Click for next page ( 58

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 57
Software User Manual 59 Risk Analysis Features of the Software One of the most important features of the software is an embedded risk analysis, which places confidence bands around a forecast designed to capture the uncertainty of future jet fuel prices and income growth. The objective is to provide airports with a way to undertake a formal risk analysis for their forecast designed to capture the range of values for these very uncertain drivers of air service. The risk analysis answers the following question: Given my current expectations about jet fuel prices and economic growth, I have created a forecast of enplanements and commercial operations for my airport; what are the High and Low forecasts if I define a range of future jet fuel prices and income growth likely to occur about 90 percent of the time? The embedded risk analysis looks at how accurately jet fuel prices and economic growth have been forecast over the past 20 years. To create an approximate 90 percent confidence band, an analysis was undertaken that compares actual to forecast values and identifies the percentage range of error (high and low) that occurs 90 percent of the time. So for example, monthly heating oil futures prices for a period 12 months forward were com- pared to actual jet fuel prices and the percentage error was measured. A confidence band was constructed, defined as the percentage range (high and low) likely to encompass the error in fore- casting the price of jet fuel 12 months into the future about 90 percent of the time. The same type of confidence band was created for errors in forecasting GDP growth. Further discussion of these embedded analyses is contained in Chapter 4. To apply the embedded risk analysis into the forecast for ACY, recall that the baseline TAF was already modified by assuming that both operations and enplanements would grow by an additional 5 percent per year. What is the confidence band around this forecast given uncertainty of jet fuel prices and income growth? To create the confidence band, go to the Baseline&Scenarios worksheet and press the two buttons : Set Jet Fuel Scenarios based Set Income Scenarios based on Futures Uncertainty on EIA GDP Uncertainty This will overwrite any other assumptions that have been made about future jet fuel prices and income growth, and automatically create Low and High Cases that correspond to a 90 percent confidence band for these two drivers. Other drivers can be modified as well, if desired. The new Baseline and Sensitivity cases can be viewed in the Projections worksheet and in the One-Page Report worksheet. The latter combines the data elements of the implied High and Low cases for jet fuel and income with the enplanements and operations graphs so the user can see the overall results on a single page. However, the graphs also reflect any other Base case or Scenario changes the user may have entered in the Baseline&Scenarios worksheet. Exhibit II-27 shows the One-Page Report for the scenario created for ACY. Interestingly, the Low case in the risk analysis shows a downside for both operations and enplanements that is 10 percent of the Baseline by 2014. The upside is far more modest--on the order of 3.5 percent. Interpreting Results Like any modeling exercise, interpreting results from the software depends almost exclusively on the assumptions the user makes. The software is designed to be a supplement to existing fore- casts. It provides a structured way to: Modify an existing forecast with updated information on existing air services Modify an existing forecast with new growth assumptions

OCR for page 57
60 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Exhibit II-27. Sample risk analysis for ACY.

OCR for page 57
Software User Manual 61 Create sensitivity cases by defining ranges for key drivers of air service including jet fuel prices, income growth, inflation, the cost of producing air service (using seat size as a proxy), competition at the airport, and competition from nearby large and medium hub airports Utilize a more formal risk analysis of the forecast based on the likely range of error in forecast- ing two key air service drivers: jet fuel prices and income growth The software will produce useful results when reasonable and consistent assumptions are applied. In such cases, it may be a useful tool for airport sponsors to examine the downside and upside of their future air services and the implications for airport finances and future development. One way to judge the results of the analysis is to compare it to the accuracy of the TAF in recent years. On the TAFHistory worksheet, the user will find a comparison of forecast and actual operations for the TAF beginning in 2003. The summary for ACY is shown in Exhibit II-28. The example for ACY illustrates one of the key motivations for the creation of the software: unanticipated spikes in jet fuel prices accompanied by sudden and unanticipated slow-down in the economy can produce dramatic and unanticipated reductions in air services. Thus, examin- ing the potential impact of sudden changes in jet fuel prices and in income growth is prudent for those interested in the consequences of changes in air services at airports. Exhibit II-28. TAFHistory worksheet. ACY Air Carrier + Air Taxi Operations: TAF Predicted vs. Actual 37,000 32,000 27,000 22,000 17,000 12,000 7,000 2,000 2004 2005 2006 2007 2008 2009 TAF 2003 TAF 2004 TAF 2005 TAF 2006 TAF 2007 TAF 2008 Actual