The paper "Econometrics Essentials" is an outstanding example of a Macro & Microeconomics assignment. The equation is given by Ln (𝑝𝑟𝑖𝑐𝑒) = 𝛽1 + 𝛽2𝑐𝑟𝑖𝑚𝑒 + 𝛽3𝑑𝑖𝑠𝑡 + 𝛽4ln (𝑛𝑜𝑥) + 𝛽5𝑙𝑜𝑤𝑠𝑡𝑎𝑡 + 𝛽6ln (𝑝𝑟𝑜𝑝𝑡𝑎𝑥) + 𝛽7𝑟𝑎𝑑𝑖𝑎𝑙 + 𝛽8𝑠𝑡𝑟𝑎𝑡𝑖𝑜 + 𝛽9𝑟𝑜𝑜𝑚𝑠 + 𝑒 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 a 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, lowstat gives a negative relationship with price, while Knox, 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 presence of heteroskedasticity (Wooldridge, 2010).
Baltagi, B. (2008). Econometric analysis of panel data. John Wiley & Sons.
Robst, J. (1994). Measurement error and the returns to excess schooling. Applied Economics Letters, 1(9), 142-144.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.