Financial Econometrics (ASB

Part 1

Use the data money.dta:

1.

Estimate

a VAR(1) and a VAR(2) for the four variables, including two dummy

variables

(dumy, dumo).

Which is preferred on AIC?

(

15 marks

)

2.

Test for autocorrelation (LM tes

ts) after estimating t

he VAR(2) and after for VAR

(

1

)

. What do

these test suggest

?

(

10 marks

)

3.

Estimate the cointegrating rank for a second order VAR, including a constant but not

a trend.

What do you .nd the cointegrating rank to be?

Repeat with a restricted trend

(this allows for a trend

in the cointegrating relationships). How do

the results

concerning the cointegrating rank change?

(

15 marks

)

Part 2

Now use the

file cons_income.dta is a STATA data file with data on r

eal personal

disposable income,

rpdi,

and real personal consumption, rc. The third variable is `time’.

T

he data are quarterly from

1947q1 to 2009q2.

Take the natural logarithms of rpdi and rc,

and then first differ

ence these variables to get the approximate growth rates, So for

income: gen Lrpdi

= ln(rpdi)

gen DLrpdi = d.Lrpdi

and similarly for consumption

.

1.

Analyse the statistical properties of the logs of the series and the diffe

rences of the

logs of

t

he

series: plot the data and look at ACF and PACF. What features do you

notice?

(

15 marks

)

2. Test the series Lrc, Lrpdi and Ls (=Lrc

–

Lrpdi) for being I(1) versus I(0).

What happens if you test Ls on the periods 1947

–

1980 and 1980

–

2009 separately?

Plot the time series for Ls and interpret your results.

(

10 marks

)

3.

Estimate alternative univariate models for the differences (

Dlrdi, Dlrc) using

observations up to

1999q3, leaving the remaini

ng observations for out

–

of sa

mple

forecasting. Start with AR

models

,

then you can try different mixed ARMA

specifications.

(

20 marks

)

Part 3

The file assignment.dta is a STATA data file containing compu

ter generated data on four

time

series, x,y,z and w and a series newt whi

ch is a quarterly time

trend running from

(1950:Q1 to

1999:Q4). Assume that the series x,y,z and w are already in logs.

1.

Investigate the order of integration of the series x,y,z and w, paying particular

attention to the form

of test regressi

on that you use

. (

5 marks

)

2.

Produce dynamic forecasts of x,y,z and w using an estimated VECM model for all 4

series using in

–

sample forecasting for

the perio

d 1990Q1 to 1999Q4. Now compare

the

1

–

step ahead forecast performance of a univariate AR(2) model for ?x with

forecasts

of the same variable using an estimated VE

CM model for all 4 series using

in

–

sample

forecasting for the period 1990Q1 to 1999Q4.

What are the gains (if any) to

using the

VECM compared to the simple time

–

series

model for the series? Are these

gains what

you’d expect?

(

10 marks

)

PLACE THIS ORDER OR A SIMILAR ORDER WITH US TODAY AND GET AN AMAZING DISCOUNT 🙂