estimate

Estimate posterior distribution of Bayesian vector autoregression (VAR) model parameters

Syntax

PosteriorMdl = estimate(PriorMdl,Y)

PosteriorMdl = estimate(PriorMdl,Y,Name,Value)

[PosteriorMdl,Summary] = estimate(___)

Description

PosteriorMdl = estimate(PriorMdl,Y) returns the Bayesian VAR(p) model PosteriorMdl that characterizes the joint posterior distributions of the coefficients Λ and innovations covariance matrix Σ. PriorMdl specifies the joint prior distribution of the parameters and the structure of the VAR model. Y is the multivariate response data. PriorMdl and PosteriorMdl might not be the same object type.

NaNs in the data indicate missing values, which estimate removes by using list-wise deletion.

PosteriorMdl = estimate(PriorMdl,Y,Name,Value) specifies additional options using one or more name-value pair arguments. For example, you can specify presample data to initialize the VAR model by using the 'Y0' name-value pair argument.

[PosteriorMdl,Summary] = estimate(___) also returns an estimation summary of the posterior distribution Summary, using any of the input argument combinations in the previous syntaxes.

Examples

Estimate Posterior Distribution

Consider the 3-D VAR(4) model for the US inflation (INFL), unemployment (UNRATE), and federal funds (FEDFUNDS) rates.

$$\left[\begin{array}{l}{\text{INFL}}_t\\ {\text{UNRATE}}_t\\ {\text{FEDFUNDS}}_t\end{array}\right]=c+\sum _{j=1}^4{\Phi }_j\left[\begin{array}{l}{\text{INFL}}_{t-j}\\ {\text{UNRATE}}_{t-j}\\ {\text{FEDFUNDS}}_{t-j}\end{array}\right]+\left[\begin{array}{c}{\epsilon }_{1,t}\\ {\epsilon }_{2,t}\\ {\epsilon }_{3,t}\end{array}\right].$$

For all $$t$$, $${\epsilon }_t$$ is a series of independent 3-D normal innovations with a mean of 0 and covariance $\Sigma$. Assume that the joint prior distribution of the VAR model parameters $\left({\left[{\Phi }_1,...,{\Phi }_4,\mathit{c}\right]}^{\prime },\Sigma \right)$ is diffuse.

Load and Preprocess Data

Load the US macroeconomic data set. Compute the inflation rate. Plot all response series.

load Data_USEconModel
seriesnames = ["INFL" "UNRATE" "FEDFUNDS"];
DataTimeTable.INFL = 100*[NaN; price2ret(DataTimeTable.CPIAUCSL)];

figure
plot(DataTimeTable.Time,DataTimeTable{:,seriesnames})
legend(seriesnames)

Stabilize the unemployment and federal funds rates by applying the first difference to each series.

DataTimeTable.DUNRATE = [NaN; diff(DataTimeTable.UNRATE)];
DataTimeTable.DFEDFUNDS = [NaN; diff(DataTimeTable.FEDFUNDS)];
seriesnames(2:3) = "D" + seriesnames(2:3);

Remove all missing values from the data.

rmDataTimeTable = rmmissing(DataTimeTable);

Create Prior Model

Create a diffuse Bayesian VAR(4) prior model for the three response series. Specify the response variable names.

numseries = numel(seriesnames);
numlags = 4;

PriorMdl = bayesvarm(numseries,numlags,'SeriesNames',seriesnames)

PriorMdl = 
  diffusebvarm with properties:

        Description: "3-Dimensional VAR(4) Model"
          NumSeries: 3
                  P: 4
        SeriesNames: ["INFL"    "DUNRATE"    "DFEDFUNDS"]
    IncludeConstant: 1
       IncludeTrend: 0
      NumPredictors: 0
                 AR: {[3×3 double]  [3×3 double]  [3×3 double]  [3×3 double]}
           Constant: [3×1 double]
              Trend: [3×0 double]
               Beta: [3×0 double]
         Covariance: [3×3 double]

PriorMdl is a diffusebvarm model object.

Estimate Posterior Distribution

Estimate the posterior distribution by passing the prior model and entire data series to estimate.

PosteriorMdl = estimate(PriorMdl,rmDataTimeTable{:,seriesnames})

Bayesian VAR under diffuse priors
Effective Sample Size:          197
Number of equations:            3
Number of estimated Parameters: 39
             |   Mean     Std  
-------------------------------
 Constant(1) |  0.1007  0.0832 
 Constant(2) | -0.0499  0.0450 
 Constant(3) | -0.4221  0.1781 
 AR{1}(1,1)  |  0.1241  0.0762 
 AR{1}(2,1)  | -0.0219  0.0413 
 AR{1}(3,1)  | -0.1586  0.1632 
 AR{1}(1,2)  | -0.4809  0.1536 
 AR{1}(2,2)  |  0.4716  0.0831 
 AR{1}(3,2)  | -1.4368  0.3287 
 AR{1}(1,3)  |  0.1005  0.0390 
 AR{1}(2,3)  |  0.0391  0.0211 
 AR{1}(3,3)  | -0.2905  0.0835 
 AR{2}(1,1)  |  0.3236  0.0868 
 AR{2}(2,1)  |  0.0913  0.0469 
 AR{2}(3,1)  |  0.3403  0.1857 
 AR{2}(1,2)  | -0.0503  0.1647 
 AR{2}(2,2)  |  0.2414  0.0891 
 AR{2}(3,2)  | -0.2968  0.3526 
 AR{2}(1,3)  |  0.0450  0.0413 
 AR{2}(2,3)  |  0.0536  0.0223 
 AR{2}(3,3)  | -0.3117  0.0883 
 AR{3}(1,1)  |  0.4272  0.0860 
 AR{3}(2,1)  | -0.0389  0.0465 
 AR{3}(3,1)  |  0.2848  0.1841 
 AR{3}(1,2)  |  0.2738  0.1620 
 AR{3}(2,2)  |  0.0552  0.0876 
 AR{3}(3,2)  | -0.7401  0.3466 
 AR{3}(1,3)  |  0.0523  0.0428 
 AR{3}(2,3)  |  0.0008  0.0232 
 AR{3}(3,3)  |  0.0028  0.0917 
 AR{4}(1,1)  |  0.0167  0.0901 
 AR{4}(2,1)  |  0.0285  0.0488 
 AR{4}(3,1)  | -0.0690  0.1928 
 AR{4}(1,2)  | -0.1830  0.1520 
 AR{4}(2,2)  | -0.1795  0.0822 
 AR{4}(3,2)  |  0.1494  0.3253 
 AR{4}(1,3)  |  0.0067  0.0395 
 AR{4}(2,3)  |  0.0088  0.0214 
 AR{4}(3,3)  | -0.1372  0.0845 
       Innovations Covariance Matrix       
           |   INFL     DUNRATE  DFEDFUNDS 
-------------------------------------------
 INFL      |  0.3028   -0.0217     0.1579  
           | (0.0321)  (0.0124)   (0.0499) 
 DUNRATE   | -0.0217    0.0887    -0.1435  
           | (0.0124)  (0.0094)   (0.0283) 
 DFEDFUNDS |  0.1579   -0.1435     1.3872  
           | (0.0499)  (0.0283)   (0.1470) 

PosteriorMdl = 
  conjugatebvarm with properties:

        Description: "3-Dimensional VAR(4) Model"
          NumSeries: 3
                  P: 4
        SeriesNames: ["INFL"    "DUNRATE"    "DFEDFUNDS"]
    IncludeConstant: 1
       IncludeTrend: 0
      NumPredictors: 0
                 Mu: [39×1 double]
                  V: [13×13 double]
              Omega: [3×3 double]
                DoF: 184
                 AR: {[3×3 double]  [3×3 double]  [3×3 double]  [3×3 double]}
           Constant: [3×1 double]
              Trend: [3×0 double]
               Beta: [3×0 double]
         Covariance: [3×3 double]

PosteriorMdl is a conjugatebvarm model object; the posterior is analytically tractable. The command line displays the posterior means (Mean) and standard deviations (Std) of all coefficients and the innovations covariance matrix. Row AR{k}(i,j) contains the posterior estimates of ${\varphi }_{\mathit{k},\mathrm{ij}}$, the lag k AR coefficient of response variable j in response equation i. By default, estimate uses the first four observations as a presample to initialize the model.

Display the posterior means of the AR coefficient matrices by using dot notation.

AR1 = PosteriorMdl.AR{1}

AR1 = 3×3

    0.1241   -0.4809    0.1005
   -0.0219    0.4716    0.0391
   -0.1586   -1.4368   -0.2905

AR2 = PosteriorMdl.AR{2}

AR2 = 3×3

    0.3236   -0.0503    0.0450
    0.0913    0.2414    0.0536
    0.3403   -0.2968   -0.3117

AR3 = PosteriorMdl.AR{3}

AR3 = 3×3

    0.4272    0.2738    0.0523
   -0.0389    0.0552    0.0008
    0.2848   -0.7401    0.0028

AR4 = PosteriorMdl.AR{4}

AR4 = 3×3

    0.0167   -0.1830    0.0067
    0.0285   -0.1795    0.0088
   -0.0690    0.1494   -0.1372

Specify Presample Data

Consider the 3-D VAR(4) model of Estimate Posterior Distribution. In this case, fit the model to the data starting in 1970.

Load and Preprocess Data

Load the US macroeconomic data set. Compute the inflation rate, stabilize the unemployment and federal funds rates, and remove missing values.

load Data_USEconModel
seriesnames = ["INFL" "UNRATE" "FEDFUNDS"];
DataTimeTable.INFL = 100*[NaN; price2ret(DataTimeTable.CPIAUCSL)];

DataTimeTable.DUNRATE = [NaN; diff(DataTimeTable.UNRATE)];
DataTimeTable.DFEDFUNDS = [NaN; diff(DataTimeTable.FEDFUNDS)];
seriesnames(2:3) = "D" + seriesnames(2:3);
rmDataTimeTable = rmmissing(DataTimeTable);

Create Prior Model

Create a diffuse Bayesian VAR(4) prior model for the three response series. Specify the response variable names.

numseries = numel(seriesnames);
numlags = 4;

PriorMdl = diffusebvarm(numseries,numlags,'SeriesNames',seriesnames);

Partition Time Base for Subsamples

A VAR(4) model requires p = 4 presample observations to initialize the AR component for estimation. Define index sets corresponding to the required presample and estimation samples.

idxpre = rmDataTimeTable.Time < datetime('1970','InputFormat','yyyy');  % Presample indices
idxest = ~idxpre;                                                   % Estimation sample indices
T = sum(idxest)

T = 
157

The effective sample size is 157 observations.

Estimate Posterior Distribution

Estimate the posterior distribution. Specify only the required presample observations by using the 'Y0' name-value pair argument.

Y0 = rmDataTimeTable{find(idxpre,PriorMdl.P,'last'),seriesnames};
PosteriorMdl = estimate(PriorMdl,rmDataTimeTable{idxest,seriesnames},...
    'Y0',Y0);

Bayesian VAR under diffuse priors
Effective Sample Size:          157
Number of equations:            3
Number of estimated Parameters: 39
             |   Mean     Std  
-------------------------------
 Constant(1) |  0.1431  0.1134 
 Constant(2) | -0.0132  0.0588 
 Constant(3) | -0.6864  0.2418 
 AR{1}(1,1)  |  0.1314  0.0869 
 AR{1}(2,1)  | -0.0187  0.0450 
 AR{1}(3,1)  | -0.2009  0.1854 
 AR{1}(1,2)  | -0.5009  0.1834 
 AR{1}(2,2)  |  0.4881  0.0950 
 AR{1}(3,2)  | -1.6913  0.3912 
 AR{1}(1,3)  |  0.1089  0.0446 
 AR{1}(2,3)  |  0.0555  0.0231 
 AR{1}(3,3)  | -0.3588  0.0951 
 AR{2}(1,1)  |  0.2942  0.1012 
 AR{2}(2,1)  |  0.0786  0.0524 
 AR{2}(3,1)  |  0.3767  0.2157 
 AR{2}(1,2)  |  0.0208  0.2042 
 AR{2}(2,2)  |  0.3238  0.1058 
 AR{2}(3,2)  | -0.4530  0.4354 
 AR{2}(1,3)  |  0.0634  0.0487 
 AR{2}(2,3)  |  0.0747  0.0252 
 AR{2}(3,3)  | -0.3594  0.1038 
 AR{3}(1,1)  |  0.4503  0.1002 
 AR{3}(2,1)  | -0.0388  0.0519 
 AR{3}(3,1)  |  0.3580  0.2136 
 AR{3}(1,2)  |  0.3119  0.2008 
 AR{3}(2,2)  |  0.0966  0.1040 
 AR{3}(3,2)  | -0.8212  0.4282 
 AR{3}(1,3)  |  0.0659  0.0502 
 AR{3}(2,3)  |  0.0155  0.0260 
 AR{3}(3,3)  | -0.0269  0.1070 
 AR{4}(1,1)  | -0.0141  0.1046 
 AR{4}(2,1)  |  0.0105  0.0542 
 AR{4}(3,1)  |  0.0263  0.2231 
 AR{4}(1,2)  | -0.2274  0.1875 
 AR{4}(2,2)  | -0.1734  0.0972 
 AR{4}(3,2)  |  0.1328  0.3999 
 AR{4}(1,3)  |  0.0028  0.0456 
 AR{4}(2,3)  |  0.0094  0.0236 
 AR{4}(3,3)  | -0.1487  0.0973 
       Innovations Covariance Matrix       
           |   INFL     DUNRATE  DFEDFUNDS 
-------------------------------------------
 INFL      |  0.3597   -0.0333     0.1987  
           | (0.0433)  (0.0161)   (0.0672) 
 DUNRATE   | -0.0333    0.0966    -0.1647  
           | (0.0161)  (0.0116)   (0.0365) 
 DFEDFUNDS |  0.1987   -0.1647     1.6355  
           | (0.0672)  (0.0365)   (0.1969) 

Display and Return Estimation Summaries

Consider the 3-D VAR(4) model of Estimate Posterior Distribution.

Load the US macroeconomic data set. Compute the inflation rate, stabilize the unemployment and federal funds rates, and remove missing values.

load Data_USEconModel
seriesnames = ["INFL" "UNRATE" "FEDFUNDS"];
DataTimeTable.INFL = 100*[NaN; price2ret(DataTimeTable.CPIAUCSL)];

DataTimeTable.DUNRATE = [NaN; diff(DataTimeTable.UNRATE)];
DataTimeTable.DFEDFUNDS = [NaN; diff(DataTimeTable.FEDFUNDS)];
seriesnames(2:3) = "D" + seriesnames(2:3);
rmDataTimeTable = rmmissing(DataTimeTable);

Create a diffuse Bayesian VAR(4) prior model for the three response series. Specify the response variable names.

numseries = numel(seriesnames);
numlags = 4;

PriorMdl = diffusebvarm(numseries,numlags,'SeriesNames',seriesnames);

You can display estimation output in three ways, or turn off the display. Compare the display types.

estimate(PriorMdl,rmDataTimeTable{:,seriesnames}); % 'table', the default

Bayesian VAR under diffuse priors
Effective Sample Size:          197
Number of equations:            3
Number of estimated Parameters: 39
             |   Mean     Std  
-------------------------------
 Constant(1) |  0.1007  0.0832 
 Constant(2) | -0.0499  0.0450 
 Constant(3) | -0.4221  0.1781 
 AR{1}(1,1)  |  0.1241  0.0762 
 AR{1}(2,1)  | -0.0219  0.0413 
 AR{1}(3,1)  | -0.1586  0.1632 
 AR{1}(1,2)  | -0.4809  0.1536 
 AR{1}(2,2)  |  0.4716  0.0831 
 AR{1}(3,2)  | -1.4368  0.3287 
 AR{1}(1,3)  |  0.1005  0.0390 
 AR{1}(2,3)  |  0.0391  0.0211 
 AR{1}(3,3)  | -0.2905  0.0835 
 AR{2}(1,1)  |  0.3236  0.0868 
 AR{2}(2,1)  |  0.0913  0.0469 
 AR{2}(3,1)  |  0.3403  0.1857 
 AR{2}(1,2)  | -0.0503  0.1647 
 AR{2}(2,2)  |  0.2414  0.0891 
 AR{2}(3,2)  | -0.2968  0.3526 
 AR{2}(1,3)  |  0.0450  0.0413 
 AR{2}(2,3)  |  0.0536  0.0223 
 AR{2}(3,3)  | -0.3117  0.0883 
 AR{3}(1,1)  |  0.4272  0.0860 
 AR{3}(2,1)  | -0.0389  0.0465 
 AR{3}(3,1)  |  0.2848  0.1841 
 AR{3}(1,2)  |  0.2738  0.1620 
 AR{3}(2,2)  |  0.0552  0.0876 
 AR{3}(3,2)  | -0.7401  0.3466 
 AR{3}(1,3)  |  0.0523  0.0428 
 AR{3}(2,3)  |  0.0008  0.0232 
 AR{3}(3,3)  |  0.0028  0.0917 
 AR{4}(1,1)  |  0.0167  0.0901 
 AR{4}(2,1)  |  0.0285  0.0488 
 AR{4}(3,1)  | -0.0690  0.1928 
 AR{4}(1,2)  | -0.1830  0.1520 
 AR{4}(2,2)  | -0.1795  0.0822 
 AR{4}(3,2)  |  0.1494  0.3253 
 AR{4}(1,3)  |  0.0067  0.0395 
 AR{4}(2,3)  |  0.0088  0.0214 
 AR{4}(3,3)  | -0.1372  0.0845 
       Innovations Covariance Matrix       
           |   INFL     DUNRATE  DFEDFUNDS 
-------------------------------------------
 INFL      |  0.3028   -0.0217     0.1579  
           | (0.0321)  (0.0124)   (0.0499) 
 DUNRATE   | -0.0217    0.0887    -0.1435  
           | (0.0124)  (0.0094)   (0.0283) 
 DFEDFUNDS |  0.1579   -0.1435     1.3872  
           | (0.0499)  (0.0283)   (0.1470) 

estimate(PriorMdl,rmDataTimeTable{:,seriesnames},...
    'Display','equation');

Bayesian VAR under diffuse priors
Effective Sample Size:          197
Number of equations:            3
Number of estimated Parameters: 39
                                                                                 VAR Equations                                                                                
           | INFL(-1)  DUNRATE(-1)  DFEDFUNDS(-1)  INFL(-2)  DUNRATE(-2)  DFEDFUNDS(-2)  INFL(-3)  DUNRATE(-3)  DFEDFUNDS(-3)  INFL(-4)  DUNRATE(-4)  DFEDFUNDS(-4)  Constant 
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 INFL      |  0.1241     -0.4809        0.1005      0.3236     -0.0503        0.0450      0.4272      0.2738        0.0523      0.0167     -0.1830        0.0067      0.1007  
           | (0.0762)    (0.1536)      (0.0390)    (0.0868)    (0.1647)      (0.0413)    (0.0860)    (0.1620)      (0.0428)    (0.0901)    (0.1520)      (0.0395)    (0.0832) 
 DUNRATE   | -0.0219      0.4716        0.0391      0.0913      0.2414        0.0536     -0.0389      0.0552        0.0008      0.0285     -0.1795        0.0088     -0.0499  
           | (0.0413)    (0.0831)      (0.0211)    (0.0469)    (0.0891)      (0.0223)    (0.0465)    (0.0876)      (0.0232)    (0.0488)    (0.0822)      (0.0214)    (0.0450) 
 DFEDFUNDS | -0.1586     -1.4368       -0.2905      0.3403     -0.2968       -0.3117      0.2848     -0.7401        0.0028     -0.0690      0.1494       -0.1372     -0.4221  
           | (0.1632)    (0.3287)      (0.0835)    (0.1857)    (0.3526)      (0.0883)    (0.1841)    (0.3466)      (0.0917)    (0.1928)    (0.3253)      (0.0845)    (0.1781) 
 
       Innovations Covariance Matrix       
           |   INFL     DUNRATE  DFEDFUNDS 
-------------------------------------------
 INFL      |  0.3028   -0.0217     0.1579  
           | (0.0321)  (0.0124)   (0.0499) 
 DUNRATE   | -0.0217    0.0887    -0.1435  
           | (0.0124)  (0.0094)   (0.0283) 
 DFEDFUNDS |  0.1579   -0.1435     1.3872  
           | (0.0499)  (0.0283)   (0.1470) 

estimate(PriorMdl,rmDataTimeTable{:,seriesnames},...
    'Display','matrix');

Bayesian VAR under diffuse priors
Effective Sample Size:          197
Number of equations:            3
Number of estimated Parameters: 39
          VAR Coefficient Matrix of Lag 1         
           | INFL(-1)  DUNRATE(-1)  DFEDFUNDS(-1) 
--------------------------------------------------
 INFL      |  0.1241     -0.4809        0.1005    
           | (0.0762)    (0.1536)      (0.0390)   
 DUNRATE   | -0.0219      0.4716        0.0391    
           | (0.0413)    (0.0831)      (0.0211)   
 DFEDFUNDS | -0.1586     -1.4368       -0.2905    
           | (0.1632)    (0.3287)      (0.0835)   
 
          VAR Coefficient Matrix of Lag 2         
           | INFL(-2)  DUNRATE(-2)  DFEDFUNDS(-2) 
--------------------------------------------------
 INFL      |  0.3236     -0.0503        0.0450    
           | (0.0868)    (0.1647)      (0.0413)   
 DUNRATE   |  0.0913      0.2414        0.0536    
           | (0.0469)    (0.0891)      (0.0223)   
 DFEDFUNDS |  0.3403     -0.2968       -0.3117    
           | (0.1857)    (0.3526)      (0.0883)   
 
          VAR Coefficient Matrix of Lag 3         
           | INFL(-3)  DUNRATE(-3)  DFEDFUNDS(-3) 
--------------------------------------------------
 INFL      |  0.4272      0.2738        0.0523    
           | (0.0860)    (0.1620)      (0.0428)   
 DUNRATE   | -0.0389      0.0552        0.0008    
           | (0.0465)    (0.0876)      (0.0232)   
 DFEDFUNDS |  0.2848     -0.7401        0.0028    
           | (0.1841)    (0.3466)      (0.0917)   
 
          VAR Coefficient Matrix of Lag 4         
           | INFL(-4)  DUNRATE(-4)  DFEDFUNDS(-4) 
--------------------------------------------------
 INFL      |  0.0167     -0.1830        0.0067    
           | (0.0901)    (0.1520)      (0.0395)   
 DUNRATE   |  0.0285     -0.1795        0.0088    
           | (0.0488)    (0.0822)      (0.0214)   
 DFEDFUNDS | -0.0690      0.1494       -0.1372    
           | (0.1928)    (0.3253)      (0.0845)   
 
     Constant Term    
 INFL      |  0.1007  
           | (0.0832) 
 DUNRATE   | -0.0499  
           |  0.0450  
 DFEDFUNDS | -0.4221  
           |  0.1781  
 
       Innovations Covariance Matrix       
           |   INFL     DUNRATE  DFEDFUNDS 
-------------------------------------------
 INFL      |  0.3028   -0.0217     0.1579  
           | (0.0321)  (0.0124)   (0.0499) 
 DUNRATE   | -0.0217    0.0887    -0.1435  
           | (0.0124)  (0.0094)   (0.0283) 
 DFEDFUNDS |  0.1579   -0.1435     1.3872  
           | (0.0499)  (0.0283)   (0.1470) 

Return the estimation summary, which is a structure that contains the same information regardless of display type.

[PosteriorMdl,Summary] = estimate(PriorMdl,rmDataTimeTable{:,seriesnames});

Bayesian VAR under diffuse priors
Effective Sample Size:          197
Number of equations:            3
Number of estimated Parameters: 39
             |   Mean     Std  
-------------------------------
 Constant(1) |  0.1007  0.0832 
 Constant(2) | -0.0499  0.0450 
 Constant(3) | -0.4221  0.1781 
 AR{1}(1,1)  |  0.1241  0.0762 
 AR{1}(2,1)  | -0.0219  0.0413 
 AR{1}(3,1)  | -0.1586  0.1632 
 AR{1}(1,2)  | -0.4809  0.1536 
 AR{1}(2,2)  |  0.4716  0.0831 
 AR{1}(3,2)  | -1.4368  0.3287 
 AR{1}(1,3)  |  0.1005  0.0390 
 AR{1}(2,3)  |  0.0391  0.0211 
 AR{1}(3,3)  | -0.2905  0.0835 
 AR{2}(1,1)  |  0.3236  0.0868 
 AR{2}(2,1)  |  0.0913  0.0469 
 AR{2}(3,1)  |  0.3403  0.1857 
 AR{2}(1,2)  | -0.0503  0.1647 
 AR{2}(2,2)  |  0.2414  0.0891 
 AR{2}(3,2)  | -0.2968  0.3526 
 AR{2}(1,3)  |  0.0450  0.0413 
 AR{2}(2,3)  |  0.0536  0.0223 
 AR{2}(3,3)  | -0.3117  0.0883 
 AR{3}(1,1)  |  0.4272  0.0860 
 AR{3}(2,1)  | -0.0389  0.0465 
 AR{3}(3,1)  |  0.2848  0.1841 
 AR{3}(1,2)  |  0.2738  0.1620 
 AR{3}(2,2)  |  0.0552  0.0876 
 AR{3}(3,2)  | -0.7401  0.3466 
 AR{3}(1,3)  |  0.0523  0.0428 
 AR{3}(2,3)  |  0.0008  0.0232 
 AR{3}(3,3)  |  0.0028  0.0917 
 AR{4}(1,1)  |  0.0167  0.0901 
 AR{4}(2,1)  |  0.0285  0.0488 
 AR{4}(3,1)  | -0.0690  0.1928 
 AR{4}(1,2)  | -0.1830  0.1520 
 AR{4}(2,2)  | -0.1795  0.0822 
 AR{4}(3,2)  |  0.1494  0.3253 
 AR{4}(1,3)  |  0.0067  0.0395 
 AR{4}(2,3)  |  0.0088  0.0214 
 AR{4}(3,3)  | -0.1372  0.0845 
       Innovations Covariance Matrix       
           |   INFL     DUNRATE  DFEDFUNDS 
-------------------------------------------
 INFL      |  0.3028   -0.0217     0.1579  
           | (0.0321)  (0.0124)   (0.0499) 
 DUNRATE   | -0.0217    0.0887    -0.1435  
           | (0.0124)  (0.0094)   (0.0283) 
 DFEDFUNDS |  0.1579   -0.1435     1.3872  
           | (0.0499)  (0.0283)   (0.1470) 

Summary

Summary = struct with fields:
               Description: "3-Dimensional VAR(4) Model"
    NumEstimatedParameters: 39
                     Table: [39×2 table]
                  CoeffMap: [39×1 string]
                 CoeffMean: [39×1 double]
                  CoeffStd: [39×1 double]
                 SigmaMean: [3×3 double]
                  SigmaStd: [3×3 double]

The CoeffMap field contains a list of the coefficient names. The order of the names corresponds to the order of all coefficient vector inputs and outputs. Display CoeffMap.

Summary.CoeffMap

ans = 39×1 string
    "AR{1}(1,1)"
    "AR{1}(1,2)"
    "AR{1}(1,3)"
    "AR{2}(1,1)"
    "AR{2}(1,2)"
    "AR{2}(1,3)"
    "AR{3}(1,1)"
    "AR{3}(1,2)"
    "AR{3}(1,3)"
    "AR{4}(1,1)"
    "AR{4}(1,2)"
    "AR{4}(1,3)"
    "Constant(1)"
    "AR{1}(2,1)"
    "AR{1}(2,2)"
    "AR{1}(2,3)"
    "AR{2}(2,1)"
    "AR{2}(2,2)"
    "AR{2}(2,3)"
    "AR{3}(2,1)"
    "AR{3}(2,2)"
    "AR{3}(2,3)"
    "AR{4}(2,1)"
    "AR{4}(2,2)"
    "AR{4}(2,3)"
    "Constant(2)"
    "AR{1}(3,1)"
    "AR{1}(3,2)"
    "AR{1}(3,3)"
    "AR{2}(3,1)"
      ⋮

Estimate Posterior with Sparse Coefficients

Consider the 3-D VAR(4) model of Estimate Posterior Distribution In this example, create a normal conjugate prior model with a fixed coefficient matrix instead of a diffuse model. The model contains 39 coefficients. For coefficient sparsity in the posterior, apply the Minnesota regularization method during estimation.

Load the US macroeconomic data set. Compute the inflation rate, stabilize the unemployment and federal funds rates, and remove missing values.

load Data_USEconModel
seriesnames = ["INFL" "UNRATE" "FEDFUNDS"];
DataTimeTable.INFL = 100*[NaN; price2ret(DataTimeTable.CPIAUCSL)];

DataTimeTable.DUNRATE = [NaN; diff(DataTimeTable.UNRATE)];
DataTimeTable.DFEDFUNDS = [NaN; diff(DataTimeTable.FEDFUNDS)];
seriesnames(2:3) = "D" + seriesnames(2:3);
rmDataTimeTable = rmmissing(DataTimeTable);

Create a normal conjugate Bayesian VAR(4) prior model for the three response series. Specify the response variable names, and set the innovations covariance matrix

$$\Sigma =\left[\begin{array}{ccc}{10}^{-5}& 0& {10}^{-4}\\ 0& 0.1& -0.2\\ {10}^{-4}& -0.2& 1.6\end{array}\right].$$

According to the Minnesota regularization method, specify the following:

• Each response is an AR(1) model, on average, with lag 1 coefficient 0.75.

• The prior self-lag coefficients have variance 100. This large variance setting allows the data to influence the posterior more than the prior.

• The prior cross-lag coefficients have variance 0.01. This small variance setting tightens the cross-lag coefficients to zero during estimation.

• Prior coefficient covariances decay with increasing lag at a rate of 10 (that is, lower lags are more important than higher lags).

numseries = numel(seriesnames);
numlags = 4;
Sigma = [10e-5 0 10e-4; 0 0.1 -0.2; 10e-4 -0.2 1.6]; 

PriorMdl = bayesvarm(numseries,numlags,'Model','normal','SeriesNames',seriesnames,...
    'Center',0.75,'SelfLag',100,'CrossLag',0.01,'Decay',10,...
    'Sigma',Sigma);

Estimate the posterior distribution, and display the posterior response equations.

PosteriorMdl = estimate(PriorMdl,rmDataTimeTable{:,seriesnames},'Display','equation');

Bayesian VAR under normal priors and fixed Sigma
Effective Sample Size:          197
Number of equations:            3
Number of estimated Parameters: 39
                                                                                 VAR Equations                                                                                
           | INFL(-1)  DUNRATE(-1)  DFEDFUNDS(-1)  INFL(-2)  DUNRATE(-2)  DFEDFUNDS(-2)  INFL(-3)  DUNRATE(-3)  DFEDFUNDS(-3)  INFL(-4)  DUNRATE(-4)  DFEDFUNDS(-4)  Constant 
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 INFL      |  0.1234     -0.4373        0.1050      0.3343     -0.0342        0.0308      0.4441      0.0031        0.0090      0.0083     -0.0003        0.0003      0.0820  
           | (0.0014)    (0.0027)      (0.0007)    (0.0015)    (0.0021)      (0.0006)    (0.0015)    (0.0004)      (0.0003)    (0.0015)    (0.0001)      (0.0001)    (0.0014) 
 DUNRATE   |  0.0521      0.3636        0.0125      0.0012      0.1720        0.0009      0.0000     -0.0741       -0.0000      0.0000      0.0007       -0.0000     -0.0413  
           | (0.0252)    (0.0723)      (0.0191)    (0.0031)    (0.0666)      (0.0031)    (0.0004)    (0.0348)      (0.0004)    (0.0001)    (0.0096)      (0.0001)    (0.0339) 
 DFEDFUNDS | -0.0105     -0.1394       -0.1368      0.0002     -0.0000       -0.1227      0.0000     -0.0000        0.0085     -0.0000      0.0000       -0.0041     -0.0113  
           | (0.0749)    (0.0948)      (0.0713)    (0.0031)    (0.0031)      (0.0633)    (0.0004)    (0.0004)      (0.0344)    (0.0001)    (0.0001)      (0.0097)    (0.1176) 
 
      Innovations Covariance Matrix     
           |  INFL   DUNRATE  DFEDFUNDS 
----------------------------------------
 INFL      | 0.0001    0        0.0010  
           |  (0)      (0)       (0)    
 DUNRATE   |  0       0.1000   -0.2000  
           |  (0)      (0)       (0)    
 DFEDFUNDS | 0.0010  -0.2000    1.6000  
           |  (0)      (0)       (0)    

Compare the results to a posterior in which you specify no prior regularization.

PriorMdlNoReg = bayesvarm(numseries,numlags,'Model','normal','SeriesNames',seriesnames,...
    'Sigma',Sigma);
PosteriorMdlNoReg = estimate(PriorMdlNoReg,rmDataTimeTable{:,seriesnames},'Display','equation');

Bayesian VAR under normal priors and fixed Sigma
Effective Sample Size:          197
Number of equations:            3
Number of estimated Parameters: 39
                                                                                 VAR Equations                                                                                
           | INFL(-1)  DUNRATE(-1)  DFEDFUNDS(-1)  INFL(-2)  DUNRATE(-2)  DFEDFUNDS(-2)  INFL(-3)  DUNRATE(-3)  DFEDFUNDS(-3)  INFL(-4)  DUNRATE(-4)  DFEDFUNDS(-4)  Constant 
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 INFL      |  0.1242     -0.4794        0.1007      0.3233     -0.0502        0.0450      0.4270      0.2734        0.0523      0.0168     -0.1823        0.0068      0.1010  
           | (0.0014)    (0.0028)      (0.0007)    (0.0016)    (0.0030)      (0.0007)    (0.0016)    (0.0029)      (0.0008)    (0.0016)    (0.0027)      (0.0007)    (0.0015) 
 DUNRATE   | -0.0264      0.3428        0.0089      0.0969      0.1578        0.0292      0.0042     -0.0309       -0.0114      0.0221     -0.1071        0.0072     -0.0873  
           | (0.0347)    (0.0714)      (0.0203)    (0.0356)    (0.0714)      (0.0203)    (0.0337)    (0.0670)      (0.0200)    (0.0326)    (0.0615)      (0.0186)    (0.0422) 
 DFEDFUNDS | -0.0351     -0.1248       -0.0411      0.0416     -0.0224       -0.1358      0.0014     -0.0302        0.1557     -0.0074     -0.0010       -0.0785     -0.0205  
           | (0.0787)    (0.0949)      (0.0696)    (0.0631)    (0.0689)      (0.0663)    (0.0533)    (0.0567)      (0.0630)    (0.0470)    (0.0493)      (0.0608)    (0.1347) 
 
      Innovations Covariance Matrix     
           |  INFL   DUNRATE  DFEDFUNDS 
----------------------------------------
 INFL      | 0.0001    0        0.0010  
           |  (0)      (0)       (0)    
 DUNRATE   |  0       0.1000   -0.2000  
           |  (0)      (0)       (0)    
 DFEDFUNDS | 0.0010  -0.2000    1.6000  
           |  (0)      (0)       (0)    

The posterior estimates of the Minnesota prior have lower magnitude, in general, compared to the estimates of the default normal conjugate prior model.

Adjust Posterior Sampling Options

Consider the 3-D VAR(4) model of Estimate Posterior Distribution In this case, assume that the coefficients and innovations covariance matrix are independent (a semiconjugate prior model).

Load the US macroeconomic data set. Compute the inflation rate, stabilize the unemployment and federal funds rates, and remove missing values.

load Data_USEconModel
seriesnames = ["INFL" "UNRATE" "FEDFUNDS"];
DataTimeTable.INFL = 100*[NaN; price2ret(DataTimeTable.CPIAUCSL)];

DataTimeTable.DUNRATE = [NaN; diff(DataTimeTable.UNRATE)];
DataTimeTable.DFEDFUNDS = [NaN; diff(DataTimeTable.FEDFUNDS)];
seriesnames(2:3) = "D" + seriesnames(2:3);
rmDataTimeTable = rmmissing(DataTimeTable);

Create a semiconjugate Bayesian VAR(4) prior model for the three response series. Specify the response variable names.

numseries = numel(seriesnames);
numlags = 4;

PriorMdl = bayesvarm(numseries,numlags,'Model','semiconjugate',...
    'SeriesNames',seriesnames);

Because the joint posterior of a semiconjugate prior model is analytically intractable, estimate uses a Gibbs sampler to form the joint posterior by sampling from the tractable full conditionals.

Estimate the posterior distribution. For the Gibbs sampler, specify an effective number of draws of 20,000, a burn-in period of 5000, and a thinning factor of 10.

rng(1) % For reproducibility
PosteriorMdl = estimate(PriorMdl,rmDataTimeTable{:,seriesnames},...
    'Display','equation','NumDraws',20000,'Burnin',5000,'Thin',10);

Bayesian VAR under semiconjugate priors
Effective Sample Size:          197
Number of equations:            3
Number of estimated Parameters: 39
                                                                                 VAR Equations                                                                                
           | INFL(-1)  DUNRATE(-1)  DFEDFUNDS(-1)  INFL(-2)  DUNRATE(-2)  DFEDFUNDS(-2)  INFL(-3)  DUNRATE(-3)  DFEDFUNDS(-3)  INFL(-4)  DUNRATE(-4)  DFEDFUNDS(-4)  Constant 
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 INFL      |  0.2243     -0.0824        0.1365      0.2515     -0.0098        0.0329      0.2888      0.0311        0.0368      0.0458     -0.0206        0.0176      0.1836  
           | (0.0662)    (0.0821)      (0.0319)    (0.0701)    (0.0636)      (0.0309)    (0.0662)    (0.0534)      (0.0297)    (0.0649)    (0.0470)      (0.0274)    (0.0720) 
 DUNRATE   | -0.0262      0.3666        0.0148      0.0929      0.1637        0.0336      0.0016     -0.0147       -0.0089      0.0222     -0.1133        0.0082     -0.0808  
           | (0.0342)    (0.0728)      (0.0197)    (0.0354)    (0.0713)      (0.0198)    (0.0334)    (0.0671)      (0.0194)    (0.0320)    (0.0606)      (0.0179)    (0.0407) 
 DFEDFUNDS | -0.0251     -0.1285       -0.0527      0.0379     -0.0256       -0.1452     -0.0040     -0.0360        0.1516     -0.0090      0.0008       -0.0823     -0.0193  
           | (0.0785)    (0.0962)      (0.0673)    (0.0630)    (0.0688)      (0.0643)    (0.0531)    (0.0567)      (0.0610)    (0.0467)    (0.0492)      (0.0586)    (0.1302) 
 
       Innovations Covariance Matrix       
           |   INFL     DUNRATE  DFEDFUNDS 
-------------------------------------------
 INFL      |  0.2984   -0.0219     0.1754  
           | (0.0305)  (0.0121)   (0.0499) 
 DUNRATE   | -0.0219    0.0890    -0.1496  
           | (0.0121)  (0.0092)   (0.0292) 
 DFEDFUNDS |  0.1754   -0.1496     1.4754  
           | (0.0499)  (0.0292)   (0.1506) 

PosteriorMdl is an empiricalbvarm model represented by draws from the full conditionals. After removing the first burn-in period draws and thinning the remaining draws by keeping every 10th draw, estimate stores the draws in the CoeffDraws and SigmaDraws properties.

Estimate VARX Model

Consider the 2-D VARX(1) model for the US real GDP (RGDP) and investment (GCE) rates that treats the personal consumption (PCEC) rate as exogenous:

$$\left[\begin{array}{l}{\text{RGDP}}_t\\ {\text{GCE}}_t\end{array}\right]=c+\Phi \left[\begin{array}{l}{\text{RGDP}}_{t-1}\\ {\text{GCE}}_{t-1}\end{array}\right]+{\text{PCEC}}_t\beta +{\epsilon }_t.$$

For all $$t$$, $${\epsilon }_t$$ is a series of independent 2-D normal innovations with a mean of 0 and covariance $\Sigma$. Assume the following prior distributions:

• ${\left[\begin{array}{ccc}\Phi & \mathit{c}& \beta \end{array}\right]}^{\prime }|\Sigma \sim {{\rm N}}_{4\times 2}\left({\rm M},\mathit{V},\Sigma \right)$, where M is a 4-by-2 matrix of means and $\mathit{V}$ is the 4-by-4 among-coefficient scale matrix. Equivalently, $\mathrm{vec}\left({\left[\begin{array}{ccc}\Phi & \mathit{c}& \beta \end{array}\right]}^{\prime }\right)|\Sigma \sim {{\rm N}}_8\left(\mathrm{vec}\left({\rm M}\right),\Sigma \otimes \mathit{V}\right)$.

• $\Sigma \sim \mathrm{Inverse}\mathrm{Wishart}\left(\Omega ,\nu \right)$, where Ω is the 2-by-2 scale matrix and $\nu$ is the degrees of freedom.

Load the US macroeconomic data set. Compute the real GDP, investment, and personal consumption rate series. Remove all missing values from the resulting series.

load Data_USEconModel
DataTimeTable.RGDP = DataTimeTable.GDP./DataTimeTable.GDPDEF;
seriesnames = ["PCEC"; "RGDP"; "GCE"];
rates = varfun(@price2ret,DataTimeTable,'InputVariables',seriesnames);
rates = rmmissing(rates);
rates.Properties.VariableNames = seriesnames;

Create a conjugate prior model for the 2-D VARX(1) model parameters.

numseries = 2;
numlags = 1;
numpredictors = 1;
PriorMdl = conjugatebvarm(numseries,numlags,'NumPredictors',numpredictors,...
    'SeriesNames',seriesnames(2:end));

Estimate the posterior distribution. Specify the exogenous predictor data.

PosteriorMdl = estimate(PriorMdl,rates{:,2:end},...
    'X',rates{:,1},'Display','equation');

Bayesian VAR under conjugate priors
Effective Sample Size:          247
Number of equations:            2
Number of estimated Parameters: 8
                 VAR Equations                 
      | RGDP(-1)   GCE(-1)  Constant     X1    
-----------------------------------------------
 RGDP |  0.0083   -0.0027    0.0078    0.0105  
      | (0.0625)  (0.0606)  (0.0043)  (0.0625) 
 GCE  |  0.0059    0.0477    0.0166    0.0058  
      | (0.0644)  (0.0624)  (0.0044)  (0.0645) 
 
Innovations Covariance Matrix
      |   RGDP       GCE   
---------------------------
 RGDP |  0.0040    0.0000  
      | (0.0004)  (0.0003) 
 GCE  |  0.0000    0.0043  
      | (0.0003)  (0.0004) 

By default, estimate uses the first p = 1 observations in the specified response data as a presample, and it removes the corresponding observations in the predictor data from the sample.

The posterior means (and standard deviations) of the regression coefficients appear below the X1 column of the estimation summary table.

Input Arguments

PriorMdl — Prior Bayesian VAR model

conjugatebvarm model object | semiconjugatebvarm model object | diffusebvarm model object | normalbvarm model object

Prior Bayesian VAR model, specified as a model object in this table.

Model Object	Description
conjugatebvarm	Dependent, matrix-normal-inverse-Wishart conjugate model returned by bayesvarm, conjugatebvarm, or estimate
semiconjugatebvarm	Independent, normal-inverse-Wishart semiconjugate prior model returned by bayesvarm or semiconjugatebvarm
diffusebvarm	Diffuse prior model returned by bayesvarm or diffusebvarm
normalbvarm	Normal conjugate model with a fixed innovations covariance matrix, returned by bayesvarm, normalbvarm, or estimate

PriorMdl can also represent a joint posterior model returned by estimate, either a conjugatebvarm or normalbvarm model object. In this case, estimate updates the joint posterior distribution using the new observations.

For a semiconjugatebvarm model, estimate uses a Gibbs sampler to estimate the posterior distribution. To tune the sampler, see Options for Semiconjugate Prior Distributions.

Y — Observed multivariate response series

numeric matrix

Observed multivariate response series to which estimate fits the model, specified as a numobs-by-numseries numeric matrix.

numobs is the sample size. numseries is the number of response variables (PriorMdl.NumSeries).

Rows correspond to observations, and the last row contains the latest observation. Columns correspond to individual response variables.

Y represents the continuation of the presample response series in Y0.

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: 'Y0',Y0,'Display','off' specifies the presample data Y0 and suppresses the estimation display.

Options for All Prior Distributions

Y0 — Presample response data

numeric matrix

Presample response data to initialize the VAR model for estimation, specified as the comma-separated pair consisting of 'Y0' and a numpreobs-by-numseries numeric matrix. numpreobs is the number of presample observations.

Rows correspond to presample observations, and the last row contains the latest observation. Y0 must have at least PriorMdl.P rows. If you supply more rows than necessary, estimate uses the latest PriorMdl.P observations only.

Columns must correspond to the response series in Y.

By default, estimate uses Y(1:PriorMdl.P,:) as presample observations, and then estimates the posterior using Y((PriorMdl.P + 1):end,:). This action reduces the effective sample size.

Data Types: double

X — Predictor data

numeric matrix

Predictor data for the exogenous regression component in the model, specified as the comma-separated pair consisting of 'X' and a numobs-by-PriorMdl.NumPredictors numeric matrix.

Rows correspond to observations, and the last row contains the latest observation. estimate does not use the regression component in the presample period. X must have at least as many observations as the observations used after the presample period.

• If you specify Y0, then X must have at least numobs rows (see Y).

• Otherwise, X must have at least numobs – PriorMdl.P observations to account for the presample removal.

In either case, if you supply more rows than necessary, estimate uses the latest observations only.

Columns correspond to individual predictor variables. All predictor variables are present in the regression component of each response equation.

Data Types: double

Display — Estimation display style

'table' (default) | 'off' | 'equation' | 'matrix'

Estimation display style printed to the command line, specified as the comma-separated pair consisting of 'Display' and a value in this table.

Value	Description
'off'	estimate does not print to the command line.
'table'	estimate prints the following: Estimation information Tabular summary of coefficient posterior means and standard deviations; each row corresponds to a coefficient, and each column corresponds to an estimate type Posterior mean of the innovations covariance matrix with standard deviations in parentheses
'equation'	estimate prints the following: Estimation information Tabular summary of posterior means and standard deviations; each row corresponds to a response variable in the system, and each column corresponds to a coefficient in the equation (for example, the column labeled Y1(-1) contains the estimates of the lag 1 coefficient of the first response variable in each equation) Posterior mean of the innovations covariance matrix with standard deviations in parentheses.
'matrix'	estimate prints the following: Estimation information Separate tabular displays of posterior means and standard deviations (in parentheses) for each parameter in the model Φ1,…, Φp, c, δ, Β, and Σ

The estimation information includes the effective sample size, the number of equations in the system, and the number of estimated parameters.

Example: 'Display','matrix'

Data Types: char | string

Options for Semiconjugate Prior Distributions

NumDraws — Monte Carlo simulation adjusted sample size

1e5 (default) | positive integer

Monte Carlo simulation adjusted sample size, specified as the comma-separated pair consisting of 'NumDraws' and a positive integer. estimate actually draws BurnIn + NumDraws*Thin samples, but bases the estimates off NumDraws samples. For details on how estimate reduces the full Monte Carlo sample, see Algorithms.

Example: 'NumDraws',1e7

Data Types: double

BurnIn — Number of draws to remove from beginning of Monte Carlo sample

5000 (default) | nonnegative scalar

Number of draws to remove from the beginning of the Monte Carlo sample to reduce transient effects, specified as the comma-separated pair consisting of 'BurnIn' and a nonnegative scalar. For details on how estimate reduces the full Monte Carlo 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',0

Data Types: double

Thin — Adjusted sample size multiplier

1 (default) | positive integer

Adjusted sample size multiplier, specified as the comma-separated pair consisting of 'Thin' and a positive integer.

The actual Monte Carlo sample size is BurnIn + NumDraws*Thin. After discarding the burn-in, estimate discards every Thin – 1 draws, and then retains the next. For details on how estimate reduces the full Monte Carlo sample, see Algorithms.

Tip

To reduce potential large serial correlation in the Monte Carlo sample, or to reduce the memory consumption of the draws stored in PosteriorMdl, specify a large value for Thin.

Example: 'Thin',5

Data Types: double

Coeff0 — Starting values of VAR model coefficients for Gibbs sampler

numeric column vector

Starting values of the VAR model coefficients for the Gibbs sampler, specified as the comma-separated pair consisting of 'Coeff0' and a numel(PriorMdl.Mu)-by-1 numeric column vector.

Elements correspond to the elements of PriorMdl.Mu (see Mu).

By default, Coeff0 is the ordinary least-squares (OLS) estimate.

Tip

• Create Coeff0 by vertically stacking the transpose of all initial coefficients in the following order (skip coefficients not in the model):

  1. All coefficient matrices ordered by lag

  2. Constant vector

  3. Linear time trend vector

  4. Exogenous regression coefficient matrix

  Specify the vectorized result Coeff0(:).

• A good practice is to run estimate multiple times using different parameter starting values. Verify that the solutions from each run converge to similar values.

Data Types: double

Sigma0 — Starting values of innovations covariance matrix for Gibbs sampler

numeric positive definite matrix

Starting values of the innovations covariance matrix for the Gibbs sampler, specified as the comma-separated pair consisting of 'Sigma0' and a numeric positive definite matrix. Rows and columns correspond to response equations.

By default, Sigma0 is the OLS residual mean squared error.

Tip

A good practice is to run estimate multiple times using different parameter starting values. Verify that the solutions from each run converge to similar values.

Data Types: double

Output Arguments

PosteriorMdl — Posterior Bayesian VAR model

conjugatebvarm model object | normalbvarm model object | empiricalbvarm model object

Posterior Bayesian VAR model, returned as a model object in the table.

Model Object	PriorMdl	Posterior Form
conjugatebvarm	conjugatebvarm or diffusebvarm	Analytically tractable
normalbvarm	normalbvarm	Analytically tractable
empiricalbvarm	semiconjugatebvarm	Analytically intractable

Summary — Summary of Bayesian estimators

structure array

Summary of Bayesian estimators, returned as a structure array containing the fields in this table.

Field	Description	Data Type
Description	Model description	String scalar
NumEstimatedParameters	Number of estimated coefficients	Numeric scalar
Table	Table of coefficient posterior means and standard deviations; each row corresponds to a coefficient, and each column corresponds to an estimate type	Table
CoeffMap	Coefficient names	String vector
CoeffMean	Coefficient posterior means	Numeric vector; rows correspond to CoeffMap
CoeffStd	Coefficient posterior standard deviations	Numeric vector; rows correspond to CoeffMap
SigmaMean	Innovations covariance posterior mean matrix	Numeric matrix; rows and columns correspond to response equations
SigmaStd	Innovations covariance posterior standard deviation matrix	Numeric matrix; rows and columns correspond to response equations

Alternatively, pass PosteriorMdl to summarize to obtain a summary of Bayesian estimators.

More About

Bayesian Vector Autoregression (VAR) Model

A Bayesian VAR model treats all coefficients and the innovations covariance matrix as random variables in the m-dimensional, stationary VARX(p) model. The model has one of the three forms described in this table.

Model	Equation
Reduced-form VAR(p) in difference-equation notation	$$y_t={\Phi }_1{y}_{t-1}+\mathrm{...}+{\Phi }_p{y}_{t-p}+c+\delta t+{\rm B}{x}_t+{\epsilon }_t.$$
Multivariate regression	$$y_t=Z_t\lambda +{\epsilon }_t.$$
Matrix regression	$$y_t={\Lambda }^{\prime }{z}_t^{\prime }+{\epsilon }_t.$$

For each time t = 1,...,T:

• y_t is the m-dimensional observed response vector, where m = numseries.

• Φ₁,…,Φ_p are the m-by-m AR coefficient matrices of lags 1 through p, where p = numlags.

• c is the m-by-1 vector of model constants if IncludeConstant is true.

• δ is the m-by-1 vector of linear time trend coefficients if IncludeTrend is true.

• Β is the m-by-r matrix of regression coefficients of the r-by-1 vector of observed exogenous predictors x_t, where r = NumPredictors. All predictor variables appear in each equation.

• $$z_t=\left[\begin{array}{ccccccc}{y}_{t-1}^{\prime }& y_{t-2}^{\prime }& \cdots & y_{t-p}^{\prime }& 1& t& x_t^{\prime }\end{array}\right],$$ which is a 1-by-(mp + r + 2) vector, and Z_t is the m-by-m(mp + r + 2) block diagonal matrix

  $$\left[\begin{array}{cccc}{z}_t& 0_z& \cdots & 0_z\\ 0_z& z_t& \cdots & 0_z\\ ⋮& ⋮& \ddots & ⋮\\ 0_z& 0_z& 0_z& z_t\end{array}\right],$$

  where 0_z is a 1-by-(mp + r + 2) vector of zeros.

• $$\Lambda ={\left[\begin{array}{ccccccc}{\Phi }_1& {\Phi }_2& \cdots & {\Phi }_p& c& \delta & {\rm B}\end{array}\right]}^{\prime }$$, which is an (mp + r + 2)-by-m random matrix of the coefficients, and the m(mp + r + 2)-by-1 vector λ = vec(Λ).

• ε_t is an m-by-1 vector of random, serially uncorrelated, multivariate normal innovations with the zero vector for the mean and the m-by-m matrix Σ for the covariance. This assumption implies that the data likelihood is

  $$\ell \left(\Lambda ,\Sigma |y,x\right)={\displaystyle \prod _{t=1}^{T}f\left(y_t;\Lambda ,\Sigma ,z_t\right)},$$

  where f is the m-dimensional multivariate normal density with mean z_tΛ and covariance Σ, evaluated at y_t.

Before considering the data, you impose a joint prior distribution assumption on (Λ,Σ), which is governed by the distribution π(Λ,Σ). In a Bayesian analysis, the distribution of the parameters is updated with information about the parameters obtained from the data likelihood. The result is the joint posterior distribution π(Λ,Σ|Y,X,Y₀), where:

• Y is a T-by-m matrix containing the entire response series {y_t}, t = 1,…,T.

• X is a T-by-m matrix containing the entire exogenous series {x_t}, t = 1,…,T.

• Y₀ is a p-by-m matrix of presample data used to initialize the VAR model for estimation.

Tips

• Monte Carlo simulation is subject to variation. If estimate uses Monte Carlo simulation, then estimates and inferences might vary when you call estimate multiple times under seemingly equivalent conditions. To reproduce estimation results, set a random number seed by using rng before calling estimate.

Algorithms

• Whenever the prior distribution PriorMdl and the data likelihood yield an analytically tractable posterior distribution, estimate evaluates the closed-form solutions to Bayes estimators. Otherwise, estimate uses the Gibbs sampler to estimate the posterior.

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

  

Version History

Introduced in R2020a