APPENDIX D The Structural Time Series Model

Although terms such as “trend” and “seasonal” are intuitively appealing, they are mental constructs as we cannot observe them directly. Therefore, we use a structural modeling approach that treats them as unobserved...

Table B3 Distribution of Pre- and Post-9/11 Long-Distance Trips by Gender, for Each Mode

Percentage trips (standard error)

NOTE: Cells with bold type indicate significant differences (p<0.05) between pre-9/11 and post-9/11 estimates.

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Figure 15 Forecasts of Air Revenue Passenger Miles

(Billions)

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SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of Transportation Statistics.

Table D1 Some of the Principal Models in the Structural Framework

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Figure 2 Industrial Production Index

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SOURCE: Economic Report of the President, TABLE B-Industrial production indexes, major industry divisions 1955-2002.

Chapter 2 Time Series Analysis of Passenger Data: Pre- and Post- September 11, 2001

In the previous chapter, we noted that some of the differences between the pre- and post-September 2001 data could be attributed to the nature of time series data. Differences due to seasonality...

Figure 6 Percentage of Pre and Post 9/11 Long-Distance Trips by Mode for International Travel

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SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of Transportation Statistics, NHTS...

Figure 8 Percentage of Pre and Post 9/11 Long-Distance Trips by Mode for Trip Distances Between 50-99 Miles

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SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of Transportation...

Table B2 Distribution of Pre- and Post-9/11 Long-Distance Trips by Age Group, for Each Mode

Percentage trips (standard error)

NOTE: Cells with a small sample size (< 30) or that have a covariance or c.v. (> 0.3) are shaded dark grey to indicate that the weighted...

Table E1 Forecasts and Forecast Errors for Air RPM

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