# forecast

Forecast vector error-correction (VEC) model responses

## Syntax

## Description

### Conditional and Unconditional Forecasts for Numeric Arrays

returns a numeric array containing paths of minimum mean squared error (MMSE)
multivariate response forecasts `Y`

= forecast(`Mdl`

,`numperiods`

,`Y0`

)`Y`

over a length
`numperiods`

forecast horizon, using the fully specified
VEC(*p* – 1) model `Mdl`

. The forecasted
responses represent the continuation of the presample data in the numeric array
`Y0`

.

uses additional options specified by one or more name-value arguments.
`Y`

= forecast(`Mdl`

,`numperiods`

,`Y0`

,`Name=Value`

)`forecast`

returns numeric arrays when all optional
input data are numeric arrays. For example,
`forecast(Mdl,10,Y0,X=Exo)`

returns a numeric array
containing a 10-period forecasted response path from `Mdl`

and the numeric matrix of presample response data `Y0`

, and
specifies the numeric matrix of future predictor data for the model regression
component in the forecast horizon `Exo`

.

To produce a conditional forecast, specify future response data in a numeric
array by using the `YF`

name-value argument.

### Unconditional Forecasts for Tables and Timetables

returns the table or timetable `Tbl2`

= forecast(`Mdl`

,`numperiods`

,`Tbl1`

)`Tbl2`

containing the length
`numperiods`

paths of multivariate MMSE response variable
forecasts, which result from computing unconditional forecasts from the VEC
model `Mdl`

. `forecast`

uses the table
or timetable of presample data `Tbl1`

to initialize the
response series.

`forecast`

selects the variables in
`Mdl.SeriesNames`

to forecast, or it selects all variables
in `Tbl1`

. To select different response variables in
`Tbl1`

to forecast, use the
`PresampleResponseVariables`

name-value argument.

uses additional options specified by one or more name-value arguments. For
example, `Tbl2`

= forecast(`Mdl`

,`numperiods`

,`Tbl1`

,`Name=Value`

)```
forecast(Mdl,10,Tbl1,PresampleResponseVariables=["GDP"
"CPI"])
```

returns a timetable of response variables containing their
unconditional forecasts from the VEC model `Mdl`

, initialized
by the data in the `GDP`

and `CPI`

variables
of the timetable of presample data in `Tbl1`

.

### Conditional Forecasts for Tables and Timetables

returns the table or timetable `Tbl2`

= forecast(`Mdl`

,`numperiods`

,`Tbl1`

,InSample=`InSample`

,ReponseVariables=`ResponseVariables`

)`Tbl2`

containing the length
`numperiods`

paths of multivariate MMSE response variable
forecasts and corresponding forecast MSEs, which result from computing
conditional forecasts from the VEC model `Mdl`

.
`forecast`

uses the table or timetable of presample
data `Tbl1`

to initialize the response series.
`InSample`

is a table or timetable of future data in the
forecast horizon that `forecast`

uses to compute
conditional forecasts and `ResponseVariables`

specifies the
response variables in `InSample`

.

uses additional options specified by one or more name-value arguments.`Tbl2`

= forecast(`Mdl`

,`numperiods`

,`Tbl1`

,InSample=`InSample`

,ReponseVariables=`ResponseVariables`

,`Name=Value`

)

## Examples

### Return Matrix of VEC Model Forecasts

Consider a VEC model for the following seven macroeconomic series. Then, fit the model to the data and forecast responses 12 quarters into the future. Supply all required data in numeric matrices.

Gross domestic product (GDP)

GDP implicit price deflator

Paid compensation of employees

Nonfarm business sector hours of all persons

Effective federal funds rate

Personal consumption expenditures

Gross private domestic investment

Suppose that a cointegrating rank of 4 and one short-run term are appropriate, that is, consider a VEC(1) model.

Load the `Data_USEconVECModel`

data set.

`load Data_USEconVECModel`

For more information on the data set and variables, enter `Description`

at the command line.

Determine whether the data needs to be preprocessed by plotting the series on separate plots.

figure tiledlayout(2,2) nexttile plot(FRED.Time,FRED.GDP) title("Gross Domestic Product") ylabel("Index") xlabel("Date") nexttile plot(FRED.Time,FRED.GDPDEF) title("GDP Deflator") ylabel("Index") xlabel("Date") nexttile plot(FRED.Time,FRED.COE) title("Paid Compensation of Employees") ylabel("Billions of $") xlabel("Date") nexttile plot(FRED.Time,FRED.HOANBS) title("Nonfarm Business Sector Hours") ylabel("Index") xlabel("Date")

figure tiledlayout(2,2) nexttile plot(FRED.Time,FRED.FEDFUNDS) title("Federal Funds Rate") ylabel("Percent") xlabel("Date") nexttile plot(FRED.Time,FRED.PCEC) title("Consumption Expenditures") ylabel("Billions of $") xlabel("Date") nexttile plot(FRED.Time,FRED.GPDI) title("Gross Private Domestic Investment") ylabel("Billions of $") xlabel("Date")

Stabilize all series, except the federal funds rate, by applying the log transform. Scale the resulting series by 100 so that all series are on the same scale.

FRED.GDP = 100*log(FRED.GDP); FRED.GDPDEF = 100*log(FRED.GDPDEF); FRED.COE = 100*log(FRED.COE); FRED.HOANBS = 100*log(FRED.HOANBS); FRED.PCEC = 100*log(FRED.PCEC); FRED.GPDI = 100*log(FRED.GPDI);

Create a VEC(1) model using the shorthand syntax. Specify the variable names.

Mdl = vecm(7,4,1); Mdl.SeriesNames = FRED.Properties.VariableNames;

`Mdl`

is a `vecm`

model object. All properties containing `NaN`

values correspond to parameters to be estimated given data.

Estimate the model using the entire data set and the default options.

EstMdl = estimate(Mdl,FRED.Variables)

EstMdl = vecm with properties: Description: "7-Dimensional Rank = 4 VEC(1) Model" SeriesNames: "GDP" "GDPDEF" "COE" ... and 4 more NumSeries: 7 Rank: 4 P: 2 Constant: [14.1329 8.77841 -7.20359 ... and 4 more]' Adjustment: [7×4 matrix] Cointegration: [7×4 matrix] Impact: [7×7 matrix] CointegrationConstant: [-28.6082 109.555 -77.0912 ... and 1 more]' CointegrationTrend: [4×1 vector of zeros] ShortRun: {7×7 matrix} at lag [1] Trend: [7×1 vector of zeros] Beta: [7×0 matrix] Covariance: [7×7 matrix]

`EstMdl`

is an estimated `vecm`

model object. It is fully specified because all parameters have known values. By default, `estimate`

imposes the constraints of the H1 Johansen VEC model form by removing the cointegrating trend and linear trend terms from the model. Parameter exclusion from estimation is equivalent to imposing equality constraints to zero.

Forecast responses from the estimated model over a three-year horizon. Specify the entire data set as presample observations.

numperiods = 12; Y0 = FRED.Variables; Y = forecast(EstMdl,numperiods,Y0);

`Y`

is a 12-by-7 matrix of forecasted responses. Rows correspond to the forecast horizon, and columns correspond to the variables in `EstMdl.SeriesNames`

.

Plot the forecasted responses and the last 50 true responses.

fh = dateshift(FRED.Time(end),"end","quarter",1:12); figure; tiledlayout(2,2) nexttile h1 = plot(FRED.Time((end-49):end),FRED.GDP((end-49):end)); hold on h2 = plot(fh,Y(:,1)); title("Gross Domestic Product"); ylabel("Index (scaled)"); xlabel("Date"); h = gca; fill([FRED.Time(end) fh([end end]) FRED.Time(end)],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2],"True","Forecast",Location="best") hold off nexttile h1 = plot(FRED.Time((end-49):end),FRED.GDPDEF((end-49):end)); hold on h2 = plot(fh,Y(:,2)); title("GDP Deflator"); ylabel("Index (scaled)"); xlabel("Date"); h = gca; fill([FRED.Time(end) fh([end end]) FRED.Time(end)],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2],"True","Forecast",Location="best") hold off nexttile h1 = plot(FRED.Time((end-49):end),FRED.COE((end-49):end)); hold on h2 = plot(fh,Y(:,3)); title("Paid Compensation of Employees"); ylabel("Billions of $ (scaled)"); xlabel("Date"); h = gca; fill([FRED.Time(end) fh([end end]) FRED.Time(end)],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2],"True","Forecast",Location="best") hold off nexttile h1 = plot(FRED.Time((end-49):end),FRED.HOANBS((end-49):end)); hold on h2 = plot(fh,Y(:,4)); title("Nonfarm Business Sector Hours"); ylabel("Index (scaled)"); xlabel("Date"); h = gca; fill([FRED.Time(end) fh([end end]) FRED.Time(end)],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2],"True","Forecast",Location="best") hold off

figure tiledlayout(2,2) nexttile h1 = plot(FRED.Time((end-49):end),FRED.FEDFUNDS((end-49):end)); hold on h2 = plot(fh,Y(:,5)); title("Federal Funds Rate"); ylabel("Percent"); xlabel("Date"); h = gca; fill([FRED.Time(end) fh([end end]) FRED.Time(end)],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2],"True","Forecast",Location="best") hold off nexttile h1 = plot(FRED.Time((end-49):end),FRED.PCEC((end-49):end)); hold on h2 = plot(fh,Y(:,6)); title("Consumption Expenditures"); ylabel("Billions of $ (scaled)"); xlabel("Date"); h = gca; fill([FRED.Time(end) fh([end end]) FRED.Time(end)],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2],"True","Forecast",Location="best") hold off nexttile h1 = plot(FRED.Time((end-49):end),FRED.GPDI((end-49):end)); hold on h2 = plot(fh,Y(:,7)); title("Gross Private Domestic Investment"); ylabel("Billions of $ (scaled)"); xlabel("Date"); h = gca; fill([FRED.Time(end) fh([end end]) FRED.Time(end)],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2],"True","Forecast",Location="best") hold off

### Compute Conditional Forecasts From Numeric Matrix of Future Response Data

This example is based on Return Matrix of VEC Model Forecasts. Forecast all response variables of the VEC model into a 3-year forecast horizon beyond the sampling data, given that the effective federal funds rate `FEDFUNDS`

is 0.5% during each future quarter.

Load the `Data_USEconVECModel`

data set.

`load Data_USEconVECModel`

Stabilize all series, except the federal funds rate, by applying the log transform. Scale the resulting series by 100 so that all series are on the same scale.

FRED.GDP = 100*log(FRED.GDP); FRED.GDPDEF = 100*log(FRED.GDPDEF); FRED.COE = 100*log(FRED.COE); FRED.HOANBS = 100*log(FRED.HOANBS); FRED.PCEC = 100*log(FRED.PCEC); FRED.GPDI = 100*log(FRED.GPDI);

Create a VEC(1) model using the shorthand syntax. Specify the variable names.

Mdl = vecm(7,4,1); Mdl.SeriesNames = FRED.Properties.VariableNames;

Estimate the model using the entire data set and the default options.

EstMdl = estimate(Mdl,FRED.Variables);

Suppose economists hypothesize that the effective federal funds rate will be at 0.5% for the next 12 quarters.

Create a matrix with the following qualities:

The matrix has 12 rows representing periods in the forecast horizon.

All columns associated with variables of

`FRED`

, except for`FEDFUNDS`

, are composed of`NaN`

values.The column corresponding to the variable

`FEDFUNDS`

is composed of 0.5.

```
numperiods = 12;
CondF = NaN(numperiods,EstMdl.NumSeries);
idxFF = string(EstMdl.SeriesNames) == "FEDFUNDS";
CondF(:,idxFF) = 0.5*ones(numperiods,1);
```

`CondF`

is a 12-by-7 matrix of `NaN`

values, except for the column associated with `FEDFUNDS`

, which is a vector composed of the value 0.5. For each period in the forecast horizon, `forecast`

fills the `NaN`

elements of the matrix with forecasts, given the values of `FEDFUNDS`

.

Forecast all variables given the hypothesis by supplying the conditioning data `CondF`

. Supply the estimation sample as a presample to initialize the model.

Y = forecast(EstMdl,numperiods,FRED.Variables,YF=CondF);

`Y`

is a 12-by-7 matrix of forecasts and the fixed values in the column corresponding to `FEDFUNDS`

.

Plot the forecasts with the last few periods of the estimation sample.

fh = dateshift(FRED.Time(end),"end","quarter",1:numperiods); idx = find(~idxFF); figure; ht = tiledlayout(2,2); for j = idx(1:4) nexttile h1 = plot(FRED.Time((end-49):end),FRED{(end-49):end,j}); hold on h2 = plot(fh,Y(:,j)); title(EstMdl.SeriesNames(j)); xlabel("Date"); h = gca; fill([FRED.Time(end) fh([end end]) FRED.Time(end)],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2],"True","Forecast",Location="best") hold off end title(ht,"Forecasts With FEDFUNDS = 0.5")

figure; ht = tiledlayout(2,1); for j = idx(5:6) nexttile h1 = plot(FRED.Time((end-49):end),FRED{(end-49):end,j}); hold on h2 = plot(fh,Y(:,j)); title(EstMdl.SeriesNames(j)); xlabel("Date"); h = gca; fill([FRED.Time(end) fh([end end]) FRED.Time(end)],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2],"True","Forecast",Location="best") hold off end title(ht,"Forecasts With FEDFUNDS = 0.5")

### Estimate Forecast Intervals

Analyze forecast accuracy using forecast intervals over a three-year horizon. This example follows from Return Matrix of VEC Model Forecasts.

Load the `Data_USEconVECModel`

data set and preprocess the data.

```
load Data_USEconVECModel
FRED.GDP = 100*log(FRED.GDP);
FRED.GDPDEF = 100*log(FRED.GDPDEF);
FRED.COE = 100*log(FRED.COE);
FRED.HOANBS = 100*log(FRED.HOANBS);
FRED.PCEC = 100*log(FRED.PCEC);
FRED.GPDI = 100*log(FRED.GPDI);
```

Estimate a VEC(1) model. Reserve the last three years of data to assess forecast accuracy. Assume that the appropriate cointegration rank is 4, and the H1 Johansen form is appropriate for the model.

bfh = FRED.Time(end) - years(3); estIdx = FRED.Time < bfh; Mdl = vecm(7,4,1); Mdl.SeriesNames = FRED.Properties.VariableNames; EstMdl = estimate(Mdl,FRED{estIdx,:});

Forecast responses from the estimated model over a three-year horizon. Specify all in-sample observations as a presample. Return the MSE of the forecasts.

numperiods = 12; Y0 = FRED{estIdx,:}; [Y,YMSE] = forecast(EstMdl,numperiods,Y0);

`Y`

is a 12-by-7 matrix of forecasted responses. `YMSE`

is a 12-by-1 cell vector of 7-by-7 matrices corresponding to the MSEs.

Extract the main diagonal elements from the matrices in each cell of `YMSE`

. Apply the square root of the result to obtain standard errors.

extractMSE = @(x)diag(x)'; MSE = cellfun(extractMSE,YMSE,UniformOutput=false); SE = sqrt(cell2mat(MSE));

Estimate approximate 95% forecast intervals for each response series.

YFI = zeros(numperiods,Mdl.NumSeries,2); YFI(:,:,1) = Y - 2*SE; YFI(:,:,2) = Y + 2*SE;

Plot the forecasted responses and the last 40 true responses.

figure ht = tiledlayout(2,2); for j = 1:4 nexttile h1 = plot(FRED.Time((end-39):end),FRED{(end-39):end,j}); hold on h2 = plot(FRED.Time(~estIdx),Y(:,j)); h3 = plot(FRED.Time(~estIdx),YFI(:,j,1),"k--"); plot(FRED.Time(~estIdx),YFI(:,j,2),"k--"); title(EstMdl.SeriesNames(j)); xlabel("Date"); h = gca; fill([bfh h.XLim([2 2]) bfh],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2 h3],"Observed","Forecast","Forecast interval", ... Location="best"); hold off end title(ht,"Forecasts and 95% Forecast Intervals")

figure ht = tiledlayout(2,2); for j = 5:7 nexttile h1 = plot(FRED.Time((end-39):end),FRED{(end-39):end,j}); hold on h2 = plot(FRED.Time(~estIdx),Y(:,j)); h3 = plot(FRED.Time(~estIdx),YFI(:,j,1),"k--"); plot(FRED.Time(~estIdx),YFI(:,j,2),"k--"); title(EstMdl.SeriesNames(j)); xlabel("Date"); h = gca; fill([bfh h.XLim([2 2]) bfh],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2 h3],"Observed","Forecast","Forecast interval", ... Location="best"); hold off end title(ht,"Forecasts and 95% Forecast Intervals")

### Return Timetable of Forecasts and Array of Forecast MSEs

Consider a VEC model for the following seven macroeconomic series, and then fit the model to a timetable of response data. This example is based on Return Matrix of VEC Model Forecasts.

**Load and Preprocess Data**

Load the `Data_USEconVECModel`

data set.

```
load Data_USEconVECModel
DTT = FRED;
DTT.GDP = 100*log(DTT.GDP);
DTT.GDPDEF = 100*log(DTT.GDPDEF);
DTT.COE = 100*log(DTT.COE);
DTT.HOANBS = 100*log(DTT.HOANBS);
DTT.PCEC = 100*log(DTT.PCEC);
DTT.GPDI = 100*log(DTT.GPDI);
```

**Prepare Timetable for Estimation**

When you plan to supply a timetable directly to `estimate`

, you must ensure it has all the following characteristics:

All selected response variables are numeric and do not contain any missing values.

The timestamps in the

`Time`

variable are regular, and they are ascending or descending.

Remove all missing values from the table.

DTT = rmmissing(DTT); T = height(DTT)

T = 240

`DTT`

does not contain any missing values.

Determine whether the sampling timestamps have a regular frequency and are sorted.

`areTimestampsRegular = isregular(DTT,"quarters")`

`areTimestampsRegular = `*logical*
0

areTimestampsSorted = issorted(DTT.Time)

`areTimestampsSorted = `*logical*
1

`areTimestampsRegular = 0`

indicates that the timestamps of DTT are irregular. `areTimestampsSorted = 1`

indicates that the timestamps are sorted. Macroeconomic series in this example are timestamped at the end of the month. This quality induces an irregularly measured series.

Remedy the time irregularity by shifting all dates to the first day of the quarter.

dt = DTT.Time; dt = dateshift(dt,"start","quarter"); DTT.Time = dt;

`DTT`

is regular with respect to time.

**Create Model Template for Estimation**

Create a VEC(1) model using the shorthand syntax. Specify the variable names.

Mdl = vecm(7,4,1); Mdl.SeriesNames = DTT.Properties.VariableNames;

`Mdl`

is a `vecm`

model object. All properties containing `NaN`

values correspond to parameters to be estimated given data.

**Fit Model to Data**

Estimate the model by supplying the timetable of data `DTT`

. By default, because the number of variables in `Mdl.SeriesNames`

is the number of variables in `DTT`

, `estimate`

fits the model to all the variables in `DTT`

.

EstMdl = estimate(Mdl,DTT);

`EstMdl`

is an estimated `vecm`

model object.

**Forecast Responses and Compute Forecast MSEs**

Forecast responses from the estimated model over a three-year horizon. Specify the entire data set `DTT`

as a presample observations.

numperiods = 12; [Tbl,YMSE] = forecast(EstMdl,numperiods,DTT); size(Tbl)

`ans = `*1×2*
12 7

tail(DTT)

Time GDP GDPDEF COE HOANBS FEDFUNDS PCEC GPDI ___________ ______ ______ ______ ______ ________ ______ ______ 01-Jan-2015 978.6 469.42 915.93 470.1 0.11 940.09 802.11 01-Apr-2015 979.8 469.97 917.34 470.57 0.13 941.25 802.29 01-Jul-2015 980.6 470.28 918.4 470.52 0.14 942.2 803.01 01-Oct-2015 981.04 470.51 919.95 471.33 0.24 942.86 802.61 01-Jan-2016 981.37 470.62 919.95 471.67 0.36 943.33 801.86 01-Apr-2016 982.28 471.19 921.5 472.09 0.38 944.88 800.22 01-Jul-2016 983.5 471.54 922.78 472.24 0.4 945.97 801.21 01-Oct-2016 984.48 472.06 923.69 472.47 0.54 947.12 804.13

head(Tbl)

Time GDP_Responses GDPDEF_Responses COE_Responses HOANBS_Responses FEDFUNDS_Responses PCEC_Responses GPDI_Responses ___________ _____________ ________________ _____________ ________________ __________________ ______________ ______________ 01-Jan-2017 985.7 472.53 924.74 472.87 0.3725 948.18 806.74 01-Apr-2017 986.82 472.93 925.75 473.21 0.33795 949.24 808.66 01-Jul-2017 987.92 473.31 926.78 473.57 0.30002 950.29 810.45 01-Oct-2017 988.99 473.67 927.82 473.94 0.27518 951.35 812.12 01-Jan-2018 990.07 474.02 928.88 474.33 0.263 952.42 813.74 01-Apr-2018 991.14 474.37 929.95 474.74 0.26045 953.49 815.32 01-Jul-2018 992.22 474.71 931.04 475.15 0.26472 954.56 816.86 01-Oct-2018 993.29 475.05 932.14 475.56 0.27283 955.64 818.35

YMSE

`YMSE=`*12×1 cell array*
{7x7 double}
{7x7 double}
{7x7 double}
{7x7 double}
{7x7 double}
{7x7 double}
{7x7 double}
{7x7 double}
{7x7 double}
{7x7 double}
{7x7 double}
{7x7 double}

YMSE{6}

`ans = `*7×7*
7.6245 1.6879 7.7978 6.3846 3.5735 5.2342 26.8879
1.6879 1.9506 1.7640 0.4391 1.6560 1.2281 4.4627
7.7978 1.7640 8.8184 6.9137 3.6937 5.4552 28.3538
6.3846 0.4391 6.9137 7.4894 2.9271 4.2783 25.3822
3.5735 1.6560 3.6937 2.9271 4.3945 2.1872 12.6306
5.2342 1.2281 5.4552 4.2783 2.1872 4.1945 18.0819
26.8879 4.4627 28.3538 25.3822 12.6306 18.0819 113.1428

`Tbl`

is a 12-by-7 matrix of forecasted responses (denoted `responseVariable`

`_Responses`

). The timestamps of `Tbl`

follow directly from the timestamps of `DTT`

, and they have the same sampling frequency. YMSE is a 12-by-1 cell array of 7-by-7 forecast MSE matrices. For example, the forecast covariance of `GDP`

and `COE`

in period 6 of the forecast horizon if element (1,3) of the matrix in `YMSE{6}`

, which is 7.7978.

### Forecast VECX Model

Consider the model and data in Return Matrix of VEC Model Forecasts.

**Load Data**

Load the `Data_USEconVECModel`

data set.

`load Data_USEconVECModel`

The `Data_Recessions`

data set contains the beginning and ending serial dates of recessions. Load this data set. Convert the matrix of date serial numbers to a datetime array.

load Data_Recessions dtrec = datetime(Recessions,ConvertFrom="datenum");

**Preprocess Data**

Remove the exponential trend from the series, and then scale them by a factor of 100.

DTT = FRED; DTT.GDP = 100*log(DTT.GDP); DTT.GDPDEF = 100*log(DTT.GDPDEF); DTT.COE = 100*log(DTT.COE); DTT.HOANBS = 100*log(DTT.HOANBS); DTT.PCEC = 100*log(DTT.PCEC); DTT.GPDI = 100*log(DTT.GPDI);

Create a dummy variable that identifies periods in which the U.S. was in a recession or worse. Specifically, the variable should be `1`

if `FRED.Time`

occurs during a recession, and `0`

otherwise. Include the variable with the `FRED`

data.

isin = @(x)(any(dtrec(:,1) <= x & x <= dtrec(:,2))); DTT.IsRecession = double(arrayfun(isin,DTT.Time));

**Prepare Timetable for Estimation**

Remove all missing values from the table.

DTT = rmmissing(DTT);

To make the series regular, shift all dates to the first day of the quarter.

dt = DTT.Time; dt = dateshift(dt,"start","quarter"); DTT.Time = dt;

`DTT`

is regular with respect to time.

**Create Model Template for Estimation**

Create a VEC(1) model using the shorthand syntax. Assume that the appropriate cointegration rank is 4. You do not have to specify the presence of a regression component when creating the model. Specify the variable names.

Mdl = vecm(7,4,1); Mdl.SeriesNames = DTT.Properties.VariableNames(1:end-1);

**Fit Model to Data**

Estimate the model using all but the last three years of data. Specify the predictor identifying whether the observation was measured during a recession.

```
bfh = DTT.Time(end) - years(3);
fh = DTT.Time(DTT.Time >= bfh);
EstSample = DTT(DTT.Time < bfh,:);
FSample = DTT(fh,:);
EstMdl = estimate(Mdl,EstSample,PredictorVariables="IsRecession");
```

**Forecast Responses**

Forecast a path of quarterly responses three years into the future.

numperiods = numel(fh); Tbl = forecast(EstMdl,numperiods,EstSample, ... InSample=FSample,PredictorVariables="IsRecession"); head(Tbl(:,endsWith(Tbl.Properties.VariableNames,"_Responses")))

Time GDP_Responses GDPDEF_Responses COE_Responses HOANBS_Responses FEDFUNDS_Responses PCEC_Responses GPDI_Responses ___________ _____________ ________________ _____________ ________________ __________________ ______________ ______________ 01-Jan-2014 974.87 468.25 911.21 467.31 0.47511 936.25 793.63 01-Apr-2014 975.81 468.6 912.19 467.82 0.63807 937.22 794.68 01-Jul-2014 976.67 468.91 913.19 468.3 0.72011 938.16 795.47 01-Oct-2014 977.53 469.21 914.16 468.77 0.76135 939.08 796.33 01-Jan-2015 978.38 469.49 915.12 469.2 0.7691 939.98 797.17 01-Apr-2015 979.22 469.77 916.06 469.62 0.75747 940.86 798 01-Jul-2015 980.05 470.04 916.99 470.02 0.73223 941.74 798.83 01-Oct-2015 980.89 470.31 917.91 470.41 0.69828 942.62 799.67

`Tbl`

is a 12-by-15 matrix of variables in `FSample`

and forecasted responses (variables named `responseVariable`

`_Responses`

, for each response * responseVariable* in the model).

Plot the forecasted responses and the last 50 true responses.

figure; tiledlayout(2,2) for j = EstMdl.SeriesNames(1:4) nexttile h1 = plot(DTT.Time((end-49):end),DTT{(end-49):end,j}); hold on h2 = plot(Tbl.Time,Tbl{:,j+"_Responses"}); title(j); xlabel("Date"); h = gca; fill([DTT.Time(end) bfh([end end]) DTT.Time(end)],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2],"True","Forecast",Location="best") hold off end

figure tiledlayout(2,2) for j = EstMdl.SeriesNames(5:7) nexttile h1 = plot(DTT.Time((end-49):end),DTT{(end-49):end,j}); hold on h2 = plot(Tbl.Time,Tbl{:,j+"_Responses"}); title(j); xlabel("Date"); h = gca; fill([DTT.Time(end) bfh([end end]) DTT.Time(end)],h.YLim([1 1 2 2]),"k", ... FaceAlpha=0.1,EdgeColor="none"); legend([h1 h2],"True","Forecast",Location="best") hold off end

### Return Timetable of Conditional Forecasts

This example is based on Return Timetable of Forecasts and Array of Forecast MSEs. Forecast all response variables of the VEC model into a 3-year forecast horizon beyond the sampling data, given that the effective federal funds rate `FEDFUNDS`

is 0.5% during each future quarter.

**Load and Preprocess Data**

Load the `Data_USEconVECModel`

data set.

```
load Data_USEconVECModel
DTT = FRED;
DTT.GDP = 100*log(DTT.GDP);
DTT.GDPDEF = 100*log(DTT.GDPDEF);
DTT.COE = 100*log(DTT.COE);
DTT.HOANBS = 100*log(DTT.HOANBS);
DTT.PCEC = 100*log(DTT.PCEC);
DTT.GPDI = 100*log(DTT.GPDI);
```

**Prepare Timetable for Estimation**

Remove all missing values from the table.

DTT = rmmissing(DTT);

To make the series regular, shift all dates to the first day of the quarter.

dt = DTT.Time; dt = dateshift(dt,"start","quarter"); DTT.Time = dt;

`DTT`

is regular with respect to time.

**Create Model Template for Estimation**

Create a VEC(1) model using the shorthand syntax. Specify the variable names.

Mdl = vecm(7,4,1); Mdl.SeriesNames = DTT.Properties.VariableNames;

`Mdl`

is a `vecm`

model object. All properties containing `NaN`

values correspond to parameters to be estimated given data.

**Fit Model to Data**

Estimate the model. Pass the entire timetable `DTT`

.

EstMdl = estimate(Mdl,DTT);

**Prepare for Conditional Forecast of Estimated Model**

Suppose economists hypothesize that the effective federal funds rate will be at 0.5% for the next 12 quarters.

Create a timetable with the following qualities:

The timestamps are regular with respect to the estimation sample timestamps and they are ordered from Q1 of 2017 through Q4 of 2019.

All variables of DTT, except for

`FEDFUNDS`

, are a 12-by-1 vector of`NaN`

values.`FEDFUNDS`

is a 12-by-1 vector, where each element is 0.5.

```
numperiods = 12;
shdt = DTT.Time(end) + calquarters(1:numperiods);
DTTCondF = retime(DTT,shdt,"fillwithmissing");
DTTCondF.FEDFUNDS = 0.5*ones(numperiods,1);
```

`DTTCondF`

is a 12-by-7 timetable that follows directly, in time, from `DTT`

, and both timetables have the same variables. All variables in `DTTCondF`

contain `NaN`

values, except for `FEDFUNDS`

, which is a vector composed of the value 0.5.

**Perform Conditional Simulation of Estimated Model**

Forecast all response variables, given the hypothesis, by supplying the conditioning data `DTTCondF`

and specifying the response variable names. Supply the estimation sample as a presample to initialize the model.

```
Tbl = forecast(EstMdl,numperiods,DTT, ...
InSample=DTTCondF,ResponseVariables=EstMdl.SeriesNames);
size(Tbl)
```

`ans = `*1×2*
12 14

```
idx = endsWith(Tbl.Properties.VariableNames,"_Responses");
head(Tbl(:,idx))
```

Time GDP_Responses GDPDEF_Responses COE_Responses HOANBS_Responses FEDFUNDS_Responses PCEC_Responses GPDI_Responses ___________ _____________ ________________ _____________ ________________ __________________ ______________ ______________ 01-Jan-2017 985.73 472.53 924.76 472.89 0.5 948.2 806.83 01-Apr-2017 986.89 472.96 925.8 473.27 0.5 949.27 808.96 01-Jul-2017 988.01 473.36 926.87 473.65 0.5 950.34 810.86 01-Oct-2017 989.12 473.74 927.94 474.04 0.5 951.42 812.62 01-Jan-2018 990.22 474.12 929.04 474.45 0.5 952.5 814.28 01-Apr-2018 991.31 474.49 930.14 474.85 0.5 953.59 815.85 01-Jul-2018 992.39 474.86 931.25 475.25 0.5 954.67 817.35 01-Oct-2018 993.47 475.24 932.36 475.65 0.5 955.76 818.79

`Tbl`

is a 12-by-14 matrix of forecasts of all response variables of the VEC model in the forecast horizon, given `FEDFUNDS`

is 0.5%. `GDP_Responses`

contains the forecasts of the transformed GDP series. `FEDFUNDS_Responses`

is a 12-by-1 vector composed of the value 0.5.

### Return Multiple Conditional Forecast Paths

This example is based on Return Timetable of Forecasts and Array of Forecast MSEs. Forecast all response variables of the VEC model into a 1-year forecast horizon beyond the sampling data, given several hypotheses economists make on the effective federal funds rate `FEDFUNDS`

during each quarter of the next year after the sampling period.

Load the `Data_USEconVECModel`

data set.

```
load Data_USEconVECModel
DTT = FRED;
DTT.GDP = 100*log(DTT.GDP);
DTT.GDPDEF = 100*log(DTT.GDPDEF);
DTT.COE = 100*log(DTT.COE);
DTT.HOANBS = 100*log(DTT.HOANBS);
DTT.PCEC = 100*log(DTT.PCEC);
DTT.GPDI = 100*log(DTT.GPDI);
```

Remove all missing values from the table.

DTT = rmmissing(DTT);

To make the series regular, shift all dates to the first day of the quarter.

dt = DTT.Time; dt = dateshift(dt,"start","quarter"); DTT.Time = dt;

`DTT`

is regular with respect to time.

Create a VEC(1) model using the shorthand syntax. Specify the variable names.

Mdl = vecm(7,4,1); Mdl.SeriesNames = DTT.Properties.VariableNames;

Estimate the model. Pass the entire timetable `DTT`

.

EstMdl = estimate(Mdl,DTT);

Assuming the effective federal funds rate is 0.1%, 0.25%, 0.5%, 0.75%, and 1% percent throughout a 1-year forecast horizon, generate a forecast path for all response variables under each scenario.

Create a timetable with the following qualities:

The timestamps are regular with respect to the estimation sample timestamps and they are ordered from Q1 of 2017 through Q4 of 2017.

The variable

`FEDFUNDS`

is a 4-by-5 matrix, where each column is composed of each of the assumptions on the value of the effective federal funds rate in the forecast horizon; the elements of the first column are 0.1, elements of the second column are 0.25, and so on.Each other response variable is a 4-by-5 matrix of

`NaN`

values to be filled with forecasted paths by`forecast`

.

```
numperiods = 4;
shdt = DTT.Time(end) + calquarters(1:numperiods);
DTTCondF = retime(DTT,shdt,"fillwithmissing");
DTTCondF = varfun(@(x)nan(numperiods,5),DTTCondF);
DTTCondF.Properties.VariableNames = EstMdl.SeriesNames;
DTTCondF.FEDFUNDS = ones(numperiods,1)*[0.1 0.25 0.5 0.75 1];
DTTCondF
```

`DTTCondF=`*4×7 timetable*
Time GDP GDPDEF COE HOANBS FEDFUNDS PCEC GPDI
___________ _______________________________ _______________________________ _______________________________ _______________________________ ___________________________________ _______________________________ _______________________________
01-Jan-2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1 0.25 0.5 0.75 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
01-Apr-2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1 0.25 0.5 0.75 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
01-Jul-2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1 0.25 0.5 0.75 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
01-Oct-2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1 0.25 0.5 0.75 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

`DTTCondF`

is a 4-by-7 timetable that follows directly, in time, from `DTT`

, and both timetables have the same variables. Each variable in `DTTCondF`

contains a 4-by-5 matrix of `NaN`

values, except for `FEDFUNDS`

, which is a matrix with each column containing a different scenario for the conditional forecasts.

Forecast all response variables, given the hypotheses, by supplying the conditioning data `DTTCondF`

and specifying the response variable names. Supply the estimation sample as a presample to initialize the model. Return the forecast MSE matrices.

```
[Tbl,YMSE] = forecast(EstMdl,numperiods,DTT, ...
InSample=DTTCondF,ResponseVariables=EstMdl.SeriesNames);
size(Tbl)
```

`ans = `*1×2*
4 14

```
idx = endsWith(Tbl.Properties.VariableNames,"_Responses");
head(Tbl(:,idx))
```

Time GDP_Responses GDPDEF_Responses COE_Responses HOANBS_Responses FEDFUNDS_Responses PCEC_Responses GPDI_Responses ___________ ______________________________________________ ______________________________________________ ______________________________________________ ______________________________________________ ___________________________________ ______________________________________________ ______________________________________________ 01-Jan-2017 985.65 985.68 985.73 985.77 985.82 472.51 472.52 472.53 472.54 472.55 924.7 924.72 924.76 924.79 924.82 472.83 472.85 472.89 472.94 472.98 0.1 0.25 0.5 0.75 1 948.14 948.16 948.2 948.23 948.27 806.54 806.65 806.83 807.01 807.2 01-Apr-2017 986.73 986.79 986.89 986.98 987.08 472.9 472.92 472.96 472.99 473.03 925.67 925.72 925.8 925.88 925.97 473.13 473.18 473.27 473.35 473.44 0.1 0.25 0.5 0.75 1 949.2 949.23 949.27 949.31 949.36 808.17 808.47 808.96 809.45 809.94 01-Jul-2017 987.83 987.9 988.01 988.12 988.24 473.26 473.29 473.36 473.42 473.48 926.69 926.76 926.87 926.97 927.08 473.5 473.55 473.65 473.74 473.84 0.1 0.25 0.5 0.75 1 950.26 950.29 950.34 950.4 950.45 810.06 810.36 810.86 811.36 811.86 01-Oct-2017 988.93 989 989.12 989.24 989.37 473.6 473.65 473.74 473.83 473.92 927.74 927.82 927.94 928.07 928.2 473.9 473.96 474.04 474.13 474.22 0.1 0.25 0.5 0.75 1 951.33 951.36 951.42 951.48 951.54 811.86 812.15 812.62 813.1 813.58

YMSE

`YMSE=`*4×1 cell array*
{7x7 double}
{7x7 double}
{7x7 double}
{7x7 double}

YMSE{4}

`ans = `*7×7*
2.9103 0.2459 2.6926 2.2954 0 1.9785 10.5522
0.2459 0.6435 0.2598 -0.2005 0 0.2656 0.1772
2.6926 0.2598 3.1251 2.3680 0 1.9150 10.3987
2.2954 -0.2005 2.3680 3.0306 0 1.5138 10.0253
0 0 0 0 0 0 0
1.9785 0.2656 1.9150 1.5138 0 1.7880 6.7155
10.5522 0.1772 10.3987 10.0253 0 6.7155 50.7359

`Tbl`

is a 4-by-14 matrix of forecasts of all response variables of the VEC model in the forecast horizon, given each assumption on `FEDFUNDS`

. `GDP_Responses`

contains the matrix of 5 forecast paths of the transformed GDP series from matrix of 5 forecast paths. Each path uses the corresponding assumption about the value of `FEDFUNDS_Responses`

.

`YMSE`

is a 4-by-1 cell vector of 7-by-7 forecast MSE matrices for each period in the forecast horizon. The MSE matrices apply to each forecast path, and all elements of each matrix corresponding to the conditioning variable are 0.

## Input Arguments

`numperiods`

— Forecast horizon

positive integer

Forecast horizon, or the number of time points in the forecast period, specified as a positive integer.

**Data Types: **`double`

`Y0`

— Presample response data

numeric matrix | numeric array

Presample response data that provides initial values for the forecasts, specified as a
`numpreobs`

-by-`numseries`

numeric matrix or a
`numpreobs`

-by-`numseries`

-by-`numprepaths`

numeric array. Use `Y0`

only when you supply optional data inputs as
numeric arrays.

`numpreobs`

is the number of presample observations.
`numseries`

is the number of response series
(`Mdl.NumSeries`

). `numprepaths`

is the number of
presample response paths.

Each row is a presample observation, and measurements in each row, among all pages,
occur simultaneously. The last row contains the latest presample observation.
`Y0`

must have at least `Mdl.P`

rows. If you
supply more rows than necessary, `forecast`

uses the latest
`Mdl.P`

observations only.

Each column corresponds to the response series name in
`Mdl.SeriesNames`

.

Pages correspond to separate, independent paths.

If you compute unconditional forecasts (that is, you do not specify the

`YF`

name-value argument),`forecast`

initializes each forecasted path (page) using the corresponding page of`Y0`

. Therefore, the output argument`Y`

has`numpaths`

=`numprepaths`

pages.If you compute conditional forecasts by specifying future response data in

`YF`

:`forecast`

takes one of these actions.If

`Y0`

is a matrix,`forecast`

initializes each response path (page) in`YF`

using the corresponding presample response in`Y0`

. Therefore,`numpaths`

is the number of paths in`YF`

, and all paths in the output argument`Y`

derive from common initial conditions.If

`YF`

is a matrix,`forecast`

generates`numprepaths`

forecast paths, initialized by each presample response path in`Y0`

, but the future response data, from which to condition the forecasts, is the same among all paths. Therefore,`numprepaths`

is the number of paths in the output argument`Y`

, and all paths evolve from possibly different initial conditions.Otherwise,

`numpaths`

is the minimum between`numprepaths`

and the number of pages in`YF`

, and`forecast`

applies`Y0(:,:,`

to initialize forecasting path)`j`

, for`j`

= 1,…,`j`

`numpaths`

.

**Data Types: **`double`

`Tbl1`

— Presample data

table | timetable

Presample response data that provides initial values for the forecasts, specified as a table or timetable with `numprevars`

variables and `numpreobs`

rows. `forecast`

returns the forecasted response variable in the output table or timetable `Tbl2`

, which is commensurate with `Tbl1`

.

Each row is a presample observation, and measurements in each row, among all paths, occur simultaneously. `numpreobs`

must be at least `Mdl.P`

. If you supply more rows than necessary, `forecast`

uses the latest `Mdl.P`

observations only.

Each selected response variable is a `numpreobs`

-by-`numprepaths`

numeric matrix. You can optionally specify `numseries`

response variables by using the `PresampleResponseVariables`

name-value argument.

Paths (columns) within a particular response variable are independent, but path

of all variables correspond, for `j`

= 1,…,`j`

`numprepaths`

. The following conditions apply:

If you compute unconditional forecasts (that is, you do not specify the

`InSample`

and`ResponseVariables`

name-value arguments),`forecast`

initializes each forecasted path per selected response variable using the corresponding path in`Tbl1`

. Therefore, each forecasted response variable in the output argument`Tbl2`

is a`numperiods`

-by-`numprepaths`

matrix.If you compute conditional forecasts by specifying future response data in

`InSample`

and corresponding response variables from the data by using`ResponseVariables`

,`forecast`

takes one of these actions.If the selected presample response variables are vectors,

`forecast`

initializes each forecast path (column) of the selected response variables in`InSample`

by using the corresponding presample variable in`Tbl1`

. Therefore, all paths in the forecasted response variables evolve from common initial conditions.If the selected response variables in

`InSample`

are vectors,`forecast`

generates`numprepaths`

forecast paths, initialized by the paths of each selected presample response variable in`Tbl1`

, but the future response data, from which to condition the forecasts, is the same among all paths. Therefore,`numpaths`

=`numprepaths`

is the number of paths in all forecasted response variables, and all paths evolve from possibly different initial conditions.Otherwise,

`numpaths`

is the minimum between`numprepaths`

and the number of paths in each selected response variable in`InSample`

. For each selected presample and future sample response variable

and each path`ResponseK`

= 1,…,`j`

`numpaths`

,`forecast`

applies`Tbl1.`

to initialize the conditional forecast for the response data in(:,`ResponseK`

)`j`

`Tbl2.`

`ResponseK`

`(:,`

.)`j`

If `Tbl1`

is a timetable, all the following conditions must be true:

`Tbl1`

must represent a sample with a regular datetime time step (see`isregular`

).The inputs

`InSample`

and`Tbl1`

must be consistent in time such that`Tbl1`

immediately precedes`InSample`

with respect to the sampling frequency and order.The datetime vector of sample timestamps

`Tbl1.Time`

must be ascending or descending.

If `Tbl1`

is a table, the following conditions hold:

The last row contains the latest presample observation.

`Tbl1.Properties.RowsNames`

must be empty.

`InSample`

— Future time series response or predictor data

table | timetable

Future time series response or predictor data, specified as a table or timetable. `InSample`

contains `numvars`

variables, including `numseries`

response variables *y _{t}* or

`numpreds`

predictor variables *x*for the model regression component. You can specify

_{t}`InSample`

only when you specify `Tbl1`

.Use `InSample`

in the following situations:

Perform conditional simulation. You must also supply the response variable names to select response data in

`InSample`

by using the`ResponseVariables`

name-value argument.Supply future predictor data for either unconditional or conditional simulation. To supply predictor data, you must specify predictor variable names in

`InSample`

by using the`PredictorVariables`

name-value argument. Otherwise,`forecast`

ignores the model regression component.

Each row corresponds to an observation in the forecast horizon, the first row is the earliest observation, and measurements in each row, among all paths, occur simultaneously. Specifically, row

of variable `j`

(`VariableK`

`InSample.`

) contains observations * VariableK*(

*,:)*

`j`

`j`

periods into the future, or the `j`

-period-ahead forecasts. `InSample`

must have at least `numperiods`

rows to cover the forecast horizon. If you supply more rows than necessary, `forecast`

uses only the first `numperiods`

rows.Each selected response variable is a numeric matrix. For each selected response variable

, columns are separate, independent paths. Specifically, path `K`

of response variable `j`

captures the state, or knowledge, of `ResponseK`

as it evolves from the presample past (for example, `ResponseK`

`Tbl1.`

) into the future. For each selected response variable `ResponseK`

:`ResponseK`

If the selected presample response variables in

`Tbl1`

are vectors,`forecast`

initializes each forecast path (column) of the selected response variables in`InSample`

by using the corresponding presample variable in`Tbl1`

. Therefore, all paths in the forecasted response variables of the output`Tbl2`

evolve from common initial conditions.If the selected response variables in

`InSample`

are vectors,`forecast`

generates`numprepaths`

forecast paths, initialized by the paths of each selected presample response variable in`Tbl1`

, but the future response data, from which to condition the forecasts, is the same among all paths. Therefore,`numpaths`

=`numprepaths`

is the number of paths in all forecasted response variables, and all paths evolve from possibly different initial conditions.Otherwise,

`numpaths`

is the minimum between`numprepaths`

and the number of paths in each selected response variable in`InSample`

. For each selected presample and future sample response variable

and each path`ResponseK`

= 1,…,`j`

`numpaths`

,`forecast`

applies`Tbl1.`

to initialize the conditional forecast for the response data in(:,`ResponseK`

)`j`

`Tbl2.`

`ResponseK`

`(:,`

.)`j`

Each predictor variable is a numeric vector. All predictor variables are present in the regression component of each response equation and apply to all response paths.

If `InSample`

is a timetable, the following conditions apply:

`InSample`

must represent a sample with a regular datetime time step (see`isregular`

).The datetime vector

`InSample.Time`

must be strictly ascending or descending.`Tbl1`

must immediately precede`InSample`

, with respect to the sampling frequency.

If `InSample`

is a table, the following conditions hold:

The last row contains the latest observation.

`InSample.Properties.RowsNames`

must be empty.

Elements of the response variables of `InSample`

can be numeric scalars or missing values (indicated by `NaN`

values). `forecast`

treats numeric scalars as deterministic future responses that are known in advance, for example, set by policy. `forecast`

forecasts responses for corresponding `NaN`

values conditional on the known values. Elements of selected predictor variables must be numeric scalars.

By default, `forecast`

computes conventional MMSE forecasts and forecast
MSEs without a regression component in the model (each selected response variable is a
`numperiods`

-by-`numprepaths`

matrix composed of
`NaN`

values indicating a complete lack of knowledge of the future
state of the responses in the forecast horizon).

For more details, see Algorithms.

**Example: **Consider forecasting one path from a model composed of two
response series, `GDP`

and `CPI`

, three
periods into the future. Suppose that you have prior knowledge about some of
the future values of the responses, and you want to forecast the unknown
responses conditional on your knowledge. Specify `InSample`

as a matrix containing the values that you know, and use
`NaN`

for values you do not know but want to forecast.
For example, ```
InSample=array2table([2 NaN; 0.1 NaN; NaN
NaN],VariableNames=["GDP" "CPI"])
```

specifies that you have no
knowledge of the future values of `CPI`

, but you know that
`GDP`

is 2, 0.1, and unknown in periods 1, 2, and 3,
respectively, in the forecast horizon.

`ResponseVariables`

— Variables to select from `InSample`

to treat as response variables *y*_{t}

string vector | cell vector of character vectors | vector of integers | logical vector

_{t}

Variables to select from `InSample`

to treat as response variables *y _{t}*, specified as a one of the following data types:

String vector or cell vector of character vectors containing

`numseries`

variable names in`InSample.Properties.VariableNames`

A length

`numseries`

vector of unique indices (integers) of variables to select from`InSample.Properties.VariableNames`

A length

`numvars`

logical vector, where`ResponseVariables(`

selects variable) = true`j`

from`j`

`InSample.Properties.VariableNames`

, and`sum(ResponseVariables)`

is`numseries`

The selected variables must be numeric vectors (single path) or matrices (columns represent multiple independent paths) of the same width.

To compute conditional forecasts, you must specify `ResponseVariables`

to select the response variables in `InSample`

for the conditioning data. `ResponseVariables`

applies only when you specify `InSample`

.

By default, computes conventional MMSE forecasts and forecast MSEs.

**Example: **`ResponseVariables=["GDP" "CPI"]`

**Example: **`ResponseVariables=[true false true false]`

or `ResponseVariable=[1 3]`

selects the first and third table variables as the response variables.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

### 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: **`forecast(Mdl,10,Y0,X=Exo)`

returns a numeric array
containing a 10-period forecasted response path from `Mdl`

and
the numeric matrix of presample response data `Y0`

, and specifies
the numeric matrix of future predictor data for the model regression component in
the forecast horizon `Exo`

.

`PresampleResponseVariables`

— Variables to select from `Tbl1`

to use for presample response data

string vector | cell vector of character vectors | vector of integers | logical vector

Variables to select from `Tbl1`

to use for presample data, specified as one of the following data types:

String vector or cell vector of character vectors containing

`numseries`

variable names in`Tbl1.Properties.VariableNames`

A length

`numseries`

vector of unique indices (integers) of variables to select from`Tbl1.Properties.VariableNames`

A length

`numprevars`

logical vector, where`PresampleResponseVariables(`

selects variable) = true`j`

from`j`

`Tbl1.Properties.VariableNames`

, and`sum(PresampleResponseVariables)`

is`numseries`

The selected variables must be numeric vectors and cannot contain missing values (`NaN`

).

`PresampleResponseNames`

does not need to contain the same names as in `Mdl.SeriesNames`

; `forecast`

uses the data in selected variable `PresampleResponseVariables(`

as a presample for * j*)

`Mdl.SeriesNames(``j`

)

.If the number of variables in `Tbl1`

matches `Mdl.NumSeries`

, the default specifies all variables in `Tbl1`

. If the number of variables in `Tbl1`

exceeds `Mdl.NumSeries`

, the default matches variables in `Tbl1`

to names in `Mdl.SeriesNames`

.

**Example: **`PresampleResponseVariables=["GDP" "CPI"]`

**Example: **`PresampleResponseVariables=[true false true false]`

or `PresampleResponseVariable=[1 3]`

selects the first and third table variables for presample data.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

`X`

— Forecasted time series of predictor data *x*_{t}

numeric matrix

_{t}

Forecasted time series of predictor data *x _{t}*
to include in the model regression component, specified as a numeric matrix containing

`numpreds`

columns. Use `X`

only when you supply
`Y0`

.`numpreds`

is the number of predictor variables
(`size(Mdl.Beta,2)`

).

Each row corresponds to an observation in the forecast horizon, and measurements in
each row occur simultaneously. Specifically, row

(`j`

`X(`

) contains the predictor
observations * j*,:)

`j`

periods into the future, or
the `j`

-period-ahead forecasts.
`X`

must have at least `numperiods`

rows. If you
supply more rows than necessary, `forecast`

uses only the earliest
`numperiods`

observations. The first row contains the earliest
observation. `forecast`

does not use the regression component in
the presample period.Each column is an individual predictor variable. All predictor variables are present in the regression component of each response equation.

`forecast`

applies `X`

to each path (page);
that is, `X`

represents one path of observed predictors.

To maintain model consistency into the forecast horizon, it is a good practice to
specify forecasted predictors when `Mdl`

has a regression
component.

By default, `forecast`

excludes the regression component,
regardless of its presence in `Mdl`

.

**Data Types: **`double`

`YF`

— Future multivariate response series

numeric matrix | numeric array

Future multivariate response series data for conditional forecasting, specified as a numeric
matrix or array containing `numseries`

columns. Use `YF`

only when you supply `Y0`

.

Each row corresponds to observations in the forecast horizon, and the first row is the
earliest observation. Specifically, row * j* in sample path

*(*

`k`

`YF(``j`

,:,`k`

)

)
contains the responses *periods into the future, or the*

`j`

`j`

-period-ahead forecasts.
`YF`

must have at least `numperiods`

rows to cover
the forecast horizon. If you supply more rows than necessary,
`forecast`

uses only the first `numperiods`

rows.Each column corresponds to the response variable name in
`Mdl.SeriesNames`

.

Each page corresponds to a separate sample path. Specifically, path
* k* (

`YF(:,:,``k`

)

)
captures the state, or knowledge, of the response series as they evolve from the presample
past (`Y0`

) into the future.If

`YF`

is a matrix,`forecast`

generates`numprepaths`

forecast paths, initialized by each presample response path in`Y0`

, but the future response data, from which to condition the forecasts, is the same among all paths. Therefore,`numprepaths`

is the number of paths in the output argument`Y`

, and all paths evolve from possibly different initial conditions.If

`Y0`

is a matrix,`forecast`

initializes each response path (page) in`YF`

using the corresponding presample response in`Y0`

. Therefore,`numpaths`

is the number of paths in`YF`

, and all paths in the output argument`Y`

derive from common initial conditions.Otherwise,

`numpaths`

is the minimum between`numprepaths`

and the number of pages in`YF`

, and`forecast`

applies`Y0(:,:,`

to initialize forecasting path)`j`

, for`j`

= 1,…,`j`

`numpaths`

.

Elements of `YF`

can be numeric scalars or missing values (indicated by
`NaN`

values). `forecast`

treats numeric scalars
as deterministic future responses that are known in advance, for example, set by policy.
`forecast`

forecasts responses for corresponding
`NaN`

values conditional on the known values.

By default, `YF`

is an array composed of `NaN`

values
indicating a complete lack of knowledge of all responses in the forecast horizon. In this
case, `forecast`

estimates conventional MMSE forecasts.

For more details, see Algorithms.

**Example: **Consider forecasting one path from a model composed of four
response series three periods into the future. Suppose that you have
prior knowledge about some of the future values of the responses, and
you want to forecast the unknown responses conditional on your
knowledge. Specify `YF`

as a matrix containing the
values that you know, and use `NaN`

for values you do
not know but want to forecast. For example, ```
'YF',[NaN 2 5 NaN;
NaN NaN 0.1 NaN; NaN NaN NaN NaN]
```

specifies that you have
no knowledge of the future values of the first and fourth response
series; you know the value for period 1 in the second response series,
but no other value; and you know the values for periods 1 and 2 in the
third response series, but not the value for period 3.

**Data Types: **`double`

`PredictorVariables`

— Variables to select from `InSample`

to treat as exogenous predictor variables *x*_{t}

string vector | cell vector of character vectors | vector of integers | logical vector

_{t}

Variables to select from `InSample`

to treat as exogenous predictor
variables *x _{t}*, specified as one of the following data types:

String vector or cell vector of character vectors containing

`numpreds`

variable names in`InSample.Properties.VariableNames`

A length

`numpreds`

vector of unique indices (integers) of variables to select from`InSample.Properties.VariableNames`

A length

`numvars`

logical vector, where`PredictorVariables(`

selects variable) = true`j`

from`j`

`InSample.Properties.VariableNames`

, and`sum(PredictorVariables)`

is`numpreds`

Regardless, selected predictor variable

corresponds to the coefficients
`j`

`Mdl.Beta(:,`

.* j*)

`PredictorVariables`

applies only when you specify
`InSample`

.

The selected variables must be numeric vectors and cannot contain missing values
(`NaN`

).

By default, `forecast`

excludes the regression component, regardless of its presence in `Mdl`

.

**Example: **`PredictorVariables=["M1SL" "TB3MS" "UNRATE"]`

**Example: **`PredictorVariables=[true false true false]`

or
`PredictorVariable=[1 3]`

selects
the first and third table variables as the response
variables.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

**Note**

`NaN`

values in`Y0`

and`X`

indicate missing values.`forecast`

removes missing values from the data by list-wise deletion. If`Y0`

is a 3-D array, then`forecast`

performs these steps.Horizontally concatenate pages to form a

`numpreobs`

-by-`numpaths*numseries`

matrix.Remove any row that contains at least one

`NaN`

from the concatenated data.

In the case of missing observations, the results obtained from multiple paths of

`Y0`

can differ from the results obtained from each path individually.For missing values in

`X`

,`forecast`

removes the corresponding row from each page of`YF`

. After row removal from`X`

and`YF`

, if the number of rows is less than`numperiods`

, then`forecast`

throws an error.`forecast`

issues an error when selected response variables from`Tbl1`

and selected predictor variables from`InSample`

contain any missing values.

## Output Arguments

`Y`

— MMSE forecasts of multivariate response series

numeric matrix | numeric array

MMSE forecasts of the multivariate response series, returned as a
`numobs`

-by-`numseries`

numeric matrix or a
`numobs`

-by-`numseries`

-by-`numpaths`

numeric array. `forecast`

returns `Y`

only when
you supply presample data `Y0`

as a numeric matrix or array.

`Y`

represents the continuation of the presample responses in
`Y0`

.

Each row is a time point in the simulation horizon. Specifically, row
* j* contains the

*-period-ahead forecasts. Values in a row, among all pages, occur simultaneously. The last row contains the latest forecasted values.*

`j`

Each column corresponds to the response series name in
`Mdl.SeriesNames`

.

Pages correspond to separate, independently forecasted paths.

If you specify future responses for conditional forecasting using the `YF`

name-value argument, the known values in `YF`

appear in the same
positions in `Y`

. However, `Y`

contains forecasted
values for the missing observations in `YF`

.

`Tbl2`

— MMSE forecasts of multivariate response series and other variables

table | timetable

MMSE forecasts of multivariate response series and other variables, returned as a table or timetable, the same data type as `Tbl1`

. `forecast`

returns `Tbl2`

only when you supply the inputs `Tbl1`

.

`Tbl2`

contains the following variables:

The forecasted response paths within the

`numperiods`

length forecast horizon of the selected response series*y*. Each forecasted response variable in_{t}`Tbl2`

is a`numperiods`

-by-`numpaths`

numeric matrix, where`numpaths`

depends on the number of response paths in the specified presample or future sample data (see`Tbl1`

or`InSample`

). Each row corresponds to a time in the forecast horizon and each column corresponds to a separate path.`forecast`

names the forecasted response variable`ResponseK`

. For example, if_Responses`ResponseK`

`Mdl.Series(`

is)`K`

`GDP`

,`Tbl2`

contains a variable for the corresponding forecasted response with the name`GDP_Responses`

. If you specify`ResponseVariables`

,

is`ResponseK`

`ResponseVariable(`

. Otherwise,)`K`

is`ResponseK`

`PresampleResponseVariable(`

.)`K`

If you specify

`InSample`

, all specified future response variables.

If `Tbl2`

is a timetable, the following conditions hold:

The row order of

`Tbl2`

, either ascending or descending, matches the row order of`InSample`

, when you specify it. If you do not specify`InSample`

, the row order of`Tbl2`

is the same as the row order`Tbl1`

.If you specify

`InSample`

, row times`Tbl2.Time`

are`InSample.Time(1:numperiods)`

. Otherwise,`Tbl2.Time(1)`

is the next time after`Tbl1(end)`

relative the sampling frequency, and`Tbl2.Time(2:numperiods)`

are the following times relative to the sampling frequency.

`YMSE`

— MSE matrices of forecasted responses

cell vector of numeric matrices

MSE matrices of the forecasted responses, returned as a `numperiods`

-by-1
cell vector of `numseries`

-by-`numseries`

numeric
matrices. Cells of `YMSE`

compose a time series of forecast error
covariance matrices. Cell * j* contains the

*-period-ahead MSE matrix.*

`j`

`YMSE`

is identical for all paths.

Because `forecast`

treats predictor variables in `X`

as exogenous and non-stochastic, `YMSE`

reflects the error covariance associated with the autoregressive component of the input model `Mdl`

only.

## Algorithms

`forecast`

estimates unconditional forecasts using the equation$$\Delta {\widehat{y}}_{t}=\widehat{A}{\widehat{B}}^{\prime}{\widehat{y}}_{t-1}+{\widehat{\Phi}}_{1}\Delta {\widehat{y}}_{t-1}+\mathrm{...}+{\widehat{\Phi}}_{p}\Delta {\widehat{y}}_{t-p}+\widehat{c}+\widehat{d}t+{x}_{t}\widehat{\beta},$$

where

*t*= 1,...,`numperiods`

.`forecast`

filters a`numperiods`

-by-`numseries`

matrix of zero-valued innovations through`Mdl`

.`forecast`

uses specified presample innovations (`Y0`

or`Tbl1`

) wherever necessary.`forecast`

estimates conditional forecasts using the Kalman filter.`forecast`

represents the VEC model`Mdl`

as a state-space model (`ssm`

model object) without observation error.`forecast`

filters the forecast data`YF`

through the state-space model. At period*t*in the forecast horizon, any unknown response is$$\Delta {\widehat{y}}_{t}=\widehat{A}{\widehat{B}}^{\prime}{\widehat{y}}_{t-1}+{\widehat{\Phi}}_{1}\Delta {\widehat{y}}_{t-1}+\mathrm{...}+{\widehat{\Phi}}_{p}\Delta {\widehat{y}}_{t-p}+\widehat{c}+\widehat{d}t+{x}_{t}\widehat{\beta},$$

where $${\widehat{y}}_{s},$$

*s*<*t*, is the filtered estimate of*y*from period*s*in the forecast horizon.`forecast`

uses specified presample values in`Y0`

or`Tbl1`

for periods before the forecast horizon.

The way

`forecast`

determines`numpaths`

, the number of paths (pages) in the output argument`Y`

, or the number of paths (columns) in the forecasted response variables in the output argument`Tbl2`

, depends on the forecast type.If you estimate unconditional forecasts, which means you do not specify the

`YF`

name-value argument, or`InSample`

and`ResponseVariables`

name-value arguments,`numpaths`

is the number of paths in the`Y0`

or`Tbl1`

input argument.If you estimate conditional forecasts and the presample data

`Y0`

and future sample data`YF`

, or response variables in`Tbl1`

and`InSample`

, have more than one path,`numpaths`

is the fewest number of paths between the presample and future sample response data. Consequently,`forecast`

uses only the first`numpaths`

paths of each response variable for each input.If you estimate conditional forecasts and either

`Y0`

or`YF`

, or response variables in`Tbl1`

or`InSample`

, has one path,`numpaths`

is the number of pages in the array with the most pages.`forecast`

uses the variables with one path to produce each output path.

`forecast`

sets the time origin of models that include linear time trends*t*_{0}to`numpreobs`

–`Mdl.P`

(after removing missing values), where`numpreobs`

is the number of presample observations. Therefore, the times in the trend component are*t*=*t*_{0}+ 1,*t*_{0}+ 2,...,*t*_{0}+`numpreobs`

. This convention is consistent with the default behavior of model estimation in which`estimate`

removes the first`Mdl.P`

responses, reducing the effective sample size. Although`forecast`

explicitly uses the first`Mdl.P`

presample responses in`Y0`

or`Tbl1`

to initialize the model, the total number of usable observations determines*t*_{0}. An observation in`Y0`

is*usable*if it does not contain a`NaN`

.

## References

[1]
Hamilton, James D. *Time Series Analysis*. Princeton, NJ: Princeton University Press, 1994.

[2]
Johansen, S. *Likelihood-Based Inference in Cointegrated Vector Autoregressive Models*. Oxford: Oxford University Press, 1995.

[3]
Juselius, K. *The Cointegrated VAR Model*. Oxford: Oxford University Press, 2006.

[4]
Lütkepohl, H. *New Introduction to Multiple Time Series Analysis*. Berlin: Springer, 2005.

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