# irf

Generate vector error-correction (VEC) model impulse responses

## Syntax

``Response = irf(Mdl)``
``Response = irf(Mdl,Name,Value)``
``[Response,Lower,Upper] = irf(___)``

## Description

The `irf` function returns the dynamic response, or the impulse response function (IRF), to a one-standard-deviation shock to each variable in a VEC(p – 1) model. A fully specified `vecm` model object characterizes the VEC model.

IRFs trace the effects of an innovation shock to one variable on the response of all variables in the system. In contrast, the forecast error variance decomposition (FEVD) provides information about the relative importance of each innovation in affecting all variables in the system. To estimate the FEVD of a VEC model characterized by a `vecm` model object, see `fevd`.

example

````Response = irf(Mdl)` returns the 20-period, orthogonalized IRF of the time series variables that compose the VEC(p – 1) model `Mdl` characterized by a fully specified `vecm` model object. `irf` shocks variables at time 0, and returns the IRF for times 0 through 19.```

example

````Response = irf(Mdl,Name,Value)` uses additional options specified by one or more name-value pair arguments. For example, `'NumObs',10,'Method',"generalized"` specifies estimating a generalized IRF for 10 time points starting at time 0, during which `irf` applies the shock, and ending at period 9.```

example

````[Response,Lower,Upper] = irf(___)` uses any of the input argument combinations in the previous syntaxes and returns lower and upper confidence bounds, for each period and variable in the IRF, of a 95% confidence interval on the true IRF. If you specify series of residuals by using the `E` name-value pair argument, then `irf` estimates the confidence bounds by bootstrapping the specified residuals.Otherwise, `irf` estimates confidence bounds by conducting Monte Carlo simulation. If `Mdl` is a custom `vecm` model object (an object not returned by `estimate` or modified after estimation), `irf` might require a sample size for the simulation `SampleSize` or presample responses `Y0`.```

## Examples

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Fit a 4-D VEC(2) model to Danish money and income rate series. Then, estimate the orthogonalized IRF from the estimated model.

Load the Danish money and income data set.

`load Data_JDanish`

The data set includes four times series in the table `DataTable`. For more details on the data set, enter `Description` at the command line.

Create a `vecm` model object that represents a 4-D VEC(2) model with two cointegrating relations. Specify the variable names.

```Mdl = vecm(4,2,2); Mdl.SeriesNames = DataTable.Properties.VariableNames;```

`Mdl` is a `vecm` model object specifying the structure of a 4-D VEC(2) model; it is a template for estimation.

Fit the VEC(2) model to the data set.

`Mdl = estimate(Mdl,DataTable.Series);`

`Mdl` is a fully specified `vecm` model object representing an estimated 4-D VEC(2) model.

Estimate the orthogonalized IRF from the estimated VEC(2) model.

`Response = irf(Mdl);`

`Response` is a 20-by-4-by-4 array representing the IRF of `Mdl`. Rows correspond to consecutive time points from time 0 to 19, columns correspond to variables receiving a one-standard-deviation innovation shock at time 0, and pages correspond to responses of variables to the variable being shocked. `Mdl.SeriesNames` specifies the variable order.

Display the IRF of the bond rate (variable 3, `IB`) when the log of real income (variable 2, `Y`) is shocked at time 0.

`Response(:,2,3)`
```ans = 20×1 0.0021 0.0057 0.0064 0.0067 0.0064 0.0061 0.0057 0.0056 0.0057 0.0058 ⋮ ```

The `armairf` function plots the IRF of VAR models characterized by AR coefficient matrices. Plot the IRF of a VEC model by:

1. Expressing the VEC(2) model as a VAR(3) model by passing `Mdl` to `varm`

2. Passing the VAR model AR coefficients and innovations covariance matrix to `armairf`

Plot the VEC(2) model IRF for 40 periods.

```VARMdl = varm(Mdl); armairf(VARMdl.AR,[],"InnovCov",VARMdl.Covariance,... "NumObs",40);```

Each plot shows the four IRFs of a variable when all other variables are shocked at time 0. `Mdl.SeriesNames` specifies the variable order.

Consider the 4-D VEC(2) model with two cointegrating relations in Estimate and Plot VEC Model IRF. Estimate the generalized IRF of the system for 50 periods.

Load the Danish money and income data set, then estimate the VEC(2) model.

```load Data_JDanish Mdl = vecm(4,2,2); Mdl.SeriesNames = DataTable.Properties.VariableNames; Mdl = estimate(Mdl,DataTable.Series);```

Estimate the generalized IRF from the estimated VEC(2) model.

`Response = irf(Mdl,"Method","generalized","NumObs",50);`

`Response` is a 50-by-4-by-4 array representing the generalized IRF of `Mdl`.

Plot the generalized IRF of the bond rate when real income is shocked at time 0.

```figure; plot(0:49,Response(:,2,3)) title("IRF of IB When Y Is Shocked") xlabel("Observation Time") ylabel("Response") grid on```

When real income is shocked, the bond rate reacts and then settles at approximately 0.0032 after 15 periods.

Consider the 4-D VEC(2) model with two cointegrating relations in Estimate and Plot VEC Model IRF. Estimate and plot its orthogonalized IRF and 95% Monte Carlo confidence intervals on the true IRF.

Load the Danish money and income data set, then estimate the VEC(2) model.

```load Data_JDanish Mdl = vecm(4,2,2); Mdl.SeriesNames = DataTable.Properties.VariableNames; Mdl = estimate(Mdl,DataTable.Series);```

Estimate the IRF and corresponding 95% Monte Carlo confidence intervals from the estimated VEC(2) model.

```rng(1); % For reproducibility [Response,Lower,Upper] = irf(Mdl);```

`Response`, `Lower`, and `Upper` are 20-by-4-by-4 arrays representing the orthogonalized IRF of `Mdl` and corresponding lower and upper bounds of the confidence intervals. For all arrays, rows correspond to consecutive time points from time 0 to 19, columns correspond to variables receiving a one-standard-deviation innovation shock at time 0, and pages correspond to responses of variables to the variable being shocked. `Mdl.SeriesNames` specifies the variable order.

Plot the orthogonalized IRF with its confidence bounds of the bond rate when real income is shocked at time 0.

```irfshock2resp3 = Response(:,2,3); IRFCIShock2Resp3 = [Lower(:,2,3) Upper(:,2,3)]; figure; h1 = plot(0:19,irfshock2resp3); hold on h2 = plot(0:19,IRFCIShock2Resp3,'r--'); legend([h1 h2(1)],["IRF" "95% Confidence Interval"]) xlabel("Time Index"); ylabel("Response"); title("IRF of IB When Y Is Shocked"); grid on hold off```

When real income is shocked, the bond rate reacts and then settles between –0.002 and 0.0095 with 95% confidence.

Consider the 4-D VEC(2) model with two cointegrating relations in Estimate and Plot VEC Model IRF. Estimate and plot its orthogonalized IRF and 90% bootstrap confidence intervals on the true IRF.

Load the Danish money and income data set, then estimate the VEC(2) model. Return the residuals from model estimation.

```load Data_JDanish Mdl = vecm(4,2,2); Mdl.SeriesNames = DataTable.Properties.VariableNames; [Mdl,~,~,E] = estimate(Mdl,DataTable.Series); T = size(DataTable,1) % Total sample size```
```T = 55 ```
`n = size(E,1) % Effective sample size`
```n = 52 ```

`E` is a 52-by-4 array of residuals. Columns correspond to the variables in `Mdl.SeriesNames`. The `estimate` function requires `Mdl.P` = 3 observations to initialize a VEC(2) model for estimation. Because presample data (`Y0`) is unspecified, `estimate` takes the first three observations in the specified response data to initialize the model. Therefore, the resulting effective sample size is `T``Mdl.P` = 52, and rows of `E` correspond to the observation indices 4 through `T`.

Estimate the orthogonalized IRF and corresponding 90% bootstrap confidence intervals from the estimated VEC(2) model. Draw 500 paths of length `n` from the series of residuals.

```rng(1); % For reproducibility [Response,Lower,Upper] = irf(Mdl,"E",E,"NumPaths",500,... "Confidence",0.9);```

Plot the orthogonalized IRF with its confidence bounds of the bond rate when real income is shocked at time 0.

```irfshock2resp3 = Response(:,2,3); IRFCIShock2Resp3 = [Lower(:,2,3) Upper(:,2,3)]; figure; h1 = plot(0:19,irfshock2resp3); hold on h2 = plot(0:19,IRFCIShock2Resp3,'r--'); legend([h1 h2(1)],["IRF" "90% Confidence Interval"]) xlabel("Time Index"); ylabel("Response"); title("IRF of IB When Y Is Shocked"); grid on hold off```

When real income is shocked, the bond rate reacts and then settles between approximately 0 and 0.010 with 90% confidence.

## Input Arguments

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VEC model, specified as a `vecm` model object created by `vecm` or `estimate`. `Mdl` must be fully specified.

### Name-Value Pair Arguments

Specify optional comma-separated pairs of `Name,Value` arguments. `Name` is the argument name and `Value` is the corresponding value. `Name` must appear inside quotes. You can specify several name and value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`.

Example: `'NumObs',10,'Method',"generalized"` specifies estimating a generalized IRF for 10 time points starting at time 0, during which `irf` applies the shock, and ending at period 9.

#### Options for All IRFs

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Number of periods for which `irf` computes the IRF, specified as the comma-separated pair consisting of `'NumObs'` and a positive integer. `NumObs` specifies the number of observations to include in the IRF (the number of rows in `Response`).

Example: `'NumObs',10` specifies the inclusion of 10 time points in the IRF starting at time 0, during which `irf` applies the shock, and ending at period 9.

Data Types: `double`

IRF computation method, specified as the comma-separated pair consisting of `'Method'` and a value in this table.

ValueDescription
`"orthogonalized"`Compute impulse responses using orthogonalized, one-standard-deviation innovation shocks. `irf` uses the Cholesky factorization of `Mdl.Covariance` for orthogonalization.
`"generalized"`Compute impulse responses using one-standard-deviation innovation shocks.

Example: `'Method',"generalized"`

Data Types: `string`

#### Options for Confidence Bound Estimation

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Number of sample paths (trials) to generate, specified as the comma-separated pair consisting of `'NumPaths'` and a positive integer.

Example: `'NumPaths',1000` generates `1000` sample paths from which the software derives the confidence bounds.

Data Types: `double`

Number of observations for the Monte Carlo simulation or bootstrap per sample path, specified as the comma-separated pair consisting of `'SampleSize'` and a positive integer.

• If `Mdl` is an estimated `vecm` model object (an object returned by `estimate` and unmodified thereafter), then the default is the sample size of the data to which the model is fit (see `summarize`).

• If `irf` estimates confidence bounds by conducting a Monte Carlo simulation (for details, see `E`), you must specify `SampleSize`.

• If `irf` estimates confidence bounds by bootstrapping residuals, the default is the length of the specified series of residuals (`size(E,1)`).

Example: If you specify `'SampleSize',100` and do not specify the `'E'` name-value pair argument, the software estimates confidence bounds from `NumPaths` random paths of length `100` from `Mdl`.

Example: If you specify `'SampleSize',100,'E',E`, the software resamples, with replacement, `100` observations (rows) from `E` to form a sample path of innovations to filter through `Mdl`. The software forms `NumPaths` random sample paths from which it derives confidence bounds.

Data Types: `double`

Presample response data that provides initial values for model estimation during the simulation, specified as the comma-separated pair consisting of `'Y0'` and a `numpreobs`-by-`numseries` numeric matrix.

Rows of `Y0` correspond to periods in the presample, and the last row contains the latest presample response. `numpreobs` is the number of specified presample responses and it must be at least `Mdl.P`. If `numpreobs` exceeds `Mdl.P`, then `irf` uses only the latest `Mdl.P` rows.

`numseries` is the dimensionality of the input VEC model `Mdl.NumSeries`. Columns must correspond to the response variables in `Mdl.SeriesNames`.

• If `Mdl` is an estimated `vecm` model object (an object returned by `estimate` and unmodified thereafter), `irf` sets `Y0` to the presample response data used for estimation by default (see `'Y0'`).

• Otherwise, you must specify `Y0`.

Data Types: `double`

Predictor data for estimating the model regression component during the simulation, specified as the comma-separated pair consisting of `'X'` and a numeric matrix containing `numpreds` columns.

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

Rows correspond to observations. `X` must have at least `SampleSize` rows. If you supply more rows than necessary, `irf` uses only the latest `SampleSize` observations. The last row contains the latest observation.

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

To maintain model consistency when `irf` estimates the confidence bounds, a good practice is to specify `X` when `Mdl` has a regression component. If `Mdl` is an estimated model, specify the predictor data used during model estimation (see `'X'`).

By default, `irf` excludes the regression component from confidence bound estimation, regardless of its presence in `Mdl`.

Data Types: `double`

Series of residuals from which to draw bootstrap samples, specified as the comma-separated pair consisting of `'E'` and a numeric matrix containing `numseries` columns. `irf` assumes that `E` is free of serial correlation.

Columns contain the residual series corresponding to the response series names in `Mdl.SeriesNames`.

If `Mdl` is an estimated `vecm` model object (an object returned by `estimate`), you can specify `E` as the inferred residuals from estimation (see `E` or `infer`).

By default, `irf` derives confidence bounds by conducting a Monte Carlo simulation.

Data Types: `double`

Confidence level for confidence bounds, specified as the comma-separated pair consisting of `'Confidence'` and a numeric scalar in [0,1].

Suppose `Confidence` = c. Then, 100(1 – c)/2 percent of the impulse responses lie outside the confidence bounds.

The default value is `0.95`, which implies that the confidence bounds represent 95% confidence intervals.

Data Types: `double`

Johansen form of the VEC(p – 1) model deterministic terms [2], specified as the comma-separated pair consisting of `'Model'` and a value in this table (for variable definitions, see Vector Error-Correction Model).

ValueError-Correction TermDescription
`"H2"`

AB´yt − 1

No intercepts or trends are present in the cointegrating relations, and no deterministic trends are present in the levels of the data.

Specify this model only when all response series have a mean of zero.

`"H1*"`

A(B´yt−1+c0)

Intercepts are present in the cointegrating relations, and no deterministic trends are present in the levels of the data.

`"H1"`

A(B´yt−1+c0)+c1

Intercepts are present in the cointegrating relations, and deterministic linear trends are present in the levels of the data.

`"H*"`A(B´yt−1+c0+d0t)+c1

Intercepts and linear trends are present in the cointegrating relations, and deterministic linear trends are present in the levels of the data.

`"H"`A(B´yt−1+c0+d0t)+c1+d1t

Intercepts and linear trends are present in the cointegrating relations, and deterministic quadratic trends are present in the levels of the data.

If quadratic trends are not present in the data, this model can produce good in-sample fits but poor out-of-sample forecasts.

For more details on the Johansen forms, see `estimate`.

### Tip

A best practice is to maintain model consistency during the simulation that estimates confidence bounds. Therefore, if `Mdl` is an estimated `vecm` model object (an object returned by `estimate` and unmodified thereafter), incorporate any constraints imposed during estimation by deferring to the default value of `Model`.

Example: `'Model',"H1*"`

Data Types: `string` | `char`

## Output Arguments

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IRF, returned as a `numobs`-by-`numseries`-by-`numseries` numeric array. `numobs` is the value of `NumObs`. Columns and pages correspond to the response variables in `Mdl.SeriesNames`.

`Response(t + 1,j,k)` is the impulse response of variable `k` to a one-standard-deviation innovation shock to variable `j` at time 0, for `t` = 0, 1, ..., `numObs` – 1, `j` = 1,2,...,`numseries`, and `k` = 1,2,...,`numseries`.

Lower confidence bounds, returned as a `numobs`-by-`numseries`-by-`numseries` numeric array. Elements of `Lower` correspond to elements of `Response`.

`Lower(t + 1,j,k)` is the lower bound of the `100*Confidence`% percentile interval on the true impulse response of variable `k` to a one-standard-deviation innovation shock to variable `j` at time 0.

Upper confidence bounds, returned as a `numobs`-by-`numseries`-by-`numseries` numeric array. Elements of `Upper` correspond to elements of `Response`.

`Upper(t + 1,j,k)` is the upper bound of the `100*Confidence`% percentile interval on the true impulse response of variable `k` to a one-standard-deviation innovation shock to variable `j` at time 0.

## More About

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### Impulse Response Function

An impulse response function (IRF) of a time series model (or dynamic response of the system) measures the changes in the future responses of all variables in the system when a variable is shocked by an impulse. In other words, the IRF at time t is the derivative of the responses at time t with respect to an innovation at time t0 (the time that innovation was shocked), tt0.

Consider a `numseries`-D VEC(p – 1) model for the multivariate response variable yt. In lag operator notation, the equivalent VAR(p) representation of a VEC(p – 1) model is:

`$\Gamma \left(L\right){y}_{t}=c+dt+\beta {x}_{t}+{\epsilon }_{t},$`

where $\Gamma \left(L\right)=I-{\Gamma }_{1}L-{\Gamma }_{2}{L}^{2}-...-{\Gamma }_{p}{L}^{p}$ and I is the `numseries`-by-`numseries` identity matrix.

In lag operator notation, the infinite lag MA representation of yt is:

`$\begin{array}{c}{y}_{t}={\Gamma }^{-1}\left(L\right)\left(c+\beta {x}_{t}+dt\right)+{\Gamma }^{-1}\left(L\right){\epsilon }_{t}\\ =\Omega \left(L\right)\left(c+\beta {x}_{t}+dt\right)+\Omega \left(L\right){\epsilon }_{t}.\end{array}$`

The general form of the IRF of yt shocked by an impulse to variable j by one standard deviation of its innovation m periods into the future is:

`${\psi }_{j}\left(m\right)={C}_{m}{e}_{j}.$`
• ej is a selection vector of length `numseries` containing a 1 in element j and zeros elsewhere.

• For the orthogonalized IRF, ${C}_{m}={\Omega }_{m}P,$ where P is the lower triangular factor in the Cholesky factorization of Σ, and Ωm is the lag m coefficient of Ω(L).

• For the generalized IRF, ${C}_{m}={\sigma }_{j}^{-1}{\Omega }_{m}\Sigma ,$ where σj is the standard deviation of innovation j.

• The IRF is free of the model constant, regression component, and time trend.

### Vector Error-Correction Model

A vector error-correction (VEC) model is a multivariate, stochastic time series model consisting of a system of m = `numseries` equations of m distinct, differenced response variables. Equations in the system can include an error-correction term, which is a linear function of the responses in levels used to stabilize the system. The cointegrating rank r is the number of cointegrating relations that exist in the system.

Each response equation can include an autoregressive polynomial composed of first differences of the response series (short-run polynomial of degree p – 1), a constant, a time trend, exogenous predictor variables, and a constant and time trend in the error-correction term.

A VEC(p – 1) model in difference-equation notation and in reduced form can be expressed in two ways:

• This equation is the component form of a VEC model, where the cointegration adjustment speeds and cointegration matrix are explicit, whereas the impact matrix is implied.

`$\begin{array}{c}\Delta {y}_{t}=A\left(B\prime {y}_{t-1}+{c}_{0}+{d}_{0}t\right)+{c}_{1}+{d}_{1}t+{\Phi }_{1}\Delta {y}_{t-1}+...+{\Phi }_{p-1}\Delta {y}_{t-\left(p-1\right)}+\beta {x}_{t}+{\epsilon }_{t}\\ =c+dt+AB\prime {y}_{t-1}+{\Phi }_{1}\Delta {y}_{t-1}+...+{\Phi }_{p-1}\Delta {y}_{t-\left(p-1\right)}+\beta {x}_{t}+{\epsilon }_{t}.\end{array}$`

The cointegrating relations are B'yt – 1 + c0 + d0t and the error-correction term is A(B'yt – 1 + c0 + d0t).

• This equation is the impact form of a VEC model, where the impact matrix is explicit, whereas the cointegration adjustment speeds and cointegration matrix are implied.

`$\begin{array}{c}\Delta {y}_{t}=\Pi {y}_{t-1}+A\left({c}_{0}+{d}_{0}t\right)+{c}_{1}+{d}_{1}t+{\Phi }_{1}\Delta {y}_{t-1}+...+{\Phi }_{p-1}\Delta {y}_{t-\left(p-1\right)}+\beta {x}_{t}+{\epsilon }_{t}\\ =c+dt+\Pi {y}_{t-1}+{\Phi }_{1}\Delta {y}_{t-1}+...+{\Phi }_{p-1}\Delta {y}_{t-\left(p-1\right)}+\beta {x}_{t}+{\epsilon }_{t}.\end{array}$`

In the equations:

• yt is an m-by-1 vector of values corresponding to m response variables at time t, where t = 1,...,T.

• Δyt = ytyt – 1. The structural coefficient is the identity matrix.

• r is the number of cointegrating relations and, in general, 0 < r < m.

• A is an m-by-r matrix of adjustment speeds.

• B is an m-by-r cointegration matrix.

• Π is an m-by-m impact matrix with a rank of r.

• c0 is an r-by-1 vector of constants (intercepts) in the cointegrating relations.

• d0 is an r-by-1 vector of linear time trends in the cointegrating relations.

• c1 is an m-by-1 vector of constants (deterministic linear trends in yt).

• d1 is an m-by-1 vector of linear time-trend values (deterministic quadratic trends in yt).

• c = Ac0 + c1 and is the overall constant.

• d = Ad0 + d1 and is the overall time-trend coefficient.

• Φj is an m-by-m matrix of short-run coefficients, where j = 1,...,p – 1 and Φp – 1 is not a matrix containing only zeros.

• xt is a k-by-1 vector of values corresponding to k exogenous predictor variables.

• β is an m-by-k matrix of regression coefficients.

• εt is an m-by-1 vector of random Gaussian innovations, each with a mean of 0 and collectively an m-by-m covariance matrix Σ. For ts, εt and εs are independent.

Condensed and in lag operator notation, the system is

`$\begin{array}{c}\Phi \left(L\right)\left(1-L\right){y}_{t}=A\left(B\prime {y}_{t-1}+{c}_{0}+{d}_{0}t\right)+{c}_{1}+{d}_{1}t+\beta {x}_{t}+{\epsilon }_{t}\\ =c+dt+AB\prime {y}_{t-1}+\beta {x}_{t}+{\epsilon }_{t}\end{array}$`

where $\Phi \left(L\right)=I-{\Phi }_{1}-{\Phi }_{2}-...-{\Phi }_{p-1}$, I is the m-by-m identity matrix, and Lyt = yt – 1.

If m = r, then the VEC model is a stable VAR(p) model in the levels of the responses. If r = 0, then the error-correction term is a matrix of zeros, and the VEC(p – 1) model is a stable VAR(p – 1) model in the first differences of the responses.

## Algorithms

• `NaN` values in `Y0`, `X`, and `E` indicate missing data. `irf` removes missing data from these arguments by list-wise deletion. Each argument, if a row contains at least one `NaN`, then `irf` removes the entire row.

List-wise deletion reduces the sample size, can create irregular time series, and can cause `E` and `X` to be unsynchronized.

• If `Method` is `"orthogonalized"`, then the resulting IRF depends on the order of the variables in the time series model. If `Method` is `"generalized"`, then the resulting IRF is invariant to the order of the variables. Therefore, the two methods generally produce different results.

• If `Mdl.Covariance` is a diagonal matrix, then the resulting generalized and orthogonalized IRFs are identical. Otherwise, the resulting generalized and orthogonalized IRFs are identical only when the first variable shocks all variables (that is, all else being the same, both methods yield the same value of `Response(:,1,:)`).

• The predictor data `X` represents a single path of exogenous multivariate time series. If you specify `X` and the VAR model `Mdl` has a regression component (`Mdl.Beta` is not an empty array), `irf` applies the same exogenous data to all paths used for confidence interval estimation.

• `irf` conducts a simulation to estimate the confidence bounds `Lower` and `Upper`.

• If you do not specify residuals `E`, then `irf` conducts a Monte Carlo simulation by following this procedure:

1. Simulate `NumPaths` response paths of length `SampleSize` from `Mdl`.

2. Fit `NumPaths` models that have the same structure as `Mdl` to the simulated response paths. If `Mdl` contains a regression component and you specify `X`, then `irf` fits the `NumPaths` models to the simulated response paths and `X` (the same predictor data for all paths).

3. Estimate `NumPaths` IRFs from the `NumPaths` estimated models.

4. For each time point t = 0,…,`NumObs`, estimate the confidence intervals by computing 1 – `Confidence` and `Confidence` quantiles (upper and lower bounds, respectively).

• If you specify residuals `E`, then `irf` conducts a nonparametric bootstrap by following this procedure:

1. Resample, with replacement, `SampleSize` residuals from `E`. Perform this step `NumPaths` times to obtain `NumPaths` paths.

2. Center each path of bootstrapped residuals.

3. Filter each path of centered, bootstrapped residuals through `Mdl` to obtain `NumPaths` bootstrapped response paths of length `SampleSize`.

4. Complete steps 2 through 4 of the Monte Carlo simulation, but replace the simulated response paths with the bootstrapped response paths.

## References

[1] Hamilton, J. 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] Pesaran, H. H., and Y. Shin. "Generalized Impulse Response Analysis in Linear Multivariate Models." Economic Letters. Vol. 58, 1998, pp. 17–29.

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