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Showing 1 to 8 of 8 for “"covariance matrix estimator"”.
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Inference in linear panel data models with serial correlation and an essay on the impact of 401 (k) participation on the wealth distribution
… multilevel data sets and discusses estimation of covariance parameters for use in GLS when the shock follows an AR(p) process. Standard estimates of the AR coefficients will typically be biased due to the inclusion of group level fixed effects, so a simple bias correction for the AR coefficients …
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Contributions to Profile Monitoring and Multivariate Statistical Process Control
… T² statistic based on the successive differences covariance matrix estimator. Part 1: Nonlinear Profile Monitoring In an increasing number of cases the quality of a product or process cannot adequately be represented by the distribution of a univariate quality variable or the multivariate …
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Essays on financial econometrics : variance and covariance estimation using price durations
Asset variance and covariance are fundamental for financial risk management and many finance applications. With the advent of tick-by-tick high-frequency data, the estimation of univariate variances and multivariate covariance matrices has attracted more attention from econometricians. Many of the …
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The Hausman test, and some alternatives, with heteroskedastic data
… the usual Hausman contrast test requires one estimator to be efficient under the null hypothesis. If data are heteroskedastic, the least squares estimator is no longer efficient. Options for carrying out a Hausman-like test in this case include estimating an artificial regression and using …
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Three Essays in Financial Economics
… The second chapter presents an improved covariance matrix estimator in the mean-variance optimization setting. Sample covariance matrix can be singular when the number of observations is less than the number of assets, and nearly singular when the number of observations exceeds the number …
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Contributions to Robust Methods: Modified Rank Covariance Matrix and Spatial-EM Algorithm
… correlation analysis are based on sample covariance matrix. Those moment-based techniques are optimal (most efficient) under the normality distributional assumption. They are, however, extremely sensitive to outlying observations, susceptible to small perturbation in data and poor in the …
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Profile Monitoring with Fixed and Random Effects using Nonparametric and Semiparametric Methods
… two different formulas for the estimated variancecovariance matrix: one based on the pooled sample variance-covariance matrix estimator and a second one based on the estimated variance-covariance matrix based on successive differences. A Monte Carlo study was performed to compare the integrated …
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Portfolio selection in case of high dimensionality
Das Problem der Portfolioauswahl war immer eines der wichtigsten Themen in der Investionstheorie. Das betrifft nicht nur das Verfahren der Portfolioauswahl an sich aber auch die Probleme der Einschätzung des Erwartunswerts und der Kovarianz. Da grosse Datenmenge heutzutage verfügbar ist, wird das …