The effect of 9/11 on the airline industry ECON 240C – Project 2 Hao Jin ChingChi Huang Bryan Watson Vineet Sharma Hilde Hesjedal.

Slides:



Advertisements
Similar presentations
Introduction Describe what panel data is and the reasons for using it in this format Assess the importance of fixed and random effects Examine the Hausman.
Advertisements

Airport Forecasting. Forecasting Demand Essential to have realistic estimates of the future demand of an airport Used for developing the airport master.
SYST 660 Airline Operating Costs and Airline Productivity
1 CHAPTER 10 FORECASTING THE LONG TERM: DETERMINISTIC AND STOCHASTIC TRENDS Figure 10.1 Economic Time Series with Trends González-Rivera: Forecasting for.
Forecasting CPI Xiang Huang, Wenjie Huang, Teng Wang, Hong Wang Benjamin Wright, Naiwen Chang, Jake Stamper.
Ka-fu Wong © 2003 Chap Dr. Ka-fu Wong ECON1003 Analysis of Economic Data.
Take Home II: US Residential Natural Gas Price Analysis and 2011 Forecast Group E Lars Hult Eric Johnson Matthew Koson Trung Le Joon Hee Lee Aygul Nagaeva.
Price of Gold and US Dollar Index Dwarakamayi Polakam Jennifer Griffeth Ashley Arlotti Rui Feng Ying Fan Qi He Qi Li Group C Presentation.
1 ECON 240C Lecture Outline Box-Jenkins Passengers Box-Jenkins Passengers Displaying the Forecast Displaying the Forecast Recoloring Recoloring.
1 Power Nine Econ 240C. 2 Outline Lab Three Exercises Lab Three Exercises –Fit a linear trend to retail and food sales –Add a quadratic term –Use both.
TAKE HOME PROJECT 2 Group C: Robert Matarazzo, Michael Stromberg, Yuxing Zhang, Yin Chu, Leslie Wei, and Kurtis Hollar.
1 Project I Econ 240c Spring Issues  Parsimonious models  2006: March or April 9.3 wks or 8.9 wks  Trend  Residual seasonality  Forecasts:
United States Imports Michael Williams Kevin Crider Andreas Lindal Jim Huang Juan Shan.
Global Warming: Is It True? Peter Fuller Odeliah Greene Amanda Smith May Zin.
1 Takehome One month treasury bill rate.
1 Lecture Eleven Econ 240C. 2 Outline Review Stochastic Time Series –White noise –Random walk –ARONE: –ARTWO –ARTHREE –ARMA(2,2) –MAONE*SMATWELVE.
Project II Troy Dewitt Emelia Bragadottir Christopher Wilderman Qun Luo Dane Louvier.
Will the Airline Industry Recover? Econ 240C Group E: Daniel Grund Daniel Jiang You Ren David Rhodes Catherine Wohletz James Young.
Revenue Passenger Miles (RPM) Brandon Briggs, Theodore Ehlert, Mats Olson, David Sheehan, Alan Weinberg.
Car Sales Analysis of monthly sales of light weight vehicles. Laura Pomella Karen Chang Heidi Braunger David Parker Derek Shum Mike Hu.
Data Sources The most sophisticated forecasting model will fail if it is applied to unreliable data Data should be reliable and accurate Data should be.
1 Power Nine Econ 240C. 2 Outline Lab Three Exercises Lab Three Exercises –Fit a linear trend to retail and food sales –Add a quadratic term –Use both.
1 Power Nine Econ 240C. 2 Outline Lab Three Exercises Lab Three Exercises –Fit a linear trend to retail and food sales –Add a quadratic term –Use both.
Arch-Garch PPIFGS. Producer Price Index Finished Goods 1982=100.
Exchange Rate YuYuan Liu Han Yu Yang Dennis Yue Jessica Chen Jo-Yu Mao.
Economics 240C Forecasting US Retail Sales. Group 3.
1 Arch-Garch Lab Nine. 2 Producer Price Index for Finished Goods, 1982 =100, –
Global Analysts Eirik Skeid, Anders Graham, Bradley Moore, Matthew Scott Tor Seim, Steven Comstock.
Will There Be Jobs For All of Us Financial Econometricians? Ben Kallo Ben Kallo James Katavolos James Katavolos Luke Panzar Luke Panzar Ryan Carl Ryan.
Forecasting Crude Oil Prices By: Keith Cochran Joseph Singh Julio Urenda Dave White Justin Adams.
Chapter 11 Multiple Regression.
Modeling Unemployment Rates June 3, 2008 Ryan DeGrazier Chun-Hung Lin Johan Rothe Chun-Kai Wang Anastasia Zavodny.
Overview of Forecasting. Two Approaches to Forecasting Forecasting Methods Model Based Judgmental (NB: Ch. 11) Using Survey Data (QMETH520) Using Past.
Forecasting. Aruoba-Diebold-Scotti (ADSA) Business Index, Fed at Philadelphia The Aruoba-Diebold-Scotti business conditions index is designed to track.
Forecasting World Wide Pandemics Using Google Flu Data to Forecast the Flu Brian Abe Dan Helling Eric Howard Ting Zheng Laura Braeutigam Noelle Hirneise.
1 Lab Four Postscript Econ 240 C. 2 Airline Passengers.
1 Power Nine Econ 240C. 2 Outline Lab Three Exercises Lab Three Exercises –Fit a linear trend to retail sales –Add a quadratic term –Use both models to.
He Loves Me, He Loves Me Not A Forecast of U.S. Jewelry Sales Alex Gates Ling-Ching Hsu Shih-Hao Lee Hui Liang Mateusz Tracz Grant Volk June 1, 2010.
U.S. Tax Revenues and Policy Implications A Time Series Approach Group C: Liu He Guizi Li Chien-ju Lin Lyle Kaplan-Reinig Matthew Routh Eduardo Velasquez.
Forecasting the US Dollar/Euro Exchange Rate Group B John Hottinger Jingyu Nie Katharina Denk Alex Brown Joel Demartini Yuanchen Wang Doug Skipper-Dotta.
Matt Mullens Gulsah Gunenc Alex Keyfes Gaoyuan Tian Andrew Booth.
The 91 Day T-Bill Rate Steven Carlson Miguel Delgado Helleseter Darren Egan Christina Louie Cambria Price Pinar Sahin.
JetBlue Cost and Productivity Analysis Greg Koch HW for OR 750.
Delta Air Lines Cost and Productivity Analysis Ujaval Patel.
Business Forecasting Chapter 4 Data Collection and Analysis in Forecasting.
Airline Operating Costs & Airline Productivity - US Airways - Seungwon Noh (Apr )
Time-Varying Volatility and ARCH Models
Time Series Analysis Introduction Averaging Trend Seasonality.
Airport Forecasting NOTE: for HW, draw cash flow diagram to solve and review engineering economics.
European Commission Directorate General Economic and Financial Affairs Using BCS data for tracking q-o-q GDP growth Andreas Reuter Business and consumer.
AVE3103 Final Exam Final Exam Preparation 6 questions Essay Format (15 points each/ Best of 4 answer) Date: TBC Time: TBC Duration: 3 hours.
Air Transport Association May 21,2002 NET INCOME U.S. Scheduled Airlines
Determinants of the velocity of money, the case of Romanian economy Dissertation Paper Student: Moinescu Bogdan Supervisor: Phd. Professor Moisă Altăr.
Special Topics in Economics Econ. 491 Chapter 11: Stock Exchange Market Report.
Homework: Airline Operating Costs and Airline Productivity Rui Neiva, April 9, 2012 Definitions: RPM: revenue passenger miles; ∑ i = 1 to All Flights (Number.
Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.
Seasonal ARMA forecasting and Fitting the bivariate data to GARCH John DOE.
Module 4 Forecasting Multiple Variables from their own Histories EC 827.
Describing Trends A Practical guide. We can describe trends in English in three different ways e.g.: Verbs of change Verbs of change Prepositions Prepositions.
How to Construct a Seasonal Index. Methods of Constructing a Seasonal Index  There are several ways to construct a seasonal index. The simplest is to.
Techniques for Seasonality
NET INCOME U.S. Scheduled Airlines $ Billions
Econometric methods of analysis and forecasting of financial markets
CHAPTER 16 ECONOMIC FORECASTING Damodar Gujarati
Square Numbers and Square Roots
Square Root.
Financial Forecasting M.Sc. in Finance – 2018/19 – 1st Semester
OUTLINE Questions? Quiz Results Quiz on Thursday Continue Forecasting
The Question … Ahmed owns a stationary manufacturing business. He has changed the prices of some of his products. Ahmed has also changed his paper supplier.
Turbulence Accidents and NTSB Research Update
Presentation transcript:

The effect of 9/11 on the airline industry ECON 240C – Project 2 Hao Jin ChingChi Huang Bryan Watson Vineet Sharma Hilde Hesjedal

The effect of 9/11 on the airline industry After the incident on sep. 11. the revenue per passenger miles dropped $22.5 billion within just one month. After the incident on sep. 11. the revenue per passenger miles dropped $22.5 billion within just one month. This paper look into how much the drop actually was if we correct for time trend and seasonality and other econometrical issues. This paper look into how much the drop actually was if we correct for time trend and seasonality and other econometrical issues.

The effect of 9/11 on the airline industry The airline industry have gone from being reasonable profitable to tough and turbulent with low margins and high losses The airline industry have gone from being reasonable profitable to tough and turbulent with low margins and high losses Can the incident on sep. 11 be the reason for some of the difficulties the industry is facing today? Can the incident on sep. 11 be the reason for some of the difficulties the industry is facing today?

Revenue Passenger Miles Definition “Revenue Passenger Miles”: One revenue passenger transported one mile in revenue service. Revenue passenger miles are computed by the summation of the products of the revenue aircraft miles flown on each inter-airport hop multiplied by the number of revenue passengers carried on that hop. Definition “Revenue Passenger Miles”: One revenue passenger transported one mile in revenue service. Revenue passenger miles are computed by the summation of the products of the revenue aircraft miles flown on each inter-airport hop multiplied by the number of revenue passengers carried on that hop.

The effect of 9/11 on the airline industry There are various reasons for the difficulties within the business, however we have examined the revenue passenger miles in the U.S. from 1996 and onwards and looked at the effect the incident on September 11 had on total revenue in the industry. There are various reasons for the difficulties within the business, however we have examined the revenue passenger miles in the U.S. from 1996 and onwards and looked at the effect the incident on September 11 had on total revenue in the industry.

Revenue Passenger Miles

First Differenced Series

Seasonal&First Differenced Series

Unit Root Test

ARMA Model

Modeling the Event Step function Step function

Conditional Heteroskedasticity ARCH Test

Correlogram of Residual Square

Add ARCH(1) and GARCH(1)

Add ARCH(1),GARCH(1) and GARCH(2)

The series is more noisy right after 9/11

Forecast

Re-coloring

95% Confidence Interval

Conclusion The loss directly associated with 9/11 is $12.8 billion (instead of $22.5). The loss directly associated with 9/11 is $12.8 billion (instead of $22.5). The total revenue in the industry today would most likely have been higher if not for the incident. The total revenue in the industry today would most likely have been higher if not for the incident. However, other factors have also influenced the airline industry. However, other factors have also influenced the airline industry.