# Forecast Observations of State-Space Model Containing Regression Component

This example shows how to estimate a regression model containing a regression component, and then forecast observations from the fitted model.

Suppose that the linear relationship between the change in the unemployment rate and the nominal gross national product (nGNP) growth rate is of interest. Suppose further that the first difference of the unemployment rate is an ARMA(1,1) series. Symbolically, and in state-space form, the model is

$\begin{array}{l}\left[\begin{array}{c}{x}_{1,t}\\ {x}_{2,t}\end{array}\right]=\left[\begin{array}{cc}\varphi & \theta \\ 0& 0\end{array}\right]\left[\begin{array}{c}{x}_{1,t-1}\\ {x}_{2,t-1}\end{array}\right]+\left[\begin{array}{c}1\\ 1\end{array}\right]{u}_{1,t}\\ {y}_{t}-\beta {Z}_{t}={x}_{1,t}+\sigma {\epsilon }_{t},\end{array}$

where:

• ${x}_{1,t}$ is the change in the unemployment rate at time t.

• ${x}_{2,t}$ is a dummy state for the MA(1) effect.

• ${y}_{1,t}$ is the observed change in the unemployment rate being deflated by the growth rate of nGNP (${Z}_{t}$).

• ${u}_{1,t}$ is the Gaussian series of state disturbances having mean 0 and standard deviation 1.

• ${\epsilon }_{t}$ is the Gaussian series of observation innovations having mean 0 and standard deviation $\sigma$.

Load the Nelson-Plosser data set, which contains the unemployment rate and nGNP series, among other things.

load Data_NelsonPlosser

Preprocess the data by taking the natural logarithm of the nGNP series, and the first difference of each series. Also, remove the starting NaN values from each series.

isNaN = any(ismissing(DataTable),2);       % Flag periods containing NaNs
gnpn = DataTable.GNPN(~isNaN);
u = DataTable.UR(~isNaN);
T = size(gnpn,1);                          % Sample size
Z = [ones(T-1,1) diff(log(gnpn))];
y = diff(u);

Though this example removes missing values, the software can accommodate series containing missing values in the Kalman filter framework.

To determine how well the model forecasts observations, remove the last 10 observations for comparison.

numPeriods = 10;                   % Forecast horizon
isY = y(1:end-numPeriods);         % In-sample observations
oosY = y(end-numPeriods+1:end);    % Out-of-sample observations
ISZ = Z(1:end-numPeriods,:);       % In-sample predictors
OOSZ = Z(end-numPeriods+1:end,:);  % Out-of-sample predictors

Specify the coefficient matrices.

A = [NaN NaN; 0 0];
B = [1; 1];
C = [1 0];
D = NaN;

Specify the state-space model using ssm.

Mdl = ssm(A,B,C,D);

Estimate the model parameters. Specify the regression component and its initial value for optimization using the 'Predictors' and 'Beta0' name-value pair arguments, respectively. Restrict the estimate of $\sigma$ to all positive, real numbers. For numerical stability, specify the Hessian when the software computes the parameter covariance matrix, using the 'CovMethod' name-value pair argument.

params0 = [0.3 0.2 0.1]; % Chosen arbitrarily
[EstMdl,estParams] = estimate(Mdl,isY,params0,'Predictors',ISZ,...
'Beta0',[0.1 0.2],'lb',[-Inf,-Inf,0,-Inf,-Inf],'CovMethod','hessian');
Method: Maximum likelihood (fmincon)
Sample size: 51
Logarithmic  likelihood:     -87.2409
Akaike   info criterion:      184.482
Bayesian info criterion:      194.141
|      Coeff       Std Err    t Stat     Prob
----------------------------------------------------------
c(1)      |  -0.31780       0.19429    -1.63572  0.10190
c(2)      |   1.21242       0.48882     2.48032  0.01313
c(3)      |   0.45583       0.63930     0.71302  0.47584
y <- z(1) |   1.32407       0.26313     5.03201   0
y <- z(2) | -24.48733       1.90115   -12.88024   0
|
|    Final State   Std Dev     t Stat    Prob
x(1)      |  -0.38117       0.42842    -0.88971  0.37363
x(2)      |   0.23402       0.66222     0.35339  0.72380

EstMdl is an ssm model, and you can access its properties using dot notation.

Forecast observations over the forecast horizon. EstMdl does not store the data set, so you must pass it in appropriate name-value pair arguments.

[fY,yMSE] = forecast(EstMdl,numPeriods,isY,'Predictors0',ISZ,...
'PredictorsF',OOSZ,'Beta',estParams(end-1:end));

fY is a 10-by-1 vector containing the forecasted observations, and yMSE is a 10-by-1 vector containing the variances of the forecasted observations.

Obtain 95% Wald-type forecast intervals. Plot the forecasted observations with their true values and the forecast intervals.

ForecastIntervals(:,1) = fY - 1.96*sqrt(yMSE);
ForecastIntervals(:,2) = fY + 1.96*sqrt(yMSE);

figure
h = plot(dates(end-numPeriods-9:end-numPeriods),isY(end-9:end),'-k',...
dates(end-numPeriods+1:end),oosY,'-k',...
dates(end-numPeriods+1:end),fY,'--r',...
dates(end-numPeriods+1:end),ForecastIntervals,':b',...
dates(end-numPeriods:end-numPeriods+1),...
[isY(end)*ones(3,1),[oosY(1);ForecastIntervals(1,:)']],':k',...
'LineWidth',2);
xlabel('Period')
ylabel('Change in the unemployment rate')
legend(h([1,3,4]),{'Observations','Forecasted responses',...
'95% forecast intervals'})
title('Observed and Forecasted Changes in the Unemployment Rate')

This model seems to forecast the changes in the unemployment rate well.

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