Panel Data Models: Fixed & Random Effects, Hausman Test

Posted by on Jun 21, 2025 in Business Statistics | 0 comments

Panel Data Models: Key Concepts & Estimators

1. General Panel Data Model

The general panel data model is expressed as:

$y_{it} = \alpha_i + X_{it}’\beta + \epsilon_{it}$

  • $\alpha_i$: Represents the individual-specific, time-invariant effect.

  • Goal: Estimate $\beta$ despite unobserved heterogeneity.

2. Fixed Effects (FE) Estimator

Methods:

  • LSDV (Least Squares Dummy Variables): Not feasible for large $N$.

  • Within Estimator:

$y_{it} – \bar{y}_i = (X_{it} – \bar{X}_i)\beta + (\epsilon_{it} – \bar{\epsilon}_i)

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Statistical Analysis of Production Models and Time Series Forecasting

Posted by on Feb 28, 2025 in Business Statistics | 0 comments

Statistical Analysis of Production Models

a) Significance parameters: Divide the model (CAN, BARRELS) using a dummy variable CAN: assign a value of 1 or 0. Barrel: βo + β2(prodtotal – 100)+ β4(prodtotal-100)2 ; Can: (βo + β1) + (β2 + β3)(prdototal – 100) + (β4 + β5)(prodtotal – 100)2 + U

β0 = Expected barrel production when total production is 100hl, β1 = Expected difference between can production and barrel production when total is 100, β2 = Expected increase of barrel production when

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