# simulate

Simulate posterior draws of Bayesian state-space model parameters

## Syntax

[Params,accept] = simulate(PriorMdl,Y,params0,Proposal)
[Params,accept] = simulate(PriorMdl,Y,params0,Proposal,Name=Value)
[Params,accept,Output] = simulate(PriorMdl,Y,params0,Proposal,Name=Value)

## Description

example

[Params,accept] = simulate(PriorMdl,Y,params0,Proposal) returns 1000 random vectors of state-space model parameters Params drawn from the posterior distribution Π(θ|Y), where PriorMdl specifies the prior distribution and data likelihood, and Y is the observed response data. params0 is the set of initial parameter values and Proposal is the covariance matrix of the proposal distribution of the Metropolis-Hastings sampler [1][2]. accept is the acceptance rate of the proposal draws.

example

[Params,accept] = simulate(PriorMdl,Y,params0,Proposal,Name=Value) specifies options using one or more name-value arguments. For example, simulate(PriorMdl,Y,params0,Proposal,NumDraws=1e6,Thin=3,DoF=10) uses the multivariate t10 distribution for the Metropolis-Hastings proposal, draws 3e6 random vectors of parameters, and thins the sample to reduce serial correlation by discarding every 2 draws until it retains 1e6 draws.

example

[Params,accept,Output] = simulate(PriorMdl,Y,params0,Proposal,Name=Value) also returns the output Output of the custom function that monitors the Markov-chain Monte Carlo (MCMC) algorithm at each iteration, specified by the OutputFunction name-value argument.

## Examples

collapse all

Simulate observed responses from a known state-space model, then treat the model as Bayesian and draw parameters from the posterior distribution.

Suppose the following state-space model is a data-generating process (DGP).

$\left[\begin{array}{c}{x}_{t,1}\\ {x}_{t,2}\end{array}\right]=\left[\begin{array}{cc}0.5& 0\\ 0& -0.75\end{array}\right]\left[\begin{array}{c}{x}_{t-1,1}\\ {x}_{t-1,2}\end{array}\right]+\left[\begin{array}{cc}1& 0\\ 0& 0.5\end{array}\right]\left[\begin{array}{c}{u}_{t,1}\\ {u}_{t,2}\end{array}\right]$

${y}_{t}=\left[\begin{array}{cc}1& 1\end{array}\right]\left[\begin{array}{c}{x}_{t,1}\\ {x}_{t,2}\end{array}\right].$

Create a standard state-space model object ssm that represents the DGP.

trueTheta = [0.5; -0.75; 1; 0.5]; A = [trueTheta(1) 0; 0 trueTheta(2)]; B = [trueTheta(3) 0; 0 trueTheta(4)]; C = [1 1]; DGP = ssm(A,B,C);

Simulate a response path from the DGP.

rng(1); % For reproducibility y = simulate(DGP,200);

Suppose the structure of the DGP is known, but the state parameters trueTheta are unknown, explicitly

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

${y}_{t}=\left[\begin{array}{cc}1& 1\end{array}\right]\left[\begin{array}{c}{x}_{t,1}\\ {x}_{t,2}\end{array}\right].$

Consider a Bayesian state-space model representing the model with unknown parameters. Arbitrarily assume that the prior distribution of ${\varphi }_{1}$, ${\varphi }_{2}$, ${\sigma }_{1}^{2}$, and ${\sigma }_{2}^{2}$ are independent Gaussian random variables with mean 0.5 and variance 1.

The Local Functions section contains two functions required to specify the Bayesian state-space model. You can use the functions only within this script.

The paramMap function accepts a vector of the unknown state-space model parameters and returns all the following quantities:

• A = $\left[\begin{array}{cc}{\varphi }_{1}& 0\\ 0& {\varphi }_{2}\end{array}\right]$.

• B = $\left[\begin{array}{cc}{\sigma }_{1}& 0\\ 0& {\sigma }_{2}\end{array}\right]$.

• C = $\left[\begin{array}{cc}1& 1\end{array}\right]$.

• D = 0.

• Mean0 and Cov0 are empty arrays [], which specify the defaults.

• StateType = $\left[\begin{array}{cc}0& 0\end{array}\right]$, indicating that each state is stationary.

The paramDistribution function accepts the same vector of unknown parameters as does paramMap, but it returns the log prior density of the parameters at their current values. Specify that parameter values outside the parameter space have log prior density of -Inf.

Create the Bayesian state-space model by passing function handles directly to paramMap and paramDistribution to bssm.

Mdl = bssm(@paramMap,@priorDistribution)
Mdl = Mapping that defines a state-space model: @paramMap Log density of parameter prior distribution: @priorDistribution 

The simulate function requires a proposal distribution scale matrix. You can obtain a data-driven proposal scale matrix by using the tune function. Alternatively, you can supply your own scale matrix.

Obtain a data-driven scale matrix by using the tune function. Supply a random set of initial parameter values, and shut off the estimation summary display.

numParams = 4; theta0 = rand(numParams,1); [theta0,Proposal] = tune(Mdl,y,theta0,Display=false);
Local minimum found. Optimization completed because the size of the gradient is less than the value of the optimality tolerance. 

Draw 1000 random parameter vectors from the posterior distribution. Specify the simulated response path as observed responses and the optimized values returned by tune for the initial parameter values and the proposal distribution.

[Theta,accept] = simulate(Mdl,y,theta0,Proposal); accept
accept = 0.4010 

Theta is a 4-by-1000 matrix of randomly drawn parameters from the posterior distribution. Rows correspond to the elements of the input argument theta of the functions paramMap and priorDistribution.

accept is the proposal acceptance probability. In this case, simulate accepts 40% of the proposal draws.

Create trace plots of the parameters.

paramNames = ["\phi_1" "\phi_2" "\sigma_1" "\sigma_2"]; figure h = tiledlayout(4,1); for j = 1:numParams nexttile plot(Theta(j,:)) hold on yline(trueTheta(j)) ylabel(paramNames(j)) end title(h,"Posterior Trace Plots")

The sampler eventually settles at near the true values of the parameters. In this case, the sample shows serial correlation and transient behavior. You can remedy serial correlation in the sample by adjusting the Thin name-value argument, and you can remedy transient effects by increasing the burn-in period using the BurnIn name-value argument.

Local Functions

This example uses the following functions. paramMap is the parameter-to-matrix mapping function and priorDistribution is the log prior distribution of the parameters.

function [A,B,C,D,Mean0,Cov0,StateType] = paramMap(theta) A = [theta(1) 0; 0 theta(2)]; B = [theta(3) 0; 0 theta(4)]; C = [1 1]; D = 0; Mean0 = []; % MATLAB uses default initial state mean Cov0 = []; % MATLAB uses initial state covariances StateType = [0; 0]; % Two stationary states end function logprior = priorDistribution(theta) paramconstraints = [(abs(theta(1)) >= 1) (abs(theta(2)) >= 1) ... (theta(3) < 0) (theta(4) < 0)]; if(sum(paramconstraints)) logprior = -Inf; else mu0 = 0.5*ones(numel(theta),1); sigma0 = 1; p = normpdf(theta,mu0,sigma0); logprior = sum(log(p)); end end

Consider the model in the example Draw Random Parameters from Posterior Distribution of Time-Invariant Model. Improve the Markov chain convergence by adjusting sampler options.

Create a standard state-space model object ssm that represents the DGP, and then simulate a response path.

trueTheta = [0.5; -0.75; 1; 0.5]; A = [trueTheta(1) 0; 0 trueTheta(2)]; B = [trueTheta(3) 0; 0 trueTheta(4)]; C = [1 1]; DGP = ssm(A,B,C); rng(1); % For reproducibility y = simulate(DGP,200);

Create the Bayesian state-space model by passing function handles directly to paramMap and paramDistribution to bssm (the functions are in Local Functions).

Mdl = bssm(@paramMap,@priorDistribution)
Mdl = Mapping that defines a state-space model: @paramMap Log density of parameter prior distribution: @priorDistribution 

Simulate random parameter vectors from the posterior distribution. Specify the simulated response path as observed responses, and obtain an optimal proposal distribution by using tune and shut off all optimization displays. The plots in Draw Random Parameters from Posterior Distribution of Time-Invariant Model suggest that the Markov chain settles after 500 draws. Therefore, specify a burn-in period of 500 (BurnIn=500). Specify thinning the sample by keeping the first draw of each set of 30 successive draws (Thin=30). Retain 2000 random parameter vectors (NumDraws=2000).

numParams = 4; theta0 = rand(numParams,1); options = optimoptions("fminunc",Display="off"); [theta0,Proposal] = tune(Mdl,y,theta0,Display=false,Options=options); [Theta,accept] = simulate(Mdl,y,theta0,Proposal, ... NumDraws=2000,BurnIn=500,Thin=30); accept
accept = 0.3885 

Theta is a 4-by-2000 matrix of randomly drawn parameters from the posterior distribution. Rows correspond to the elements of the input argument theta of the functions paramMap and priorDistribution.

accept is the proposal acceptance probability. In this case, simulate accepts 39% of the proposal draws.

Create trace plots and correlograms of the parameters.

paramNames = ["\phi_1" "\phi_2" "\sigma_1" "\sigma_2"]; figure h = tiledlayout(4,1); for j = 1:numParams nexttile plot(Theta(j,:)) hold on yline(trueTheta(j)) ylabel(paramNames(j)) end title(h,"Posterior Trace Plots")

figure h = tiledlayout(4,1); for j = 1:numParams nexttile autocorr(Theta(j,:)); ylabel(paramNames(j)); title([]); end title(h,"Posterior Sample Correlograms")

The sampler quickly settles near the true values of the parameters. The sample shows little serial correlation and no transient behavior.

Local Functions

This example uses the following functions. paramMap is the parameter-to-matrix mapping function and priorDistribution is the log prior distribution of the parameters.

function [A,B,C,D,Mean0,Cov0,StateType] = paramMap(theta) A = [theta(1) 0; 0 theta(2)]; B = [theta(3) 0; 0 theta(4)]; C = [1 1]; D = 0; Mean0 = []; % MATLAB uses default initial state mean Cov0 = []; % MATLAB uses initial state covariances StateType = [0; 0]; % Two stationary states end function logprior = priorDistribution(theta) paramconstraints = [(abs(theta(1)) >= 1) (abs(theta(2)) >= 1) ... (theta(3) < 0) (theta(4) < 0)]; if(sum(paramconstraints)) logprior = -Inf; else mu0 = 0.5*ones(numel(theta),1); sigma0 = 1; p = normpdf(theta,mu0,sigma0); logprior = sum(log(p)); end end

Consider the following time-varying, state-space model for a DGP:

• From periods 1 through 250, the state equation includes stationary AR(2) and MA(1) models, respectively, and the observation model is the weighted sum of the two states.

• From periods 251 through 500, the state model includes only the first AR(2) model.

• ${\mu }_{0}=\left[\begin{array}{cccc}0.5& 0.5& 0& 0\end{array}\right]$ and ${\Sigma }_{0}$ is the identity matrix.

Symbolically, the DGP is

$\begin{array}{l}\begin{array}{c}\left[\begin{array}{c}{x}_{1t}\\ {x}_{2t}\\ {x}_{3t}\\ {x}_{4t}\end{array}\right]=\left[\begin{array}{cccc}{\varphi }_{1}& {\varphi }_{2}& 0& 0\\ 1& 0& 0& 0\\ 0& 0& 0& \theta \\ 0& 0& 0& 0\end{array}\right]\left[\begin{array}{c}{x}_{1,t-1}\\ {x}_{2,t-1}\\ {x}_{3,t-1}\\ {x}_{4,t-1}\end{array}\right]+\left[\begin{array}{cc}{\sigma }_{1}& 0\\ 0& 0\\ 0& 1\\ 0& 1\end{array}\right]\left[\begin{array}{c}{u}_{1t}\\ {u}_{2t}\end{array}\right]\\ {y}_{t}={c}_{1}\left({x}_{1t}+{x}_{3t}\right)+{\sigma }_{2}{\epsilon }_{t}.\end{array}\phantom{\rule{0.2777777777777778em}{0ex}}for\phantom{\rule{0.2777777777777778em}{0ex}}t=1,...,250,\\ \begin{array}{c}\left[\begin{array}{c}{x}_{1t}\\ {x}_{2t}\end{array}\right]=\left[\begin{array}{cccc}{\varphi }_{1}& {\varphi }_{2}& 0& 0\\ 1& 0& 0& 0\end{array}\right]\left[\begin{array}{c}{x}_{1,t-1}\\ {x}_{2,t-1}\\ {x}_{3,t-1}\\ {x}_{4,t-1}\end{array}\right]+\left[\begin{array}{c}{\sigma }_{1}\\ 0\end{array}\right]{u}_{1t}\\ {y}_{t}={c}_{2}{x}_{1t}+{\sigma }_{3}{\epsilon }_{t}.\end{array}\phantom{\rule{0.2777777777777778em}{0ex}}for\phantom{\rule{0.2777777777777778em}{0ex}}t=251,\\ \begin{array}{c}\left[\begin{array}{c}{x}_{1t}\\ {x}_{2t}\end{array}\right]=\left[\begin{array}{cc}{\varphi }_{1}& {\varphi }_{2}\\ 1& 0\end{array}\right]\left[\begin{array}{c}{x}_{1,t-1}\\ {x}_{2,t-1}\end{array}\right]+\left[\begin{array}{c}{\sigma }_{1}\\ 0\end{array}\right]{u}_{1t}\\ {y}_{t}={c}_{2}{x}_{1t}+{\sigma }_{3}{\epsilon }_{t}.\end{array}\phantom{\rule{0.2777777777777778em}{0ex}}for\phantom{\rule{0.2777777777777778em}{0ex}}t=252,...,500.\end{array}$

where:

• The AR(2) parameters $\left\{{\varphi }_{1},{\varphi }_{2}\right\}=\left\{0.5,-0.2\right\}$ and ${\sigma }_{1}=0.4$.

• The MA(1) parameter $\theta =0.3$.

• The observation equation parameters $\left\{{\mathit{c}}_{1},{\mathit{c}}_{2}\right\}=\left\{2,3\right\}$ and $\left\{{\sigma }_{2},{\sigma }_{3}\right\}=\left\{0.1,0.2\right\}$.

Simulate a response path of length 500 from the model. The function timeVariantParamMapBayes.m, stored in matlabroot/examples/econ/main, specifies the state-space model structure.

params = [0.5; -0.2; 0.4; 0.3; 2; 0.1; 3; 0.2]; numObs = 500; numParams = numel(params); [A,B,C,D,mean0,Cov0,stateType] = timeVariantParamMapBayes(params,numObs); DGP = ssm(A,B,C,D,Mean0=mean0,Cov0=Cov0,StateType=stateType); rng(1) % For reproducibility y = simulate(DGP,numObs); plot(y) ylabel("y")

Create a time-varying, Bayesian state-space model that uses the structure of the DGP, but all parameters are unknown and the prior density is flat (the function flatPriorBSSM.m in matlabroot/examples/econ/main is the prior density).

Create a bssm object representing the Bayesian state-space model object.

Mdl = bssm(@(params)timeVariantParamMapBayes(params,numObs),@flatPriorBSSM);

Draw a sample from the posterior distribution. Initialize the parameter values to a random set of positive values in [0,0.5]. Set the proposal distribution to multivariate ${\mathit{t}}_{25}$ with a scale matrix proportional. Set the proportionality constant to 0.005.

params0 = 0.5*rand(numParams,1); options = optimoptions("fminunc",Display="off"); [params0,Proposal] = tune(Mdl,y,params0,Options=options,Display=false); [PostParams,accept] = simulate(Mdl,y,params0,Proposal, ... DoF=25,Proportion=0.005); accept
accept = 0.7460 

PostParams is an 8-by-1000 matrix of 1000 random draws from the posterior distribution. The Metropolis-Hastings sampler accepted 75% of the proposed draws.

When you work with a state-space model that contains a deflated response variable, you must have data for the predictors.

Consider a regression of the US unemployment rate onto and real gross national product (rGNP) rate, and suppose the resulting innovations are an ARMA(1,1) process. The state-space form of the relationship is

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

where:

• ${x}_{1,t}$ is the ARMA process.

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

• ${y}_{t}$ is the observed unemployment rate deflated by a constant and the rGNP rate (${Z}_{t}$).

• ${u}_{t}$ is an iid Gaussian series with mean 0 and standard deviation 1.

Load the Nelson-Plosser data set, which contains a table DataTable that has the unemployment rate and rGNP series, among other series.

load Data_NelsonPlosser

Create a variable in DataTable that represents the returns of the raw rGNP series. Because price-to-returns conversion reduces the sample size by one, prepad the series with NaN.

DataTable.RGNPRate = [NaN; price2ret(DataTable.GNPR)]; T = height(DataTable);

Create variables for the regression. Represent the unemployment rate as the observation series and the constant and rGNP rate series as the deflation data ${\mathit{Z}}_{\mathit{t}}$.

Z = [ones(T,1) DataTable.RGNPRate]; y = DataTable.UR;

The functions armaDeflateYBayes.m and flatPriorDeflateY.m in matlabroot/examples/econ/main specify the state-space model structure and likelihood, and the flat prior distribution.

Create a bssm object representing the Bayesian state-space model. Specify the parameter-to-matrix mapping function as a handle to a function solely of the parameters.

numParams = 5; Mdl = bssm(@(params)armaDeflateYBayes(params,y,Z),@flatPriorDeflateY)
Mdl = Mapping that defines a state-space model: @(params)armaDeflateYBayes(params,y,Z) Log density of parameter prior distribution: @flatPriorDeflateY 

Draw a sample from the posterior distribution. Initialize the MCMC algorithm with a random set of positive values in [0,0.5]. Obtain an optimal set of proposal distribution moments for the sampler by using tune. Set the proportionality constant to 0.01. Set a burn-in period of 2000 draws, set a thinning factor of 50, and specify retaining 1000 draws.

rng(1) % For reproducibility params0 = 0.5*rand(numParams,1); options = optimoptions("fminunc",Display="off"); [params0,Proposal] = tune(Mdl,y,params0,Options=options, ... Display=false); [PostParams,accept] = simulate(Mdl,y,params0,Proposal,Proportion=0.01, ... BurnIn=2000,NumDraws=1000,Thin=50); accept
accept = 0.9200 

PostParams is a 5-by-1000 matrix of 1000 draws from the posterior distribution. The Metropolis-Hastings sampler accepts 92% of the proposed draws.

paramNames = ["\phi" "\theta" "\sigma" "\beta_0" "\beta_1"]; figure h = tiledlayout(numParams,1); for j = 1:numParams nexttile plot(PostParams(j,:)) hold on ylabel(paramNames(j)) end title(h,"Posterior Trace Plots")

All samples appear to suffer from autocorrelation. To improve the Markov chain, experiment with the Thin option.

The estimate and simulate functions do not return posterior draws of the degrees of freedom parameters of multivariate t-distributed state disturbances and observation innovations. However, you can write an output function that stores the degrees of freedom draws at each iteration of the MCMC algorithm, and pass it to simulate to obtain the draws.

Consider the following DGP.

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

${y}_{t}=\left[\begin{array}{cc}1& 3\end{array}\right]\left[\begin{array}{c}{x}_{t,1}\\ {x}_{t,2}\end{array}\right].$

• The true value of the state-space parameter set $\Theta =\left\{{\varphi }_{1},\text{\hspace{0.17em}}{\varphi }_{2},\text{\hspace{0.17em}}{\sigma }_{1},{\sigma }_{2}\right\}=\left\{0.5,-0.75,1,0.5\right\}$.

• The state disturbances ${\mathit{u}}_{1,\mathit{t}}$ and ${\mathit{u}}_{2,\mathit{t}}$ are jointly a multivariate Student's $\mathit{t}$ random series with ${\nu }_{\mathit{u}}=5$ degrees of freedom.

Create a vector autoregression (VAR) model that represents the state equation of the DGP.

trueTheta = [0.5; -0.75; 1; 0.5]; trueDoF = 5; phi = [trueTheta(1) 0; 0 trueTheta(2)]; Sigma = [trueTheta(3)^2 0; 0 trueTheta(4)^2]; DGP = varm(AR={phi},Covariance=Sigma,Constant=[0; 0]);

Filter a random 2-D multivariate central $\mathit{t}$ series of length 500 through the VAR model to obtain state values. Set the degrees of freedom to 5.

rng(10) % For reproducibility T = 500; trueU = mvtrnd(eye(DGP.NumSeries),trueDoF,T); X = filter(DGP,trueU);

Obtain a series of observations from the DGP by the linear combination ${\mathit{y}}_{\mathit{t}}={\mathit{x}}_{1,\mathit{e}}+3{\mathit{x}}_{2,\mathit{t}}$.

C = [1 3]; y = X*C';

Consider a Bayesian state-space model representing the model with parameters $\Theta$ and ${\nu }_{\mathit{u}}$ treated as unknown. Arbitrarily assume that the prior distribution of the parameters in $\Theta$ are independent Gaussian random variables with mean 0.5 and variance 1. Assume that the prior on the degrees of freedom ${\nu }_{\mathit{u}}$ is flat. The functions in Local Functions specify the state-space structure and prior distributions.

Create the Bayesian state-space model by passing function handles to the paramMap and priorDistribution functions to bssm. Specify that the state disturbance distribution is multivariate Student's $\mathit{t}$ with unknown degrees of freedom.

PriorMdl = bssm(@paramMap,@priorDistribution,StateDistribution="t");

PriorMdl is a bssm object representing the Bayesian state-space model with unknown parameters.

In Local Functions, write a function called posteriorDrawsNuU that accepts the input structure and returns the posterior draws of ${\nu }_{\mathit{u}}$.

function StateDoF = posteriorDrawsStateDoF(inputstruct) StateDoF = inputstruct.StateDoF; end 

Simulate draws from the posterior distribution of $\Theta ,{\nu }_{\mathit{u}}|\mathit{y}$ by using simulate. Specify a random set of positive values in [0,1] to initialize the Kalman filter.

Set the burn-in period of the MCMC algorithm to 1000 draws, draw a sample of 10000 from the posterior, specify the univariate treatment of multivariate series for faster computations, and set the proposal scale matrix to a diagonal matrix with small values along the diagonal to increase the proposal acceptance rate.

numParamsTheta = 4; theta0 = rand(numParamsTheta,1); [ThetaPostDraws,accept,StateDoFDraws] = simulate(PriorMdl,y,theta0, ... eye(numParamsTheta)/1000,BurnIn=1e3,NumDraws=1e4, ... Univariate=true,OutputFunction=@posteriorDrawsStateDoF); [numThetaParams,numDraws] = size(ThetaPostDraws)
numThetaParams = 4 
numDraws = 10000 
accept
accept = 0.4066 
size(StateDoFDraws)
ans = 1×2 1 10000 

ThetaPostDraws is a 4-by-10000 matrix containing the posterior draws of the parameters $\Theta$. accept is the Metropolis-Hastings sampler acceptance probability, in this case 41%, which means that simulate rejected 59% of the posterior draws. StateDoFDraws is a 1-by-10000 vector containing the posterior draws of ${\nu }_{\mathit{u}}$.

To diagnose the Markov chain induced by the Metropolis-within-Gibbs sampler, create a trace plot of the posterior draws of ${\nu }_{\mathit{u}}$.

figure plot(StateDoFDraws) title("Posterior Trace Plot of \nu_u")

The posterior sample shows significant serial correlation. You can remedy these behaviors by adjusting the Thin name-value argument. In general, simulate has name-value arguments that enable you to control several aspects of the MCMC sampler, which can improve the quality of the posterior sample.

Local Functions

This example uses the following functions. paramMap is the parameter-to-matrix mapping function and priorDistribution is the log prior distribution of the parameters.

function [A,B,C,D,Mean0,Cov0,StateType] = paramMap(theta) A = [theta(1) 0; 0 theta(2)]; B = [theta(3) 0; 0 theta(4)]; C = [1 3]; D = 0; Mean0 = []; % MATLAB uses default initial state mean Cov0 = []; % MATLAB uses initial state covariances StateType = [0; 0]; % Two stationary states end function logprior = priorDistribution(theta) paramconstraints = [(abs(theta(1)) >= 1) (abs(theta(2)) >= 1) ... (theta(3) < 0) (theta(4) < 0)]; if(sum(paramconstraints)) logprior = -Inf; else mu0 = 0.5*ones(numel(theta),1); sigma0 = 1; p = normpdf(theta,mu0,sigma0); logprior = sum(log(p)); end end function StateDoF = posteriorDrawsStateDoF(inputstruct) StateDoF = inputstruct.StateDoF; end 

## Input Arguments

collapse all

Prior Bayesian state-space model, specified as a bssm model object return by bssm or ssm2bssm.

The function handles of the properties PriorMdl.ParamDistribution and PriorMdl.ParamMap determine the prior and the data likelihood, respectively.

Observed response data, from which simulate forms the posterior distribution, specified as a numeric matrix or a cell vector of numeric vectors.

• If PriorMdl is time invariant with respect to the observation equation, 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. T is the sample size and n is the number of observations per period. The last row of Y contains the latest observations.

• If PriorMdl is time varying with respect to the observation equation, 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, outputs of PriorMdl.ParamMap, C{t}, and D{t} must be consistent with the matrix in Y{t} for all periods. The last cell of Y contains the latest observations.

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

Data Types: double | cell

Initial parameter values for the parameters Θ, specified as a numParams-by-1 numeric vector. Elements of params0 must correspond to the elements of the first input arguments of PriorMdl.ParamMap and Mdl.ParamDistribution.

Data Types: double

Metropolis-Hastings sampler proposal distribution covariance/scale matrix for the parameters Θ, up to the proportionality constant Proportion, specified as a numParams-by-numParams, positive-definite numeric matrix. Elements of Proposal must correspond to elements in params0.

The proposal distribution is multivariate normal or Student's t with DoF degrees of freedom (for details, see DoF).

Data Types: double

### Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: simulate(Mdl,Y,params0,Proposal,NumDraws=1e6,Thin=3,DoF=10) uses the multivariate t10 distribution for the Metropolis-Hastings proposal, draws 3e6 random vectors of parameters, and thins the sample to reduce serial correlation by discarding every 2 draws until it retains 1e6 draws.

Kalman Filter Options

collapse all

Univariate treatment of a multivariate series flag, specified as a value in this table.

ValueDescription
trueApplies the univariate treatment of a multivariate series, also known as sequential filtering
falseDoes not apply sequential filtering

The univariate treatment can accelerate and improve numerical stability of the Kalman filter. However, all observation innovations must be uncorrelated. That is, DtDt' must be diagonal, where Dt (t = 1, ..., T) is the output coefficient matrix D of PriorMdl.ParamMap and PriorMdl.ParamDistribution.

Example: Univariate=true

Data Types: logical

Square root filter method flag, specified as a value in this table.

ValueDescription
trueApplies the square root filter method for the Kalman filter
falseDoes not apply the square root filter method

If you suspect that the eigenvalues of the filtered state or forecasted observation covariance matrices are close to zero, then specify SquareRoot=true. The square root filter is robust to numerical issues arising from the finite precision of calculations, but requires more computational resources.

Example: SquareRoot=true

Data Types: logical

Posterior Sampling Options

collapse all

Number of Metropolis-Hastings sampler draws from the posterior distribution Π(θ|Y), specified as a positive integer.

Example: NumDraws=1e7

Data Types: double

Number of draws to remove from the beginning of the sample to reduce transient effects, specified as a nonnegative scalar. For details on how simulate reduces the full sample, see Algorithms.

Tip

To help you specify the appropriate burn-in period size:

1. Determine the extent of the transient behavior in the sample by setting the BurnIn name-value argument to 0.

2. Simulate a few thousand observations by using simulate.

3. Create trace plots.

Example: BurnIn=1000

Data Types: double

Adjusted sample size multiplier, specified as a positive integer.

The actual sample size is BurnIn + NumDraws*Thin. After discarding the burn-in, simulate discards every Thin1 draws, and then retains the next draw. For more details on how simulate reduces the full sample, see Algorithms.

Tip

To reduce potential large serial correlation in the posterior sample, or to reduce the memory consumption of the output sample, specify a large value for Thin.

Example: Thin=5

Data Types: double

Proposal distribution degrees of freedom for parameter updates using the Metropolis-Hastings sampler, specified as a value in this table.

ValueMetropolis-Hastings Proposal Distribution
Positive scalarMultivariate t with DoF degrees of freedom
InfMultivariate normal

The following options specify other aspects of the proposal distribution:

Example: DoF=10

Data Types: double

Proposal scale matrix proportionality constant, specified as a positive scalar.

Tip

For higher proposal acceptance rates, experiment with relatively small values for Proportion.

Example: Proportion=1

Data Types: double

Proposal distribution center, specified as a value in this table.

ValueDescription
numParams-by-1 numeric vectorsimulate uses the independence Metropolis-Hastings sampler. Center is the center of the proposal distribution.
[] (empty array)simulate uses the random-walk Metropolis-Hastings sampler. The center of the proposal density is the current state of the Markov chain.

Example: Center=ones(10,1)

Data Types: double

Proposal standard deviation for the degrees of freedom parameter of the t-distributed state disturbance or observation innovation process, specified as a positive numeric scalar.

StdDoF applies to the corresponding process when PriorMdl.StateDistribution.Name is "t" or PriorMdl.ObservationDistribution.Name is "t", and their associated degrees of freedom are estimable (the DoF field is NaN or a function handle). For example, if the following conditions hold, StdDof applies only to the t-distribution degrees of freedom of the state disturbance process:

• PriorMdl.StateDistribution.Name is "t".

• PriorMdl.StateDistribution.DoF is NaN.

• The limiting distribution of the observation innovations is Gaussian (PriorMdl.ObservationDistribution.Name is "Gaussian" or PriorMdl.ObservationDistribution is struct("Name","t","DoF",Inf).

Example: StdDoF=0.5

Data Types: double

Function that simulate calls after each MCMC iteration, specified as a function handle. simulate saves the outputs of the specified function after each iteration and returns them in the Output output argument. You write the function, which can perform arbitrary calculations, such as computing or plotting custom statistics at each iteration.

Suppose outputfcn is the name of the MATLAB® function associated with specified function handle. outputfcn must have this form.

function Output = outputfcn(Input) ... end
At each iteration, simulate passes Input as a structure array with fields in this table. In other words, values in this table are available for operations in the function.

FieldDescriptionValue
IterationCurrent iterationNumeric scalar
ParametersCurrent values of the parameters θNumeric vector
LogPosteriorDensityLog posterior density evaluated at current parameters and conditioned on latent variablesNumeric scalar
StateDoFCurrent degree of freedom of the t-distributed state disturbancesNumeric scalar
ObservationDoFCurrent degree of freedom of the t-distributed observation innovationNumeric scalar
StateVarianceCurrent latent variance variable associated with the state disturbanceT-by-1 numeric vector
ObservationVarianceCurrent latent variance variable associated with the observation innovation, a T-by-1 numeric vectorT-by-1 numeric vector
ACoefficient A evaluated at current parametersNumeric matrix for a dimension-invariant coefficient or a T-by-1 cell vector of numeric matrices for a dimension-varying coefficient
BCoefficient B evaluated at current parametersNumeric matrix for a dimension-invariant coefficient or a T-by-1 cell vector of numeric matrices for a dimension-varying coefficient
CCoefficient C evaluated at current parametersNumeric matrix for a dimension-invariant coefficient or a T-by-1 cell vector of numeric matrices for a dimension-varying coefficient
DCoefficient D evaluated at current parametersNumeric matrix for a dimension-invariant coefficient or a T-by-1 cell vector of numeric matrices for a dimension-varying coefficient
StatesCurrent state draws obtained by simulation smootherNumeric matrix for a dimension-invariant coefficient or a T-by-1 cell vector of numeric matrices for a dimension-varying coefficient
YObservationsThe value of the input Y
PreviousOutputOutput of the previous iterationStructure array with fields in this table

Output depends on the computations in outputfcn. However, if Output is a k-by-1 homogeneous column vector, simulate concatenates the outputs of all iterations and returns a k-by-NumDraws matrix of the same type. If simulate cannot horizontally concatenate the outputs of all iterations, simulate returns a 1-by-NumDraws cell array instead, where successive cells contain the output of corresponding iterations.

Example: OutputFunction=@outputfcn

Data Types: function_handle

## Output Arguments

collapse all

Posterior draws of the state-space model parameters Θ, returned as a numParams-by-NumDraws numeric matrix. Each column is a separate draw from the distribution. Each row corresponds to the corresponding element of params0.

Because the Metropolis-Hastings sampler is a Markov chain, successive draws are correlated.

Proposal acceptance rate, returned as a positive scalar in [0,1].

Custom function OutputFunction output concatenated over all MCMC iterations, returned as a matrix or cell vector. Output has NumDraws columns, and the number of rows depends on the size of the output at each iteration. The data type of the entries of Output depends on the operations of the output function.

collapse all

### Latent Variance Variables of t-Distributed Errors

To facilitate posterior sampling, multivariate Student's t-distributed state disturbances and observation innovations are each represented as a inverse-gamma scale mixture, where the inverse-gamma random variable is the latent variance variable.

Explicitly, suppose the m-dimensional state disturbances ut are iid multivariate t distributed with location 0, scale Im, and degrees of freedom νu. As an inverse-gamma scale mixture

${u}_{t}=\sqrt{{\zeta }_{t}}{\stackrel{˜}{u}}_{t},$

where:

• The latent variable ζt is inverse-gamma with shape and scale νu/2.

• ${\stackrel{˜}{u}}_{t}$ is an m-dimensional multivariate standard Gaussian random variable.

Multivariate t-distributed observation innovations can be similarly decomposed.

## Tips

• Acceptance rates from accept that are relatively close to 0 or 1 indicate that the Markov chain does not sufficiently explore the posterior distribution. To obtain an appropriate acceptance rate for your data, tune the sampler by implementing one of the following procedures.

• Use the tune function to search for the posterior mode by numerical optimization. tune returns optimized parameters and the proposal scale matrix, which is proportional to the negative Hessian matrix evaluated at the posterior mode. Pass the optimized parameters and the proposal scale to simulate, and tune the proportionality constant by using the Proportion name-value argument. Small values of Proportion tend to increase the proposal acceptance rates of the Metropolis-Hastings sampler.

• Perform a multistage simulation:

1. Choose an initial value for the input Proposal and simulate draws from the posterior.

2. Compute the sample covariance matrix, and pass the result to simulate as an updated value for Proposal.

3. Repeat steps 1 and 2 until accept is reasonable for your data.

## Algorithms

• simulate performs posterior simulation of model parameters, states, and latent variables as follows:

1. simulate generates parameter draws by the Metropolis-Hastings sampler. The posterior density is proportional to the product of the prior density PriorMdl.ParamDistribution and the likelihood obtained by the Kalman filter of the state-space model PriorMdl.ParamMap.

2. simulate updates states by the simulation smoother of the state-space model.

3. simulate samples the latent variance variables of state disturbance and observation innovation distributions from posterior conditional distributions. For t-distributed state disturbances or observation innovations, latent variables are inverse-gamma distributed.

• If the input prior model PrioirMdl is a Gaussian linear state-space model and you do not specify the custom output function OutputFunction, simulate marginalizes the states, and the Metropolis-Hastings sampler simulates only parameters. Otherwise, simulate simulates parameters Θ, states xt, and latent variables ζt by the Metropolis-within-Gibbs sampler, which is more computationally intensive compared to the standard Metropolis-Hastings sampler.

• This figure shows how simulate reduces the sample by using the values of NumDraws, Thin, and BurnIn. Rectangles represent successive draws from the distribution. simulate removes the white rectangles from the sample. The remaining NumDraws black rectangles compose the sample.

## References

[1] Hastings, Wilfred K. "Monte Carlo Sampling Methods Using Markov Chains and Their Applications." Biometrika 57 (April 1970): 97–109. https://doi.org/10.1093/biomet/57.1.97.

[2] Metropolis, Nicholas, Rosenbluth, Arianna. W., Rosenbluth, Marshall. N., Teller, Augusta. H., and Teller, Edward. "Equation of State Calculations by Fast Computing Machines." The Journal of Chemical Physics 21 (June 1953): 1087–92. https://doi.org/10.1063/1.1699114.

## Version History

Introduced in R2022a