# tune

Tune Bayesian state-space model posterior sampler

## Syntax

[params,Proposal] = tune(PriorMdl,Y,params0)
[params,Proposal] = tune(PriorMdl,Y,params0,Name=Value)

## Description

The tune function searches for the posterior mode to form proposal distribution moments of the Metropolis-Hastings sampler [1][2]. To improve the proposal distribution, for example, increase the acceptance rate of proposed posterior draws, pass the outputs of tune to simulate.

example

[params,Proposal] = tune(PriorMdl,Y,params0) returns proposal distribution parameter mean vector params and scale matrix Proposal to improve the Metropolis-Hastings sampler. PriorMdl is the Bayesian state-space model that specifies the state-space model structure (likelihood) and prior distribution, Y is the data for the likelihood, and params0 is the vector of initial values for the unknown state-space model parameters θ in PriorMdl.

example

[params,Proposal] = tune(PriorMdl,Y,params0,Name=Value) specifies additional options using one or more name-value arguments. For example, tune(Mdl,Y,params0,Hessian="opg",Display=false) uses the outer-product of gradients method to compute the Hessian matrix and suppresses the display of the optimized values.

## Examples

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Simulate observed responses from a known state-space model, then treat the model as Bayesian and draw parameters from the posterior distribution. Tune the proposal distribution of the Metropolis-Hastings sampler by using tune.

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 the 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.

numParams = 4; theta0 = rand(numParams,1); [theta0,Proposal] = tune(Mdl,y,theta0);
Local minimum found. Optimization completed because the size of the gradient is less than the value of the optimality tolerance. Optimization and Tuning | Params0 Optimized ProposalStd ---------------------------------------- c(1) | 0.6968 0.4459 0.0798 c(2) | 0.7662 -0.8781 0.0483 c(3) | 0.3425 0.9633 0.0694 c(4) | 0.8459 0.3978 0.0726 

theta0 is a 4-by-1 estimate of the posterior mode and Proposal is the Hessian matrix. Both outputs are the optimized moments of the proposal distribution, the latter of which is up to a proportionality constant. tune displays convergence information and an estimation table, which you can suppress by using the Display options of the optimizer and tune.

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.

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);

Obtain optimized values for the proposal distribution moments by using tune. Initialize the parameter values to a random set of positive values in [0,0.1]. Suppress all tuning disaplys. Use the Hessian matrix returned by the optimizer of the posterior mode.

params0 = 0.1*rand(numParams,1); options = optimoptions("fminunc",Display="off"); [params0,Proposal] = tune(Mdl,y,params0,Options=options,Display=false, ... Hessian="optimizer");

Draw a sample from the posterior distribution. Supply the optimized parameter estimates. Set the proposal distribution to multivariate ${\mathit{t}}_{25}$ with a scale matrix proportional. Set the proportionality constant to 0.005.

[PostParams,accept] = simulate(Mdl,y,params0,Proposal, ... Dof=25,Proportion=0.1); accept
accept = 0.7950 

PostParams is an 8-by-1000 matrix of 1000 random draws from the posterior distribution. The Metropolis-Hastings sampler accepted 80% of the proposed draws.

The log joint prior distribution function specifies parameter constraints by attributing a probability of -Inf for arguments outside the support of the distribution. Because posterior sampling does not occur during proposal distribution tuning, it is good practice to additionally specify constraints when you call tune.

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 the prior distribution: @flatPriorDeflateY 

Tune the proposal distribution. Initialize the Kalman filter with a random set of positive values in [0,0.5]. Suppress the optimization displays. The log prior joint density function flatPriorDeflateY.m specifies that the AR coefficient theta(1) must be within the unit circle and that the observation error standard deviation must be positive theta(3). Specify these constraints for proposal distribution optimization. Use the Hessian matrix returned by fmincon.

rng(1) % For reproducibility params0 = 0.5*rand(numParams,1); options = optimoptions("fmincon",Display="off"); % Constrained optimization requires FMINCON lb = -Inf*ones(numParams,1); % Preallocation ub = Inf*ones(numParams,1); % Preallocation lb([1; 3]) = [-1; 0]; up(1) = 1; [params0,Proposal] = tune(Mdl,y,params0,Options=options, ... Display=false,Hessian="optimizer",Lower=lb,Upper=ub);

Draw a sample from the posterior distribution. Supply the proposal moments returned by tune. Set the proportionality constant to 0.1. Set a burn-in period of 2000 draws, set a thinning factor of 50, and specify retaining 1000 draws.

[PostParams,accept] = simulate(Mdl,y,params0,Proposal,Proportion=0.1, ... BurnIn=2000,NumDraws=1000,Thin=50); accept
accept = 0.7506 

PostParams is a 5-by-1000 matrix of 1000 draws from the posterior distribution. The Metropolis-Hastings sampler accepts 75% 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")

## Input Arguments

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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 tune 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, 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

### 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: tune(Mdl,Y,params0,Hessian="opg",Display=false) uses the outer-product of gradients method to compute the Hessian matrix and suppresses the display of the optimized values.

Kalman Filter Options

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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

Proposal Tuning Options

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Hessian approximation method for the Metropolis-Hastings proposal distribution scale matrix, specified as a value in this table.

ValueDescription
"difference"Finite differencing
"diagonal"Diagonalized result of finite differencing
"opg"Outer product of gradients, ignoring the prior distribution
"optimizer"Posterior distribution optimized by fmincon or fminunc. Specify optimization options by using the Options name-value argument.

Tip

The Hessian="difference" setting can be computationally intensive and inaccurate, and the resulting scale matrix can be nonnegative definite. Try one of the other options for better results.

Example: Hessian="opg"

Data Types: char | string

Parameter lower bounds when computing the Hessian matrix (see Hessian), specified as a numParams-by-1 numeric vector. If you specify Lower, tune uses

Lower(j) specifies the lower bound of parameter theta(j), the first input argument of PriorMdl.ParamMap and PriorMdl.ParamDistribution.

The default value [] specifies no lower bounds.

Note

Lower does not apply to posterior simulation. To apply parameter constraints on the posterior, code them in the log prior distribution function PriorMdl.ParamDistribution by setting the log prior of values outside the distribution support to -Inf.

Example: Lower=[0 -5 -1e7]

Data Types: double

Parameter lower bounds when computing the Hessian matrix (see Hessian), specified as a numParams-by-1 numeric vector.

Upper(j) specifies the upper bound of parameter theta(j), the first input argument of PriorMdl.ParamMap and PriorMdl.ParamDistribution.

The default value [] specifies no upper bounds.

Note

Upper does not apply to posterior simulation. To apply parameter constraints on the posterior, code them in the log prior distribution function PriorMdl.ParamDistribution by setting the log prior of values outside the distribution support to -Inf.

Example: Upper=[5 100 1e7]

Data Types: double

Optimization options for the setting Hessian="optimizer", specified as an optimoptions optimization controller. Options replaces default optimization options of the optimizer. For details on altering default values of the optimizer, see the optimization controller optimoptions, the constrained optimization function fmincon, or the unconstrained optimization function fminunc in Optimization Toolbox™.

For example, to change the constraint tolerance to 1e-6, set options = optimoptions(@fmincon,ConstraintTolerance=1e-6,Algorithm="sqp"). Then, pass Options by using Options=options.

By default, tune uses the default options of the optimizer.

Simplex search flag to improve initial parameter values, specified as a value in this table.

ValueDescription
trueApply simplex search method to improve initial parameter values for proposal optimization. For more details, see fminsearch Algorithm.
falseDoes not apply simplex search method.

tune applies simplex search when the numerical optimization exit flag is not positive.

Example: Simplex=false

Data Types: logical

Proposal tuning results display flag, specified as a value in this table.

ValueDescription
trueDisplays tuning results
falseSuppresses tuning results display

Example: Display=false

Data Types: logical

## Output Arguments

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Optimized parameter values for the Metropolis-Hastings sampler, returned as a numParams-by-1 numeric vector. params(j) contains the optimized value of parameter theta(j), where theta is the first input argument of PriorMdl.ParamMap and PriorMdl.ParamDistribution.

When you call simulate, pass params as the initial parameter values input params0.

Proposal distribution covariance/scale matrix for the Metropolis-Hastings sampler, specified as a numParams-by-numParams numeric matrix. Rows and columns of Proposal correspond to elements in params.

Proposal is the scale matrix up to a proportionality constant, which is specified by the Proportion name-value argument of estimate and simulate.

The proposal distribution is multivariate normal or Student's t.

When you call simulate, pass Proposal as the proposal distribution scale matrix input Proposal.

Data Types: double

## Algorithms

• The Metropolis-Hastings sampler requires a carefully specified proposal distribution. Under the assumption of a Gaussian linear state-space model, tune tunes the sampler by performing numerical optimization to search for the posterior mode. A reasonable proposal for the multivariate normal or t distribution is the inverse of the negative Hessian matrix, which tune evaluates at the resulting posterior mode.

• When tune tunes the proposal distribution, the optimizer that tune uses to search for the posterior mode before computing the Hessian matrix depends on your specifications.

## 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