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Gasoline Prices, Vehicle Spending and National Employment: Vector Error Correction Estimates Implying a Structurally Adapting, Integrated System, 1949-2011.

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Presentation on theme: "Gasoline Prices, Vehicle Spending and National Employment: Vector Error Correction Estimates Implying a Structurally Adapting, Integrated System, 1949-2011."— Presentation transcript:

1 Gasoline Prices, Vehicle Spending and National Employment: Vector Error Correction Estimates Implying a Structurally Adapting, Integrated System, By: D. J. Santini and D. A. Poyer* Presented at: 32nd U.S. and International Associations for Energy Economics North American Conference Anchorage, Alaska: July 28-31, 2013 Sponsor: J. Ward, DOE Vehicle Technologies Program * Argonne Consultant and Morehouse College Professor The submitted manuscript has been created by Argonne National Laboratory, a U.S. Department of Energy laboratory managed by UChicago Argonne, LLC, under Contract No. DE-AC02-06CH The U.S. Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government

2 Research Problem  Assess the dynamic relationship between real gasoline prices and macroeconomic activity  Assess the direct effect of real gasoline prices on real motor vehicle expenditures and employment  Assess real motor vehicle expenditures on employment  Assess structural changes in the dynamic relationship in real gasoline prices, real motor vehicle expenditures, and total employment over the post World War II period (between 1949q2 to 1987q4 and 1988q1 to 2011q3) 2 Method(s)  Vector Error Correction econometric model,  Sub-vs. full-period tests for cointegration, structural change  Consideration of both directions in bi-directional dynamic VECM  Theoretical interpretation in context of selected literature

3 Interpretation Results is Being Prepared in the Context of the Following Literature: Author(s)Key arguments, observations Hamilton (JPE, 1983; Brookings, 2009; others Exogenous oil price shocks cause post WWII macro declines ~ one year later, acting via the vehicle technology sector Kilian (JEL, 2008; AER, 2009; U.M. paper, 2011) Gasoline vs. oil discussion, gasoline prices are treated endogenously and cause vehicle sector shifts to imports & cars (vs. trucks) Ramey & Vine, NBER Working Paper, 2010 Estimates gas price effects on spending, size mix & capacity utilization of motor vehicles, argues probable macro effect Santini, IJES, 1985 “Energy Squeeze” Model Essentially a descriptive model (flow chart) & simple statistics, arguing real consumer energy prices can rise as demand declines Santini, ANL/ES-153 Report, 1987 Estimates annual gasoline price and spending on next year’s GDP & unemployment, ; tests with money work. Santini, Trans. Research A Presents evidence that fuel technology switches in the transport sector cause major macro transition problems Santini, Ch. on Capital Spending After Energy Price Shock. Moroney ed. Book, JAI Press, 1992 Purely theoretical model. … long-run model … spanning a long- lasting, but not permanent energy price shift far greater than any capital price shift. Capital & transport energy substitute. More capital used if energy price high. Santini & Poyer, 2009 Rejected AEJ submission Vector Autoregression - Real Motor Vehicle Output “Granger Causes” Other GDP & is Cointegrated. 3

4 Data Used and Transformations Made for Model Variables 4 MnemonicDefinitionSource Unit of Measure a LNS Q Seasonally Adjusted Employment Level Bureau of Labor Statistics Thousands RMVE= (DMOTRC1/DMOTRG3)100 Real motor vehicle & parts expenditures Bureau of Economic Analysis Billions 2005 $ realgasprice= (DGOERG3/DPCERG3) Real price of gasoline & other energy goods b Bureau of Economic Analysis Index (2005=1) a Values are quarterly. b Ratio of the price indices for real gasoline and other goods, and personal consumption expenditures.

5 Descriptive Statistics for Model Variables, by Estimation Period 5 VariableObsMeanStd. Dev.MinMax 1949q2 to 2011q3 LNS Q RMVE259$184$119$27$426 realgasprice q2 to 1987q4 LNS Q RMVE164$106$57$27$268 realgasprice q1 to 2011q3 LNS Q RMVE95$318$66$210$426 realgasprice

6 The Vector Error Correction Model (VECM) Accounts for Past Impulses & Past Levels to Estimate Current Impulses Source: Smith, principles of Econometrics, 4 th ed. Ch, 12, pp With regard to levels … when e t = y t - β 1 - β 2 x t is a stationary I(0) process In this case y t and x t are said to be cointegrated Cointegration implies that y t and x t share similar stochastic trends, and, since the difference e t is stationary, they never diverge too far from each other (Smith, p. 59) If y and x are cointegrated, it means that there is a long-run relationship between them … which can be written in the form:

7 A Vector Error Correction Model (VECM) Includes Long-run Levels & Short-Run Error Correction Parts  Series … which can be made stationary [I(0)] by taking the first difference, are said to be integrated of order one, and denoted as I(1)  If variables are … I(1) and cointegrated, we can estimate a regression relationship between the levels of those variables without fear of encountering a spurious regression. The equation –Is called an error correction equation –This is a very popular model because: It allows for an underlying or fundamental link between variables (the long-run relationship) It allows for short-run adjustments (i.e. changes) between variables, including adjustments to achieve the cointegrating relationship 7 Source: Hill, et al, Principles of Econometrics, 4 th ed. Ch, 12, pp

8 Real Gasoline Price First Difference Changes Have Increased in Volatility Throughout the Full Sample Period, but Appear Stationary [ I(0)] 8

9 Employment First Differences Appear Stationary [I(0)]. Variation Dropped Sharply During the “Great Moderation” – Which Ended Badly. 9 >

10 Real Motor Vehicle Expenditure First Difference Changes Were Least in the Great Moderation & Appear Stationary [ I(0)] for the Full Period 10

11 The Levels of Real Gasoline Prices Were Dropping and Low at 1 st -3 rd Longest Times Between Recessions 11 1 st Longest 3 rd Longest 2 nd Longest Great Moderation

12 Statistical tests estimated the unrestricted and semi-restricted models to be nearly identical and better than the two restricted models. Semi- restricted results are presented hereafter.  Four experiments were conducted & five models estimated: –Restricted: Same structure over the 1949q2 to 2011q3 period (one- period model) –Unrestricted: normalizing on employment and testing for cointegration with motor vehicle spending and real gasoline price – over the 1949q2 to 1987q4 and 1988q1 to 2011q3 periods (all coefficients may vary from one period to the other - two-period test) –Semi-restricted: same cointegrating structure ( β ) for the two-period models, but unrestricted difference coefficients (δ) for the 1949q2 to 1987q4 versus the 1988q1 to 2011q3 periods. α also allowed to vary. –ϋber-restricted: same cointegrating structure from the two-period model imposed on the restricted model 12

13 When Separately Estimated, the β Values for Both Periods: the Null Hypothesis of No Difference Could Not be Rejected. Cointegration Terms Coefficients Variables & direction (if applicable) 1 st period Coefficients 10%, by period 2 nd period β1 β1 Constant N/A β2 β2 N1.0N/A1.0 β3 β3 RMVE-0.38 a Yes,1: Yes, a β4β4 Gas-0.19 a Yes,1: Yes, a αNαN N a Yes,1: No, α RMVE RMVE0.769 a Yes,1: Yes, a α Gas Gas0.150No,1: Yes, a 13

14 The Short-run Effect of Real Gasoline Price Level on Real Vehicle Spending Change is Consistently Negative. Effects on Employment are Weak & Inconsistent Δ estimate of Gas Level on: α (- β 4 ) = ? Significant ? α (- β 4 ) Significant ? N -(-0.043)[-( -.19)] =.008Yes-(0.011)[-( -.21)] = -.002No RMVE -(0.769)[-(-0.19)] = -.146Yes-(0.121)[-(-0.21)] = -.025Yes 14

15 Within Year Timing of Reactions to Real Gas Price Impulses is Critical to Interpretation. RMVE Effects are Immediate 15 Impulse Response Function, Full Equation

16 Real Gasoline Price Impulse (Increase) Causes an Employment Decline With a Delay of Nearly a Year 16 Impulse Response Function, Full Equation

17 Employment Response to a Motor Vehicle Spending Impulse is Fairly Prompt, Mostly in < 1 Year 17 Impulse Response Function, Full Equation

18 If Employment Had Dropped Immediately After a Gasoline Price Impulse, It Could Have Been the Cause of “in-Year” Motor Vehicle Spending Decline (But it Didn’t) 18 Impulse Response Function, Full Equation

19 More Jobs Apparently Lead to More Spending on Vehicles and Fuel, Pushing Fuel Demand & Price Up 19 Impulse Response Function, Full Equation

20 So Far, Signs of Qrtrly “IRFs” Were the Same in Both Periods, Though Sizes Differed. However, for Real Motor Vehicle Spending on Gasoline Price, Signs Change 20 Impulse Response Function, Full Equation

21 The Period Ends With 2 Decades of Sharp Reduction in Fuel Use Per Vehicle. Reduced Demand for Gasoline Should Lower Gasoline Price (and Did). 21 Comparison of new vehicle on-road fuel use to fleet fuel use (per vehicle),

22 Conclusion: Motor Vehicle Spending Remains as Important to the Economy in as in Impulse Response Function, Full Equation

23 Motor Vehicle Spending’s Response to Gasoline Prices Was Higher in than in Quarterly Impulse Response Function, Real Consumer Motor Vehicle Spending to Real Gasoline Price Santini and Poyer, 2013, VECM Monthly Response of Real Consumer Motor Vehicle Spending to a Shock to Real Gasoline Price Ramey and Vine, 2010 © VAR Note: The real gasoline prices are defined differently. Ramey & Vine multiply by energy share fraction, << 1.0.

24 High Gasoline Price Levels Lead to a Shift in Demand to Small Cars &/or Cars vs. Light Trucks 24 Ramey and Vine, 2010 © Response of Real Consumption by Expenditure Item with 1 and 2 Standard Error Bands Kilian, JEL, 2008

25 Conclusions  Real gasoline price, motor vehicle spending and employment are cointegrated identically for the full sample  Error correction coefficients & adjustment parameters are collectively significantly different across subperiods  The specific sectoral shifts hypothesis advocated by J. Hamilton, which focuses on motor vehicles as the key oil price shock transmission path, is supported  Kilian’s arguments that gasoline may be as/more important than oil, and that gasoline prices are endogenous, is supported  Patterns of predicted impulse response of motor vehicle spending to a gasoline price shock are consistent with Ramey and Vine’s 2010 estimate.  Kilian’s argument that it is important to be able to produce small cars domestically to mitigate gasoline price shock impacts is supported. CAFE is credited.  Motor vehicle spending remains as important in as in  fleet efficiency gains, via CAFE regulation, endogenously pushed gasoline prices down, enabling a shift to profitable large domestic vehicles, contributing to the Great Moderation. Current high real gasoline prices, which restrict the recovery, probably result from inadequate gains in fleet fuel efficiency to date.  Dramatic variations in the domestic output of motor vehicles are a fundamental cause of Post WWII isolated recessions, the double dip recessions and Great Recession (also considers 2008 Santini and Poyer estimates). 25

26 Conclusions (continued) Causes of fluctuation in domestic motor vehicle output are certainly not confined to gasoline price shocks and include:  Purchase delay effects, including: –Gruenspecht effects (various technology standards) –Bernanke and Pindyck fuel price uncertainty effects –Santini capital for energy substitution effects  Depreciation allowances  Subsidies  Taxes and fees  Vehicle and network performance capabilities  Interest rates  Credit rating systems  Strikes  Natural disasters  Income  Employment 26

27 Conclusions (continued) Causes of fluctuation in domestic motor vehicle output (continued) 27 Emergence of delayed evidence on vehicle reliability and safety Fuel reformulation or switching Fueling infrastructure technology change Vehicle operating network infrastructure funding and design Enabling technology feasibility shocks (such as battery technology, fuel cells, steam engines, electric street railways, internal combustion engines)

28 Selected References (related to slide 3 & VECM):  Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press, Princeton, NJ.  Hill, R. Carter, William E. Griffiths, and Guay C. Lim, 2011, Principles of Econometrics, John Wiley & Sons, Hoboken, NJ.  Hamilton, J.D. (1983). “Oil and the macroeconomy since World War II.” J. of Political Economy, 91,  Hamilton, J.D. (1996). Analysis of the Transmission of Oil Price Shocks through the Macroeconomy. Paper presented at the DOE Conference, “International Energy Security: Economic Vulnerability to Oil Price Shocks, Washington, D.C., October.  Kilian, L. (2008). "The Economic Effects of Energy Price Shocks." Journal of Economic Literature, 46(4):  Ramey, V.A. and D.J. Vine (2010). Oil, Automobiles and the U.S. Economy: How Much Have Things Really Changed? National Bureau of Economic Research Working Paper Cambridge, MA  Santini, D.J. (1985). The Energy Squeeze Model: Energy Price Dynamics in U.S. Business Cycles. International Journal of Energy Systems, 5 (1):  Santini, D.J. (1989). Interactions among transportation fuel substitution, vehicle quantity growth, and national economic growth. Transportation Research A, 23(3), (May).  Santini, D.J. (1992), Energy and the Macroeconomy: Capital Spending After an Energy Cost Shock, Ch. 3 of Advances in the Economics of Energy and Resources, Vol. 7, Energy, Growth and the Environment, J. Moroney, ed., J.A.I. Press, Greenwich, CT, pp. 101–124 (1992).  Santini, D.J., and D. Poyer (2009). Motor Vehicle Output and Other GDP. The 66 th International Atlantic Economic Conference, Montreal, Canada (Oct. 9-12, 2008). 28

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