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Topic: Heteroscedasticity

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Heteroskedasticity

Homoskedasticity

Autoregressive conditional heteroskedasticity

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	SAS/ETS Examples -- A Simple Regression Model with Correction of Heteroscedasticity
		One of the classical assumptions of the ordinary regression model is that the disturbance variance is constant, or homogeneous, across observations.
		If this assumption is violated, the errors are said to be "heteroscedastic." Heteroscedasticity often arises in the analysis of cross-sectional data.
		If heteroscedasticity is present and a regression of spending on per capita income by state and its square is computed, the parameter estimates are still consistent but they are no longer efficient.
	support.sas.com /rnd/app/examples/ets/hetero/index.htm (877 words)

	FRONTIER ESTIMATION AND FIRM-SPECIFIC INEFFICIENCY MEASURES IN THE PRESENCE OF HETEROSCEDASTICITY Magazine: Journal of ... (Site not responding. Last check: 2007-10-20)
		Heteroscedasticity is a specification error often associated with the estimation of cost functions, and the presence of heteroscedasticity is likely to affect these inefficiency measures.
		To allow for heteroscedasticity in the frontier estimation, the model is parameterized in terms of beta, sigma[sub v], and sigma[sub w].
		The heteroscedastic frontier parameter estimates are about evenly split between those that are larger than the OLS or regular frontier parameter estimates and those that are smaller.
	mgv.mim.edu.my /Articles/00363/960110.Htm (3648 words)

	HIA98 - Abstracts (Site not responding. Last check: 2007-10-20)
		We know that tests for heteroscedasticity are important because, in the presence of heteroscedasticity of error variances, least squares method gives inefficient parameter estimates and biased variance estimates.
		The performance of this test for conditional heteroscedasticity is compared with three other tests; one of them is a variant of the Kamstra test (Kamstra (1993)); this last test is a neural network test for heteroscedasticity but with a different test statistics.
		In a conditional heteroscedastic context, we are going to include past squared disturbance terms as explanatory variables (or input variables) in the network to capture dynamics.
	www.econ.unian.it /ospiti/siec/HIA98/papers/Abs10.htm (663 words)

	Heteroscedasticity (Site not responding. Last check: 2007-10-20)
		In this case, the classic correction for heteroscedasticity is the HC0 estimator proposed by Huber (1967) and White (1980).
		There are a number of tests for heteroscedasticity, so it seems natural to conduct a test, then use a correction if the test suggest heteroscedasticity.
		The trouble with this is that the tests often fail to detect heteroscedasticity, leading you to neglect the correction when it is actually needed.
	www.sociology.ohio-state.edu /people/ptv/faq/heteroscedasticity.htm (616 words)

	GAUSS Programming for Econometricians: Chapter VI (Site not responding. Last check: 2007-10-20)
		Heteroscedasticity is a common problem with cross-sectional data, in which unequal model variance is observed.
		The consequence of ordinary least squares estimation with heterogeneously distributed error structure is inefficient parameter estimates of the regression equation.
		For a heteroscedastic regression model, a consistent estimate of the variance-covariance matrix is obtained by approximating the heteroscedastic variances with the squared of estimated residuals from an ordinary least squares estimation.
	eclab.econ.pdx.edu /gpe/chap6.htm (992 words)

	Multiple Regression Assumptions. ERIC Digest.
		According to Berry and Feldman (1985), slight heteroscedasticity has little effect on significance tests; however, when heteroscedasticity is marked, it can lead to serious distortion of findings and seriously weaken the analysis, thus increasing the possibility of a Type I error.
		Heteroscedasticity is indicated when the residuals are not evenly scattered around the line.
		There are many forms heteroscedasticity can take, such as a bow-tie or fan shape.
	www.ericdigests.org /2003-3/multiple.htm (1616 words)

	Estimation under Heteroscedasticity
		In the errors-in-variables model all the components of a measurement vector are corrupted by noise.
		When the structure of the model is polynomial, linearization of the estimation problem introduces data-dependent (heteroscedastic) noise.
		Bogdan Matei: Heteroscedastic errors-in-variables models in computer vision.
	www.caip.rutgers.edu /riul/research/hetero.html (267 words)

	[No title]
		The international scientific and standardization bodies recommend that the uncertainty of patients' results obtained in clinical laboratories should be known [2, 3]; the rationale for this recommendation is that full interpretation of the value of a quantity obtained by measurement requires also evaluation of the doubt attached to its value.
		With regard to day-to-day imprecision, the phenomenon called heteroscedasticity should be taken into account: day-to-day metrological variance depends on the value of the measurand (the opposite phenomenon is called homoscedasticity).
		In some cases of heteroscedasticity, in spite of variance differences with the measurand value, the coefficient of variation reminds constant; in these cases, the calculation of the variance due to day-to day imprecision is easy to carry out (knowing the measured value and the constant coefficient of variation).
	www.ifcc.org /ejifcc/vol14no1/140103200309n.htm (593 words)

	Correcting the standard errors of regression slopes for heteroscedasticity
		To understand these methods, the most important point to know is that the patterns of heteroscedasticity that most distort statistical inference are not the most common or obvious cases.
		In a problem with 8 regressors (predictors), suppose we're thinking of one of the 8 as the independent variable of interest, and are thinking of the other 7 regressors as covariates which are in the regression in order to control them statistically.
		Because of this, the simple-regression method for controlling heteroscedasticity can be extended to multiple regression by computing X.C and Y.C, then deleting from the sample the cases closest to the mean on X.C. (That mean is always exactly 0, since residuals always have a mean of 0.) Then proceed as described above.
	comp9.psych.cornell.edu /Darlington/heterosc.htm (1664 words)

	St. Louis Fed: WP 1998-011A "Conditional Heteroskedasticity in Qualitative Response Models of Time Series:A Gibbs ...
		Previous time series applications of qualitative response models have ignored features of the data, such as conditional heteroscedasticity, that are routinely addressed in time-series econometrics of financial data.
		This article addresses this issue by adding Markov-switching heteroscedasticity to a dynamic ordered probit model of discrete changes in the bank prime lending rate and estimating via the Gibbs sampler.
		In addition, the extension to regime-switching parameters and conditional heteroscedasticity is easy to implement under Gibbs sampling.
	research.stlouisfed.org /wp/more/1998-011 (191 words)

	The Statistical Distribution Of Daily Exchange Rate Price Changes: Dependent Vs Independent Models
		In the cases where there is significant first-order heteroscedasticity in the data set, the GARCH models are superior only 50% of the time.
		Results indicate that independence should not be overlooked, and future research should not focus on the search for the perfect GARCH model, but attempt to develop models that incorporate the pronounced volatility clustering found in exchange rate price series and the independent behavior that exists in the data.
		Generalized Autoregressive Conditional Heteroscedastic in the Mean (GARCH-M) In the GARCH-M model the conditional mean is a function of the conditional variance equation.
	www.studyfinance.com /jfsd/htmlfiles/v12n2/johnston.html (2301 words)

	soci209 - module 12 - heteroscedasticity & weighted least squares (Site not responding. Last check: 2007-10-20)
		heteroscedasticity means that OLS standard errors of the estimates are incorrect (often underestimated); therefore statistical inference is invalid
		When heteroscedasticity is present transforming the variables or the use of WLS may be undesirable when
		if there is heteroscedasticity look first for a reasonable transformation that might stabilize the variances of the errors, but without introducing problems of interpretation or upsetting the functional relationship of Y with the independent variables; if such a transformation is found it is a desirable solution
	www.unc.edu /~nielsen/soci209/m12/m12.htm (1687 words)

	Heteroscedasticity models on the BSE... (Site not responding. Last check: 2007-10-20)
		In this article, we study conditional heteroscedasticity in a marketindex on the Bombay Stock Exchange, from April 1979 to March 1995.
		We find strong evidence of heteroscedasticity in daily, weekly and monthly returns.
		The results with weekly and daily data are not as drastic -- while strong evidence of the regime shift and of seasonality is found in daily and weekly data, even after controlling for these, returns are still ARCH, and still exhibit a fair degree of persistence.
	db.socionet.nw.ru /RuPEc/xml/wpa/paper-wuwpfi/wpawuwpfi9507007.xml (269 words)

	[No title]
		A characterization of the heteroscedasticity: Well defined estimators and methods for testing hypotheses will be obtainable if the heteroscedasticity is “well behaved” in the sense that (i / (i (i (0 as n ((.
		VIR: For the heteroscedasticity to be substantive wrt estimation and inference by LS the weights must be correlated with xs and/or their squares.
		One difference is that the relatively crisp results for the model of heteroscedasticity are replaced with relatively fuzzy, somewhat imprecise results here.
	www.bus.ucf.edu /kim/eco7426/Greene-Notes16.doc (1222 words)

	Heteroscedasticity
		The residuals of an estimation are used to investigate the heteroscedasticity of the true disturbances.
		White's test is general because it makes no assumptions about the form of the heteroscedasticity (White 1980).
		There are two methods for improving the efficiency of the parameter estimation in the presence of heteroscedastic errors.
	www.ualberta.ca /CNS/RESEARCH/Software/SAS.old/ets/chap14/sect39.htm (980 words)

	[No title]
		Heteroscedasticity often occurs in cross-section data when there is a wide range to the X variables, e.g.
		Estimation when heteroscedasticity is present There are a number of ways of solving the heteroscedasticity problem.
		White’s heteroscedasticity consistent standard errors (HCSE’s) Since it is only the standard errors rather than the coefficients themselves which are biased (and inconsistent), another approach is to find alternative estimates of the standard errors.
	www.sussex.ac.uk /Units/economics/qm1/lectures/hetero.doc (1076 words)

	GAUSS Programming for Econometricians: Chapter VII (Site not responding. Last check: 2007-10-20)
		Following from the treatment of the heteroscedasticity consistent covariance matrix introduced in Chapter IV, we can keep the unbiased parameter estimators but correct for the variance-covariance estimator with an autocorrelation consistent covariance matrix.
		Combining both problems of heteroscedasticity and autocorrelation, the Newey-West estimator of heteroscedasticity autocorrelation consistent covariance matrix is a simple approach to deal with an unspecified structure of heteroscedasticity and autocorrelation.
		For a regression model with an unspecified structure of heteroscedasticity and autocorrelation, the consistent estimator of the variance-covariance matrix is
	eclab.econ.pdx.edu /gpe/chap7.htm (2397 words)

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