Relevant bibliographies by topics / Homoskedasticita

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters

Academic literature on the topic 'Homoskedasticita'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Homoskedasticita"

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Erb, Jack, and Douglas G. Steigerwald. "Accurately sized test statistics with misspecified conditional homoskedasticity." Journal of Statistical Computation and Simulation 81, no. 6 (June 2011): 729–47. http://dx.doi.org/10.1080/00949650903463574.

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Djalic, Irena, and Svetlana Terzic. "Violation of the assumption of homoscedasticity and detection of heteroscedasticity." Decision Making: Applications in Management and Engineering 4, no. 1 (March 15, 2021): 1–18. http://dx.doi.org/10.31181/dmame2104001d.

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In this paper, it is assumed that there is a violation of homoskedasticity in a certain classical linear regression model, and we have checked this with certain methods. Model refers to the dependence of savings on income. Proof of the hypothesis was performed by data simulation. The aim of this paper is to develop a methodology for testing a certain model for the presence of heteroskedasticity. We used the graphical method in combination with 4 tests (Goldfeld-Quantum, Glejser, White and Breusch-Pagan). The methodology that was used in this paper showed that the assumption of homoskedasticity was violated and it showed existence of heteroskedasticity.
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Hodoshima, Jiro, and Masakazu Ando. "Bootstrapping stochastic regression models under homoskedasticity: wild bootstrapvs. pairs bootstrap." Journal of Statistical Computation and Simulation 80, no. 11 (November 2010): 1225–35. http://dx.doi.org/10.1080/00949650903014971.

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Baltagi, Badi H., Georges Bresson, and Alain Pirotte. "Joint LM test for homoskedasticity in a one-way error component model." Journal of Econometrics 134, no. 2 (October 2006): 401–17. http://dx.doi.org/10.1016/j.jeconom.2005.06.029.

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Jun, Sung Jae, and Joris Pinkse. "ADDING REGRESSORS TO OBTAIN EFFICIENCY." Econometric Theory 25, no. 1 (February 2009): 298–301. http://dx.doi.org/10.1017/s0266466608090567.

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It is well known that in standard linear regression models with independent and identically distributed data and homoskedasticity, adding “irrelevant regressors” hurts (asymptotic) efficiency unless such irrelevant regressors are orthogonal to the remaining regressors. But we have found that under (conditional) heteroskedasticity “irrelevant regressors” can always be found such that one can achieve the asymptotic variance of the generalized least squares estimator by adding the “irrelevant regressors” to the model.
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Kew, Hsein, and David Harris. "HETEROSKEDASTICITY-ROBUST TESTING FOR A FRACTIONAL UNIT ROOT." Econometric Theory 25, no. 6 (December 2009): 1734–53. http://dx.doi.org/10.1017/s0266466609990314.

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This paper shows how fractional unit root tests originally derived under stationarity can be made robust to heteroskedasticity. This is done by using existing tests nested in a regression framework and then implementing these tests using White’s heteroskedasticity consistent standard errors (White, 1980). We show this approach is effective both asymptotically and in finite samples. We also provide some evidence on the asymptotic local power of different implementations of the tests, under both homoskedasticity and heteroskedasticity.
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Smeekes, Stephan, and A. M. Robert Taylor. "BOOTSTRAP UNION TESTS FOR UNIT ROOTS IN THE PRESENCE OF NONSTATIONARY VOLATILITY." Econometric Theory 28, no. 2 (September 13, 2011): 422–56. http://dx.doi.org/10.1017/s0266466611000387.

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Three important issues surround testing for a unit root in practice: uncertainty as to whether or not a linear deterministic trend is present in the data; uncertainty as to whether the initial condition of the process is (asymptotically) negligible or not, and the possible presence of nonstationary volatility in the data. Assuming homoskedasticity, Harvey, Leybourne, and Taylor (2011, Journal of Econometrics, forthcoming) propose decision rules based on a four-way union of rejections of quasi-differenced (QD) and ordinary least squares (OLS) detrended tests, both with and without a linear trend, to deal with the first two problems. In this paper we first discuss, again under homoskedasticity, how these union tests may be validly bootstrapped using the sieve bootstrap principle combined with either the independent and identically distributed (i.i.d.) or wild bootstrap resampling schemes. This serves to highlight the complications that arise when attempting to bootstrap the union tests. We then demonstrate that in the presence of nonstationary volatility the union test statistics have limit distributions that depend on the form of the volatility process, making tests based on the standard asymptotic critical values or, indeed, the i.i.d. bootstrap principle invalid. We show that wild bootstrap union tests are, however, asymptotically valid in the presence of nonstationary volatility. The wild bootstrap union tests therefore allow for a joint treatment of all three of the aforementioned issues in practice.
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Mubyarjati, Dhea Kurnia, Abdul Hoyyi, and Hasbi Yasin. "PEMODELAN REGRESI ROBUST S-ESTIMATOR UNTUK PENANGANAN PENCILAN MENGGUNAKAN GUI MATLAB (Studi Kasus : Faktor-Faktor yang Mempengaruhi Produksi Ikan Tangkap di Jawa Tengah)." Jurnal Gaussian 8, no. 1 (February 28, 2019): 81–92. http://dx.doi.org/10.14710/j.gauss.v8i1.26616.

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Multiple Linear Regression can be solved by using the Ordinary Least Squares (OLS). Some classic assumptions must be fulfilled namely normality, homoskedasticity, non-multicollinearity, and non-autocorrelation. However, violations of assumptions can occur due to outliers so the estimator obtained is biased and inefficient. In statistics, robust regression is one of method can be used to deal with outliers. Robust regression has several estimators, one of them is Scale estimator (S-estimator) used in this research. Case for this reasearch is fish production per district / city in Central Java in 2015-2016 which is influenced by the number of fishermen, number of vessels, number of trips, number of fishing units, and number of households / fishing companies. Approximate estimation with the Ordinary Least Squares occur in violation of the assumptions of normality, autocorrelation and homoskedasticity this occurs because there are outliers. Based on the t- test at 5% significance level can be concluded that several predictor variables there are the number of fishermen, the number of ships, the number of trips and the number of fishing units have a significant effect on the variables of fish production. The influence value of predictor variables to fish production is 88,006% and MSE value is 7109,519. GUI Matlab is program for robust regression for S-estimator to make it easier for users to do calculations. Keywords: Ordinary Least Squares (OLS), Outliers, Robust Regression, Fish Production, GUI Matlab.
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Hsiao, Cheng, and Qi Li. "A CONSISTENT TEST FOR CONDITIONAL HETEROSKEDASTICITY IN TIME-SERIES REGRESSION MODELS." Econometric Theory 17, no. 1 (February 2001): 188–221. http://dx.doi.org/10.1017/s0266466601171069.

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We show that the standard consistent test for testing the null of conditional homoskedasticity (against conditional heteroskedasticity) can be generalized to a time-series regression model with weakly dependent data and with generated regressors. The test statistic is shown to have an asymptotic normal distribution under the null hypothesis of conditional homoskedastic error. We also discuss extension of our test to the case of testing the null of a parametrically specified conditional variance. We advocate using a bootstrap method to overcome the issue of slow convergence of this test statistic to its limiting distribution.
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10

Attfield, C. L. F. "A Bartlett adjustment to the likelihood ratio test for homoskedasticity in the linear model." Economics Letters 37, no. 2 (October 1991): 119–23. http://dx.doi.org/10.1016/0165-1765(91)90118-5.

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Dissertations / Theses on the topic "Homoskedasticita"

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Studený, Marek. "Modelování tržní ceny nemovitosti mnohonásobnou lineární regresí." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2013. http://www.nusl.cz/ntk/nusl-232776.

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The main subject of the diploma thesis is a market price modeling by real estates. As a tool for modeling, is used a multiple linear regression. As starting points, are used an econometrical theory and knowledge about real estate valuation. The main goal is to find optimal model for best capture in the time and place.
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Darandari, Eqbal Z. M. Tate Richard L. "Robustness of hierarchical linear model parameter estimates under violations of second-level residual homoskedasticity and independence assumptions." 2004. http://etd.lib.fsu.edu/theses/available/etd-01062004-023244.

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Thesis (Ph. D.)--Florida State University, 2004.
Advisor: Dr. Richard L. Tate, Florida State University, College of Education, Dept. of Educational Psychology and Learning Systems. Title and description from dissertation home page (viewed June 16, 2004). Includes bibliographical references.
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Books on the topic "Homoskedasticita"

1

Hodoshima, Jiro. Identification and estimation in linear simultaneous equations models with structural change under limited information: Gains by homoskedasticity. Louvain-la-Neuve: CORE, 1985.

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Book chapters on the topic "Homoskedasticita"

1

Ciuiu, Daniel. "Testing the Homoskedasticity/Heteroskedasticity of the Errors Using the White Test: Pattern Classification by k-Variances and Informational Criteria." In Optimization Theory, Decision Making, and Operations Research Applications, 305–18. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5134-1_22.

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You might also be interested in the extended bibliographies on the topic 'Homoskedasticita' for particular source types:
Journal articles
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