IRIS Toolbox Reference Manual

## regress

Centred population regression for selected model variables

#### Syntax

    [B,CovRes,R2] = regress(M,Lhs,Rhs,...)

#### Input arguments

- `M` [ model ] - Model on whose covariance matrices the popolation regression will be based.

- `Lhs` [ char | cellstr ] - Lhs variables in the regression; each of the variables must be part of the state-space vector.

- `Rhs` [ char | cellstr ] - Rhs variables in the regression; each of the variables must be part of the state-space vector, or must refer to a larger lag of a transition variable present in the state-space vector.

#### Output arguments

- `B` [ namedmat | numeric ] - Population regression coefficients.

- `CovRes` [ namedmat | numeric ] - Covariance matrix of residuals from the population regression.

- `R2` [ numeric ] - Coefficient of determination (R-squared).

#### Options

- `'output='` [ `'namedmat'` | `'numeric'` ] - Output matrices will be either namedmat objects or plain numeric arrays.

#### Description

Population regressions calculated by this function are always centred. This means the regressions are always calculated as if estimated on observations with their uncondional means (the steady-state levels) removed from them.

The Lhs and Rhs variables that are log-variables must include `log(...)` explicitly in their names. For instance, if `X` is declared to be a log variable, then you must refer to `log(X)` or `log(X{-1})`.

#### Example

    [B,C] = regress('log(R)',{'log(R{-1})','log(dP)'});

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