# inv, invpd¶

## Purpose¶

`inv()`

returns the inverse of an invertible matrix.
`invpd()`

returns the inverse of a symmetric, positive definite matrix.

## Format¶

## Examples¶

```
// Set random seed
rndseed 90701980;
// Set number of observations
n = 4000;
// Generate random x
x1 = rndn(n, 1);
// Add constant
x = ones(n, 1)~x1;
// Set true coefficients
btrue = { 1, 0.5 };
/*
** Generate linear data with
** random normal disturbances
*/
y = x*btrue + rndn(n, 1);
// Compute OLS estimates of coefficients
bols = invpd(x'x)*x'y;
```

After the code above, *bols* will be equal to:

```
1.0108262804
0.4633302971
```

This example simulates some data and computes the `ols()`

coefficient estimator using the `invpd()`

function. First, the number of observations is specified. Second, a vector *x1* of standard Normal random variables is generated and is concatenated with a vector of `ones()`

(to create a constant term). The true coefficients are specified, and the dependent variable *y* is created. Then the `ols()`

coefficient estimates are computed.

When computing least-squares problems with poorly conditioned matrices, the slash operator `/`

and the function `olsqr()`

will provide greater accuracy.

## Remarks¶

*x* can be any legitimate expression that returns a matrix or array that
is legal for the function.

If *x* is an array, the result will be an array containing the inverses of
each 2-dimensional array described by the two trailing dimensions of *x*.
In other words, for a 10x4x4 array, the result will be an array of the
same size containing the inverses of each of the 10 4x4 arrays contained
in *x*.

For `inv()`

, if *x* is a matrix, it must be square and invertible. Otherwise,
if *x* is an array, the 2-dimensional arrays described by the last two
dimensions of *x* must be square and invertible.

For `invpd()`

, if *x* is a matrix, it must be symmetric and positive definite.
Otherwise, if *x* is an array, the 2-dimensional arrays described by the
last two dimensions of *x* must be symmetric and positive definite.

If the input matrix is not invertible by these functions, they will
either terminate the program with an error message or return an error
code which can be tested for with the `scalerr()`

function. This depends on
the trap state as follows:

If trap is set to 1, they will return a scalar errorcode:

inv |
invpd |

50 | 20 |

If trap is set to 0, they will terminate with an error message:

inv |
invpd |

“Matrix singular” | “Matrix not positive definite” |

If the input to `invpd()`

is not symmetric, it is possible that the function
will (erroneously) appear to operate successfully.

Positive definite matrices can be inverted by `inv()`

. However, for
symmetric, positive definite matrices (such as moment matrices), `invpd()`

is about twice as fast as `inv()`

.