The paper “ Econometric Analysis of Cross Section and Panel Data” is an engrossing variant of the math problem on macro & microeconomics. The equation is given by: Ln (𝑝𝑟𝑖𝑐𝑒) = 𝛽1 + 𝛽2𝑐𝑟𝑖𝑚𝑒 + 𝛽3𝑑𝑖𝑠𝑡 + 𝛽4ln (𝑛𝑜𝑥) + 𝛽5𝑙𝑜𝑤𝑠𝑡𝑎𝑡 + 𝛽6ln (𝑝𝑟𝑜𝑝𝑡𝑎𝑥) + 𝛽7𝑟𝑎𝑑𝑖𝑎𝑙 + 𝛽8𝑠𝑡𝑟𝑎𝑡𝑖𝑜 + 𝛽9𝑟𝑜𝑜𝑚𝑠 + 𝑒 The regression results are shown in the table below From the results, the estimated equation will be given by; LPRICE = -0.00899022863382*LOWSTAT + 0.176450565448*LNOX 0.00119235090003*DIST - 0.00667964959875*CRIME + 0.231670438192*LPROPTAX - 0.037923718021*NOX + 3.62441426754e-05*PRICE - 0.00822618311505*PROPTAX + 0.00479484711547*RADIAL - 0.0462807286603*ROOMS - 0.00114227351782*STRATIO + 8.4075273902. From the results, it is clear that distance, lnox, nox, and stratio are not statistically significant while the rest of the variables are statistically with p-value < 0.05. Using a significance level of 0.15 on rooms, the regression equation is given below From the results, the p-value is 0.00< 0.15 meaning that the null hypothesis is rejected and accepting the alternative hypothesis that it adds less than 15% with a coefficient of 0.368664 indicating the positive relationship of the variables. Plot the residuals of your regression against dist and comment From the graph, there is a strong correlation between the regression residual and the distance.
The correlations also show positive correlation. This is shown by the highly condense dotted line in the graph (Wooldridge, 2010). White’ s test (without cross products) to test for heteroskedasticity errors Dependent Variable: LPRICE Method: Least Squares Sample: 1 506 Included observations: 506 White heteroskedasticity-consistent standard errors & covariance From the estimates, a low state gives a negative relationship with price, while lnox, distance, nox, and protax are not statistically significant in this case. Crime, LPROPTAX, PRICE, and ROOMS are statistically significant with most of them negatively correlated. R-squared is 0.953864 meaning that the null hypothesis is rejected since there is a presence of heteroskedasticity (Wooldridge, 2010). Conduct a Goldfeld-Quandt test of the null hypothesis of no heteroskedasticity against the alternative that the variances of the errors are changing with dist
ReferenceWooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.