us motor gasoline consumption models

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APPLIED ECONOMETRICS U.S. MOTOR GASOLINE FORECASTING Tavatchai Engbunmeesakul

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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

Page 1: US motor gasoline consumption models

APPLIED ECONOMETRICSU.S. MOTOR GASOLINE FORECASTING

Tavatchai Engbunmeesakul

Page 2: US motor gasoline consumption models

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

Page 3: US motor gasoline consumption models

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

Page 4: US motor gasoline consumption models

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

Page 5: US motor gasoline consumption models

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

Page 6: US motor gasoline consumption models

Structural models: Multiple Linear Regression (MLR)

Model with strong correlation variables

Average age and Household income are not statisticallysignificant at 90% confidence level

household

Page 7: US motor gasoline consumption models

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)

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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

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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

Page 10: US motor gasoline consumption models

Structural models: Comparison

MLR Regression Tree

Neural NetworkMLR has better

performance than NN and CART

Page 11: US motor gasoline consumption models

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.

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Time Series forecasting models: ARIMA

ARIMA(1,1,3)

For ARIMA model, the best model is ARIMA(1,1,3)

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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.

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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.

Page 15: US motor gasoline consumption models

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

Page 16: US motor gasoline consumption models

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.

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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.