Multivariate methods for quantitative and qualitative data

Keller, Wouter J.; Wansbeek, Tom

doi:10.1016/0304-4076(83)90095-7

Cited by 25 publications

(9 citation statements)

References 13 publications

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“…The advantage of defining ρ instead of ρ c as an association measure between two vectors of random variables is that ρ can be easily generalized to the case of non normal variables, like for example ordinal variables in our case. Keller and Wansbeek (1983) mention that the canonical coefficients can be obtained when ψ has a particular form. They also use the SEM in (1) with categorical variables, and show the relationships between the resulting models and Correspondence Analysis.…”

Section: Proposition 1 For the Sem (1) The Correlation Coefficient ρmentioning

confidence: 99%

Predicting House Prices with Spatial Dependence: Impacts of Alternative Submarket Definitions

2008

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Much of the trading activity in Equity markets is directed to brokerage houses. In exchange they provide so-called "soft dollars" which basically are amounts spent in "research" for identifying profitable trading opportunities. Soft dollars represent about USD 1 out of every USD 10 paid in commissions. Obviously they are costly, and it is interesting for an institutional investor to determine whether soft dollar inputs are worth being used (and indirectly paid for) or not, from a statistical point of view. To address this question, we develop association measures between what broker-dealers predict and what markets realize. Our data are ordinal predictions by two broker-dealers and realized values on several markets, on the same ordinal scale. We develop a structural equation model with latent variables in an ordinal setting which allows us to test broker-dealer predictive ability of financial market movements. We use a multivariate logit model in a latent factor framework, develop a tractable estimator based on a Laplace approximation, and show its consistency and asymptotic normality. Monte Carlo experiments reveal that both the estimation method and the testing procedure perform well in small samples. The method is then used to analyze our dataset.

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Section: Proposition 1 For the Sem (1) The Correlation Coefficient ρmentioning

confidence: 99%

Predicting House Prices with Spatial Dependence: Impacts of Alternative Submarket Definitions

2008

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“…Linear "errors-in-variables" models have also been generalized to the case where there are k linear restrictions among p variables (p > k), see for instance Anderson (1976), Keller andWansbeek (1983), andAmemiya andFuller (1984). They are then referred to as multivariate errors-invariables functional (MEVF) models.…”

Section: Introductionmentioning

confidence: 98%

Alternate test statistics for a functional errors-in-variables model

Provost¹

1990

Communications in Statistics - Theory and Methods

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K e y W o r d s and Phrases: simultaneous equations models, errors-in-variables models, test of hypotheses, exact density, approximate density.ABSTRACT Some test statistics for the structural coefficients of a simultaneous equations mode1 often referred to as the multivariate linear functional relationship model are proposed in this article. The following cases are considered: the covariance matrix of errors is either unknown, known up to a proportionality factor, or completely known. The exact and approximate distributions of the proposed test statistics, as well as those of some that are known, are also given.

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“…Recently, the MLFR model and other errors-in-variables models have been analyzed in Keller and Wansbeck [9] where their connections to several multivariate statistical techniques were pointed out.…”

Section: Introductionmentioning

confidence: 99%

Some test statistics for the structural coefficients of the multivariate linear functional relationship model

Provost

1986

Ann Inst Stat Math

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SummaryFor the testing problem concerning the coefficients of the multivariate linear functional relationship model, the distribution of a statistic previously proposed by A. P. Basu depends on the unknown covariance matrix V of errors, so limiting its applicability. This article proposes new test statistics with sampling distributions which are independent of the unknown parameters for the cases where V is either unknown or known only up to a proportionality factor. The exact distributions of the test statistics are also discussed.

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Multivariate methods for quantitative and qualitative data

Cited by 25 publications

References 13 publications

Predicting House Prices with Spatial Dependence: Impacts of Alternative Submarket Definitions

Predicting House Prices with Spatial Dependence: Impacts of Alternative Submarket Definitions

Alternate test statistics for a functional errors-in-variables model

Some test statistics for the structural coefficients of the multivariate linear functional relationship model

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