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What are the Stylized Facts that we might hope to explain in building an econometric model of the automotive industry? Industry Characteristics U. S. Industry Retail Deliveries (millions of units) Years Ended December 31,

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What are the Stylized Facts that we might hope to explain in building an econometric model of the automotive industry?

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What are the Stylized Facts that we might hope to explain in building an econometric model of the automotive industry?


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 8.6

Trucks………………………… 8.7 7.8 7.2 6.9 6.5

--- --- --- --- ---

Total............ 17.4 16.0 15.5 15.5 15.1

==== ==== ==== ==== ====


Industry Characteristics

  • The 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%) = 2743.79

When 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 1995

CARS

Small............... 16.1% 16.9% 18.1% 19.1% 19.6%

Middle.............. 23.7 23.6 24.7 25.6 26.4

Large............... 3.0 3.4 3.9 3.9 4.3

Luxury.............. 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

TRUCKS

Compact Pickup...... 6.2% 6.7 6.4 6.2 6.8

Compact Bus/Van/Utility 22.1 21.1 20.0 19.0 18.0

Full-Size Pickup.... 12.7 12.4 12.0 12.6 11.5

Full-Size Bus/Van/Utility 6.5 6.5 6.1 5.0 4.4

Medium/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%


How Might Gasoline Efficiency Be Modeled?

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

Change in Gasoline Efficiency (GE)

DGEn = GEn - GEn-1 = k*(M - GEn-1)GEn-1 where M = 22 miles per gallon

and

OLS est. k = 0.004


Change in Gasoline Efficiency (GE)

DGEt = 0.004*(22 - GEt-1)GEt-1 or,

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


This type of variable may be more useful to explain segment demand rather than overall demand.


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


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.


Converting Back to Level Terms


Table 1 - Elasticities of Motor Vehicle Demand

IncomeCar Price

New Equation 1.57 -0.60

Commerce Dept.* 1.56 -1.11

* 1970-1982


Motor Vehicles Consumption Equation from QUEST

University of Maryland’s Econometric Model

cdmvpc$ is per capita consumption of motor vehicles in constant dollars

= cdmv$/pop

cR is Personal consumption in real terms; pop is population

cRpc = cR/pop

Create ypcR, real disposable income per capita

Price pidisaR = pidisa/cD

ypcR = pidisaR/pop

dypcR = ypcR - ypcR[1]

DEFINE Interest rate * ypcR to represent credit conditions

rtbXypc = .01*rtb*ypcR

DEFINE Motor Vehicle wear out variable by accumulating

the 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 PerformanceFactors

Domestic Supply(Production + Change in Inventories)

U.S. Motor Vehicle Demand

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

Imports

Industry Profits

Demand, Supply and Profit Linkages


More Issues

  • 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?


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

Ordinary Least Squares

MONTHLY 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 3.4760

R Sq 0.4186 R Bar Sq 0.4137 F (3,358)85.9083

D.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

Equation Tries to Capture Cost Side Pressure: (1) Labor Cost; (2) Material Costs; (3) Cost of Holding Excessive Inventory.


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.


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