Commandeur & Koopman, State Space Time Series Analysis

[TomDoan]

The attached zip has the examples and data sets from Commandeur & Koopman, An Introduction to State Space Time Series Analysis, Oxford University Press 2007. These are all examples of the use of the DLM instruction for analyzing state space models; in almost all cases, these are applications of structural time series models (local level or local trend with or without seasonality) which decompose a time series into the sum of two or more uncorrelated components. The examples are the most straightforward of any from the books that we have on the subject.

This book is available for purchase from Estima. For more information, see http://www.estima.com/textbook_commandeur.shtml.

commandeur-koopman.zip

Examples/data

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Example	Description	RATS Level
chapter1.rpf	Descriptive statistics;graphs	Basic
chapter2_norway.rpf	Local level model, estimation of variances	Intermediate
chapter2_uk.rpf	Local level model	Intermediate
chapter3_finland.rpf	Local trend model, estimation, Kalman smoothing	Intermediate
chapter3_uk.rpf	Local trend model, estimation, Kalman smoothing	Intermediate
chapter4_inflation.rpf	UC model with seasonal	Intermediate
chapter4_uk.rpf	UC model with seasonal	Intermediate
chapter5_uk.rpf	Local level model with exogenous shift	Intermediate
chapter6_uk.rpf	Local level model with intervention variable	Intermediate
chapter7_inflation.rpf	Seasonal model with intervention variables	Intermediate
chapter7_uk.rpf	Seasonal model with exogenous shifts	Intermediate
chapter8_nor_fin_forecast.rpf	Local trend model, out-of-sample predictions	Intermediate
chapter8_norway.rpf	Local level model, out-of-sample predictions	Intermediate
chapter8_uk_diags.rpf	UC model with seasonal; diagnostics	Intermediate
chapter8_uk_forecast.rpf	UC model with seasonal; out-of-sample predictions	Intermediate
chapter8_uk_missing.rpf	UC model with seasonal; missing data handling	Intermediate
chapter8_uk.rpf	UC model with seasonal	Intermediate
chapter9_bivariate.rpf	Seasonal model with multiple observables	Advanced

[hardmann]

Dear Tom:
In code of Chapter 8 - Norway & Finland data, forecasts, commandure & koopman(2007), there is a spelling error.
In finland code:
nonlin sigsqeps sigsqxi=0.0 sigsqzeta
dlm(a=at,c=ct,sv=sigsqeps,f=ft,sw=%diag(||sigsqxi,sigsqzeta||),exact,y=logfinland,$
method=bfgs,yhat=vhat,svhat=svhat) * 2008:1 xstates vstates
set forecast 1970:3 2008:1 = %if(t<=2003:1,%scalar(yhat),%scalar(xstates(t)))
set fvariance 1970:3 2008:1 = %if(t<=2003:1,%scalar(svhat),%scalar(vstates(t)))

vhat and yhat should be same.


Best Regard.
Hardmann

[TomDoan]

Dear Tom:
In code of Chapter 8 - Norway & Finland data, forecasts, commandure & koopman(2007), there is a spelling error.
In finland code:
nonlin sigsqeps sigsqxi=0.0 sigsqzeta
dlm(a=at,c=ct,sv=sigsqeps,f=ft,sw=%diag(||sigsqxi,sigsqzeta||),exact,y=logfinland,$
method=bfgs,yhat=vhat,svhat=svhat) * 2008:1 xstates vstates
set forecast 1970:3 2008:1 = %if(t<=2003:1,%scalar(yhat),%scalar(xstates(t)))
set fvariance 1970:3 2008:1 = %if(t<=2003:1,%scalar(svhat),%scalar(vstates(t)))

vhat and yhat should be same.


Best Regard.
Hardmann

Yes. It should read

dlm(a=at,c=ct,sv=sigsqeps,f=ft,sw=%diag(||sigsqxi,sigsqzeta||),exact,y=logfinland,$
method=bfgs,yhat=yhat,svhat=svhat) * 2008:1 xstates vstates

[hardmann]

Dear Tom

I use STAMP 6.3 to estime the each model, the Log-Likelihood is very different from the one estimated from RATS, even is different from result from the Commandeur & Koopman book, so does AIC.
For example, as for the local determininstic level model for uk log KSI, the Log-Likelihood is 334.331 for STAMP, 63.3139 for RATS, 0.329757 for C&K book.
Why is these very different .

Best Regard
Hardmann

[TomDoan]

Have you checked whether the difference is due to the (log) integrating constants? RATS includes those, but the inclusion/exclusion has no effect on any of the estimates.

There are different ways to define AIC. @REGCRITS standardizes AIC by dividing by the number of observations---that doesn't change the ordering of models.