Econ 299 Chapter 04a.. - University of Alberta - Edmonton_ Alberta

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Econ 299 Chapter 04a.. - University of Alberta - Edmonton_ Alberta Powered By Docstoc
					          4. Models with Multiple
          Explanatory Variables
Up until this point, we have assumed that our
   dependent variable (Y) is affected by only
   ONE explanatory variable (X).

Sometimes this is the case. Example: The
  amount of dollars in your bank account is
  equal to the number of cents in your bank

Usually, this is not the case. Example: your
  mark on the midterm depends on how well
  you study and your innate intelligence.
       4. Multi Variable Examples:
Demand = f( price of good, price of substitutes,
  income, price of compliments)

Consumption = f( income, tastes, wages)

Graduation rates = f( tuition, school quality,
   student quality)

Christmas present satisfaction = f (cost, timing,
   knowledge of person, presence of card, age,
   etc.)                                      2
        4. The Partial Derivative
It is often impossible to analyze all various
     movements of explanatory variables and
     their impact on the dependent variable.

Instead, we analyze one variable’s impact,

We do this through the partial derivative.
This chapter uses the partial derivative to
   expand the topics introduced in chapter 2.
   4. Calculusand Applications
involving More than One Variable
4.1 Derivatives of Functions of More Than
  One Variable
4.2 Applications Using Partial Derivatives
4.3 Partial and Total Derivatives
4.4 Unconstrained Optimization
4.5 Constrained Optimization

          4.1 Partial Derivatives
Consider the function z=f(x,y). As this function
  takes into account 3 variables, it must be
  graphed on a 3-dimensional graph.

A partial derivative calculates the slope of a
   2-dimensional “slice” of this 3-dimensional

The partial derivative ∂z/∂x asks how x affects z
   while y is held constant (ceteris paribus).

          4.1 Partial Derivatives
In taking the partial derivative, all other variables
    are kept constant and hence treated as
    constants (the derivative of a constant is 0).
There are a variety of ways to indicate the
    partial derivative:
1) ∂y/∂x
2) ∂f(x,z)/∂x
3) fx(x,z)
Note: dy=dx is equivalent to ∂y/∂x if y=f(x); ie: if
    y only has x as an explanatory variable.
Therefore often these are used interchangeably   6
    in economic shorthand
          4.1 Partial Derivatives
Let y = 2x2+3xz+8z2

∂y/ ∂x = 4x+3z+0
∂y/ ∂z = 0+3x+16z
            (0’s are dropped)
Let y = xln(zx)
∂ y/ ∂ x = ln(zx) + zx/zx
       = ln(zx) + 1
∂ y/ ∂ z = x(1/zx)x
         4.1 Partial Derivatives
Let y = 3x2z+xz3-3z/x2

∂ y/ ∂ z=3x2+3xz2-3/x2

∂ y/ ∂ x=6xz+z3+6z/x3

Try these:

    4.1.1 Higher Partial Derivatives
Higher order partial derivates are evaluated
     exactly like normal higher order derivatives.
It is important, however, to note what variable to
     differentiate with respect to:

From before:
Let y = 3x2z+xz3-3z/x2
∂ y/ ∂ z=3x2+3xz2-3/x2

∂ 2y/ ∂ z2=6xz
∂ 2y/ ∂ z ∂ x=6x+3z2+6/x3                     9
    4.1.1 Higher Partial Derivatives
From before:
Let y = 3x2z+xz3-3z/x2
∂ y/ ∂ x=6xz+z3+6z/x3

∂ 2y/ ∂ x2=6-18z/x4
∂ 2y/ ∂ x ∂ z=6x+3z2+6/x3

Notice that d2y/dxdz=d2y/dzdx
This is reflected by YOUNG’S THEOREM, order
   of differentiation doesn’t matter for higher
   order partial derivatives
4.2 Applications using Partial Derivatives
As many real-world situations involve many
   variables, Partial Derivatives can be used to
   analyze these situations, using tools

 Interpreting coefficients
 Partial Elasticities
 Marginal Products

      4.2.1 Interpreting Coefficients
Given a function a=f(b,c,d), the dependent
     variable a is determined by a variety of
     explanatory variables b, c, and d.
If all dependent variables change at once, it is
     hard to determine if one dependent variables
     has a positive or negative effect on a.
A partial derivative, such as ∂ a/ ∂ c, asks how
     one explanatory variable (c), affects the
     dependent variable, a, HOLDING ALL
     CONSTANT (ceteris paribus)
      4.2.1 Interpreting Coefficients
A second derivative with respect to the same
    variable discusses curvature.
A second cross partial derivative asks how the
    impact of one explanatory variable changes
    as another explanatory variable changes.
Ie: If Happiness = f(food, tv),
∂ 2h/ ∂ f ∂tv asks how watching more tv affects
    food’s effect on happiness (or how food
    affects tv’s effect on happiness). For
    example, watching TV may not increase
    happiness if someone is starving.
           4.2.1 Corn Example
Consider the following formula for corn

Corn = 500+100Rain-Rain2+50Scare*Fertilizer
Corn = bushels of corn
Rain = centimeters of rain
Scare=number of scarecrows
Fertilizer = tonnes of fertilizer

Explain this formula
           4.2.1 Corny Example
Intercept = 500
    -if it doesn’t rain, there are no scarecrows
    and no fertilizer, the farmer will harvest 500
    -positive until rain=50, then negative
    -more rain increases the harvest at a
    decreasing rate until rain hits 50cm, then
    additional rain decreases the harvest at an
    increasing rate
(∂ 2corn/ ∂ rain2=-2<0, concave)
            4.2.1 Corny Example
    -More scarecrows will increase the harvest 50
    for every tonne of fertilizer
    -if no fertilizer is used, scarecrows are useless
(∂ 2corn/ ∂ scare2=0, straight line, no curvature)
    -More fertilizer will increase the harvest 50 for
    every scarecrow
    -if no scarecrows are used, fertilizer is useless
(∂ 2corn/ ∂ fertilizer2=0, straight line, no curvature)
         4.2.1 Demand Example
Consider the demand formula:
Q = β1 + β2 Pown + β3 Psub + β4 INC
Where quantity demanded depends on a
   product’s own price, price of substitutes, and
Here ∂ Q/ ∂ Pown= β2 = the impact on quantity
   when the product’s price changes
Here ∂ Q/ ∂ Psub= β3 = the impact on quantity
   when the substitute’s price changes
Here ∂ Q/ ∂ INC= β4 = the impact on quantity
   when income changes
         4.2.3 Partial Elasticities
Furthermore, partial elasticities can also be
   calculated using partial derivatives:
Own-Price Elasticity = ∂ Q/ ∂ Pown(Pown/Q)
                      = β2(Pown/Q)
Cross-Price Elasticity = ∂ Q/ ∂ Psub(Psub/Q)
                       = β3(Psub/Q)
Income Elasticity = ∂ Q/ ∂ INC(INC/Q)
                      = β4(INC/Q)

4.2.2 Cobb-Douglas Production Function
A favorite function of economists is the Cobb-
    Douglas Production Function of the form
Where L=labour, K=Capital, and O=Other
    (education, technology, government, etc.)
This is an attractive function because if b+c+f=1,
    the demand function is homogeneous of
    degree 1. (Doubling all inputs doubles
    outputs…a happy concept)

    4.2.2 Cobb-Douglas University
Consider a production function for university


Where L=Labour, K=Capital (excluding
  computes), and C=Computers
More simply, labour can reflect professors,
  capital can reflect classrooms, and
  computers can reflect computers.
    4.2.2 Cobb-Douglas University
Finding partial derivatives:
∂ Q/ ∂ L =abLb-1KcCf
            =b* average product of labour
-in other words, adding an additional professor
    will contribute a fraction of the average
    product of each current professor
-this partial elasticity gives us the MARGINAL
    PRODUCT of labour                         21
    4.2.2 Cobb-Douglas Professors
For example, if 20 professors are employed by
   the department, and 500 students graduate
   yearly, and b=0.5:

∂ Q/ ∂ L   =0.5(500/20)

Ie: Hiring another professor will graduate 12.5
    more students. The marginal product of
    professors is 12.5
         4.2.2 Marginal Product
Consider the function Q=f(L,K,O)
The partial derivative reveals the MARGINAL
   PRODUCT of a factor, or incremental effect
   on output that a factor can have when all
   other factors are held constant.
∂ Q/ ∂ L=Marginal Product of Labour (MPL)
∂ Q/ ∂ K=Marginal Product of Capital (MPK)
∂ Q/ ∂ L=Marginal Product of Other (MPO)

    4.2.2 Cobb-Douglas Elasticities
Since the “Professor Elasticity of Demand” (or
   PED…an arbitrary name) is expressed:
PED = ∂ Q/ ∂ L(L/Q)

We can find that
PED =b(Q/L)(L/Q)

The partial elasticity with respect to labour is b.
   Find the remaining derivatives and
   elasticities.                               24
          4.2.2 Logs and Cobbs
We can highlight elasticities easier by using

We now find that:
PED= ∂ ln(Q)/ ∂ ln(L)=b

Therefore using logs, elasticities more apparent.

         4.2.2 Logs and Demand

Reverting to a log-log demand example:
Ln(Qdx)=ln(β1) +β2 ln(Px)+ β3 ln(Py)+ β4 ln(I)

We now find that:
Own Price Elasticity = β2
Cross-Price Elasticity = β3
Income Elasticity = β4

                  4.2.2 ilogs
Considering the demand for the ipad, assume:
Ln(Qdipad)=2.7 -1ln(Pipad)+4 ln(Pnetbook)+0.1 ln(I)

We now find that:
Own Price Elasticity = -1, demand is unit elastic
Cross-Price Elasticity = β3, a 1% increase in the
   price of netbooks causes a 4% increase in
   quantity demanded of ipads
Income Elasticity = 0.1, a 1% increase in income
   causes a 0.1% increase in quantity
   demanded for ipads                        27