7310AFE Econometric Methods

Question:

A domestic financial firm has hired you to study the empirical links between the Australian All Ordinaries Index, the Hong Kong based Hang Seng, and the US Dow Jones Industrial Average. The file allord.wf1 has data on the daily returns (in percentage points) on these three financial indicators. You are to estimate an ARDL(P,Q) model for Australian returns  

1. Select the appropriate lag structures to ensure that your model (i) has desirable goodness of fit properties, and (ii) is free of autocorrelation.   Give your estimated equation and provide appropriate supporting outputs to demonstrate the quality of your model.

2. Write a paragraph explaining on how you settled on your specification in the previous question. Why is it better than other possible specifications?

3. ining the dynamics of returns as estimated by your model. Which foreign index seems more important for returns in Australia? How long does it take for movements in these markets to impact upon the Australian index? How well does your model fit the data, and what does this imply about financial modelling?

Social scientists are often interested in the factors that make marriages successful, versus those that predict familial break up.  One such factor is the presence of an extramarital affair.  In the data file affairs.wf1 you have 601 observations (obtained from a confidential survey conducted by a US magazine) on this phenomenon. The variables are defined as follows:

Your task is to produce an econometric model that will yield useful information for understanding the incidence of extramarital affairs. Be sure to highlight the strengths and weaknesses of your model. Also include an Eviews output of your final model and of other related outputs that helped you make your decisions.

You may like to (i) use alternative functional forms and (ii) include different transformations of the same variable in your chosen regression.  You should also consider at least some of the following:

(1) The quality of the fit

(2) The significance of the coefficients

(3) Whether the specification of your model is appropriate for the data

(4) The parsimony of your model (i.e. there is a slight preference for simple models over complex ones)

(5) Whether the standard errors are correct

(6) The extent to which multicollinearity affects your model

(7) The social implications of your research.

Note that there is no ‘correct’ answer and that a great many specifications will perform well. You will be marked on both the model and the explanation you provide with an emphasis on the explanation.