• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Economic Regression Analysis Presentation
 

Economic Regression Analysis Presentation

on

  • 14,094 views

This project was completed as part of my Economic Analysis for Managers MBA class. The purpose of the project was to conduct a regression analysis for the airline industry.

This project was completed as part of my Economic Analysis for Managers MBA class. The purpose of the project was to conduct a regression analysis for the airline industry.

Statistics

Views

Total Views
14,094
Views on SlideShare
14,075
Embed Views
19

Actions

Likes
1
Downloads
30
Comments
0

1 Embed 19

http://www.slideshare.net 19

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Economic Regression Analysis Presentation Economic Regression Analysis Presentation Presentation Transcript

    • Joseph J. Giarmo III Economic Analysis for Managers MBA 679 October 14, 2008
    • Develop an economic regression model for average United  States domestic passenger airfares. Explain the price of airfares through the identification of  independent variables that have a causal relationship with the dependent variable.
    • The airline industry (worldwide) consists of:  ◦ 2,000 airlines ◦ 23,000 aircraft ◦ 3,700 airports The U.S. accounts for 1/3rd of the world’s total air traffic  In 2006, U.S. airlines carried 754 million passengers  compared to the over 2 billion passengers that were carried worldwide
    • World Economy  Government regulation  Global events  Fuel prices  Terrorism  Supply & Demand 
    • Airlines have restructured The result:  Airlines have the capability  Increased demand for fuel-  to carry 20.4% more efficient aircraft passengers Modification of existing  Aircraft use 3% fewer  aircraft gallons of fuel than in 2000 Reduced aircraft weight  $5 billion profit in 2007 
    • In 2007, inflation adjusted (real) airfares fell 1.4%  Growth Rates (1978-present): Unadjusted terms  ◦ Airfares: 53% ◦ Milk: 154% ◦ New vehicles: 345% ◦ Single-family homes: 345% ◦ Prescription drugs: 499% ◦ Public college tuition: 799% The decrease in airfares and their low growth rate has been due to:  ◦ Economic deregulation ◦ Competitive markets ◦ Advances in technology ◦ More efficient operations
    • Deregulation  ◦ Open sky agreements ◦ Elimination of traffic rights restrictions ◦ Competitive air travel market Demand for fuel-efficient planes  ◦ Due to increased fuel prices ◦ Every $10 increase in a barrel of crude oil = $3.4 billion cost for the airline industry Mergers  ◦ To generate value for the airlines, their shareholders, and their employees ◦ Northwest Airlines and Delta Airlines
    • Dependent Variable: Average U.S. Domestic Passenger Airfares Based on fares reported from the United States top 100 airports  o This excludes Alaska, Hawaii, and Puerto Rico Airfares are measured per ticket and are based on domestic itinerary  fares, round-trip, or one-way for which no return is purchased Airfares include taxes and applicable fees but do not include frequent flyer  fares and unusually high reported fares Fares are reported on a quarterly basis by the U.S. Department of  Transportation: Bureau of Transportation Statistics (BTS)
    • Airfares ($) 100 150 200 250 300 350 400 50 0 Mar-95 Sep-95 Mar-96 Sep-96 Mar-97 Sep-97 Mar-98 Sep-98 Mar-99 Sep-99 Mar-00 Sep-00 Mar-01 Sep-01 Date Mar-02 Sep-02 Mar-03 Sep-03 Mar-04 Sep-04 Mar-05 Sep-05 Average U.S. Domestic Passenger Airfares Mar-06 Source: Data provided by the U.S. Department of Transportation: Bureau of Transportation Statistics (BTS) Sep-06 Mar-07 Sep-07 Mar-08
    • Labor Costs  Food and Beverage Costs  Fuel Costs  Other Operating Expenses  Seasonal Dummy Variables 
    • 9/11  Professional Services  Landing Fees  Aircraft Insurance  Non-Aircraft Insurance  Passenger Commissions  Advertising and Promotion 
    • Independent Variables Null Hypotheses (Ho) Alternative Hypotheses (H1) B≤0 B>0 Labor Costs B≤0 B>0 Food/Beverage Costs B≤0 B>0 Fuel Costs B≤0 B>0 Other Operating Expenses B≤0 B>0 Q1 B≤0 B>0 Q2
    • Is the model Logical? Are the slope terms significantly positive or negative? What is the explanatory power of the model? Does serial correlation exist? Does multicollinearity exist?
    • Coefficients Standard Error t Stat P-Value Intercept 149.472 23.354 6.400 0.000 Labor 0.010 0.002 4.722 0.000 Fuel 0.004 0.001 4.021 0.000 Other Operating 0.009 0.003 2.630 0.012 Exp. Food/Beverage 0.074 0.028 2.618 0.012 Q1 14.825 4.479 3.310 0.002 Q2 11.147 4.396 2.536 0.015 Source: Data provided by the U.S. Department of Transportation: Bureau of Transportation Statistics (BTS), the Air Transport Association, and Microsoft Excel
    • For labor costs, reject Ho because |4.72| > 1.684  For fuel costs, reject Ho because |4.02| > 1.684  For other operating expenses, reject Ho because |2.63| > 1.684  For food and beverage costs, reject Ho because |2.61| > 1.684  For Q1, reject Ho because |3.31| > 1.684  For Q2, reject Ho because |2.53| > 1.684 
    • Multiple R .763 R Square .583 Adjusted R Square .528 Standard Error 12.896 Durbin Watson .66 Observations 53 Source: Data provided by the U.S. Department of Transportation: Bureau of Transportation Statistics (BTS), the Air Transport Association, and Microsoft Excel
    • Test Value of the Calculated DW Result Satisfied/Unsatisfied 1 (4-1.175) < .66 < 4 Negative serial Unsatisfied correlation exists 2 (4-1.854) < .66 < (4-1.175) Result is Unsatisfied indeterminate 3 2 < .66 < (4-1.854) No serial correlation Unsatisfied exists 4 1.854 < .66 < 2 No serial correlation Unsatisfied exists 5 1.175 < .66 < 1.854 Result is Unsatisfied indeterminate 6 0 < .66 < 1.175 Positive serial Satisfied correlation exists Source: Table 4-3 and Table 4-4 from Managerial Economics: An Economic Foundation for Business Decisions
    • Labor Fuel Other Operating Food/Beverage Exp. Labor Costs 1 Fuel Costs 0.057 1 Other Operating - 0.106 0.154 1 Exp. Food/Beverage 0.145 -0.618 0.048 1 Costs Source: Data provided by the Air Transport Association and Microsoft Excel
    • Actual Airfares ($) vs. Predicted Airfares ($) 400 350 300 250 Airfares ($) 200 150 Actual Airfares ($) 100 Predicted Airfares ($) 50 0 Aug-01 Feb-98 Jun-00 Feb-05 Jun-07 Dec-96 Jul-97 Sep-98 Jan-01 Dec-03 Jul-04 Sep-05 Jan-08 Mar-95 Apr-99 Nov-99 Mar-02 Apr-06 Nov-06 Oct-95 Oct-02 May-96 May-03 Date Source: Data provided by the U.S. Department of Transportation: Bureau of Transportation Statistics (BTS) and Microsoft Excel
    • The model is useful but should be used with caution  Why?  Positive serial correlation exists There are likely many more independent variables that  could and should be considered The airline industry is vulnerable to many external and  internal factors making it a somewhat unpredictable industry