Optimality of least squares in the seemingly unrelated regression equation model

1978 ◽  
Vol 7 (3) ◽  
pp. 391-395 ◽  
Author(s):  
T.D Dwivedi ◽  
V.K Srivastava
Keyword(s):  
Least Squares ◽  
Equation Model ◽  
Regression Equation ◽  
Seemingly Unrelated Regression ◽  
Unrelated Regression

An Evaluation of the Use of Ordinary Least Squares for Estimating Tourism Demand Models

1997 ◽  
Vol 35 (4) ◽  
pp. 69-73 ◽  
Author(s):  
Clive L. Morley
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Percentage Error ◽  
Regression Estimation ◽  
Tourism Demand ◽  
Constant Variance ◽  
Unrelated Regression ◽  
Demand Models ◽  
The Common

Estimating tourism demand models involves a set of related equations with errors that may not satisfy the common assumptions of being independent, with constant variance and normal distribution. In such circumstances, seemingly unrelated regression estimation may be considered a better estimation technique than ordinary least squares. Results from a simulation exercise, however, show that generally there is little difference between ordinary least squares and seemingly unrelated regression. The ordinary least squares technique performs well, and the results give little reason to use more complex estimation techniques. Another feature of tourism data is that strong growth in tourist numbers is often observed. This feature implies that models in which such series are the dependent variable are not consistently estimated by least squares methods. A percentage error loss function is proposed as a more appropriate criterion for estimating tourist data of this type.

A Comparison of Three Methods for Estimating Tourism Demand Models

1996 ◽  
Vol 2 (3) ◽  
pp. 223-234 ◽  
Author(s):  
Clive L. Morley
Keyword(s):  
Least Squares ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Small Samples ◽  
Tourism Demand ◽  
Regression Methods ◽  
Unrelated Regression ◽  
Demand Models ◽  
The Common

Estimation of tourism demand models involves a set of related equations with errors which may not satisfy the common assumptions of regression modelling. Results from a simulation exercise show that, for the error types and small samples considered, the Generalized Method of Moments is less accurate on average than the Ordinary Least Squares and Seemingly Unrelated Regression methods, which had very similar accuracies. Overall, the Ordinary Least Squares technique performs well and the results give little reason to use the more complex estimation techniques.

scholarly journals Effect of Inflation on Sri Lankan Banks’ Performance: Mediation Effect of Interest Income and Expenses

2021 ◽  
Vol 6 (3) ◽  
pp. 176-180
Author(s):  
B. W. C. M. Amarasena ◽  
T. U. I. Peiris
Keyword(s):  
Least Squares ◽  
Seemingly Unrelated Regression ◽  
Mediation Effect ◽  
Bank Performance ◽  
Sri Lankan ◽  
Least Squares Regression ◽  
Interest Income ◽  
Unrelated Regression ◽  
Sure Model ◽  
Three Stage Least Squares

This study hypothesizes that the inflationary effects on bank performance are not straightforward but mediates through interest income and expenditure. This hypothesis is tested in the Sri Lankan context, referring to 17 licensed banks from 2011 to 2019. Findings were obtained using the Seemingly Unrelated Regression (SURE) model, and the results were tested for robustness by applying Three-Stage Least Squares Regression (3SLS) model. Results indicated that interest expenses negatively significant as a mediator between inflation and banks’ performance. In contrast, the effect of interest income is positive. Therefore, a significantly negative overall effect can be expected because interest expenses are relatively more substantial than the influence of interest income on the banks' performance. Furthermore, although banks with excess savings can grant loans to the customers, customers are reluctant to gain the loans because they have to spend more interest in an inflationary situation.

scholarly journals Effectiveness of State-level Pecan Promotion Programs: The Case of the Texas Pecan Checkoff Program

HortScience ◽  
2009 ◽  
Vol 44 (7) ◽  
pp. 1914-1920 ◽  
Author(s):  
Eli D. Moore ◽  
Gary W. Williams ◽  
Marco A. Palma ◽  
Leonardo Lombardini
Keyword(s):  
Cost Analysis ◽  
Least Squares ◽  
State Level ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Regression Estimator ◽  
Benefit Cost Analysis ◽  
Unrelated Regression ◽  
Ordinary Least Squares Estimator ◽  
Benefit Cost

The Texas Pecan Board was established in 1998 to administer the Texas Pecan Checkoff Program and is financed through a half cent per pound assessment on grower pecan sales. The Board spends the assessment collections on a variety of advertising campaigns in an attempt to expand demand for Texas pecans and to increase the welfare of Texas pecan growers. This article presents an evaluation of the economic effectiveness of the Texas Pecan Checkoff Program in expanding sales of Texas pecans. First, the effects of Texas Pecan Board promotion on sales of all Texas pecans are determined using the ordinary least squares estimator followed by a test for differential effects of Texas Pecan Board promotion activities on sales of improved and native Texas pecan varieties using the seemingly unrelated regression estimator. The analysis indicates that the Texas Pecan Checkoff Program has effectively increased sales of improved varieties of Texas pecans but has had no statistically measurable impact on sales of native varieties of Texas pecans. A benefit–cost analysis determines that $35.0 in additional sales revenues are generated for every dollar invested in promotion, indicating that the Texas pecan promotion program has been financially successful. The per unit return is large but on a very few dollars available for investment in promotion implying the need for more investment for more meaningful returns.

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