# Quantitative Methods Of Economics. Essay

3229 words - 13 pages

The Econometric analysis will test the variable "GDP % Growth" as the dependant variable, and how it is affected or explained by the independent variables: "Agricultural % Growth", "Industry % Growth", "Manufacturing % Growth" & "Services % Growth". The data is cross-sectional spread over 77 countries and all the growth rates are for the period 1990-2001.Y = a + B1X1 + B2X2 + B3X3 + B4X4Y = GDP % Growtha = constantX1 = Services % GrowthX2 = Agricultural % GrowthX3 = Industry % GrowthX4 = Manufacturing % GrowthI predict the coefficients to be positive, however the highest impact on GDP % Growth from services and industry growth. This is, as the growth of GDP will encompass the combined effects of the growth of the economy's various sectors, however in most developed countries the services will play the largest part in determining GDP growth, whilst similarly manufacturing and industry will be in the developing countries. These variables will have higher coefficients and t-values if the prediction is correct.The variables "manufacturing growth" and "industry growth" were defined differently in a technical classification sense, however are likely to be very similar and have a degree of collinearity so, it may be that one of these variables will have to be dropped, depending upon how large the covariance is.It will be important to recognise the conditions for accurate ordinary least squares regression, so to obtain accurate estimates with in the regressions. This aims to minimise the sum of squares of the residuals, giving the best fit of the estimated model. These conditions are given as the Gauss-Markov conditions, which state;* Expected value of the disturbance term in any observation should be E(ui) = 0, and therefore should not have a trend in any direction.* Variance of the disturbance term should be constant for all observations, Var(ui) = ui2 ) for all i(no heteroskedasticity)* Covariance between residuals should be 0, therfore there should be no correlation between the two, therefore E(uiuj) = 0, (no auto correlation)* The disturbance term should be independent of the explanatory variable, Cov(xiuI) = 0In addition to these assumptions, there is a normality assumption which states that the disturbance term should be normally distributed, so that the regression coefficients will also be normally distributed, therefore making the t and F hypothesis tests more reliable. To test this we perform the JB test, although we may experience difficulty interpreting the results, as we require a sample of above 100 for this, as the results are only valid asymptotically.The following is a summary of my data from STATA. sum GDP Agriculture Industry Manufac ServicesVariable | Obs Mean Std. Dev. Min Max-------------+-----------------------------------------------------GDP | 77 3.409091 2.462037 -8.5 10Agriculture | 77 2.294805 2.490978 -5.9 9Industry | 77 3.468831 3.886592 -13.2 13.1Manufac | 77 2.914286 4.291108 -12.6 12.8Services | 77 3.646753 2.326596...

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