An Example of Reporting Key Stata Output Question 8 The dataset XXXX has data on average net wealth of households in 517 regions in Australia in 2003-04. (a) Obtain the summary statistics. Do the data appear to have greater kurtosis than the normal distribution? Explain. (b) Generate a new variable, squared of the wealth. Run a regression y on x1 and x2 (a) STATA OUTPUT - For Q8 , Part (a) sum net_worth_per_hh, detail Net worth per household $ thousands Percentiles Smallest 1% 223.5 154.3 5% 267.2 202.1 10% 294 211.6 Obs 517 25% 344.6 214.4 Sum of Wgt. 517 50% 406.6 Mean 446.6569 Largest Std. Dev. 181.3074 75% 489.1 1386.5 90% 630.5 1388.5 Variance 32872.37 95% 787.8 1530.1 Skewness 3.024729 99% 1178 1927 Kurtosis 17.90566 There are 3 indicators… (b) STATA OUTPUT - For Q8 , Part (b) gen networth_sq=net_worth^2 kdensity networth_sq . reg y x1 x2 Source | SS df MS Number of obs = 1500F( 2, 1497) = 11.45 Model | 30457457.5 2 15228728.7 Prob > F = 0.0000 Residual | 1.9909e+09 1497 1329954.57 R-squared = 0.0151 Adj R-squared = 0.0138 Total | 2.0214e+09 1499 1348498.63 Root MSE = 1153.2 - y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -- X1 | -671.5362 152.4207 -4.41 0.000 -970.517 -372.5555 X2 | -79.90985 36.19838 -2.21 0.027 -150.9148 -8.904931 _cons | 1697.235 97.63377 17.38 0.000 1505.721 1888.748 X1 means that while we are….. . Q1- 5 marks Use "school of economics" assignment cover sheet – and clearly write your name , SID and ›‘‘‹ƒŽ‰‘’„‡ȋ†ƒ›ƒ†‹‡‘ˆ›‘‘‹ƒŽȌƒ†tutor's nameƒ‡‹…Ž†‡†‹ ›‘•„‹••‹‘ Q2- 20 marks (each 5) Use the data in KIELMC.dta, only for the year 1981, to answer the following questions. The data are for houses that sold during 1981 in North Andover, Massachusetts;1981 was the year construction began on a local garbage incinerator. (i) To study the effects of the incinerator location on housing price, consider the simple regression model ݑ ൅ ሻݐݏ݅݀ሺ݃݋݈ଵߚ ൅ ଴ߚ ሻ ൌ݁ܿ݅ݎ݌ሺ݃݋݈ where price is housing price in dollars and dist is distance from the house to the incinerator measured in feet. Interpreting this equation causally, what sign do you expect for ߚଵ if the presence of the incinerator depresses housing prices? Estimate this equation and interpret the results. (ii) To the simple regression model in part (i), add the variables log(intst), log(area), log(land), rooms, baths, and age, where intst is distance from the home to the interstate, area is square footage of the house, land is the lot size in square feet, rooms is total number of rooms, baths is number of bathrooms, and age is age of the house in years. Now, what do you conclude about the effects of the incinerator? Explain why (i) and (ii) give conflicting results. (iii) Add [log(intst)]2 to the model from part (ii). Now what happens? What do you conclude about the importance of functional form? (iv) Is the square of log(dist) significant when you add it to the model from part (iii)? Q3- 75 marks (each 10 – except part c , which is 15) In this problem set, you will use multivariate regressions to analyze energy consumption by American households. The data for this problem are "energy_con.dta".The file has the following set of variables: x hd16- heating degree-days (this is a measure of how much heating is required over the year to warm the house up to 16.5 degrees, a decrease in the outside temperature of one degree for one day of the year would increase hd16by one unit) x cd16 - cooling degree-days (this is a measure of how much cooling is requried over the year to cool the house down to 16.5 degrees, an increase in the outside temperature of one degree for one day of the year would increase cd16.5 by one unit) x totrooms - number of rooms in the house x yearbuilt - year in which the house was built x washload - number of loads of laundry done each week x kwh - kilowatt-hours of electricity used annually