heteroskedasticity exists in a number of monthly time series from major statistical agencies.
		A statistical test for seasonal heteroskedasticity is presented and applied to a number of Census Bureau series on housing starts and building permits.
		It is shown how seasonal noise can be separated from nonsystematic noise and included in the seasonal adjustment of a time series.
	www.census.gov /srd/www/jsm2005tmt_abs.html (226 words)

	[No title]
		ü Apö°ûð Ðýÿ@?ü¬rªöøV 3ûÿÿ,ÿÿÿ¥ÿÿdd The Nature of Heteroskedasticity , ÿ•ÿÿdd2ªöÍû¶ ³ ÿ•ÿÿdd"Heteroskedasticity is a systematic pattern in the errors where the variances of the errors are not constant.
		Decide which variable is proportional to the heteroskedasticity (xt in previous example). 2.
		Divide all terms in the original model by the square root of that variable (divide by xt). 3.
	www.bilkent.edu.tr /~alii/econ302/Chap10.ppt (908 words)

	SSRN-A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix by Whitney ...
		This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction.
		It also establishes consistency of the estimated covariance matrix under fairly general conditions.
		Newey, Whitney K. and West, Kenneth D., "A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix" (April 1986).
	papers.ssrn.com /sol3/papers.cfm?abstract_id=225071 (377 words)

	Amazon.com: The economics of conditional heteroskedasticity: evidence from Canadian and U.S. stock and futures markets. ... (Site not responding. Last check: 2007-10-12)
		Amazon.com: The economics of conditional heteroskedasticity: evidence from Canadian and U.S. stock and futures markets.
		From the author: This paper provides insight into the sources of time variation and persistence in volatility by presenting new evidence concerning the price behavior of three index futures contracts and associated stock price indexes (the New York Stock Exchange Composite index, Standard and Poor's 500 index, and Toronto 35 index).
		Although persistence in the second moments of stock returns distribution is widely documented, the economic explanation for generalized autoregressive conditional heteroskedasticity is not established.
	www.amazon.com /exec/obidos/tg/detail/-/B00097UFEA?v=glance (377 words)

	Examining and Correcting for Heteroskedasticity using Stata (Site not responding. Last check: 2007-10-12)
		Using the state crime data regress the crime rate on the employment and urbanization rates.
		Note that heteroskedasticity is not apparent based on anything displayed in the analysis.
		Testing for heteroskedasticity in Stata is quite easy.
	www.polsci.wvu.edu /duval/ps602/Notes/STATA/heteroskedasticity.htm (112 words)

	SSRN-Long-Run Performance Evaluation: Correlation and Heteroskedasticity-Consistent Tests by Narasimhan Jegadeesh, ...
		Specifically, industry clustering or overlapping returns in the sample contribute to test misspecification.
		We propose a new test of long-run performance that uses the average long-run abnormal return for each monthly cohort of event firms, but weights these average abnormal returns in a way that allows for heteroskedasticity and autocorrelation.
		Our tests work well in random samples and in samples with industry clustering and with overlapping returns, without a reduction in power compared to the methodologies of Lyon, Barber and Tsai (1999).
	papers.ssrn.com /sol3/papers.cfm?abstract_id=532503 (260 words)

	Campbell R. Harvey: Supplementary Research Results
		Response of economic variables to liberalizations: 60-months before/after, heteroskedasticity and AR1 correction
		Response of economic variables to liberalizations: 60-months before/after, heteroskedasticity and AR1 correction, fixed effects
		Response of economic variables to liberalizations: 36-months before/after, heteroskedasticity and AR1 correction, fixed effects
	www.duke.edu /~charvey/Research/indexr.htm (498 words)

	Economics 421: LM Tests for Heteroskedasticity (Site not responding. Last check: 2007-10-12)
		There seems to be confusion over the LM tests for Heteroskedasticity.
		Listed below are links to weblogs that reference LM Tests for Heteroskedasticity:
		I just got confused about the "p" on the subscript of the alphas and Zs used for the LM tests.
	economistsview.typepad.com /economics421/2006/02/lm_tests_for_he.html (221 words)

	[No title]
		Bad: Don’t know about “power” of test relative some test with a more specific Ho Good: news is that (asymptotically) it fixes any heteroskedasticity problem Heteroskedasticity Correction: GLS Estimation See pp.
		P-R 148-51 GLS is a new estimator that is more efficient than OLS when errors are heteroskedastic.
		Q: How did we derive OLS estimator A: Minimize the ESS WLS estimator is derived (almost) the same way.
	www.ag.auburn.edu /agec/courses/agec0659/hetero.doc (727 words)

	WHITETST: Stata module to perform White's test for heteroskedasticity
		whitetst computes White's test for heteroskedasticity following regress or cnsreg.
		This test is a special case of the Breusch-Pagan test (q.v.
		Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using
	ideas.repec.org /c/boc/bocode/s390601.html (243 words)

	S-WoPEc: Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations
		S-WoPEc: Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations
		Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations
		Abstract: In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure.
	swopec.hhs.se /hastef/abs/hastef0577.htm (273 words)

	Glossary of research economics
		Breusch-Pagan statistic: A diagnostic test of a regression.
		It is a statistic for testing whether dependent variable y is heteroskedastic as a function of regressors X. If it is, that suggests use of GLS or SUR estimation in place of OLS.
		Large values of test statistic reject the hypothesis that y is homoskedastic in X. The meaning of 'large' varies with the number of variables in X. Quoting almost directly from the Stata manual: The Breusch and Pagan (1980) chi-squared statistic -- a Lagrange multiplier statistic -- is given by
	www.econterms.com /econtent.html (14590 words)

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