This function retrieves the fixed effects from a `fixest`

estimation. It is useful only when there are one or more fixed-effect dimensions.

# S3 method for fixest fixef( object, notes = getFixest_notes(), sorted = TRUE, nthreads = getFixest_nthreads(), fixef.tol = 1e-05, fixef.iter = 10000, ... )

object | |
---|---|

notes | Logical. Whether to display a note when the fixed-effects coefficients are not regular. |

sorted | Logical, default is |

nthreads | The number of threads. Can be: a) an integer lower than, or equal to, the maximum number of threads; b) 0: meaning all available threads will be used; c) a number strictly between 0 and 1 which represents the fraction of all threads to use. The default is to use 50% of all threads. You can set permanently the number of threads used within this package using the function |

fixef.tol | Precision used to obtain the fixed-effects. Defaults to |

fixef.iter | Maximum number of iterations in fixed-effects algorithm (only in use for 2+ fixed-effects). Default is 10000. |

... | Not currently used. |

A list containing the vectors of the fixed effects.

If there is more than 1 fixed-effect, then the attribute “references” is created. This is a vector of length the number of fixed-effects, each element contains the number of coefficients set as references. By construction, the elements of the first fixed-effect dimension are never set as references. In the presence of regular fixed-effects, there should be Q-1 references (with Q the number of fixed-effects).

If the fixed-effect coefficients not regular, then several reference points need to be set, leading to the coefficients to be NOT interpretable. If this is the case, then a warning is raised.

`plot.fixest.fixef`

. See also the main estimation functions `femlm`

, `feols`

or `feglm`

. Use `summary.fixest`

to see the results with the appropriate standard-errors, `fixef.fixest`

to extract the fixed-effect coefficients, and the function `etable`

to visualize the results of multiple estimations.

Laurent Berge

data(trade) # We estimate the effect of distance on trade => we account for 3 fixed-effects est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination+Product, trade) # Obtaining the fixed-effects coefficients: fe_trade = fixef(est_pois) # The fixed-effects of the first fixed-effect dimension: head(fe_trade$Origin) #> AT BE DE DK ES FI #> 22.67038 23.72304 24.86999 23.60329 25.12917 21.80906 # Summary information: summary(fe_trade) #> Fixed_effects coefficients #> Origin Destination Product #> Number of fixed-effects 15 15 20 #> Number of references 0 1 1 #> Mean 23.5 3.09 0.0127 #> Standard-deviation 1.28 1.11 1.36 #> #> COEFFICIENTS: #> Origin: AT BE DE DK ES #> 22.67 23.72 24.87 23.6 25.13 ... 10 remaining #> ----- #> Destination: AT BE DE DK ES #> 2.436 2.696 4.323 2.451 4.043 ... 10 remaining #> ----- #> Product: 1 2 3 4 5 #> 0 1.414 0.656 1.449 -1.521 ... 15 remaining # Plotting them: plot(fe_trade)