# Introduction¶

Panel data includes observations on multiple entities – individuals, firms, countries – over multiple time periods. In most classical applications of panel data the number of entities, N, is large and the number of time periods, T, is small (often between 2 and 5). Most asymptotic theory for these estimators has been developed under an assumption that N will diverge while T is fixed.

Most panel models are designed to estimate the parameters of a model which can be described

$y_{it} = x_{it}\beta + \alpha_i + \epsilon_{it}$

where i indexes the entities and t indexes time. $$\beta$$ contains the parameters of interest. $$\alpha_i$$ are entity-specific components that are not usually identified in the standard setup, and so cannot be consistently estimated and $$\epsilon_{it}$$ are idiosyncratic errors uncorrelated with $$\alpha_i$$ and the covariates $$x_{it}$$.

All models require two inputs

• dependent - The variable to be modeled, $$y_{it}$$ in the model

• exog - The regressors, $$x_{it}$$ in the model.

and use different techniques to address the presence of $$\alpha_i$$.

In particular,

• PanelOLS uses fixed effect (i.e., entity effects) to eliminate the entity specific components. This is mathematically equivalent to including a dummy variable for each entity, although the implementation does not do this for performance reasons.

• BetweenOLS averages within an entity and then regresses the time-averaged values using OLS.

• FirstDifferenceOLS takes the first difference to eliminate the entity specific effect.

• RandomEffects uses a quasi-difference to efficiently estimate $$\beta$$ when the entity effect is independent from the regressors. It is, however, not consistent when there is dependence between the entity effect and the regressors.

• PooledOLS ignores the entity effect and is consistent but inefficient when the effect is independent of the regressors.

PanelOLS is somewhat more general than the other estimators and can be used to model 2 effects (e.g., entity and time effects).

Model specification is similar to statsmodels. This example estimates a fixed effect regression on a panel of the wages of working men modeling the log wage as a function of squared experience, a dummy if the man is married and a dummy indicating if the man is a union member.

from linearmodels.panel import PanelOLS
from linearmodels.datasets import wage_panel
import statsmodels.api as sm
data = data.set_index(['nr','year'])
dependent = data.lwage
mod = PanelOLS(dependent, exog, entity_effects=True)
res


While the result contains many properties containing specific quantities of interest (e.g., params or tstats), the string representation of the result is a summary table.

                          PanelOLS Estimation Summary
================================================================================
Dep. Variable:                  lwage   R-squared:                        0.1365
Estimator:                   PanelOLS   R-squared (Between):             -0.0674
No. Observations:                4360   R-squared (Within):               0.1365
Date:                Wed, Apr 19 2017   R-squared (Overall):              0.0270
Time:                        17:48:58   Log-likelihood                   -1439.0
F-statistic:                      200.87
Entities:                         545   P-value                           0.0000
Avg Obs:                       8.0000   Distribution:                  F(3,3812)
Min Obs:                       8.0000
Max Obs:                       8.0000   F-statistic (robust):             200.87
P-value                           0.0000
Time periods:                       8   Distribution:                  F(3,3812)
Avg Obs:                       545.00
Min Obs:                       545.00
Max Obs:                       545.00

Parameter Estimates
==============================================================================
Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI
------------------------------------------------------------------------------
const          1.3953     0.0123     113.50     0.0000      1.3712      1.4194
expersq        0.0037     0.0002     19.560     0.0000      0.0033      0.0041
married        0.1073     0.0182     5.8992     0.0000      0.0717      0.1430
union          0.0828     0.0198     4.1864     0.0000      0.0440      0.1215
==============================================================================

F-test for Poolability: 9.3360
P-value: 0.0000
Distribution: F(544,3812)

Included effects: Entity


Like statsmodels, panel models can be specified using a R-like formula. This model is identical to the previous. Note the use of the special variable EntityEffects to include the fixed effects.

mod = PanelOLS.from_formula('lwage ~ 1 + expersq + union + married + EntityEffects',data)