Estimation of seemingly unrelated regression equations

Srivastava, V. K.; Dwivedi, T. D.

doi:10.1016/0304-4076(79)90061-7

Cited by 126 publications

(48 citation statements)

References 21 publications

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“…In this example, the univariate analysis has shown the same relationship, with a significant difference between boys and girls for SBP but not for DBP. Therefore, we used sex as the outcome specific covariate for SBP and we did not include it for the DBP model [14][15][16]. The final models are then:…”

Section: Cardiovascular Diseases Risk Factors In Portuguese Youngtersmentioning

confidence: 99%

Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

Oliveira¹,

Teixeira‐Pinto²

2015

J Biom Biostat

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In health related research it is common to have multiple outcomes of interest in a single study. These outcomes are often analysed separately, ignoring the correlation between them. One would expect that a multivariate approach would be a more efficient alternative to individual analyses of each outcome. Surprisingly, this is not always the case. In this article we discuss different settings of linear models and compare the multivariate and univariate approaches. We show that for linear regression models, the estimates of the regression parameters associated with covariates that are shared across the outcomes are the same for the multivariate and univariate models while for outcome-specific covariates the multivariate model performs better in terms of efficiency.

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Section: Cardiovascular Diseases Risk Factors In Portuguese Youngtersmentioning

confidence: 99%

Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

Oliveira¹,

Teixeira‐Pinto²

2015

J Biom Biostat

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“…Note that conventional seemingly unrelated regressions (SUR) does not have this property if the numbers of variables differ between the equations, even though it is one of the most successful and efficient methods for estimating SUR. The resulting model has stimulated countless theoretical and empirical results in econometrics and other areas (see Zellner, 1962;Srivastava and Giles, 1987;Chib and Greenberg, 1995). The benefit of this model for us is that the ISUR estimators utilize the information present in the cross regression (or equations) error correlation and hence it is more efficient than other estimation methods such as ordinary least squares (OLS).…”

Section: The Systemic Specificationmentioning

confidence: 99%

Using the SUR model of tourism demand for neighbouring regions in Sweden and Norway

Salman¹

2011

Advances in Econometrics - Theory and Applications

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“…Some notion of the magnitude of the gains from SUR may be gleaned from Kmenta and Gilbert's (1968) monte carlo experiments concerning a two equation system, where each equation contained two independent variables, and subsequent experiments (Srivastava and Dwivedi, 1979). These experiments demonstrate that the gains in efficiency from SUR can vary substantially depending on the correlations between the equation disturbances and the correlations between the sets of explanatory variables of these equations.…”

Section: Ols Coefficient Standard Error (I)mentioning

confidence: 99%

Sales response modeling: Gains in efficiency from system estimation

Bolton¹

1989

Journal of Business Research

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This study investigates gains in efficiency from Zellner's Seemingly Unrelated Regressions (SUR) procedure using an optical scanner data base supplied by Information Resources, Inc. A total of 162 sales equations describing different brands, product categories, and cities is estimated with both SUR and ordinary least squares (OLS). The coefficient standard errors obtained from SUR are about 25% smaller than those obtained from OLS. Gains in efficiency from SUR are found to be related to characteristics of the sales models and data bases. Large gains in efficiency are observed for coefficients of equations describing the sales of heavily advertised, high-market-share brands, sold at locations utilizing different marketing policies to attract a similar clientele. These features should be recognized in the data collection and analysis processes.

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Estimation of seemingly unrelated regression equations

Cited by 126 publications

References 21 publications

Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

Using the SUR model of tourism demand for neighbouring regions in Sweden and Norway

Sales response modeling: Gains in efficiency from system estimation

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