What are the Stylized Facts that we might hope to explain in building an

econometric model of the automotive industry?

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U.S. Motor Vehicle Sales

Industry Characteristics

U. S. Industry Retail Deliveries

(millions of units) Years Ended December 31, -------------------------------------------- 1999 1998 1997 1996 1995 ----- ----- ----- ----- -----Cars……………………………… 8.7 8.2 8.3 8.6 8.6Trucks………………………… 8.7 7.8 7.2 6.9 6.5 --- --- --- --- ---Total............ 17.4 16.0 15.5 15.5 15.1 ==== ==== ==== ==== ====

Industry CharacteristicsThe profitability of vehicle sales is

affected by many factors,including the following (Ford’s

Perspective):

• Unit sales volume• The mix of vehicles and options sold• The margin of profit on each vehicle sold• The level of "incentives" (price discounts) and other marketing costs• The costs for customer warranty claims and other customer satisfaction actions• The costs for government-mandated safety, emission and fuel economy• Technology and equipment• The ability to manage costs• The ability to recover cost increases through higher prices

U.S. Car Market Shares* ----------------------------------------------------- Years Ended December 31, ----------------------------------------------------- 1999 1998 1997 1996 1995 Ford**........... 19.9% 20.4% 20.8% 21.6% 21.9% General Motors... 29.3 29.8 32.2 32.3 33.9 DaimlerChrysler*** 10.3 10.7 10.2 10.9 10.0 Toyota............ 10.2 10.6 9.9 9.3 9.2 Honda............. 9.8 10.6 10.0 9.2 8.6 Nissan............ 4.6 5.0 5.7 5.9 6.0 All Other****..... 15.9 12.9 11.2 10.8 10.4 ---- ---- ---- ---- ---- Total U.S. Car Retail Deliveries 100.0% 100.0% 100.0% 100.0% 100.0%

U.S. Truck Market Shares* ----------------------------------------------------- Years Ended December 31, ----------------------------------------------------- 1999 1998 1997 1996 1995 Ford............. 28.2% 30.2% 31.1% 31.1% 31.9% General Motors... 27.8 27.5 28.8 29.0 29.9 DaimlerChrysler*** 22.2 23.2 21.9 23.4 21.3 Toyota............ 6.7 6.3 5.7 5.3 4.5 Honda.............. 2.6 1.9 1.5 0.8 0.8 Nissan............. 3.2 2.7 3.6 3.6 3.9 All Other****...... 9.3 8.2 7.4 6.8 7.7 ---- ---- ---- ---- ---- Total U.S. Truck Retail Deliveries 100.0% 100.0% 100.0% 100.0% 100.0%

U.S. Combined Car and Truck Market Shares* ------------------------------------------------------- Years Ended December 31, ------------------------------------------------------- 1999 1998 1997 1996 1995 Ford**............ 24.1% 25.2% 25.6% 25.8% 26.2% General Motors.... 28.5 28.7 30.6 30.8 32.2 DaimlerChrysler*** 16.3 16.8 15.6 16.5 14.8 Toyota............ 8.5 8.5 7.9 7.5 7.2 Honda............. 6.2 6.3 6.0 5.5 5.3 Nissan............ 3.9 3.9 4.7 4.8 5.1 All Other****..... 12.5 10.6 9.6 9.1 9.2 ---- ---- ---- ---- ---- Total U.S. Car and Truck Retail Deliveries 100.0% 100.0% 100.0% 100.0% 100.0%

TABLE NOTES* All U.S. retail sales data are based on publicly available information from the media and trade publications.** Ford purchased Volvo Car on March 31, 1999. The figures shown here include Volvo Car on a pro forma basis for the periods prior to its acquisition by Ford. During the period from 1995 through 1998, Volvo Car represented no more than 1.2 percentage points of total market share during any one year.*** Chrysler and Daimler-Benz merged in late 1998. The figures shown here combine Chrysler and Daimler-Benz (excluding Freightliner and Sterling Heavy Trucks) on a pro forma basis for the periods prior to their merger.**** "All Other" includes primarily companies based in various European countries and in Korea. The increase in combined market share shown for "All Others" reflects primarily increases in market share for Volkswagen AG and the Korean manufacturers.

Herfindahl Index -- Based on 1999 U.S. Combined Car & Truck Market

General Motors: 0.285Ford: 0.241DaimlerChrysler: 0.163Toyota: 0.085Honda: 0.062

HI (top 5 normalized on 79%) = 2743.79When the HI exceeds 1,800 the industry is more concentrated and less rivalry exists. Firms in the same industry attempting to merge generally will be challenged by the Justice Department when the HI will exceed 1800.

Top Four Firms Concentration: 72.8%

U.S. Industry Vehicle Sales by Segment -------------------------------------------------- Years Ended December 31, -------------------------------------------------- 1999 1998 1997 1996 1995CARSSmall............... 16.1% 16.9% 18.1% 19.1% 19.6%Middle.............. 23.7 23.6 24.7 25.6 26.4Large............... 3.0 3.4 3.9 3.9 4.3Luxury.............. 7.1 7.1 6.7 6.7 6.8 ---- ---- ---- ---- ----Total U.S. Industry Car Sales.......... 49.9 51.0 53.4 55.3 57.1

TRUCKSCompact Pickup...... 6.2% 6.7 6.4 6.2 6.8Compact Bus/Van/Utility 22.1 21.1 20.0 19.0 18.0Full-Size Pickup.... 12.7 12.4 12.0 12.6 11.5Full-Size Bus/Van/Utility 6.5 6.5 6.1 5.0 4.4Medium/Heavy........ 2.6 2.3 2.1 1.9 2.2 ---- ---- ---- ---- ----Total U.S. Industry Truck Sales....... 50.1 49.0 46.6 44.7 42.9 Total U.S. Industry Vehicle Sales..... 100.0% 100.0% 100.0% 100.0% 100.0%

0098969492908886848280787674727068666462605856

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Average Age of a Vehicle

Car TRUCK

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Source: Energy Information Agency

Gasoline EfficiencyMiles Traveled Per Gallon Consumed

FormF yn = yn - yn-1 = k*(M - yn-1) yn-1

How Might Gasoline Efficiency Be Modeled?

Change in Gasoline Efficiency (GE) CGEn = GEn - GEn-1 = k*(M - GEn-1) GEn-1

where M = 22 miles per gallonand

OLS est. k = 0.004

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Estimated

Actual

Gasoline Efficiency Modeling

Change in Gasoline Efficiency (GE) CGEt = 0.004*(22 - GEt-1) GEt-1

or,GEt = [0.004*(22 - GEt-1) GEt-1] GEt-1

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Fuel Cost Per Mile Traveledvs. New Vehicle Sales

Fuel Impact Variable (Left Scale) Sales (Right Scale)

This type of variable may be more useful to explain segment

demand rather than overall demand.

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Motor Vehicle Industry Capacity UtilizationWith Decade Averages

1960s = 85.1%1970s = 80.31980s = 71.81990s = 75.9

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Motor Vehicle Industry Sales & Domestic Production

Sales Production

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Inventory-to-Sales Ratio: Motor Vehicles

Stylized Facts about U.S. Motor Vehicle Industry

Product demand is cyclical. Product is durable and average holding period has increased. Industry has inventory. Industry has high overhead cost structure with

high barriers of entry Industry structure is as an oligopoly with a shift

towards even greater concentration. Consumer demand manipulated with leasing,

incentives and other financing packages.

Basic Formulation: Motor Vehicle Sales Growth

Ordinary Least Squares MONTHLY data for 266 periods from JAN 1978 to FEB 2000

sm6(motor) = 1.56866 * sm6(mydp96[-2]) - 0.60088 * sm6(custseta01[-1]) (3.84854) (1.57140)

+ 0.46759 * sm6(relcarprice [-1]) - 1.32058 * mf1405[-1] + 7.07891 (3.86749) (3.75933) (2.90520)

Sum Sq 43613.8 Std Err 12.9268 LHS Mean 1.5324 R Sq 0.1907 R Bar Sq 0.1783 F 4,261 15.3738 D.W.( 1) 1.2616 D.W.(12) 1.7468

Note: SM6 is a percentage change formula = (((x/((1/12)*(x.1+x.2+x.3+x.4+x.5+x.6+x.7+x.8+x.9+x.10+x.11+x.12)))**(12/6.5)-1)*100.

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Actual Predicted

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Converting Back to Level Terms

Table 1 - Elasticities of Motor Vehicle Demand

Income Car Price

New Equation 1.57 -0.60

Commerce Dept.* 1.56 -1.11

* 1970-1982

Motor Vehicles Consumption Equation from QUESTUniversity of Maryland’s Econometric Model

cdmvpc$ is per capita consumption of motor vehicles in constant dollars = cdmv$/popcR is Personal consumption in real terms; pop is population cRpc = cR/popCreate ypcR, real disposable income per capitaPrice pidisaR = pidisa/cDypcR = pidisaR/popdypcR = ypcR - ypcR[1]DEFINE Interest rate * ypcR to represent credit conditions rtbXypc = .01*rtb*ypcRDEFINE Motor Vehicle wear out variable by accumulatingthe purchases of automobiles with a wear out rate of 8 percent per quarter. = @cum(y,x,s) creates y by y(t) = (1-s)*y(t-1) + x(t)Define ub08 = @cum(ub08,1.,.08)DEFINE mvWear = @cum(mvSt,cdmv$[4],.08)/(ub08*pop)

Key Feature of this formulation:

Assume that we are satisfied with our demand equation for industry output . . .

Demand = f( real disposable income, new car price, relative price of used cars to new cars, short-term interest rate).

How do you forecast the input variables?

One Answer: Treat them as EXOGENOUS VARIABLES and Forecast them SEPARATELY.

Or, endogenous some or all of them (that is, make an equation for them). This leads to a broader or more complete structure.

In our demand system, what might be included that is not from the single equation? How can we

capture more of those stylized facts?

A good starting point is to conceptualize the problem in a flow

chart.

Economic Performance

Factors

Domestic Supply(Production + Change in

Inventories)

U.S. Motor Vehicle Demand

Imports

Cost of Production(Labor, Materials, Interest Cost, Etc.)

Industry Profits

Demand, Supply and Profit Linkages

• How Might the Price Equation be Specified?

How Might the Inventory Aspect be Specified?

• How Might we pick up the Changing Shares of the Market Segments (e.g., small vs. luxury

car demand)?

• Should we Include Dummy or Qualitative Variables? For what? -- Strikes, Regulation?

Corporate Purchasing Efficiency? NAFTA production?

More Issues

One Attempt to Estimate Price Equation . . . With Lots of Room for Improvement -- SUGGESTIONS?

Equation Tries to

Capture Cost Side

Pressure: (1) Labor Cost; (2) Material Costs; (3)

Cost of Holding

Excessive Inventory.

Ordinary Least SquaresMONTHLY data for 362 periods from JAN 1970 to FEB 2000

pchya(custseta01) <--- % CHG in New Vehicle Prices

= 0.095*pchya(wrhp371_u.2)+0.179*pchya(s20s.9) (2.906) (6.844)

+ 0.236*(ki371.3/shp371.3)*mf1405.3 + 0.857 (5.188) (3.302)

Sum Sq 1774.18 Std Err 2.2262 LHS Mean 3.4760R Sq 0.4186 R Bar Sq 0.4137 F (3,358)85.9083D.W.( 1)0.1254 D.W.(12) 1.6068

WRHP371 = Average Hourly Earnings, SIC 371S20s = PPI for Intermediate Material PricesKI371 = Nominal Inventory Spending, SIC 371SHP371 = Nominal Shipments, SIC 371MF1405 = 3-Month Treasury Bill Rate

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Fitted Actual

Industry Consumption Forecasts = Extrapolated Pattern

Equation 1: Other Durable HousefurnishingsMCEDHOT = 0.02399 * mydp92 + 0.21091 * pch(custsa0) - 64.5759

2002 2001 2000 1999 1998 1997 1996 1995 1994Actual 65.6 61.8 57.9 54.6 52.3 % Change 6.2 6.7 6.0 4.4Estimate 76.9 73.5 70.3 67.6 63.9 60.3 57.0 53.7 52.3 % Change 4.7 4.6 3.9 3.0 6.1 5.6 6.2 2.7InputsConsumer Durables 788.5 769.3 750.5 742.3 725.7 668.6 626.1 589.1 561.2 % Change 2.5 2.5 1.1 2.3 8.5 6.8 6.3 5.0Real Disposable Income 5876.8 5733.4 5593.6 5487.2 5342.3 5183.1 5043.1 4906.1 4773.0 % Change 2.5 2.5 1.9 2.7 3.1 2.8 2.8 2.8Consumer Price Index 181.4 177.0 172.7 167.5 163.2 160.6 157.0 152.5 148.3 % Change 2.5 2.5 3.1 2.6 1.6 2.3 3.0 2.8

Equation 2: Sporting GoodsMCEDOWS = 0.00840 * mydp92 + 0.21450 * pch(custsa0) - 26.8218

2002 2001 2000 1999 1998 1997 1996 1995 1994Actual 19.0 17.7 16.7 15.6 14.7 % Change 7.5 6.0 7.1 6.1Estimate 23.1 21.9 20.8 19.8 18.4 17.2 16.2 15.0 14.7 % Change 5.5 5.0 5.0 4.3 6.9 6.4 7.8 2.0InputsConsumer Durables 788.5 769.3 750.5 742.3 725.7 668.6 626.1 589.1 561.2 % Change 2.5 2.5 1.1 2.3 8.5 6.8 6.3 5.0Real Disposable Income 5876.8 5733.4 5593.6 5487.2 5342.3 5183.1 5043.1 4906.1 4773.0 % Change 2.5 2.5 1.9 2.7 3.1 2.8 2.8 2.8Consumer Price Index 181.4 177.0 172.7 167.5 163.2 160.6 157.0 152.5 148.3 % Change 2.5 2.5 3.1 2.6 1.6 2.3 3.0 2.8

EXCEL Sample Format for Model Equations

Forecasting Often Requires Assumptions

Clearly show your “exogenous variables” or assumption variables for your modeling effort

in tabular form. Explain how you got those forecasts (used consensus, trend extrapolation, judgment, other forms of expert opinion, side

models, etc.).

If you are not comfortable with your exogenous variable forecasts, use scenarios. If you want to show how sensitive your model is to alternative

outcomes, use scenarios.