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Bayesian Stochastic Search Variable Selection

This example shows how to implement stochastic search variable selection (SSVS), a Bayesian variable selection technique for linear regression models.


Consider this Bayesian linear regression model.


  • The regression coefficients βk|σ2N(μj,σ2Vk).

  • k=0,...,p.

  • The disturbances εtN(0,σ2).

  • The disturbance variance σ2IG(A,B), where IG(A,B) is the inverse gamma distribution with shape A and scale B.

The goal of variable selection is to include only those predictors supported by data in the final regression model. One way to do this is to analyze the 2p permutations of models, called regimes, where models differ by the coefficients that are included. If p is small, then you can fit all permutations of models to the data, and then compare the models by using performance measures, such as goodness-of-fit (for example, Akaike information criterion) or forecast mean squared error (MSE). However, for even moderate values of p, estimating all permutations of models is inefficient.

A Bayesian view of variable selection is a coefficient, being excluded from a model, has a degenerate posterior distribution. That is, the excluded coefficient has a Dirac delta distribution, which has its probability mass concentrated on zero. To circumvent the complexity induced by degenerate variates, the prior for a coefficient being excluded is a Gaussian distribution with a mean of 0 and a small variance, for example N(0,0.12). Because the prior is concentrated around zero, the posterior must also be concentrated around zero.

The prior for the coefficient being included can be N(μ,V), where V is sufficiently away from zero and μ is usually zero. This framework implies that the prior of each coefficient is a Gaussian mixture model.

Consider the latent, binary random variables γk, k=0,...,p, such that:

  • γk=1 indicates that βkN(0,σ2V1k) and that βk is included in the model.

  • γk=0 indicates that βkN(0,σ2V2k) and that βk is excluded from the model.

  • γkBernoulli(gk).

  • The sample space of γk has a cardinality of 2p+1, and each element is a p+1-D vector of zeros or ones.

  • V2k is a small, positive number and V1k>V2k.

  • Coefficients βj and βk, jk are independent, a priori.

One goal of SSVS is to estimate the posterior regime probabilities gk, the estimates that determine whether corresponding coefficients should be included in the model. Given βk, γk is conditionally independent of the data. Therefore, for k=0,...,p, this equation represents the full conditional posterior distribution of the probability that variable k is included in the model:


where ϕ(μ,σ2) is the pdf of the Gaussian distribution with scalar mean μ and variance σ2.

Econometrics Toolbox™ has two Bayesian linear regression models that specify the prior distributions for SSVS: mixconjugateblm and mixsemiconjugateblm. The framework presented earlier describes the priors of the mixconjugateblm model. The difference between the models is that β and σ2 are independent, a priori, for mixsemiconjugateblm models. Therefore, the prior variance of βk is Vk1 (γk=1) or Vk2 (γk=0).

After you decide which prior model to use, call bayeslm to create the model and specify hyperparameter values. Supported hyperparameters include:

  • Intercept, a logical scalar specifying whether to include an intercept in the model.

  • Mu, a (p + 1)-by-2 matrix specifying the prior Gaussian mixture means of β. The first column contains the means for the component corresponding to γk=1, and the second column contains the means corresponding to γk=0. By default, all means are 0, which specifies implementing SSVS.

  • V, a (p + 1)-by-2 matrix specifying the prior Gaussian mixture variance factors (or variances) of β. Columns correspond to the columns of Mu. By default, the variance of the first component is 10 and the variance of the second component is 0.1.

  • Correlation, a (p + 1)-by-(p + 1) positive definite matrix specifying the prior correlation matrix of β for both components. The default is the identity matrix, which implies that the regression coefficients are uncorrelated, a priori.

  • Probability, a (p + 1)-D vector of prior probabilities of variable inclusion (gk, k = 0,...,_p_) or a function handle to a custom function. γj and γk, jk, are independent, a priori. However, using a function handle (@functionname), you can supply a custom prior distribution that specifies dependencies between γj and γk. For example, you can specify forcing x2 out of the model if x4 is included.

After you create a model, pass it and the data to estimate. The estimate function uses a Gibbs sampler to sample from the full conditionals, and estimate characteristics of the posterior distributions of β and σ2. Also, estimate returns posterior estimates of gk.

For this example, consider creating a predictive linear model for the US unemployment rate. You want a model that generalizes well. In other words, you want to minimize the model complexity by removing all redundant predictors and all predictors that are uncorrelated with the unemployment rate.

Load and Preprocess Data

Load the US macroeconomic data set Data_USEconModel.mat.

load Data_USEconModel

The data set includes the MATLAB® timetable DataTimeTable, which contains 14 variables measured from Q1 1947 through Q1 2009; UNRATE is the US unemployment rate. For more details, enter Description at the command line.

Plot all series in the same figure, but in separate subplots.

for j = 1:size(DataTimeTable,2)

All series except FEDFUNDS, GS10, TB3MS, and UNRATE appear to have an exponential trend.

Apply the log transform to those variables with an exponential trend.

hasexpotrend = ~ismember(DataTimeTable.Properties.VariableNames,...
    ["FEDFUNDS" "GD10" "TB3MS" "UNRATE"]);
DataTimeTableLog = varfun(@log,DataTimeTable,'InputVariables',...
DataTimeTableLog = [DataTimeTableLog ...

DataTimeTableLog is a timetable like DataTimeTable, but those variables with an exponential trend are on the log scale.

Coefficients that have relatively large magnitudes tend to dominate the penalty in the lasso regression objective function. Therefore, it is important that variables have a similar scale when you implement lasso regression. Compare the scales of the variables in DataTimeTableLog by plotting their box plots on the same axis.

h = gcf;
h.Position(3) = h.Position(3)*2.5;
title('Variable Box Plots');

The variables have fairly similar scales.

To tune the prior Gaussian mixture variance factors, follow this procedure:

  1. Partition the data into estimation and forecast samples.

  2. Fit the models to the estimation sample and specify, for all k, V1k={10,50,100} and V2k={0.05,0.1,0.5}.

  3. Use the fitted models to forecast responses into the forecast horizon.

  4. Estimate the forecast MSE for each model.

  5. Choose the model with the lowest forecast MSE.

George and McCulloch suggest another way to tune the prior variances of β in [1].

Create estimation and forecast sample variables for the response and predictor data. Specify a forecast horizon of 4 years (16 quarters).

fh = 16;
y = DataTimeTableLog.UNRATE(1:(end - fh));
yF = DataTimeTableLog.UNRATE((end - fh + 1):end);
isresponse = DataTimeTable.Properties.VariableNames == "UNRATE";
X = DataTimeTableLog{1:(end - fh),~isresponse};
XF = DataTimeTableLog{(end - fh + 1):end,~isresponse};
p = size(X,2); % Number of predictors
predictornames = DataTimeTableLog.Properties.VariableNames(~isresponse);

Create Prior Bayesian Linear Regression Models

Create prior Bayesian linear regression models for SSVS by calling bayeslm and specifying the number of predictors, model type, predictor names, and component variance factors. Assume that β and σ2 are dependent, a priori (mixconjugateblm model).

V1 = [10 50 100];
V2 = [0.05 0.1 0.5];
numv1 = numel(V1);
numv2 = numel(V2);

PriorMdl = cell(numv1,numv2); % Preallocate

for k = 1:numv2
    for j = 1:numv1
        V = [V1(j)*ones(p + 1,1) V2(k)*ones(p + 1,1)];
        PriorMdl{j,k} = bayeslm(p,'ModelType','mixconjugateblm',...

PriorMdl is a 3-by-3 cell array, and each cell contains a mixconjugateblm model object.

Plot the prior distribution of log_GDP for the models in which V2 is 0.5.

for j = 1:numv1
    [~,~,~,h] = plot(PriorMdl{j,3},'VarNames',"log_GDP");
    title(sprintf("Log GDP, V1 = %g, V2 = %g",V1(j),V2(3)));
    h.Tag = strcat("fig",num2str(V1(j)),num2str(V2(3)));

The prior distributions of β have the spike-and-slab shape. When V1 is low, more of the distribution is concentrated around 0, which makes it more difficult for the algorithm to attribute a high value for beta. However, variables the algorithm identifies as important are regularized, in that the algorithm does not attribute a high magnitude to the corresponding coefficients.

When V1 is high, more density occurs well away from zero, which makes it easier for the algorithm to attribute non-zero coefficients to important predictors. However, if V1 is too high, then important predictors can have inflated coefficients.

Perform SSVS Variable Selection

To perform SSVS, estimate the posterior distributions by using estimate. Use the default options for the Gibbs sampler.

PosteriorMdl = cell(numv1,numv2);
PosteriorSummary = cell(numv1,numv2);

rng(1); % For reproducibility
for k = 1:numv2
    for j = 1:numv1
        [PosteriorMdl{j,k},PosteriorSummary{j,k}] = estimate(PriorMdl{j,k},X,y,...

Each cell in PosteriorMdl contains an empiricalblm model object storing the full conditional posterior draws from the Gibbs sampler. Each cell in PosteriorSummary contains a table of posterior estimates. The Regime table variable represents the posterior probability of variable inclusion (gk).

Display a table of posterior estimates of gk.

RegimeTbl = table(zeros(p + 2,1),'RowNames',PosteriorSummary{1}.Properties.RowNames);
for k = 1:numv2
    for j = 1:numv1
        vname = strcat("V1_",num2str(V1(j)),"__","V2_",num2str(V2(k)));
        vname = replace(vname,".","p");
        tmp = table(PosteriorSummary{j,k}.Regime,'VariableNames',vname);
        RegimeTbl = [RegimeTbl tmp];
RegimeTbl.Var1 = [];
RegimeTbl=15×9 table
                    V1_10__V2_0p05    V1_50__V2_0p05    V1_100__V2_0p05    V1_10__V2_0p1    V1_50__V2_0p1    V1_100__V2_0p1    V1_10__V2_0p5    V1_50__V2_0p5    V1_100__V2_0p5
                    ______________    ______________    _______________    _____________    _____________    ______________    _____________    _____________    ______________

    Intercept           0.9692                 1                 1            0.9501                1                 1           0.9487           0.9999                 1    
    log_COE             0.4686            0.4586            0.5102            0.4487           0.3919            0.4785           0.4575           0.4147            0.4284    
    log_CPIAUCSL        0.9713            0.3713            0.4088             0.971           0.3698            0.3856            0.962           0.3714            0.3456    
    log_GCE             0.9999                 1                 1            0.9978                1                 1           0.9959                1                 1    
    log_GDP             0.7895            0.9921            0.9982            0.7859           0.9959                 1           0.7908           0.9975            0.9999    
    log_GDPDEF          0.9977                 1                 1                 1                1                 1           0.9996                1                 1    
    log_GPDI                 1                 1                 1                 1                1                 1                1                1                 1    
    log_GS10                 1                 1            0.9991                 1                1            0.9992                1           0.9992             0.994    
    log_HOANBS          0.9996                 1                 1            0.9887                1                 1           0.9763                1                 1    
    log_M1SL                 1                 1                 1                 1                1                 1                1                1                 1    
    log_M2SL            0.9989            0.9993            0.9913            0.9996           0.9998            0.9754           0.9951           0.9983            0.9856    
    log_PCEC            0.4457            0.6366            0.8421            0.4435           0.6226            0.8342           0.4614            0.624              0.85    
    FEDFUNDS            0.0762            0.0386            0.0237            0.0951           0.0465            0.0343           0.1856           0.0953             0.068    
    TB3MS               0.2473            0.1788            0.1467            0.2014           0.1338            0.1095           0.2234           0.1185            0.0909    
    Sigma2                 NaN               NaN               NaN               NaN              NaN               NaN              NaN              NaN               NaN    

Using an arbitrary threshold of 0.10, all models agree that FEDFUNDS is an insignificant or redundant predictor. When V1 is high, TB3MS borders on being insignificant.

Forecast responses and compute forecast MSEs using the estimated models.

yhat = zeros(fh,numv1*numv2);
fmse = zeros(numv1,numv2);

for k = 1:numv2
    for j = 1:numv1
        idx = ((k - 1)*numv1 + j); 
        yhat(:,idx) = forecast(PosteriorMdl{j,k},XF);
        fmse(j,k) = sqrt(mean((yF - yhat(:,idx)).^2));

Identify the variance factor settings that yield the minimum forecast MSE.

minfmse = min(fmse,[],'all');
[idxminr,idxminc] = find(abs(minfmse - fmse) < eps);
bestv1 = V1(idxminr)
bestv1 = 100
bestv2 = V2(idxminc)
bestv2 = 0.0500

Estimate an SSVS model using the entire data set and the variance factor settings that yield the minimum forecast MSE.

XFull = [X; XF];
yFull = [y; yF];
EstMdl = estimate(PriorMdl{idxminr,idxminc},XFull,yFull);
Method: MCMC sampling with 10000 draws
Number of observations: 201
Number of predictors:   14
              |   Mean      Std          CI95         Positive  Distribution  Regime 
 Intercept    |  29.4598  4.2723   [21.105, 37.839]     1.000     Empirical    1     
 log_COE      |   3.5380  3.0180   [-0.216,  9.426]     0.862     Empirical   0.7418 
 log_CPIAUCSL |  -0.6333  1.7689   [-5.468,  2.144]     0.405     Empirical   0.3711 
 log_GCE      |  -9.3924  1.4699  [-12.191, -6.494]     0.000     Empirical    1     
 log_GDP      |  16.5111  3.7131   [ 9.326, 23.707]     1.000     Empirical    1     
 log_GDPDEF   |  13.0146  2.3992   [ 9.171, 19.131]     1.000     Empirical    1     
 log_GPDI     |  -5.9537  0.6083   [-7.140, -4.756]     0.000     Empirical    1     
 log_GS10     |   1.4485  0.3852   [ 0.680,  2.169]     0.999     Empirical   0.9868 
 log_HOANBS   | -16.0240  1.5361  [-19.026, -13.048]    0.000     Empirical    1     
 log_M1SL     |  -4.6509  0.6815   [-5.996, -3.313]     0.000     Empirical    1     
 log_M2SL     |   5.3320  1.3003   [ 2.738,  7.770]     0.999     Empirical   0.9971 
 log_PCEC     |  -9.9025  3.3904  [-16.315, -2.648]     0.006     Empirical   0.9858 
 FEDFUNDS     |  -0.0176  0.0567   [-0.125,  0.098]     0.378     Empirical   0.0269 
 TB3MS        |  -0.1436  0.0762   [-0.299,  0.002]     0.026     Empirical   0.0745 
 Sigma2       |   0.2891  0.0289   [ 0.238,  0.352]     1.000     Empirical    NaN   

EstMdl is an empiricalblm model representing the result of performing SSVS. You can use EstMdl to forecast the unemployment rate given future predictor data , for example.


[1] George, E. I., and R. E. McCulloch. "Variable Selection Via Gibbs Sampling." Journal of the American Statistical Association. Vol. 88, No. 423, 1993, pp. 881–889.

See Also


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