Disturbances in the linear model, estimation and hypothesis testing

1. Front Matter

   Pages I-VII

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2. Introduction

   • C. Dubbelman

   Pages 1-23

3. Tabulable quadratic ratio tests

   • C. Dubbelman

   Pages 24-49

4. BLUF disturbance estimation

   • C. Dubbelman

   Pages 50-63

5. An empirical Ω

   • C. Dubbelman

   Pages 64-90

6. Evaluation of the tests

   • C. Dubbelman

   Pages 91-106

7. Back Matter

   Pages 107-109

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1. 1. The general linear model All econometric research is based on a set of numerical data relating to certain economic quantities, and makes infer­ ences from the data about the ways in which these quanti­ ties are related (Malinvaud 1970, p. 3). The linear relation is frequently encountered in applied econometrics. Let y and x denote two economic quantities, then the linear relation between y and x is formalized by: where {31 and {32 are constants. When {31 and {32 are known numbers, the value of y can be calculated for every given value of x. Here y is the dependent variable and x is the explanatory variable. In practical situations {31 and {32 are unknown. We assume that a set of n observations on y and x is available. When plotting the ob­ served pairs (x l' YI)' (x ' Y2)' . . . , (x , Y n) into a diagram with x 2 n measured along the horizontal axis and y along the vertical axis it rarely occurs that all points lie on a straight line. Generally, no b 1 and b exist such that Yi = b + b x for i = 1,2, . . . ,n. Unless 2 l 2 i the diagram clearly suggests another type of relation, for instance quadratic or exponential, it is customary to adopt linearity in order to keep the analysis as simple as possible.

• econometrics
• research
• value-at-risk

• Econometric Institute, Erasmus University Rotterdam, Netherlands

  C. Dubbelman

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