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From page 67... ... 67 C H A P T E R 4 This chapter presents quantitative results from econometric models of annual airport passenger enplanements. The analysis compares the results from model specifications that incorporate a specific type of disaggregated socioeconomic data into a set of baseline models of airport passenger demand. Read the entire page →
From page 68... ... 68 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies Specifying Simple Models for Airport Enplanements Regression models of air passenger demand or enplanements typically use historical data on regional economics or demographics as independent variables that underlie the demand for air travel that drives airport passenger enplanements from year to year. Examples of the types of analyses and organizations that make use of models like these are discussed in Chapter 2. Read the entire page →
From page 69... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 69 For each of the case study airports or airport systems, the analysis was conducted in the following steps: 1. For the MSA served by each of the case study airports, socioeconomic data was collected, using databases from Woods and Poole. Read the entire page →
From page 70... ... 70 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies Case Study Model Estimation Results and Model Performance Results from the case study model regressions using the model specifications outlined earlier are reported in this chapter, following a discussion of variable correlation issues in the socio economic data (aggregated and disaggregated) Read the entire page →
From page 71... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 71 the baseline and the case study alternative regression specifications, and the disaggregated socio economic variable -- the percentage of Phoenix MSA households with annual incomes exceed ing $100,000 (in 2009 dollars) -- used in the alternative regression specification. Read the entire page →
From page 72... ... 72 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies adding very little explanatory power compared to a model estimated with fewer of these inde pendent variables. We will see numerous examples of this in the case study regression model estimates. Read the entire page →
From page 73... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 73 used for the analysis, the percentage of Tulsa MSA households with incomes exceeding $100,000 (in 2009 dollars) Read the entire page →
From page 74... ... 74 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies Variable Population Emp Total Earn Wages & Salaries GRP Avg HH Inc % > $100K DB1B Enp Real Oil Price Population 1 Employment 0.936 1 Total Earnings 0.932 0.940 1 Wages & Salaries 0.947 0.975 0.983 1 GRP 0.954 0.951 0.992 0.988 1 Avg HH Income 0.916 0.928 0.991 0.972 0.990 1 % > $100K 0.919 0.976 0.949 0.973 0.951 0.928 1 DB1B Enplanements 0.141 0.383 0.115 0.208 0.137 0.127 0.330 1 Real Oil Price 0.687 0.630 0.755 0.714 0.783 0.826 0.605 –0.107 1 Table 30. Correlations among Tulsa case study model variables 1990 to 2010. Read the entire page →
From page 75... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 75 year intervals over the 1990 to 2010 sample period. All dollar values are inflation adjusted to 2015 dollars. Read the entire page →
From page 76... ... 76 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies The strong correlations, or collinearity, between the socioeconomic variables reported for each of the regions served by the case study airports raise the same statistical challenges for modeling approaches that would use more than one socioeconomic variable as explanatory fac tors. This is because the close similarity in behavior over time by the socioeconomic variables in each region (a similarity expressed in the high positive correlation between the variables) Read the entire page →
From page 77... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 77 Case Study Model Estimation Results Tables 35 and 36 present the regression results for all the equation specifications used for the eight case study airports. Table 35 shows the regression results for seven baseline regression specifications (including the specification that includes only the disaggregated socioeconomic variable) Read the entire page →
From page 78... ... 78 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies Aggregate Socioeconomic Variable Regression Variable BaltimoreWashington EUG LAX MDT MSO PHX PVD TUL Population Constant coefficient -12.45 -1.45 -4.99 -4.50 0.49 0.93 -123.16 10.28 t-statistic -3.766 -0.476 -0.794 -1.170 0.611 0.781 -8.188 3.863 Oil Price coefficient -0.087 -0.106 -0.016 -0.189 0.026 -0.297 -0.184 -0.080 t-statistic -1.460 -1.455 -0.328 -3.572 0.676 -4.346 -1.842 -1.568 Population coefficient 3.28 2.50 2.37 2.96 2.55 1.93 18.79 0.63 t-statistic 8.474 4.485 3.391 4.626 12.901 11.444 9.058 1.515 Adj R squared 0.847 0.707 0.424 0.465 0.948 0.836 0.797 0.043 Employment Constant coefficient -6.20 4.68 1.00 4.39 5.61 1.93 -46.21 10.83 t-statistic -3.624 2.782 0.213 2.264 13.856 1.695 -9.434 9.025 Oil Price coefficient -0.160 -0.062 -0.044 -0.121 0.062 -0.313 -0.320 -0.100 t-statistic -3.521 -0.931 -0.773 -2.848 1.754 -4.356 -3.811 -2.482 Employment coefficient 2.74 1.57 1.84 1.58 1.54 1.98 9.20 0.60 t-statistic 12.718 4.467 3.252 4.603 13.313 11.051 12.113 2.909 Adj R squared 0.922 0.705 0.437 0.464 0.945 0.824 0.905 0.254 Total Earnings Constant coefficient 3.21 5.22 7.40 7.21 4.03 3.94 -18.71 12.41 t-statistic 2.749 2.976 2.888 5.150 8.174 4.590 -6.533 11.507 Oil Price coefficient -0.095 -0.037 -0.044 -0.131 0.050 -0.216 -0.371 -0.091 t-statistic -1.942 -0.543 -0.801 -2.866 1.480 -3.658 -3.939 -1.783 Total Earnings coefficient 1.10 0.86 0.74 0.67 1.03 1.08 3.26 0.21 t-statistic 10.609 3.983 3.478 4.362 14.077 12.313 11.133 1.775 Adj R squared 0.905 0.672 0.448 0.416 0.943 0.878 0.873 0.074 Wages & Salaries Constant coefficient 2.52 5.71 5.85 7.21 4.12 3.81 -20.49 11.47 t-statistic 2.202 3.579 1.895 5.406 9.745 4.490 -8.284 9.058 Oil Price coefficient -0.099 -0.058 -0.059 -0.125 0.019 -0.218 -0.352 -0.099 t-statistic -2.146 -0.824 -0.998 -2.889 0.605 -3.770 -4.583 -2.118 Wages & Salaries coefficient 1.18 0.84 0.90 0.69 1.08 1.12 3.53 0.32 t-statistic 11.433 4.067 3.391 4.579 16.225 12.610 13.610 2.254 Adj R squared 0.915 0.679 0.454 0.448 0.960 0.886 0.909 0.148 GRP Constant coefficient 2.47 5.81 5.25 6.88 3.35 3.01 -18.95 11.78 t-statistic 2.066 4.442 1.818 4.939 6.223 3.888 -6.201 9.465 Oil Price coefficient -0.106 -0.123 -0.114 -0.134 0.009 -0.201 -0.397 -0.109 t-statistic -2.202 -1.748 -1.747 -3.035 0.254 -4.132 -3.928 -2.037 GRP coefficient 1.12 0.78 0.90 0.67 1.08 1.11 3.16 0.27 t-statistic 10.998 4.892 3.832 4.626 14.172 14.867 10.512 2.043 Adj R squared 0.905 0.732 0.478 0.455 0.945 0.906 0.843 0.112 Avg HH Income Constant coefficient -6.08 -6.45 4.81 -2.03 -11.75 -15.24 -39.18 10.15 t-statistic -3.602 -1.894 2.045 -0.648 -7.312 -5.373 -7.236 5.400 Oil Price coefficient -0.134 -0.047 -0.125 -0.138 0.032 -0.213 -0.491 -0.123 t-statistic -3.094 -0.856 -2.280 -3.216 0.923 -3.099 -4.201 -2.214 Avg HH Income coefficient 1.99 1.72 1.05 1.38 2.12 2.76 4.83 0.40 t-statistic 12.823 5.467 4.893 4.900 14.091 10.452 9.660 2.217 Adj R squared 0.932 0.769 0.584 0.484 0.943 0.844 0.809 0.133 Pct HH Inc>100k Constant coefficient 18.91 13.97 18.67 14.72 14.95 18.62 21.15 15.31 (Disaggregated t-statistic 87.867 28.728 37.169 44.059 43.103 53.301 26.287 41.074 Socioeconomic Oil Price coefficient -0.029 0.049 0.040 -0.111 0.015 -0.009 -0.354 -0.090 Variable) t-statistic -1.004 0.875 1.194 -2.692 0.380 -0.200 -3.625 -2.312 Pct HH Inc>100k coefficient 1.80 0.72 1.52 0.56 1.26 1.95 3.21 0.39 t-statistic 16.678 4.085 4.841 4.595 12.332 13.443 10.572 2.815 Adj R squared 0.949 0.687 0.608 0.453 0.951 0.906 0.830 0.243 Statistical significance of coefficient estimates: Not significantly different from 0 20% level or better (t-statistic > 1.282) Read the entire page →
From page 79... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 79 Aggregate Socioeconomic Variable Regression Variable BaltimoreWashington EUG LAX MDT MSO PHX PVD TUL Population Constant coefficient 27.40 2.75 11.51 4.33 7.16 12.16 -33.10 26.10 t-statistic 4.431 0.351 1.460 0.525 2.798 3.254 -0.990 4.679 Oil Price coefficient -0.001 -0.068 0.014 -0.161 0.008 -0.124 -0.319 -0.041 t-statistic -0.030 -0.689 0.328 -2.833 0.227 -1.573 -3.324 -0.946 Population coefficient -0.90 1.84 0.75 1.62 1.40 0.72 7.10 -1.48 t-statistic -1.373 1.434 0.910 1.263 3.063 1.736 1.622 -1.938 Pct HH Inc>100k coefficient 2.24 0.23 1.25 0.29 0.61 1.28 2.13 0.88 t-statistic 6.729 0.583 2.836 1.207 2.707 3.114 2.909 3.084 Adj R squared 0.952 0.698 0.599 0.467 0.962 0.909 0.866 0.362 Employment Constant coefficient 23.53 5.41 35.71 9.36 9.51 12.88 -20.62 11.31 t-statistic 2.646 0.801 3.221 1.090 4.309 3.791 -1.552 1.694 Oil Price coefficient -0.002 -0.054 0.138 -0.118 0.035 -0.124 -0.361 -0.099 t-statistic -0.041 -0.546 1.930 -2.716 0.949 -1.546 -4.516 -2.329 Employment coefficient -0.51 1.45 -1.84 0.83 0.91 0.69 5.75 0.54 t-statistic -0.519 1.271 -1.538 0.625 2.491 1.697 3.148 0.600 Pct HH Inc>100k coefficient 2.12 0.06 2.66 0.28 0.54 1.32 1.33 0.04 t-statistic 3.384 0.111 3.329 0.595 1.792 3.346 2.049 0.073 Adj R squared 0.946 0.688 0.621 0.438 0.954 0.909 0.917 0.210 Total Earnings Constant coefficient 22.43 11.75 19.80 14.83 7.67 16.26 -2.58 25.57 t-statistic 5.525 1.205 3.689 1.872 2.738 2.290 -0.200 6.917 Oil Price coefficient -0.008 0.027 0.050 -0.110 0.033 -0.044 -0.378 0.006 t-statistic -0.222 0.229 0.831 -2.199 0.912 -0.383 -4.076 0.119 Total Earnings coefficient -0.25 0.22 -0.08 -0.01 0.69 0.17 1.95 -0.86 t-statistic -0.868 0.229 -0.210 -0.014 2.611 0.332 1.841 -2.786 Pct HH Inc>100k coefficient 2.18 0.55 1.64 0.57 0.43 1.65 1.35 1.41 t-statistic 4.824 0.681 2.543 0.977 1.319 1.746 1.280 3.654 Adj R squared 0.946 0.668 0.585 0.421 0.949 0.901 0.876 0.466 Wages & Salaries Constant coefficient 22.76 10.18 25.53 11.43 4.94 16.55 -19.11 24.50 t-statistic 4.717 1.100 3.452 1.069 1.645 1.896 -1.473 3.947 Oil Price coefficient -0.008 -0.001 0.103 -0.118 0.017 -0.039 -0.354 -0.036 t-statistic -0.202 -0.008 1.359 -2.468 0.529 -0.290 -4.405 -0.690 Wages & Salaries coefficient -0.28 0.39 -0.50 0.30 1.00 0.16 3.41 -0.81 t-statistic -0.798 0.410 -0.929 0.308 3.352 0.237 3.106 -1.483 Pct HH Inc>100k coefficient 2.21 0.40 2.17 0.32 0.10 1.68 0.11 1.18 t-statistic 4.257 0.491 2.845 0.398 0.275 1.465 0.108 2.136 Adj R squared 0.946 0.669 0.593 0.422 0.958 0.901 0.905 0.308 GRP Constant coefficient 23.02 5.69 16.54 10.35 7.11 6.78 1.31 25.67 t-statistic 5.043 1.289 2.841 1.434 2.388 1.134 0.095 5.007 Oil Price coefficient -0.004 -0.125 0.012 -0.126 0.005 -0.157 -0.389 0.022 t-statistic -0.100 -1.175 0.143 -2.593 0.143 -1.835 -3.973 0.332 GRP coefficient -0.28 0.79 0.15 0.38 0.73 0.84 1.57 -0.86 t-statistic -0.900 1.885 0.367 0.606 2.646 1.984 1.441 -2.026 Pct HH Inc>100k coefficient 2.23 -0.01 1.33 0.26 0.42 0.48 1.67 1.30 t-statistic 4.578 -0.028 2.173 0.491 1.285 0.636 1.505 2.770 Adj R squared 0.947 0.716 0.587 0.430 0.950 0.907 0.856 0.374 Avg HH Income Constant coefficient 23.87 -42.55 11.16 -16.02 -2.99 38.33 16.02 24.59 t-statistic 2.623 -2.984 2.287 -0.665 -0.436 2.458 0.483 3.313 Oil Price coefficient -0.006 -0.193 -0.058 -0.156 0.020 0.117 -0.368 -0.003 t-statistic -0.107 -2.602 -0.816 -2.900 0.583 1.072 -2.764 -0.036 Avg HH Income coefficient -0.40 4.72 0.59 2.53 1.43 -1.61 0.41 -0.75 t-statistic -0.545 3.965 1.549 1.276 2.613 -1.264 0.155 -1.252 Pct HH Inc>100k coefficient 2.15 -1.48 0.81 -0.49 0.43 3.05 2.95 0.97 t-statistic 3.329 -2.588 1.471 -0.586 1.308 3.471 1.686 2.001 Adj R squared 0.946 0.814 0.624 0.461 0.951 0.905 0.822 0.290 Statistical significance of coefficient estimates: Not significantly different from 0 20% level or better (t-statistic > 1.282) 10% level or better (t-statistic > 1.645) Read the entire page →
From page 80... ... 80 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies These two tables are somewhat complex, but they present all the estimation results in an organized fashion, making it possible to read both how well the range of independent variables performed for each of the case study airports and regions and to see how coefficient estimates for a given independent variable compared across the eight airports and regions. In the remainder of this subsection we present observations on the parameter estimates reported in the tables. Read the entire page →
From page 81... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 81 The remaining case study airports, PVD and TUL, have somewhat anomalous patterns in the parameter estimates for the independent socioeconomic variables included in the baseline regressions. For PVD, the estimates are consistently higher than those for the same variables in the regression for the six case study locations. Read the entire page →
From page 82... ... 82 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies independent variable for the case study airport's annual enplanements. For each case study air port, model performance can be assessed by comparing the "forecast" values to the actual annual enplanement totals for the years 2011 to 2015. Read the entire page →
From page 83... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 83 addition of the disaggregated household income variable improves the accuracy of the alterna tive regression forecast (which uses the disaggregated income variable) compared to the baseline regression forecast. Read the entire page →
From page 84... ... 84 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 8,000,000 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 A nn ua l O & D E np la ne m en ts (P H X) Actual PHX Model 1990-2010 Medium Forecast High Forecast Low Forecast Forecast Using Actual Figure 9. Read the entire page →
From page 85... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 85 For TUL, the actual enplanement series is much more volatile than the baseline model esti mates, which is reflected in the low adjusted Rsquared scores for the TUL models. The forecast ing performance of the baseline model is also quite inaccurate. Read the entire page →
From page 86... ... 86 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies The withinsample (1990 to 2010) model estimates and the outofsample forecasts for the TUL alternative model differ in some ways from those shown above in the TUL baseline model estimates. Read the entire page →
From page 87... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 87 airport and the accuracy of the forecasts produced by the model (using the scenario assumptions about the future values of the independent variables) Read the entire page →
From page 88... ... 88 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies demand, and do not take sufficient account of the role of supply side decisions by airlines on the availability of seats and travel opportunities for prospective passengers. This may be especially significant for models of passenger enplanements at smaller airports. Read the entire page →
From page 89... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 89 variables in air passenger demand models for the Baltimore–Washington airport system provides different approaches that can extend beyond the simple case study comparisons described earlier. More Detailed Analysis of the Baltimore -- Washington Region The case study regression models used a fairly simple functional form with a limited num ber of variables in each model. Read the entire page →
From page 90... ... 90 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies To calculate this measure from the Woods & Poole data, it is necessary to make two calculations: • Determine the household income that corresponds to the 90th percentile of the cumulative distribution • Estimate the average household income for all households below the 90th percentile of the income distribution Calculating the average household income for households below the 90th percentile, rather than directly calculating the total income for the 10% of households above the 90th percentile, was necessary due to the uncertainty about the shape of the income distribution above $200,000. The percent of total personal income received by households in the top 10% by income can then be easily calculated from the average income for all households and the average income for households below the 90th percentile. Read the entire page →
From page 91... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 91 and oil price gave statistically significant coefficients for all variables. In contrast, the same model with the percent of households with incomes of $100,000 in place of the percent of total personal income received by the top 10% of households resulted in a statistically significant coefficient for the percent of households with incomes of $100,000 but the coefficients for the average household income and oil price became statistically insignificant and the coefficient for average household income had a counterintuitive sign. Read the entire page →
From page 92... ... 92 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies the projected traffic exceeded the actual traffic. Based on the resulting pattern of this ratio, year specific dummy variables were defined to account for the level of overprediction in each year. Read the entire page →
From page 93... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 93 the dummy variables can be omitted from the forecasts or used to define future scenarios in which these effects continue but at a different level of frequency. In contrast, developing models that rely on a limited set of continuous variables without considering any yearspecific effects runs the risk of the resulting models accounting for these effects by distorting the estimated coefficients of the continuous variables. Read the entire page →
From page 94... ... 94 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies declining over the period from 1990 to 2010 and as airfares decline some households and busi nesses are likely to have chosen to use the savings for other purposes rather than purchasing more air travel. The estimated coefficient for employment per capita also appears reasonable. Read the entire page →
From page 95... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 95 nearperfect fit from 2001 on, since this is forced by the choice of dummy variables. However, the fit from 1990 to 2000 (which did not include the effect of any dummy variables) Read the entire page →
From page 96... ... 96 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies after 2010. It was found that keeping their combined effect constant at their values in 2010 gave a closer fit to the actual traffic than allowing them to continue changing at the rates estimated for the period up to 2010. Read the entire page →
From page 97... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 97 discussion will begin with the results from the case study regressions that were based on models with a limited number of explanatory variables, and then turn to the more detailed analysis of the Baltimore–Washington airport system. Several largely statistical factors contribute to the case study results. Read the entire page →
From page 98... ... 98 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies the estimated regression coefficients for the other socioeconomic variables. Although it may at first appear that the simpler models that use only aggregate socioeconomic variables perform well enough without the disaggregated household income variable, this places undue emphasis on model fit over the reasonableness of the estimated values of the model coefficients. Read the entire page →
From page 99... ... Case Studies in Modeling Airport Passenger Enplanements Using Disaggregated Socioeconomic Data 99 as noted above, model goodness of fit is not the only consideration in deciding whether any particular variable adds value to a model. As found with the initial case study results, the inclusion of the disaggregated household income variable in the more detailed models of Baltimore–Washington airport system O&D enplanements led to a significant change in the parameter estimate for the aggregate household income variable that was included in both the baseline specification and the specification that also included the disaggregated variable. Read the entire page →

From page 67...

... 67 C H A P T E R 4 This chapter presents quantitative results from econometric models of annual airport passenger enplanements. The analysis compares the results from model specifications that incorporate a specific type of disaggregated socioeconomic data into a set of baseline models of airport passenger demand.