Partial likelihood methods for panel data

Partial (pooled) likelihood estimation for panel data is a quasi-maximum likelihood method for panel analysis that assumes that density of y i t {\displaystyle y_{it}} given x i t {\displaystyle x_{it}} is correctly specified for each time period but it allows for misspecification in the conditional density of y i = ( y i 1 , … , y i T ) {\displaystyle y_{i}=(y_{i1},\dots ,y_{iT})} given x i = ( x i 1 , … , x i T ) {\displaystyle x_{i}=(x_{i1},\dots ,x_{iT})} . == Description == Concretely, partial likelihood estimation uses the product of conditional densities as the density of the joint conditional distribution.

Source: Wikipedia — Partial likelihood methods for panel data (CC BY-SA 4.0)

Partial likelihood methods for panel data

Partial (pooled) likelihood estimation for panel data is a quasi-maximum likelihood method for panel analysis that assumes that density of y i t {\displaystyle y_{it}} given x i t {\displaystyle x_{it}} is correctly specified for each time period but it allows for misspecification in the conditional density of y i = ( y i 1 , … , y i T ) {\displaystyle y_{i}=(y_{i1},\dots ,y_{iT})} given x i = ( x i 1 , … , x i T ) {\displaystyle x_{i}=(x_{i1},\dots ,x_{iT})} . == Description == Concretely, partial likelihood estimation uses the product of conditional densities as the density of the joint conditional distribution.

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Source: Wikipedia "Partial likelihood methods for panel data" · CC BY-SA 4.0

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