# Presentation: 226802

## Gasoline Demand Elasticities 2007 EIA Energy Outlook, Modeling and Data Conference 

**Carol Dahl, Professor and Director**

## Coming Attractions-Gasoline Demand Survey Update

**"Find a relationship that survives long enough to be useful."  Hendry and Juselius (2001)**
**	****	****1. Study in Context**
**	****	****2. What are  Elasticities?  **
**	****	****3. Why are They Important?**
**	****	****4. Demand Methodologies**
**	****	****5. Summary of Survey Work to Date**
**	****	****6. Demand for Transportation Fuels **
**	****	****	****Nasser Al Dossary**

## Scope of Study - Critical Review All Econometric Demand Elasticities

**All countries - all products**
**	****>1500 studies**
**Levels of aggregation**
**	****E, C, El, O, Ng, Biomass**
**	****O – tr & ntr**
**	****O – G, K, D, Fo-lt, Fo-hv**
**	****Sector r, c, i, e, tr, ii ****	**
**On line data base**

## Demand Elasticities

**D**

**P**

**Q**

## Demand Elasticities – What Are They?

**How consumption responds to a variety of variables?****    **
**e.g. price elasticity**
**	****Consumption response to prices?**

## Elasticities –Why Important?

## How Easily a Market Can Respond to Disruption,

## Renewable phase in

**Renewable phase in**

## Responses to the Business Cycle

## Estimation Issues: Qi = o + PPi + YYi 

**	****Is P exogenous?**
**	****Is equation identified? **
**	****Is equation correctly specified?**
**	****	****correct variables, correct functional form**
**	****	****statistical tests**
**	****	****recent – cubic splines, nonparametric****	**
**	****Is relationship stable and symmetric?**
**	****	****statistical tests**
**	****Aggregation Issues**
**	**

**Notes:**
Conventional Wisdom – betaP = 0

## Measuring Short Run and Long Run

**1.  Lags**
**2.  Vehicle Stock and Use Choice**
**3.  Data: Cross Sections versus Time Series**

## Short Run and Long Run Using Lags

**Time Series Issues**
**	****Variables are non-stationary**
**	****	****spurious regression**
**	****	****lose consistency**
**	****	****	****lagged endogenous**
**	**

## Non-stationarity

**Three causes of non-stationarity**
**	****y****t **** = ********               +   y****t-1**** +        ********t + ********t**
**	****      ****drift        random     time**
**                                  ****walk         trend**
**First difference – make stationary I(1)**
**	****run**
**	****may lose variation – lower efficiency**

## Non-stationarity

**All integrated of order 1 or same order**
**	****linear combination I(0) stationary**
**	****	****cointegrated**
**	****	****OLS consistent (super) **
**	****	****	****measure long run relationship **
**	****	****	****error correction model**
**	****	****	****	****short run deviations**

## Endogenous RHS

**	**
**	****biased and inconsistent**
**	****time series – all could be endogenous**
**	****system approach – vector autoregression**
**	****cointegration may save the day**
**	****	****may be > 1 cointegrating relationships**
**	****	**

## Stock Changes (LR) Utilization (SR)

**Gallons = Miles/(Miles/Gallon)**
**log (Gallons) = log(Miles) – log(Miles/Gallon)**
_****__**log (Gallons)**_** = **_****__** log(Miles)**_** – **_****__**log(Miles/Gallon)**_
******log (Price)         ********log (Price)    ********log (Price)**
**Rebound effect (Cafe standards)**
**	****MPG ******** Cost per mile******** Miles ******** **
**	****cancelling some of  efficiency gains**
**Greening, Greene, and Difiglio (2000) – 10% sr and up to 30% long run**

## Cross Section (LR) - Time Series (SR)

**Cross section **
**	****adjusted to large differences**
**    ****price and income**
**Time series **
**	****how much adjustment in periodicity of data**

## Slide 18

## Espy 96’ Meta-analysis on Gasoline Elasticities

**Determine effects on gasoline demand elasticities by**
**	****	****Functional form **
**	****	****Lag structure**
**	****	****Estimation technique **
**	****	****Other study differences**
**Analysis on US studies without household panel data**

## Price Elasticity Results of Espy 96’

**Explains 1/3 to 1/2 variation in long run elasticities**
**No significant from non-price & income variables**
**Static & dynamic no significanty difference long run estimates **
**Per capita or per household demand less elastic than total demand or demand per vehicle **

## Price Elasticity Results of Espy 96’

**No difference in LR estimates for monthly, quarterly, or annual data **
**No difference in linear or log linear models**
**No difference national or state cross section time series **
**Cross section time series of non-US find 50% more elastic price response  **

## Price Elasticity Results of Espy 96’

**Random coefficient techniques < elastic price response**
**Demand was more price elastic prior to 1974 **
**Other**
**	****Hughes, Knittel, and Sperling (2006           **
**         – ****monthly, static                **
**                                ****Price       Income****	**
**	****	****1/75-1/80      -0.30            0.47        **
**	****	****3/01-3/06      -0.04            0.53**

## Income Elasticity Results Espy 96

**Vehicle ownership lowers the elasticity **
**Non-price and income variables do not lower elasticity  **
**Linear models are not different than non-linear**
**Monthly and quarterly data find a smaller long run income response**

## Income Elasticity Results Espy 96

**Higher income elasticity (10% significance level)  **
**	****Per capita **
**	****Per household**
**	****Per vehicle**
**Income elasticity less elastic before 1974 (10%) **
**No difference national and cross section time series of states  **
**Random coefficient find more elastic income response **

## Global Demand for Transportation FuelsNasser Aldossary (PhD Candidate)

**Scope: ****	****	****Estimate elasticities (price, income)  ****	****	****	****45 countries.**
**Methodology: ****	****Building an econometrics model ****	****	****	****for each country.**
**Fuels of focus:****	****Gasoline and Diesel. **
**Sample years:****	****1978-2004**
**Total consumption of selected countries:**
**Gasoline 88%  and 85% Diesel of world’s consumption**

## Sum Up

**Numerous Surveys**
**	****conventional wisdom **
**	**** ****	****little or no price response**
**	****	****	****all surveys conclude there is****	**
**	****	****	****most come to numerical conclusion****	**
**	****	****income near 1****	**
**	****	****	****not so inconsistent with surveys**
**	****	****my best guesses****	****	****	****	****	**** **
**	****	****	********p****(annual -0.2) ********p****(lr -0.6 to -0.8) ****	****	****                      **
**	****	****	********p****(annual 0.3 to 0.5) ********p****(lr <1) ****	****	****                      **