In the first chapter, we develop a dynamic model of collusion in city-pair routes for selected US airlines and specify the first order conditions using a state-space representation that is estimated by Kalman-filtering techniques using the Databank 1A (DB1A) Department of Transportation (DOT) data during the period 1979I-1988IV. We consider two airlines, American (AA) and United (UA) and four city pairs. Our measure of market power is based on the shadow value of long-run profits in a two person strategic dynamic game and we find evidence of relative market power of UA in three of the four city pairs we analyze. The second chapter explores three models of forecasting airline energy demand: Trend line, ARIMA and Structural Model based on results from Chapter 1 and find that none of them is a dominant winner in American (AA) and United (UA) between Chicago and Salt Lake City. In the third chapter, we use Model Averaging and Forecast Combination Techniques to provide a decisive conclusion focusing on discussing Equal Weighted Averaging, Mean Square Weighted Averaging and Optimized Weighted Averaging on UA and AA in City-Pairs Chicago -Seattle and Chicago-San Diego.