Practical Examples using Eviews

1. Practical Examples using Eviews Presented by 顏廣杰 2013/10/24

2. P.40-P.43 File: SandPhedge.xls

3. Estimation of an optimal hedge ratio • This section shows how to run a bivariate regression using Eviews. • We focus on the relationship between SPOT and FUTURES: • Level regression (long run relationship) • Return regression (short run relationship) • The appropriate hedge ratio will be the slope estimate, , in a regression where the dependent variable is the spot returns and the independent variable is the futures return. • Test whether or not, we can View  Coeff. Tests  Coeff. Restrictions. Type C(2)=1.

4. Input Data

5. Descriptive Statistics • Genr type rfutures=100*dlog(futures) rspot=100*dlog(spot) • Do not forget to Save the workfile.

6. Run Regression • If you want to save the summary statistics, you must name them by clicking Name and then choose a name, e.g. Descstats. • We can now proceed to estimate the regression.

7. Name  returnreg • In the same way, we also obtain levelreg

8. Test Coefficients of Regression • Suppose now that we wanted to test the null hypothesis that rather than .

9. P.77-P.80 File: capm.xls

10. Example for CAPM

11. Generate New Variables • RSANDP=100*DLOG(SANDP) • RFORD=100*DLOG(FORD) • USTB3M=USTB3M/12 • ERSANDP=RSANDP-USTB3M

13. CAPM test • To estimate the CAPM equation, click on Equation • Type in the equation window ERFORD C ERSANDP Or 100*DLOG(FORD)-USTB3M C 100*DLOG(SANDP)-USTB3M

14. P.99-P.104 File: macro.xls Period: 1986/03~2007/04

15. APT-style Model • In the spirit of APT, the following example will examine regressions that seek to determine whether the monthly returns on Microsoft stock an be explained by reference to unexpected changes in a set of macroeconomic and financial variables. • Press Genr or type in the Command window Genrdspread = d(baa_aaa_spread) Genrdprod = d(industrial_production) Genrdcredit = d(consumer_credit) Genrrmsoft = 100*dlog(microsoft) Genrrsandp= 100*dlog(sandp) Genrdmoney = d(m1money_supply) Genrinflation = 100*dlog(cpi) Genr term = ustb10y – ustb3m

16. Press Genr Genrdinflation = d(inflation) Genrmustb3m = ustb3m/12 Genrrterm = d(term) Genrermsoft= rmsoft – mustb3m Genrersandp= rsandp– mustb3m • Use Least Squares over the whole sample period. (ermsoft c ersandpdproddcreditdinflationdmoneydspreadrterm)

20. Stepwise regression

21. P.136-P.139 File: macro.wfl Period: 1986/03~2007/04

22. Testing for heteroscedasticity • If the residuals of the regression have systematically changing variability over the sample, that is a sign of heteroscedasticity. • It is hard to see any clear pattern, so we need to run the formal statistical test. (White’s test)

23. To test for heteroscedasticity using White’s test. V V  ambiguous!! X

24. Using White’s modified standard error estimates in EViews The heteroscedasticity-consistent s.d. errors are smaller than OLS Durbin-Watson (DW) is a test for first order autocorrelation.

25. Detecting autocorrelation • Breusch-Godfrey test:

26. Testing for non-normality • The Bera-Jarque normality tests • View  Residual Tests  Histogram  Normality Test

27. Multicollinearity • Quick/Group  Statistics/Correlations • In the dialog box that appears: Ersandpdproddcreditdinflationdmoneydspreadrterm

28. RESET tests (p.177) • View  Stability tests  Ramsey RESET test

29. It would be concluded that the linear model for the Microsoft returns is appropriate.

30. Stability tests (p.188) • View  Stability Tests  Chow Breakpoint Test

32. P.234-P.238 File: UKHP.wfl Period: 1991/03~2007/05

33. Constructing ARMA models in Eviews • We use the monthly UK house price series in the chapter one to build an ARMA model for the house price changes. • Autocorrelation • Partial autocorrelation

34. Estimating the autocorrelation coefficients for up to 12 lags • Double click DHP  View/Correlation  Lag 12  OK

35. Using information criteria to decide on model orders • Quick  Estimate Equation

36. This specify an ARMA(1,1). The output is given in the table below.

37. One more example:“dhp c ar(1) ar(2) ar(3) ar(4) ar(5) ma(1) ma(2) ma(3) ma(4) ma(5)” • Using AIC to decide which one model is good. • Smaller AIC imlies better model. AIC

38. Forecasting using ARMA models in Eviews • Suppose that the AR(2) model selected for the house price percentage changes series were estimated using observations Feb. 1991-Dec. 2004, leaving 29 remaining observations to construct forecasts. • Quick Equation Estimation 

39. Forecast  dynamic/static

40. Simultaneous equations modelling using EViews • What is the relationship between inflation and stock returns? • In EViews, to do this we need to specify a list of instruments, which would be all of the variables from the reduced form equation. The reduced form equations: • Quick  Estimation Equation

42. The coefficients are all not significant. • The fitted relationship between the stock returns and inflation series is positive (albeit not significantly so). • The adjusted is negative.

43. P.308 File: currencies.wfl Period: 1991/03~2007/05

44. Vector autoregressive models • The simplest case: • Open “currencies.wfl” Quick  Estimate VAR

46. How to decide the length of lagged term? • View  Lag Structure  Lag Length Criteria 10 • Conclusion: choose VAR(1). • Granger causality test  very little evidence of lead-lag interactions between the series.