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arma2ar

Convert ARMA model to AR model

Syntax

``ar = arma2ar(ar0,ma0)``
``ar = arma2ar(ar0,ma0,numLags)``

Description

example

````ar = arma2ar(ar0,ma0)` returns the coefficients of the truncated, infinite-order AR model approximation to an ARMA model having AR and MA coefficients specified by `ar0` and `ma0`, respectively.`arma2ar:` Accepts: Vectors or cell vectors of matrices in difference-equation notation.`LagOp` lag operator polynomials corresponding to the AR and MA polynomials in lag operator notation. Accommodates time series models that are univariate or multivariate (i.e., `numVars` variables compose the model), stationary or integrated, structural or in reduced form, and invertible.Assumes that the model constant c is 0. ```

example

````ar = arma2ar(ar0,ma0,numLags)` returns the first nonzero `numLags` lag-term coefficients of the infinite-order AR model approximation of an ARMA model having AR coefficients `ar0` and MA coefficients `ma0`.```

Examples

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Find the lag coefficients of the truncated, AR approximation of this univariate, stationary, and invertible ARMA model

The ARMA model is in difference-equation notation because the left side contains only and its coefficient 1. Create a vector containing the AR lag term coefficients in order starting from t - 1.

`ar0 = [0.2 -0.1];`

Alternatively, you can create a cell vector of the scalar coefficients.

Create a vector containing the MA lag term coefficient.

`ma0 = 0.5;`

Convert the ARMA model to an AR model by obtaining the coefficients of the truncated approximation of the infinite-lag polynomial.

`ar = arma2ar(ar0,ma0)`
```ar = 0.7000 -0.4500 0.2250 -0.1125 0.0562 -0.0281 0.0141 ```

`ar` is a numeric vector because `ar0` and `ma0` are numeric vectors.

The approximate AR model truncated at 7 lags is

Find the first five lag coefficients of the AR approximation of this univariate and invertible MA(3) model

The MA model is in difference-equation notation because the left side contains only and its coefficient of 1. Create a cell vector containing the MA lag term coefficient in order starting from t - 1. Because the second lag term of the MA model is missing, specify a `0` for its coefficient.

```ma0 = {-0.2 0 0.5}; ```

Convert the MA model to an AR model with at most five lag coefficents of the truncated approximation of the infinite-lag polynomial. Because there is no AR contribution, specify an empty cell (`{}`) for the AR coefficients.

```numLags = 5; ar0 = {}; ar = arma2ar(ar0,ma0,numLags) ```
```ar = 1x5 cell array {[-0.2000]} {[-0.0400]} {[0.4920]} {[0.1984]} {[0.0597]} ```

`ar` is a cell vector of scalars because at least one of `ar0` and `ma0` is a cell vector.

The approximate AR(5) model is

Find the coefficients of the truncated, structural VAR equivalent of the structural, stationary, and invertible VARMA model

where and .

The VARMA model is in lag operator notation because the response and innovation vectors are on opposite sides of the equation.

Create a cell vector containing the VAR matrix coefficients. Because this model is a structural model, start with the coefficient of and enter the rest in order by lag. Because the equation is in lag operator notation, include the sign in front of each matrix. Construct a vector that indicates the degree of the lag term for the corresponding coefficients.

```var0 = {[1 0.2 -0.1; 0.03 1 -0.15; 0.9 -0.25 1],... -[-0.5 0.2 0.1; 0.3 0.1 -0.1; -0.4 0.2 0.05],... -[-0.05 0.02 0.01; 0.1 0.01 0.001; -0.04 0.02 0.005]}; var0Lags = [0 4 8];```

Create a cell vector containing the VMA matrix coefficients. Because this model is a structural model, start with the coefficient of and enter the rest in order by lag. Construct a vector that indicates the degree of the lag term for the corresponding coefficients.

```vma0 = {eye(3),... [-0.02 0.03 0.3; 0.003 0.001 0.01; 0.3 0.01 0.01]}; vma0Lags = [0 4];```

`arma2ar` requires `LagOp` lag operator polynomials for input arguments that comprise structural VAR or VMA models. Construct separate `LagOp` polynomials that describe the VAR and VMA components of the VARMA model.

```VARLag = LagOp(var0,'Lags',var0Lags); VMALag = LagOp(vma0,'Lags',vma0Lags);```

`VARLags` and `VMALags` are `LagOp` lag operator polynomials that describe the VAR and VMA components of the VARMA model.

Convert the VARMA model to a VAR model by obtaining the coefficients of the truncated approximation of the infinite-lag polynomial.

`VAR = arma2ar(VARLag,VMALag)`
```VAR = 3-D Lag Operator Polynomial: ----------------------------- Coefficients: [Lag-Indexed Cell Array with 4 Non-Zero Coefficients] Lags: [0 4 8 12] Degree: 12 Dimension: 3 ```

`VAR` is a `LagOP` lag operator polynomial. All coefficients except those corresponding to lags 0, 4, 8, and 12 are 3-by-3 matrices of zeros.

Convert the coefficients to difference-equation notation by reflecting the VAR lag operator polynomial around lag zero.

`VARDiffEqn = reflect(VAR);`

Display the nonzero coefficients of the resulting VAR models.

```lag2Idx = VAR.Lags + 1; % Lags start at 0. Add 1 to convert to indices. varCoeff = toCellArray(VAR); varDiffEqnCoeff = toCellArray(VARDiffEqn); fprintf (' Lag Operator | Difference Equation\n')```
``` Lag Operator | Difference Equation ```
```for j = 1:numel(lag2Idx) fprintf('_________________________Lag %d_________________________\n',... lag2Idx(j) - 1) fprintf('%8.3f %8.3f %8.3f | %8.3f %8.3f %8.3f\n',... [varCoeff{lag2Idx(j)} varDiffEqnCoeff{lag2Idx(j)}]') fprintf('_______________________________________________________\n') end```
```_________________________Lag 0_________________________ 1.000 0.200 -0.100 | 1.000 0.200 -0.100 0.030 1.000 -0.150 | 0.030 1.000 -0.150 0.900 -0.250 1.000 | 0.900 -0.250 1.000 _______________________________________________________ _________________________Lag 4_________________________ 0.249 -0.151 -0.397 | -0.249 0.151 0.397 -0.312 -0.099 0.090 | 0.312 0.099 -0.090 0.091 -0.268 -0.029 | -0.091 0.268 0.029 _______________________________________________________ _________________________Lag 8_________________________ 0.037 0.060 -0.012 | -0.037 -0.060 0.012 -0.101 -0.007 0.000 | 0.101 0.007 -0.000 -0.033 0.029 0.114 | 0.033 -0.029 -0.114 _______________________________________________________ _________________________Lag 12_________________________ 0.014 -0.007 -0.034 | -0.014 0.007 0.034 0.000 -0.000 -0.001 | -0.000 0.000 0.001 -0.010 -0.018 0.002 | 0.010 0.018 -0.002 _______________________________________________________ ```

The coefficients of lags 4, 8, and 12 are opposites between `VAR` and `VARDiffEqn`.

Find the lag coefficients and constant of the truncated AR approximation of this univariate, stationary, and invertible ARMA model.

The ARMA model is in difference-equation notation because the left side contains only and its coefficient of 1. Create separate vectors for the AR and MA lag term coefficients in order starting from t - 1.

```ar0 = [0.2 -0.1]; ma0 = 0.5;```

Convert the ARMA model to an AR model by obtaining the first five coefficients of the truncated approximation of the infinite-lag polynomial.

```numLags = 5; ar = arma2ar(ar0,ma0,numLags)```
```ar = 0.7000 -0.4500 0.2250 -0.1125 0.0562 ```

To compute the constant of the AR model, consider the ARMA model in lag operator notation.

or

Part of the conversion involves premultiplying both sides of the equation by the inverse of the MA lag operator polynomial, as in this equation.

To compute the inverse of MA lag operator polynomial, use the lag operator left-division object function `LagOp.mldivide`.

```Theta = LagOp([1 0.5]); ThetaInv = mldivide(Theta,1,'RelTol',1e-5);```

`ThetaInv` is a `LagOp` lag operator polynomial.

The application of lag operator polynomials to constants results in the product of the constant with the sum of the coefficients. Apply `ThetaInv` to the ARMA model constant to obtain the AR model constant.

`arConstant = 1.5*sum(cell2mat(toCellArray(ThetaInv)))`
```arConstant = 1.0000 ```

The approximate AR model is

Input Arguments

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Autoregressive coefficients of the ARMA(p,q) model, specified as a numeric vector, cell vector of square, numeric matrices, or a `LagOp` lag operator polynomial object. If `ar0` is a vector (numeric or cell), then the coefficient of yt is the identity. To specify a structural AR polynomial (i.e., the coefficient of yt is not the identity), use `LagOp` lag operator polynomials.

• For univariate time series models, `ar0` is a numeric vector, cell vector of scalars, or a one-dimensional `LagOp` lag operator polynomial. For vectors, `ar0` has length p and the elements correspond to lagged responses composing the AR polynomial in difference-equation notation. That is, `ar0(j)` or `ar0{j}` is the coefficient of yt-j.

• For `numVars`-dimensional time series models, `ar0` is a cell vector of `numVars`-by-`numVars` numeric matrices or an `numVars`-dimensional `LagOp` lag operator polynomial. For cell vectors:

• `ar0` has length p.

• `ar0` and `ma0` must contain `numVars`-by-`numVars` matrices.

• The elements of `ar0` correspond to the lagged responses composing the AR polynomial in difference equation notation. That is, `ar0{j}` is the coefficient matrix of yt-j.

• Row k of an AR coefficient matrix contains the AR coefficients in the equation of the variable yk. Subsequently, column k must correspond to variable yk, and the column and row order of all autoregressive and moving average coefficients must be consistent.

• For `LagOp` lag operator polynomials:

• The first element of the `Coefficients` property corresponds to the coefficient of yt (to accommodate structural models). All other elements correspond to the coefficients of the subsequent lags in the `Lags` property.

• To construct a univariate model in reduced form, specify `1` for the first coefficient. For `numVars`-dimensional multivariate models, specify `eye(numVars)` for the first coefficient.

• When you work from a model in difference-equation notation, negate the AR coefficients of the lagged responses to construct the lag-operator polynomial equivalent. For example, consider ${y}_{t}=0.5{y}_{t-1}-0.8{y}_{t-2}+{\epsilon }_{t}-0.6{\epsilon }_{t-1}+0.08{\epsilon }_{t-2}$. The model is in difference-equation form. To convert to an AR model, enter the following into the command window.

`ar = arma2ar([0.5 -0.8], [-0.6 0.08]);`

The ARMA model written in lag-operator notation is $\left(1-0.5L+0.8{L}^{2}\right){y}_{t}=\left(1-0.6L+0.08{L}^{2}\right){\epsilon }_{t}.$ The AR coefficients of the lagged responses are negated compared to the corresponding coefficients in difference-equation format. In this form, to obtain the same result, enter the following into the command window.

```ar0 = LagOp({1 -0.5 0.8}); ma0 = LagOp({1 -0.6 0.08}); ar = arma2ar(ar0, ma0);```

It is a best practice for `ar0` to constitute a stationary or unit-root stationary (integrated) time series model.

If the ARMA model is strictly an MA model, then specify `[]` or `{}` for `ar0`.

Moving average coefficients of the ARMA(p,q) model, specified as a numeric vector, cell vector of square, numeric matrices, or a `LagOp` lag operator polynomial object. If `ma0` is a vector (numeric or cell), then the coefficient of εt is the identity. To specify a structural MA polynomial (i.e., the coefficient of εt is not the identity), use `LagOp` lag operator polynomials.

• For univariate time series models, `ma0` is a numeric vector, cell vector of scalars, or a one-dimensional `LagOp` lag operator polynomial. For vectors, `ma0` has length q and the elements correspond to lagged innovations composing the AR polynomial in difference-equation notation. That is, `ma0(j)` or `ma0{j}` is the coefficient of εt-j.

• For `numVars`-dimensional time series models, `ma0` is a cell vector of numeric `numVars`-by-`numVars` numeric matrices or an `numVars`-dimensional `LagOp` lag operator polynomial. For cell vectors:

• `ma0` has length q.

• `ar0` and `ma0` must both contain `numVars`-by-`numVars` matrices.

• The elements of `ma0` correspond to the lagged responses composing the AR polynomial in difference equation notation. That is, `ma0{j}` is the coefficient matrix of yt-j.

• For `LagOp` lag operator polynomials:

• The first element of the `Coefficients` property corresponds to the coefficient of εt (to accommodate structural models). All other elements correspond to the coefficients of the subsequent lags in the `Lags` property.

• To construct a univariate model in reduced form, specify `1` for the first coefficient. For `numVars`-dimensional multivariate models, specify `eye(numVars)` for the first coefficient.

It is a best practice for `ma0` to constitute an invertible time series model.

Maximum number of lag-term coefficients to return, specified as a positive integer.

If you specify `'numLags'`, then `arma2ar` truncates the output polynomial at a maximum of `numLags` lag terms, and then returns the remaining coefficients. As a result, the output vector has `numLags` elements or is at most a degree `numLags` `LagOp` lag operator polynomial.

By default, `arma2ar` determines the number of lag coefficients to return by the stopping criteria of `mldivide`.

Data Types: `double`

Output Arguments

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Coefficients of the truncated AR model approximation of the ARMA model, returned as a numeric vector, cell vector of square, numeric matrices, or a `LagOp` lag operator polynomial object. `ar` has `numLags` elements, or is at most a degree `numLags` `LagOp` lag operator polynomial.

The data types and orientations of `ar0` and `ma0` determine the data type and orientation of `ar`. If `ar0` or `ma0` are of the same data type or have the same orientation, then `ar` shares the common data type or orientation. If at least one of `ar0` or `ma0` is a `LagOp` lag operator polynomial, then `ar` is a `LagOp` lag operator polynomial. Otherwise, if at least one of `ar0` or `ma0` is a cell vector, then `ar` is a cell vector. If `ar0` and `ma0` are cell or numeric vectors and at least one is a row vector, then `ar` is a row vector.

If `ar` is a cell or numeric vector, then the order of the elements of `ar` corresponds to the order of the coefficients of the lagged responses in difference-equation notation starting with the coefficient of yt-1. The resulting AR model is in reduced form.

If `ar` is a `LagOp` lag operator polynomial, then the order of the coefficients of `ar` corresponds to the order of the coefficients of the lagged responses in lag operator notation starting with the coefficient of yt. If Φ0InumVars, then the resulting AR model is structural. To view the coefficients in difference-equation notation, pass `ar` to `reflect`.

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Difference-Equation Notation

A linear time series model written in difference-equation notation positions the present value of the response and its structural coefficient on the left side of the equation. The right side of the equation contains the sum of the lagged responses, present innovation, and lagged innovations with corresponding coefficients.

In other words, a linear time series written in difference-equation notation is

`${\Phi }_{0}{y}_{t}=c+{\Phi }_{1}{y}_{t-1}+...+{\Phi }_{p}{y}_{t-p}+{\Theta }_{0}{\epsilon }_{t}+{\Theta }_{1}{\epsilon }_{t-1}+...+{\Theta }_{q}{\epsilon }_{t-q},$`
where

• yt is an `numVars`-dimensional vector representing the responses of `numVars` variables at time t, for all t and for `numVars` ≥ 1.

• εt is an `numVars`-dimensional vector representing the innovations at time t.

• Φj is the `numVars`-by-`numVars` matrix of AR coefficients of the response yt-j, for j = 0,...,p.

• Θk is the `numVars`-by-`numVars` matrix of MA coefficients of the innovation εt-k., k = 0,...,q.

• c is the n-dimensional model constant.

• For models in reduced form, Φ0 = Θ0 = InumVars, which is the `numVars`-dimensional identity matrix.

Lag Operator Notation

A time series model written in lag-operator notation positions a p-degree lag operator polynomial on the present response on the left side of the equation. The right side of the equation contains the model constant and a q-degree lag operator polynomial on the present innovation.

In other words, a linear time series model written in lag-operator notation is

`$\Phi \left(L\right){y}_{t}=c+\Theta \left(L\right){\epsilon }_{t},$`
where

• yt is an `numVars`-dimensional vector representing the responses of `numVars` variables at time t, for all t and for `numVars` ≥ 1.

• $\Phi \left(L\right)={\Phi }_{0}-{\Phi }_{1}L-{\Phi }_{2}{L}^{2}-...-{\Phi }_{p}{L}^{p}$, which is the autoregressive, lag operator polynomial.

• L is the back-shift operator, in other words, ${L}^{j}{y}_{t}={y}_{t-j}$.

• Φj is the `numVars`-by-`numVars` matrix of AR coefficients of the response yt-j, for j = 0,...,p.

• εt is an `numVars`-dimensional vector representing the innovations at time t.

• $\Theta \left(L\right)={\Theta }_{0}+{\Theta }_{1}L+{\Theta }_{2}{L}^{2}+...+{\Theta }_{q}{L}^{q}$, which is the moving average, lag operator polynomial.

• Θk is the `numVars`-by-`numVars` matrix of MA coefficients of the innovation εt-k., k = 0,...,q.

• c is the `numVars`-dimensional model constant.

• For models in reduced form, Φ0 = Θ0 = InumVars, which is the `numVars`-dimensional identity matrix.

When comparing lag operator notation to difference equation notation, the signs of the lagged AR coefficients appear negated relative to the corresponding terms in difference equation notation. The signs of the moving average coefficients are the same and appear on the same side.

For more details on lag operator notation, see Lag Operator Notation.

Tips

• To accommodate structural ARMA models, specify the input arguments `ar0` and `ma0` as `LagOp` lag operator polynomials.

• To access the cell vector of the lag operator polynomial coefficients of the output argument `ar`, enter `toCellArray(ar)`.

• To convert the model coefficients of the output argument from lag operator notation to the model coefficients in difference-equation notation, enter

`arDEN = toCellArray(reflect(ar));`
`arDEN` is a cell vector containing at most `numLags` + 1 coefficients corresponding to the lag terms in `ar.Lags` of the AR model equivalent of the input ARMA model in difference-equation notation. The first element is the coefficient of yt, the second element is the coefficient of yt–1, and so on.

Algorithms

• The software computes the infinite-lag polynomial of the resulting AR model according to this equation in lag operator notation:

`${\Theta }^{-1}\left(L\right)\Phi \left(L\right){y}_{t}={\epsilon }_{t},$`
where $\Phi \left(L\right)=\sum _{j=0}^{p}{\Phi }_{j}{L}^{j}$ and $\Theta \left(L\right)=\sum _{k=0}^{q}{\Theta }_{k}{L}^{k}.$

• `arma2ar` approximates the AR model coefficients whether `ar0` and `ma0` compose a stable polynomial (a polynomial that is stationary or invertible). To check for stability, use `isStable`.

`isStable` requires a `LagOp` lag operator polynomial as input. For example, if `ar0` is a vector, enter the following code to check `ar0` for stationarity.

```ar0LagOp = LagOp([1 -ar0]); isStable(ar0LagOp)```

A `0` indicates that the polynomial is not stable.

You can similarly check whether the AR approximation to the ARMA model (`ar`) is stationary.

References

[1] Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. Time Series Analysis: Forecasting and Control 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1994.

[2] Hamilton, J. D. Time Series Analysis. Princeton, NJ: Princeton University Press, 1994.

[3] Lutkepohl, H. New Introduction to Multiple Time Series Analysis. Springer-Verlag, 2007.