By Connor R. Walsh
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Additional info for Airline Industry: Strategies, operations, safety
Additionally, these coefficients are estimated for various periods around the transition period to deregulation, from which inferences are drawn about deregulation‘s effect on behavior. S. Airline Industry 23 To further test the difference in earnings behavior between the two regulatory periods, the following model is estimated: Pit/Ait - Pit-1/Ait-1 = α + (β1+ β2D)[Pit-1/Ait-1 – E(Pit-1/Ait-1)] + (β3+ β4D)[Pit-2/Ait-2 – E (Pit-2/Ait-2)] + (β5+ β6D) [Pit-1/Ait-1 – Pt-2/Ait-2] + (β7+ β8D) [Pit-2/Ait-2 – Pt-3/Ait-3] + εt (4) where D is a dummy variable taking the value of 1 in the post-regulation period, 0 else, and all other variables are identical to those in equation (2).
The results are presented in Table 3 and reveal that the first and second-order autocorrelations appear to be the only significant autocorrelations for most of the carriers. Hence, the Fama and French (2000) mean-reversion model (Equation 2) is modified to include the second-order autocorrelation. Table 1. Common Earnings’ Descriptive Statistics Using annual data, this table shows descriptive statistics for the airline industry‘s three major carrier‘s common earnings divided by book value of assets.
The estimation of the expected value is explained below. e. the lead change in scaled profitability), followed by an error term. From above, expected profitability is estimated in a first-stage regression: Pit/Ait = θ0 + θ1SPt + θ2SPt-1 +θ3CPIt-2 + θ4CRUDEt-2 + εit (3) If earnings are mean-reverting, then β2 and/or β3 from the second-stage regression should be less than unity. Additionally, these coefficients are estimated for various periods around the transition period to deregulation, from which inferences are drawn about deregulation‘s effect on behavior.