Structural Break in the Variance/Covariance Matrix State Space

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macroWB
Posts: 3
Joined: Tue Jun 09, 2015 7:03 am

Structural Break in the Variance/Covariance Matrix State Space

Is it possible in the Eviews State Space to incorporate structural change in the variance-covariance matrix? Imagine a simple trend-cycle GDP model but I want to include a break for the great moderation (lower volatility in GDP).

Signals
y = trend + cycle

States
trend = c(1) + trend(-1) + [var=c(10)^2)]
cycle = c(2)*cycle(-1) + c(3)*cycle(-2) + [var=c(20)^2)]

Could I write something along the following lines for the state equations?

States Breaks
trend = c(1) + trend(-1) + [var=(c(10)+c(11)*@during("1960 1984"))^2)]
cycle = c(2)*cycle(-1) + c(3)*cycle(-2) + [var=(c(20)+c(21)*@during("1960 1984"))^2)]

Thanks,

Dave

EViews Glenn
EViews Developer
Posts: 2616
Joined: Wed Oct 15, 2008 9:17 am

Re: Structural Break in the Variance/Covariance Matrix State Space

I don't have the time to test this right now, but it should work.

All that the variance equation is doing is evaluating the expression to get the appropriate variance for a give observation.

macroWB
Posts: 3
Joined: Tue Jun 09, 2015 7:03 am

Re: Structural Break in the Variance/Covariance Matrix State Space

Thanks for the quick reply Glenn. I had a go at coding up a simple trend/cycle model of US per capita GDP and it seems to work ok. If anyone sees anything that looks wrong with the syntax I have used please comment below.

Thanks again,

Dave

Code: Select all

'___________________________________________________________________________________________________________________________________________________________________________

'  ESTIMATE UNOBSERVED COMPONENT MODELS
' Estimates two models:
' 1. Simple trend/cycle model of GDP
' 2. Simple trend/cycle model of GDP with structural break in variance-covariance matrix
'___________________________________________________________________________________________________________________________________________________________________________
close @all

wfcreate q 1947Q1 2019Q2
fetch(d=ffred) a939rx0q048sbea

sample ssest 1947q1 2006q4

series lgdppc = log(a939rx0q048sbea)*100
series dlgdppc = dlog(a939rx0q048sbea)*100

series dum_mod = 0
smpl @first 1984q4
dum_mod =1

' Setup coefficient vectors
coef(1) delta
coef(2) phi
coef(3) sigma
coef(6) alpha

' Establish starting values using frequency domain filter estimates of trend and cycle

smpl @all

' HP filter
lgdppc.hpf(lamda=1600)  lgdppct_ini
series lgdppcc_ini = lgdppc - lgdppct_ini

' Estimate initial coefficients
' Trend
smpl ssest
equation eq_lgdppct.ls lgdppct_ini =delta(1)+lgdppct_ini(-1)

' Store estimates for later use
!delta1=delta(1)
!sigma1=eq_lgdppct.@se

' Cycle
smpl ssest
equation eq_phi.ls lgdppcc_ini = phi(1)*lgdppcc_ini(-1) + phi(2)*lgdppcc_ini(-2)

' Store estimates for later use
!phi1 = phi(1)
!phi2 =phi(2)
!sigma2=eq_phi.@se

' Estimate simple trend/cycle state space model using HP estimates as starting values (uses MLE)
sspace eq_gdpc
eq_gdpc.append @param delta(1) !delta1 phi(1) !phi1 phi(2) !phi2 sigma(1) !sigma1 sigma(2) !sigma2
eq_gdpc.append @signal dlgdppc = delta(1) + ygap0 - ygap1+ [var=(sigma(1)^2)]
eq_gdpc.append @state ygap0 = phi(1)*ygap0(-1) + phi(2)*ygap1(-1) + [var=(sigma(2)^2)]
eq_gdpc.append @state ygap1 = ygap0(-1)

smpl ssest
eq_gdpc.ml(optmethod=legacy)
eq_gdpc.makestates(t=smooth) *

' Estimate simple trend/cycle state space model with structural break in variance-covariance matrix
sspace eq_gdpc_break
eq_gdpc_break.append @param delta(1) !delta1 phi(1) !phi1 phi(2) !phi2 sigma(1) !sigma1 sigma(2) !sigma2
eq_gdpc_break.append @signal dlgdppc = delta(1) + ygap0 - ygap1+ [var=(sigma(1)+sigma(3)*dum_mod)^2]
eq_gdpc_break.append @state ygap0 = phi(1)*ygap0(-1) + phi(2)*ygap1(-1) + [var=(sigma(2)^2)]
eq_gdpc_break.append @state ygap1 = ygap0(-1)

smpl ssest
eq_gdpc_break.ml
eq_gdpc_break.makestates(t=smooth) *_break

plot ygap0 ygap0_break

EViews Glenn
EViews Developer
Posts: 2616
Joined: Wed Oct 15, 2008 9:17 am

Re: Structural Break in the Variance/Covariance Matrix State Space

Looking at the variance specs only, it looks okay to me.