Nov 8 2006 LAB 6 ECON 240A 1 L Phillips Exploratory Data Analysis Scatterplots Regression and ANOVA I This first example uses the Anscombe data set four data files of eleven observations each on the dependent and explanatory variable Open the data file in Eviews and select the four variables x1 x2 x3 x4 along with y1 y2 y3 and y4 Go to the View menu open selected one window one group You should see the eight variables by observation in the spreadsheet view or table A common practice is to rush and run a regression This can often be fatal to understanding the relationship between the variables For example go to the quick menu and select estimate equation In the equation specification box type y1 c x1 and hit the OK button Note that the estimated intercept is 3 0 and the estimated slope is 0 5 and the coefficient of determination is 0 666 For diagnostics in the equation window go to the view menu and select actual fitted residual graph Now repeat this procedure for each of the other three data sets Are you enlightened yet As an alternative exploratory procedure return to the workfile window In the main Eviews menu select quick graph and in the window type in x1 y1 and hit the OK button For graph type choose scatter diagram and hit the option button and select the regression line box and hit OK Repeat this procedure for the remaining three data sets Sometimes a picture is worth a 1000 words This is one of the points of using visual techniques in exploratory data analysis before wheeling up the heavy artillery I The Returns Generating Process The second exercise uses a data file from Chapter 17 of the text Xr17 47 problem 17 47 p 608 This monthly data begins in January 1993 and ends with December 1996 The authors do not use net returns i e net of the risk free rate a Show that this affects the interpretation of the intercept but not of the slope or the coefficient of determination In Eviews go to the File menu select new and workfile In the box click monthly and for the dates 93 01 and 96 12 and click OK In the workfile window select procs import read text Lotus Excel Select the Xr17 47 file in the Lab Six folder from Econ240a folder in the classes folder and hit the open button The data begins in cell A2 Type in 2 for the number of series and hit the OK button In the Nov 8 2006 LAB 6 ECON 240A 2 L Phillips Exploratory Data Analysis Scatterplots Regression and ANOVA workfile window select GE and s p index01 In the view menu select open selected one window one group You should be in the spreadsheet view In the view menu go to multiple graphs line and you will see plots of each series against time In the view menu choose descriptive stats common sample since each have 48 observations Close the group window and select GE Go to the view menu open selected one window In the view menu choose descriptive statistics histogram stats The coefficient of skewness zero for the normal distribution is not significant and the coefficient of kurtosis three for a normal distribution is not significant either as reflected by the Jarque Bera statistic with probability 0 545 Thus the 48 monthly returns for the GE stock are not significantly different from normal Select the stock index Standard and Poor s Composite and repeat this procedure It also looks normal Go to the quick menu graph and in the window type in s p index01 GE and hit the OK button For graph type choose scatter diagram and hit the option button and select the regression line box and hit OK Go to the quick menu select estimate equation and type in ge c s p index01 b Is the slope significantly different from one What does this finding mean c How much of the variation in the monthly returns to GE stock is attributable to the market Go to the view menu and select actual fitted residuals graph Does the equation look OK Go to the view menu residual tests histogram normality test d Are the residuals normal II House Price and Multiple Regression The third exercise is from the text and is a preview of coming attractions in Econ 240B This is the data file XM18 02 example 18 2 p 646 There are 100 observations on homes with price number of bedrooms house size in square feet and lot size in square feet This data set was imported into EViews Select bedrooms lot size01 house size01 and price In the view menu select open selected one window one group You should be in the spreadsheet view In the view menu go to multiple graphs scatter matrix of all pairs In the last row you will see the scatter plots of price against the other three variables It looks like price is positively associated with all three variables Nov 8 2006 LAB 6 ECON 240A 3 L Phillips Exploratory Data Analysis Scatterplots Regression and ANOVA The text regresses price against an intercept number of bedrooms house size and lot size However from the scatter plots it is apparent that house size and lot size are highly correlated Try a scatter plot of just these two variables by selecting these two going to the quick menu graph and selecting scatter for type with a regression line as an option Also you can select these two variables go to the view menu select open selected one window one group You should be in the spreadsheet view In the view menu select correlations The correlation coefficient is 0 994 These two explanatory variables are highly correlated and are not providing separate variation explaining house price This is called multicollinearity between the explanatory variables and causes large standard errors for the slope coefficients for these explanatory variables and hence low tstatistics eventhough the coefficient of determination is high One remedy is to regress price against a constant bedrooms and house size a Interpret the estimated regression coefficient on house size b Interpret the estimated coefficient on bedrooms IV Exercises 1 An alternative to the regression of price on a constant number of bedrooms and house size would be to estimate a separate intercept for two bedroom houses three bedroom houses etc similar to the approach in Lab Five where we estimated separate intercepts for each industry in the regression of lnassets on lnsales Select bedrooms go to the view menu open selected one window In the view menu choose descriptive statistics histogram stats The number of bedrooms ranges from two to five Create the dummy variables and run the regression Are there any anomalies 2 Percent of Household Income Spent on Lotteries This exercise uses XR18 13 This data was
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