# Fixed effects model

**fixed effectsFixed effectFixed effects estimationFixed effects estimatorfixed effects factorfixed effects models**

In statistics, a fixed effects model is a statistical model in which the model parameters are fixed or non-random quantities.wikipedia

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### Mixed model

**mixed effects modelslinear mixed modellinear mixed models**

This is in contrast to random effects models and mixed models in which all or some of the model parameters are considered as random variables.

A mixed model (or more precisely mixed error-component model) is a statistical model containing both fixed effects and random effects.

### Random effects model

**random effectsrandom effectvariance component**

This is in contrast to random effects models and mixed models in which all or some of the model parameters are considered as random variables.

In econometrics, random effects models are used in the analysis of hierarchical or panel data when one assumes no fixed effects (it allows for individual effects).

### Panel data

**longitudinal datapanel(panel)**

In panel data where longitudinal observations exist for the same subject, fixed effects represent the subject-specific means.

Two important models are the fixed effects model and the random effects model.

### Durbin–Wu–Hausman test

**Hausman specification testHausman testThe Hausman test, ''or'' Hausman specification test**

The Durbin–Wu–Hausman test is often used to discriminate between the fixed and the random effects models.

The Hausman test can be also used to differentiate between fixed effects model and random effects model in panel data.

### Fixed-effect Poisson model

* Fixed-effect Poisson model

Their outcome of interest was the number of patents filed by firms, where they wanted to develop methods to control for the firm fixed effects.

### Statistics

**statisticalstatistical analysisstatistician**

In statistics, a fixed effects model is a statistical model in which the model parameters are fixed or non-random quantities.

### Statistical model

**modelprobabilistic modelstatistical modeling**

In statistics, a fixed effects model is a statistical model in which the model parameters are fixed or non-random quantities.

### Parameter

**parametersparametricargument**

In statistics, a fixed effects model is a statistical model in which the model parameters are fixed or non-random quantities.

### Econometrics

**econometriceconometricianeconometric analysis**

In many applications including econometrics and biostatistics a fixed effects model refers to a regression model in which the group means are fixed (non-random) as opposed to a random effects model in which the group means are a random sample from a population.

### Biostatistics

**biostatisticianbiometrybiometrician**

In many applications including econometrics and biostatistics a fixed effects model refers to a regression model in which the group means are fixed (non-random) as opposed to a random effects model in which the group means are a random sample from a population.

### Regression analysis

**regressionmultiple regressionregression model**

In many applications including econometrics and biostatistics a fixed effects model refers to a regression model in which the group means are fixed (non-random) as opposed to a random effects model in which the group means are a random sample from a population.

### Estimator

**estimatorsestimateestimates**

In panel data analysis the term fixed effects estimator (also known as the within estimator) is used to refer to an estimator for the coefficients in the regression model including those fixed effects (one time-invariant intercept for each subject).

### Coefficient

**coefficientsleading coefficientfactor**

In panel data analysis the term fixed effects estimator (also known as the within estimator) is used to refer to an estimator for the coefficients in the regression model including those fixed effects (one time-invariant intercept for each subject).

### Omitted-variable bias

**omitted variable biasomitted variablesomitted variable**

Such models assist in controlling for omitted variable bias due to unobserved heterogeneity when this heterogeneity is constant over time.

### Finite difference

**difference operatorfinite differencesNewton series**

This heterogeneity can be removed from the data through differencing, for example by subtracting the group-level average over time, or by taking a first difference which will remove any time invariant components of the model.

### Efficiency (statistics)

**efficientefficiencyinefficient**

If the random effects assumption holds, the random effects estimator is more efficient than the fixed effects estimator. If the error terms u_{it} are homoskedastic with no serial correlation, the fixed effects estimator is more efficient than the first difference estimator.

### Consistent estimator

**consistentconsistencyinconsistent**

However, if this assumption does not hold, the random effects estimator is not consistent.

### Endogeneity (econometrics)

**endogenousendogeneityreverse causality**

Strict exogeneity with respect to the idiosyncratic error term u_{it} is still required.

### Errors and residuals

**residualserror termresidual**

### Controlling for a variable

**controlcontrollingaccounted for**

Since \alpha_{i} is not observable, it cannot be directly controlled for.

### Multicollinearity

**collinearitymulticollinearperfect multicollinearity**

One is to add a dummy variable for each individual i>1 (omitting the first individual because of multicollinearity).

### Homoscedasticity

**homoscedastichomogeneity of variancehomoskedastic**

If the error terms u_{it} are homoskedastic with no serial correlation, the fixed effects estimator is more efficient than the first difference estimator.

### Autocorrelation

**autocorrelation functionserial correlationautocorrelated**

If the error terms u_{it} are homoskedastic with no serial correlation, the fixed effects estimator is more efficient than the first difference estimator.

### Random walk

**random walkssimple random walkRandom walks on graphs**

If u_{it} follows a random walk, however, the first difference estimator is more efficient.

### Minimum distance estimation

**minimum distance rule**

:which can be estimated by minimum distance estimation.