Structural Break in the Variance/Covariance Matrix State Space

[macroWB]

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]

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]

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]

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