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  • Search: subject:"Cramér–von Mises statistic"
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Year of publication
Subject
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Anderson-Darling statistic 2 Cramér-von Mises statistic 2 Critical values 2 Goodness of fit tests 2 AR(1) 1 Anderson - Darling statistic 1 Asymptotic normality 1 Bera-Jarque statistic 1 Brownian bridge 1 Copula 1 Cramer-Von Mises statistic 1 Cramer-von Mises statistic 1 Cramér - Von Mises statistic 1 Cramér–von Mises statistic 1 Crámer-von Mises distance 1 Distribution-free asymptotics 1 Einkommensverteilung 1 Empirical Distribution function 1 Empirical characteristic function 1 Empirical distribution 1 Empirical distribution function 1 Estimation 1 Estimation theory 1 Hermite-Gauss 1 Income distribution 1 Independence 1 Kernel function 1 Kolmogorov - Smirnov statistic 1 Kolmogorov-Smirnov statistic 1 Kuiper Statistic 1 Kuiper statistic 1 Minimum distance estimator 1 Minimum distance estimators 1 Missing data 1 Monte-Carlo Simulations 1 Monte-Carlo simulations 1 Multivariate time series 1 Null distribution 1 Pareto interpolation 1 Schätztheorie 1
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Online availability
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Free 6
Type of publication
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Article 3 Book / Working Paper 3
Type of publication (narrower categories)
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Article 2 Working Paper 2 Arbeitspapier 1 Graue Literatur 1 Non-commercial literature 1
Language
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English 6
Author
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Alessi, Lucia 2 Barigozzi, Matteo 2 Capasso, Marco 2 Fagiolo, Giorgio 2 Cho, Jin Seo 1 Hong, Yongmiao 1 Hürlimann, Werner 1 Liebscher, Eckhard 1 Park, Myung-Ho 1 Phillips, Peter C. B. 1
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Institution
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Laboratory of Economics and Management (LEM), Scuola Superiore Sant'Anna 1
Published in...
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Annals of Economics and Finance 1 Cowles Foundation discussion paper 1 Journal of Statistical and Econometric Methods 1 LEM Papers Series 1 LEM Working Paper Series 1 Statistical Papers 1
Source
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EconStor 3 RePEc 2 ECONIS (ZBW) 1
Showing 1 - 6 of 6
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Fitting copulas in the case of missing data
Liebscher, Eckhard - In: Statistical Papers 65 (2024) 6, pp. 3681-3711
missing data. We construct a specific Cramér–von Mises statistic as a sum of such statistics for the several missing data … parameter estimators and of the Cramér–von Mises statistic. …
Persistent link: https://www.econbiz.de/10015358820
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Minimum distance testing and top income shares in Korea /
Cho, Jin Seo; Park, Myung-Ho; Phillips, Peter C. B. - 2015
Persistent link: https://www.econbiz.de/10011312313
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On non-Gaussian AR(1) inflation modeling
Hürlimann, Werner - In: Journal of Statistical and Econometric Methods 1 (2012) 1, pp. 93-101
A severe limitation of the original autoregressive process of order one or AR(1) process is the Gaussian nature of the assumed residual error distribution while the observed sample residual errors tend to be much more skewed and have a much higher kurtosis than is allowed by a normal...
Persistent link: https://www.econbiz.de/10010286831
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On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters
Capasso, Marco; Alessi, Lucia; Barigozzi, Matteo; … - Laboratory of Economics and Management (LEM), Scuola … - 2007
This note discusses some problems possibly arising when approximating via Monte-Carlo simulations the distributions of goodness-of-fit test statistics based on the empirical distribution function. We argue that failing to re-estimate unknown parameters on each simulated Monte-Carlo sample -- and...
Persistent link: https://www.econbiz.de/10005650091
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Testing for Independence between Two stationary Time Series via the Empirical Characteristic Function
Hong, Yongmiao - In: Annals of Economics and Finance 2 (2001) 1, pp. 123-164
This paper proposes an asymptotic one-sided N(0, 1) test for independence between two stationary time series using the empirical characteristic function. Unlike the tests based on the cross-correlation function (e.g. Haugh, 1976; Hong, 1996; Koch & Yang 1986), the proposed test has power against all...
Persistent link: https://www.econbiz.de/10009145677
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On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters
Capasso, Marco; Alessi, Lucia; Barigozzi, Matteo; … - 2007
This note discusses some problems possibly arising when approximating via Monte-Carlo simulations the distributions of goodness-of-fit test statistics based on the empirical distribution function. We argue that failing to reestimate unknown parameters on each simulated Monte-Carlo sample - and...
Persistent link: https://www.econbiz.de/10010328518
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