us motor gasoline consumption models
Post on 14-Sep-2014
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DESCRIPTION
The world biggest motor gasoline consumer is the United States of America with approximately 9 million barrels per day. With the global concern on world energy’s problem, there are many researches those revolve around attempts to understand and to forecast the U.S. motor gasoline consumption. This study uses applied Econometrics to showcase the major driving factors for motor gasoline consumption. The study uses the structural models and time series models to forecast future U.S. motor gasoline consumption.TRANSCRIPT
APPLIED ECONOMETRICSU.S. MOTOR GASOLINE FORECASTING
Tavatchai Engbunmeesakul
Executive Summary
The major motor gasoline consumer is the United States with approximately 500% bigger than China, the second largest consumer.
*As of December 2008
Executive Summary (Cont.)Structural models in Econometrics such as
Regression, Neural Network and CART are useful to identify major driving factors of motor gasoline consumption.
Compared States by States, factors those have strong correlation are:1.Total Highway in the States2.Number of person per household3.Number of white population4.Number of registered motor vehicles5.Number of population under 5 years old
Executive Summary (Cont.)
Time series models are more accurate to forecast the motor gasoline consumption but they can’t identify major driving factors.
EIATIME SERIES
MODELSTRUCTURAL
MODELForecasting 8.822 8.824 11.488% Error (compare to EIA) - 0% 30%
2013 motor gasoline consumption Forecasting
Result from the best model of time series and structural forecasting
Prediction Parameters:1. Number of registered of motor vehicle in each
States: Federal Highway Administration2. Number of highway in each States: Federal
Highway Administration3. Demographic and Geographic information of
people in each States: U.S. Census Bereau4. Historical liquid fuel consumption in U.S.:
Energy Information Administration
DatasetOutput:
Motor gasoline consumption acquired from Federal Highway Administration
Structural models: Multiple Linear Regression (MLR)
Model with strong correlation variables
Average age and Household income are not statisticallysignificant at 90% confidence level
household
1. White Population2. Total Highway3. Person per household4. Average Age DROP 5. Household income DROP
Model after dropping insignificant variables
Structural models: Multiple Linear Regression (MLR)
Structural models: Regression Tree (CART)
Six possible outcomes:
667,105.80 gallons1,888,626.25 gallons3,057,667 gallons3,723,813.2 gallons5,150,959.2 gallons9,707,025.75 gallons
Structural models: Neural Network (NN)
StatesPercentage
Error
Forecasted Consumption (gallons)
Actual Consumption
(gallons)
Washington 0.50% 3,241,958.48 3,225,691.00
Iow a 0.63% 2,167,030.62 2,153,512.00
Minnesota 1.94% 3,112,573.55 3,174,006.00
Georgia 2.04% 5,880,987.57 6,003,544.00
North Carolina 2.86% 5,120,374.37 5,271,088.00
Ohio 3.10% 6,659,388.35 6,459,306.00
Pennsylvania 3.41% 6,662,220.15 6,442,720.00
Wisconsin 3.41% 3,075,012.21 3,183,592.00
Arizona 4.42% 3,623,960.74 3,470,462.00
Kentucky 4.94% 2,768,948.98 2,912,990.00
Indiana 4.95% 4,072,006.34 4,283,985.00
California 4.95% 16,865,506.50 17,744,540.00
Texas 5.17% 15,166,677.73 15,992,908.00
Montana 106.99% 1,474,190.06 712,196.00
Idaho 125.98% 1,961,864.19 868,153.00
North Dakota 153.39% 1,357,410.18 535,690.00
Alaska 156.23% 1,296,995.00 506,192.00
South Dakota 158.82% 1,556,162.08 601,259.00
Delaw are 162.24% 1,272,840.08 485,373.00
Rhode Island 174.54% 1,242,801.99 452,682.00
Haw aii 187.99% 1,412,787.49 490,566.00
Vermont 191.39% 1,118,402.29 383,814.00
District of Columbia 618.98% 916,969.38 127,537.00
StatesPercentage
Error
Forecasted Consumption (gallons)
Actual Consumption
(gallons)
NN shows good performance in big States and poor performance in
small States
Structural models: Comparison
MLR Regression Tree
Neural NetworkMLR has better
performance than NN and CART
Forecasting from Structural modelsModel Formula for MLRMotor gasoline consumption = -3,513,469.25 + 8.231(number of highway) +0.598(number of white population) + 1,376,686 (number of person per household)
Significance variables from Structural models1. Total Highway2. Number of person per household3. Number of white population4. Number of registered motor vehicles5. Number of population under 5 years old
District of Columbia 433.86% (425,797.92) 127,537.00
Texas 4.34% 15,298,978.00 15,992,908.00
California 4.62% 18,564,785.73 17,744,540.00
StatesPercentage
Error
Forecasted Consumption
(gallons)
Actual Consumption
(gallons)
The model does a good job in forecasting
consumption in big states such as Texas
and California.
Time Series forecasting models: ARIMA
ARIMA(1,1,3)
For ARIMA model, the best model is ARIMA(1,1,3)
Time Series forecasting models: ARIMAARIMA(1,1,3)
17.6
18.0
18.4
18.8
19.2
19.6
2012Q1 2012Q2 2012Q3 2012Q4
CONFSTAT_LOWERCONFSTAT_UPPERCONFSTAT_INHistory Total Consumption (million bbl/day)
Pseudo within sample forecast shows that the actual consumption falls within 95% control limit.
Time Series forecasting models: ARIMA
18.0
18.4
18.8
19.2
19.6
20.0
2010 2011 2012 2013
CONF_OUTHistory Total Consumption (million bbl/day)
17.2
17.6
18.0
18.4
18.8
19.2
2013Q1 2013Q2 2013Q3 2013Q4
EIAFORE CONF_OUTUPPER LOWER
The out of sample forecast for year 2013 indicates a downward trend for
the whole year which is unlikely.
Compared with EIA’s forecast, the forecast falls outside the 95% control
limit. This means the model is not accurate enough.
Time Series forecasting models: SARIMA
SARIMA(2,1,1)(0,1,1)12
17.6
18.0
18.4
18.8
19.2
19.6
20.0
2012Q1 2012Q2 2012Q3 2012Q4
IN_LOWERIN_UPPERCONFHistory Total Consumption (million bbl/day)
For SARIMA model, the best model is
SARIMA(2,1,1)(0,1,1)12
Time Series forecasting models: ARIMASARIMA(2,1,1)(0,1,1)12
18.0
18.4
18.8
19.2
19.6
20.0
2010 2011 2012 2013
CONF_OUTHistory Total Consumption (million bbl/day)
17.5
18.0
18.5
19.0
19.5
20.0
20.5
2013Q1 2013Q2 2013Q3 2013Q4
EIA CONF_OUTOUT_LOWER OUT_UPPER
Compared with EIA’s, the out of sample forecast for year 2013 and
EIA’s forecast the same trend.
Conclusion
1. The structural model can be used to identify statistically significant factors those have strong correlation with motor gasoline consumption.
2. The time series model is more accurate than the structural model in terms of forecasting motor gasoline consumption.
3. To improve the structural model, more variables are needed to prevent from omitted variables bias.