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Recursive linear regression

`recreg`

recursively estimates coefficients
(*β*) and their standard errors in a multiple
linear regression model of the form $$y=X\beta +\epsilon $$ by
performing successive regressions using nested or rolling windows. `recreg`

has
options for OLS, HAC, and FGLS estimates, and for iterative plots
of the estimates.

`recreg(X,y)`

`recreg(Tbl)`

`recreg(___,Name,Value)`

```
[Coeff,SE]
= recreg(___)
```

`recreg(`

uses the
data in the tabular array `Tbl`

)`Tbl`

. The first `numPreds`

columns
are the predictors (`X`

) and the last column is the
response (`y`

).

`recreg(___,`

uses
any of the input arguments in the previous syntaxes and additional
options specified by one or more `Name,Value`

)`Name,Value`

pair
arguments. For example, you can specify the estimation method using `'`

`Estimator`

`'`

or
whether to include an intercept in the multiple regression model using `'`

`Intercept`

`'`

.

Plots of nested-window estimates typically show volatility during
a “burn-in” period, in which the number of subsample
observations is only slightly larger than the number of coefficients
in the model. After this period, any further volatility is evidence
of coefficient instability. Sudden changes in coefficient values can
indicate a structural change, and sustained changes can indicate
model misspecification. For structural change tests, see `cusumtest`

and `chowtest`

.

[1] Enders, W. *Applied Econometric
Time Series.* New York: John Wiley & Sons, Inc., 2009.

[2] Johnston, J. and J. DiNardo. *Econometric
Methods.* New York: McGraw Hill, 1997.

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