Class: ssm

Refine initial parameters to aid state-space model estimation




refine(Mdl,Y,params0) finds a set of initial parameter values to use when fitting the state-space model Mdl to the response data Y, using the crude set of initial parameter values params0. The software uses several routines, and displays the resulting loglikelihood and initial parameter values for each routine.


refine(Mdl,Y,params0,Name,Value) displays results of the routines with additional options specified by one or more Name,Value pair arguments. For example, you can include a linear regression component composed of predictors and an initial value for the coefficients.


Output = refine(___) returns a structure array (Output) containing a vector of refined, initial parameter values, the loglikelihood corresponding the initial parameter values, and the method the software used to obtain the values. You can use any of the input arguments in the previous syntaxes.


  • Likelihood surfaces of state-space models can be complicated, for example, they might contain multiple local maxima. If estimate fails to converge, or converges to an unsatisfactory solution, then refine might find a better set of initial parameter values to pass to estimate.

  • The refined initial parameter values returned by refine might appear similar to each other and to params0. Choose a set yielding estimates that make economic sense and correspond to relatively large loglikelihood values.

  • If a refinement attempt fails, then the software displays errors and sets the corresponding loglikelihood to -Inf. It also sets its initial parameter values to [].

Input Arguments

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Mdl — Standard state-space modelssm model object

Standard state-space model containing unknown parameters, specified as an ssm model object returned by ssm.

  • For explicitly created state-space models, the software estimates all NaN values in the coefficient matrices (Mdl.A, Mdl.B, Mdl.C, and Mdl.D) and the initial state means and covariance matrix (Mdl.Mean0 and Mdl.Cov0). For details on explicit and implicit model creation, see ssm.

  • For implicitly created state-space models, you specify the model structure and the location of the unknown parameters using the parameter-to-matrix mapping function. Implicitly create a state-space model to estimate complex models, impose parameter constraints, and estimate initial states. The parameter-to-mapping function can also accommodate additional output arguments.

    Note:   Mdl does not store observed responses or predictor data. Supply the data wherever necessary, using the appropriate input and name-value pair arguments.

Y — Observed response datanumeric matrix | cell vector of numeric vectors

Observed response data to which Mdl is fit, specified as a numeric matrix or a cell vector of numeric vectors.

  • If Mdl is time invariant with respect to the observation equation, then Y is a T-by-n matrix. Each row of the matrix corresponds to a period and each column corresponds to a particular observation in the model. Therefore, T is the sample size and n is the number of observations per period. The last row of Y contains the latest observations.

  • If Mdl is time varying with respect to the observation equation, then Y is a T-by-1 cell vector. Y{t} contains an nt-dimensional vector of observations for period t, where t = 1,...,T. The corresponding dimensions of the coefficient matrices in Mdl.C{t} and Mdl.D{t} must be consistent with the matrix in Y{t} for all periods. The last cell of Y contains the latest observations.

Suppose that you create Mdl implicitly by specifying a parameter-to-matrix mapping function, and the function has input arguments for the observed responses or predictors. Then, the mapping function establishes a link to observed responses and the predictor data in the MATLAB® workspace, which overrides the value of Y.

NaN elements indicate missing observations. For details on how the Kalman filter accommodates missing observations, see Algorithms.

Data Types: double | cell

params0 — Initial values of unknown parametersnumeric vector

Initial values of unknown parameters for numeric maximum likelihood estimation, specified as a numeric vector.

The elements of params0 correspond to the unknown parameters in the state-space model matrices A, B, C, and D, and, optionally, the initial state mean Mean0 and covariance matrix Cov0.

  • If you created Mdl explicitly (that is, by specifying the matrices without a parameter-to-matrix mapping function), then the software maps the elements of params to NaNs in the state-space model matrices and initial state values. The software searches for NaNs column-wise, following the order A, B, C, D, Mean0, Cov0.

  • If you created Mdl implicitly (that is, by specifying the matrices with a parameter-to-matrix mapping function), then set initial parameter values for the state-space model matrices, initial state values, and state types within the parameter-to-matrix mapping function.

Data Types: double

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

'Beta0' — Initial values of regression coefficientsnumeric matrix

Initial values of regression coefficients, specified as the comma-separated pair consisting of 'Beta0' and a d-by-n numeric matrix. d is the number of predictor variables (see Predictors) and n is the number of observed response series (see Y).

By default, Beta0 is the ordinary least-squares estimate of Y onto Predictors.

Data Types: double

'Predictors' — Predictor data[] (default) | numeric matrix

Predictor data for the regression component in the observation equation, specified as the comma-separated pair consisting of 'Predictors' and a T-by-d numeric matrix. T is the number of periods and d is the number of predictor variables. Row t corresponds to the observed predictors at period t (Zt) in the expanded observation equation


In other words, the predictor series serve as observation deflators. β is a d-by-n time-invariant matrix of regression coefficients that the software estimates with all other parameters.

  • For n observations per period, the software regresses all predictor series onto each observation.

  • If you specify Predictors, then Mdl must be time invariant. Otherwise, the software returns an error.

  • By default, the software excludes a regression component from the state-space model.

Data Types: double

Output Arguments

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Output — Information about initial parameter valuesstructure array

Information about the initial parameter values, returned as a 1-by-5 structure array. The software uses five algorithms to find initial parameter values, and each element of Output corresponds to an algorithm.

This table describes the fields of Output.


Refinement algorithm.

Each element of Output corresponds to one of these algorithms:

'Loose bound interior point'
'Nelder-Mead algorithm'
'Starting value perturbation'
'Starting value shrinkage'
LoglikelihoodLoglikelihood corresponding to the initial parameter values.
ParametersVector of refined initial parameter values. The order of the parameters is the same as the order in params0. If you pass these initial values to estimate, then the estimation results can improve.


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Refine Parameters When Fitting Time-Invariant State-Space Model

Suppose that a latent process is a random walk. Subsequently, the state equation is

$$x_t = x_{t-1} + u_t,$$

where $u_t$ is Gaussian with mean 0 and standard deviation 1.

Generate a random series of 100 observations from $x_t$, assuming that the series starts at 1.5.

T = 100;
rng(1); % For reproducibility
u = randn(T,1);
x = cumsum([1.5;u]);
x = x(2:end);

Suppose further that the latent process is subject to additive measurement error. Subsequently, the observation equation is

$$y_t = x_t + \varepsilon_t,$$

where $\varepsilon_t$ is Gaussian with mean 0 and standard deviation 1.

Use the random latent state process (x) and the observation equation to generate observations.

y = x + randn(T,1);

Together, the latent process and observation equations compose a state-space model. Assume that the state is a stationary AR(1) process. Then the state-space model to estimate is

$$\begin{array}{c}x_t = \phi x_{t-1} + \sigma_1u_t \\ y_t = x_t +

Specify the coefficient matrices. Use NaN values for unknown parameters.

A = NaN;
B = NaN;
C = 1;
D = NaN;

Specify the state-space model using the coefficient matrices. Specify that the initial state distribution is stationary using the StateType name-value pair argument.

StateType = 0;
Mdl = ssm(A,B,C,D,'StateType',StateType);

Mdl is an ssm model. The software sets values for the initial state mean and variance. Verify that the model is specified correctly using the display in the Command Window.

Pass the observations to estimate to estimate the parameters. For the params0 parameters that are unlikely to correspond to their true values. Also, specify lower bound constraints of 0 for the standard deviations.

params0 = [-1e7 1e-6 2000];
EstMdl = estimate(Mdl,y,params0,'lb',[-Inf,0,0]);
Warning: Covariance matrix of estimators cannot be computed precisely due to
inversion difficulty. Check parameter identifiability. Also try different
starting values and other options to compute the covariance matrix. 

Method: Maximum likelihood (fmincon)
Sample size: 100
Logarithmic  likelihood:     -2464.23
Akaike   info criterion:      4934.46
Bayesian info criterion:      4942.27
      |      Coeff           Std Err       t Stat     Prob  
 c(1) |   -9.99977e+06      9.99977e+05  -10.00000   0      
 c(2) |    1.23086e+05      1.91567e+13    0.00000  1.00000 
 c(3) | 2006.86501          3.12341e+11    0.00000  1.00000 
      |     Final State      Std Dev        t Stat    Prob  
 x(1) |   -3.37649       1999.42392       -0.00169  0.99865 

estimate failed to converge, and so the results are undesirable.

Refine params0 using refine.

Output = refine(Mdl,y,params0);
logL = cell2mat({Output.LogLikelihood})';
[~,maxLogLIndx] = max(logL)
refinedParams0 = Output(maxLogLIndx).Parameters
Description = Output(maxLogLIndx).Description
maxLogLIndx =


refinedParams0 =

    0.9705   -0.8934    0.9330

Description =

Nelder-Mead simplex

The algorithm that yields the highest loglikelihood value is Loose bound interior point, which is the third struct in the structure array Output.

Estimate Mdl using refinedParams0, which is the vector of refined initial parameter values.

EstMdl = estimate(Mdl,y,refinedParams0,'lb',[-Inf,0,0]);
Method: Maximum likelihood (fmincon)
Sample size: 100
Logarithmic  likelihood:     -181.379
Akaike   info criterion:      368.758
Bayesian info criterion:      376.574
      |     Coeff       Std Err   t Stat     Prob  
 c(1) |  0.97050       0.02863   33.90367   0      
 c(2) |  0.89343       0.18521    4.82401  0.00000 
 c(3) |  0.93303       0.15176    6.14806   0      
      |   Final State   Std Dev    t Stat    Prob  
 x(1) | -3.93007       0.72066   -5.45343   0      

estimate converged, making the parameter estimates much more desirable. The AR model coefficient is within two standard errors of 1, which suggests that the state processes is a random walk.

Refine Estimation of State-Space Model Containing Regression Component

Suppose that the relationship between the unemployment rate and the nominal gross national product (nGNP) is linear. Suppose further that the unemployment rate is an AR(1) series. Symbolically, and in state-space form, the model is

x_{t} = \phi x_{t - 1} + \sigma u_{t}\\
{y_t} - \beta {Z_t} = {x_{t}},


  • $x_{t}$ is the unemployment rate at time t.

  • $y_{t}$ is the observed unemployment rate being deflated by the log of nGNP ( $Z_t$).

  • $u_{t}$ is the Gaussian series of state disturbances having mean 0 and unknown standard deviation $\sigma$.

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

load Data_NelsonPlosser

Preprocess the data by taking the first difference of the unemployment rate and converting nGNP to a series of returns. Remove the observations corresponding to the string of NaN values at the beginning of the unemployment rate series.

isNaN = any(ismissing(DataTable),2);       % Flag periods containing NaNs
gnpn = DataTable.GNPN(~isNaN);
y = DataTable.UR(~isNaN);
y = diff(y);
T = size(y,1);
Z = [ones(T,1) price2ret(gnpn)];

This example continues using the series without NaN values. However, using the Kalman filter framework, the software can accommodate series containing missing values.

Specify the coefficient matrices.

A = NaN;
B = NaN;
C = 1;

Specify the state-space model using ssm.

Mdl = ssm(A,B,C);

Find a good set of starting parameters to use for estimation.

params0 = [150 1000]; % Initial values chosen arbitrarily
Beta0 = [1 -100];
Output = refine(Mdl,y,params0,'Predictors',Z,'Beta0',Beta0);

Output is a 1-by-5 structure array containing the recommended initial parameter values.

Choose the initial parameter values corresponding to the largest loglikelihood.

logL = cell2mat({Output.LogLikelihood})';
[~,maxLogLIndx] = max(logL)
refinedParams0 = Output(maxLogLIndx).Parameters
Description = Output(maxLogLIndx).Description
maxLogLIndx =


refinedParams0 =

    0.0000   -1.3441    1.3477  -24.4336

Description =

Nelder-Mead simplex

Estimate Mdl using the refined initial parameter values refinedParams0.

EstMdl = estimate(Mdl,y,refinedParams0(1:(end - 2)),'Predictors',Z,...
    'Beta0',refinedParams0((end - 1):end));
Method: Maximum likelihood (fminunc)
Sample size: 61
Logarithmic  likelihood:     -103.321
Akaike   info criterion:      214.642
Bayesian info criterion:      223.085
           |      Coeff       Std Err    t Stat     Prob  
 c(1)      |   0.20499       0.12217     1.67793  0.09336 
 c(2)      |  -1.31586       0.08283   -15.88649   0      
 y <- z(1) |   1.38082       0.23315     5.92241   0      
 y <- z(2) | -24.87986       1.76909   -14.06365   0      
           |    Final State   Std Dev     t Stat    Prob  
 x(1)      |   1.19607        0           Inf      0      

estimate returns reasonable parameter estimates and their corresponding standard errors.


The Kalman filter accommodates missing data by not updating filtered state estimates corresponding to missing observations. In other words, suppose that your data has a missing observation at period t. Then, the state forecast for period t, based on the previous t – 1 observations, is equivalent to the filtered state for period t.

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