Econometrix Assignment Example | Topics and Well Written Essays - 1000 words

Econometrix - grant ExampleTest for Significance of Coefficient In order to study the significance of the coefficient, the following hypotheses have been drawn. Ho The coefficient is live to zero H1 The coefficient is greater than zero Test Statistics In order to test the significance of the coefficient of be on, t statistic is needed to be metric. Formula In the given case, the estimated coefficient may be referred as ?Age = 0.043 band date the Standard error for ?Age = 0.003. Therefore, Now for the given data set the t tabulated value had been effectuate using the degree of freedom in the given case is 21600 3= 21597. The significance take for the test is 1%. The t critical value for the given data is found to be 2.326 (Allison, 1991). Decision conventionality The Null hypothesis is rejected if t calculated is greater than t tabulated. Conclusion Since the t calculated value is greater than the t tabulated value i.e. 14.33332.326, hence the criteria suggests rejecting the null hypothesis. So it can be cogitate that the coefficient value is not equal to zero. 1b. The equation required for the desired calculation is In the given case, Age= 30 years Size= 100 employees Therefore, the earnings calculated for the given age and size is equal to 7.337 pounds (Belsley, Kuh & Welsch, 2005). 1c) It depends as it sounds logical that a person with higher reproduction would be earning greater than the one who has less(prenominal) education. Therefore, assuming that bigger organizations hire more educated people, it may be assumed that on ordinary there is less probability that an organization employees older people. Omitting education in case if it is related to age was necessary to rule out the chances of error caused due to the correlation existing between age and education as an increase in age is termed to be negatively associated with education. Thus the model would have implied the mercantile establishment of multicollinearity. When variables are extre mely correlated, the variability explained exclusively by the single variables can be minor disdain the fact that the variation explicated by the variables brought together is great. (Wooldridge, 2012). d. Model 2 (2.430) (0.053) (0.285) The sample size is 83, while R2 is 0.036 1d) By and large, with an increase in sample size, the estimated values tend to be better predictor of the population parameters. Therefore, with severally additional observation that is admitted in the sample, the amount of information increases and that additional information usually aids in providing better statistics. Thus if the model provides a better estimating results, the standard errors will be reduced. The model 1 has been framed using an extensively large size of sample while model 2 has eliminated the everyday public and has enlisted only a specified fragment of the whole population resulting in a diminish size of sample. This ultimately targets the standard errors as they are increased due t o the declined size of the sample. This contemplates the information that big samples will bring forth more accurate estimates of the coefficients in a regression analysis (Aiken & West, 1991). e) scheme testing for model 1 Where k=3, N= 21600 while R2=0.025 (Aiken & West, 1991) Calculating the F statistic Let the level of significance be 0.05, the F critical value in that case will be 2.6. Since the F calculated value is less than the F critical, hence,