# logLik.systemfit: Log-Likelihood value of systemfit object In systemfit: Estimating Systems of Simultaneous Equations

## Description

This method calculates the log-likelihood value of a fitted object returned by `systemfit`.

## Usage

 ```1 2``` ``` ## S3 method for class 'systemfit' logLik( object, residCovDiag = FALSE, ... ) ```

## Arguments

 `object` an object of class `systemfit`. `residCovDiag` logical. If this argument is set to `TRUE`, the residual covaraince matrix that is used for calculating the log-likelihood value is assumed to be diagonal, i.e. all covariances are set to zero. This may be desirable for models estimated by OLS, 2SLS, WLS, and W2SLS. `...` currently not used.

## Details

The residual covariance matrix that is used for calculating the log-likelihood value is calculated based on the actually obtained (final) residuals (not correcting for degrees of freedom). In case of systems of equations with unequal numbers of observations, the calculation of the residual covariance matrix is only based on the residuals/observations that are available in all equations.

## Value

A numeric scalar (the log-likelihood value) with 2 attributes: `nobs` (total number of observations in all equations) and `df` (number of free parameters, i.e. coefficients + elements of the residual covariance matrix).

## Author(s)

Arne Henningsen [email protected]

`systemfit`, `logLik`
 ``` 1 2 3 4 5 6 7 8 9 10``` ```data( "Kmenta" ) eqDemand <- consump ~ price + income eqSupply <- consump ~ price + farmPrice + trend system <- list( demand = eqDemand, supply = eqSupply ) ## perform a SUR estimation fitsur <- systemfit( system, "SUR", data = Kmenta ) ## residuals of all equations logLik( fitsur ) ```