Appendix A

40
Adapted from Vera Tabakova’s notes ECON 4550 Econometrics Memorial University of Newfoundland

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ECON 4550 Econometrics Memorial University of Newfoundland. Review of Math Essentials. Appendix A. Adapted from Vera Tabakova’s notes. Appendix A: Review of Math Essentials. A.1 Summation A.2 Some Basics A.3 Linear Relationships A.4 Nonlinear Relationships. A.1 Summation. - PowerPoint PPT Presentation

Transcript of Appendix A

Page 1: Appendix A

Adapted from Vera Tabakova’s notes

ECON 4550Econometrics Memorial University of Newfoundland

Page 2: Appendix A

A.1 Summation

A.2 Some Basics

A.3 Linear Relationships

A.4 Nonlinear Relationships

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Σ is the capital Greek letter sigma, and means “the sum of.”

The letter i is called the index of summation. This letter is arbitrary and may also appear as t, j, or k.

The expression is read “the sum of the terms xi, from i equal one to n.”

The numbers 1 and n are the lower limit and upper limit of summation.

1 21

n

i ni

x x x x

1

n

ii

x

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Rules of summation operation:

1 21

n

i ni

x x x x

1 1

n n

i ii i

ax a x

1

n

i

a a a a na

1 1 1

( )n n n

i i i ii i i

x y x y

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1 1 1

1 1 2

1 1 1 1 1 1

( )

( ) 0

n n n

i i i ii i i

n

ii n

n n n n n n

i i i i ii i i i i i

ax by a x b y

xx x x

xn n

x x x x x nx x x

This is the mean (or average) of n values of X

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1 21

( ) ( ) ( ) ( )

( ) ("Sum over all values of the index ")

( ) ("Sum over all possible values of ")

n

i ni

ii

x

f x f x f x f x

f x i

f x X

Often you will see an abbreviated

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1 1 1 1

1 21 1 1

1 1 1 1

( , ) ( )

( , ) [ ( , ) ( , ) ( , )]

( , ) ( , )

m n m n

i j i ji j i j

m n m

i j i i i ni j i

m n n m

i j i ji j j i

f x y x y

f x y f x y f x y f x y

f x y f x y

Double summation: If f(x,y)=x+y

The order of summation does notmatter…

Work from the innermostIndex outwards. Example:Set i=1 first and sum over all values of j, then set i=2 etc…

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Product Operator

i 1

n

x i x 1 x 2 x 3 . . .x n

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A.2.1Numbers

Integers are the whole numbers, 0, ±1, ±2, ±3, …. .

Rational numbers can be written as a/b, where a and b are integers, and b ≠ 0.

The real numbers can be represented by points on a line. There are an uncountable number of real numbers and they are not all rational. Numbers such as are said to be irrational since they cannot be expressed as ratios, and have only decimal representations. Numbers like are not real numbers.

3.1415927 and 2

2

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The absolute value of a number is denoted . It is the positive part

of the number, so that

Basic rules about Inequalities:

a

3 3 and 3 3.

If , then

if 0If , then

if 0

If and , then

a b a c b c

ac bc ca b

ac bc c

a b b c a c

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A.2.2Exponents

(n terms) if n is a positive integer

x0 = 1 if x ≠ 0. 00 is not defined

Rules for working with exponents, assuming x and y are real, m and n are integers, and a and b are rational:

nx xx x

1 if 0n

nx x

x

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1/

/ 1/

/ 1/

n n

mm n n

mm n n

a b a b

a a

a

x x

x x

x x

x x x

x x

y y

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5 7

5 7

2

510,000 .00000034 (5.1 10 ) (3.4 10 )

(5.1 3.4) (10 10 )

17.34 10

.1734

5 512

7 7

510,000 5.1 10 5.1 101.5 10

.00000034 3.4 10 3.4 10

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ln ln

Rules:

ln( ) ln( ) ln( )

ln( / ) ln( ) ln( )

ln( ) ln( )

b

a

x e b

xy x y

x y x y

x a x

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The exponential function is the antilogarithm because we can recover

the value of x using it.

ln exp lnxx e x

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The exponential function is the antilogarithm because we can recover the value of x using it

In STATA:

generate newname = log(x) or ln(x) will generate the natural logarithm of xgenerate newname2 = exp(newname) will recover x

Type also “help scalar” to learn how to handle scalarsClick “define” in the dialogs options, to obtain a pull down menu

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The exponential function is the antilogarithm because we can recover the value of x using it

In SHAZAM

GENR NEW =LOG(x) will generate the natural logarithm of x

GENR NEW2=EXP(NEW) will recover x

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1 2y x

2y x

1 2 1 2 10y x

Which we call the intercept

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Figure A.1 A linear relationship

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2

dy

dx

1 2 2 3 3y x x

2 32

3 23

given that is held constant

given that is held constant

yx

xy

xx

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1 2 3Q L K

2 given that capital is held constant

, the marginal product of labor inputL

QK

L

MP

2 32 3

,y y

x x

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yx

y y y x xslope

x x x y y

% 100y

yy

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Figure A.2 A nonlinear relationship

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2dy dx

yx

dy y dy x xslope

dx x dx y y

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Figure A.3 Alternative Functional Forms

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If β3 > 0, then the curve is U-shaped, and representative of average or

marginal cost functions, with increasing marginal effects. If β3 < 0,

then the curve is an inverted-U shape, useful for total product curves,

total revenue curves, and curves that exhibit diminishing marginal

effects.

21 2 3y x x

2 3 2 32 0, or / (2 )dy dx x x

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Cubic functions can have two inflection points, where the function

crosses its tangent line, and changes from concave to convex, or vice versa.

Cubic functions can be used for total cost and total product curves in economics. The derivative of total cost is marginal cost, and the derivative of total product is marginal product.

If the “total” curves are cubic, as usual, then the “marginal” curves are quadratic functions, a U-shaped curve for marginal cost, and an inverted-U shape for marginal product.

2 31 2 3 4y x x x

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Example: the Phillips Curve

11 2 1 2

1y x

x

1

1

1 2

% 100

1%

t tt

t

tt

w ww

w

wu

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In order to use this model all values of y and x must be positive. The slopes of these curves change at every point, but the elasticity is constant and equal to β2.

1 2ln( ) ln( )y x

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Both its slope and elasticity change at each point and are the same sign as β2. Note that this is also an exponential function:

The slope at any point is β2y, which for β2 > 0 means that the marginal

effect increases for larger values of y.

1 2ln( )y x

1exp ln( ) exp( )y y x 2

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1 0 1 00

1ln ln( ) ( )y y y y

y

1 0 1 00 0

ln(1 )

1ln ln( ) ln ( ) = relative change in

x x

yy y y y y y

y y

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1 0

0

100 ln( ) 100 ln( ) ln( )

100

% percentage change in

y y y

y

y

y y

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10

1 1

1

% 100 100( 1)

1 ln% 100 ln( )% approximation error 100 100

100 ln( ) ln( )

yy y

y

y yy y

y y

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1 0

2 1 0

2

100 ln( ) ln( ) %

100

(100 )

y y y

x x

x

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0 1 2 0

1 1 2 1

ln( )

ln( )

y x

y x

1 0 2 1 0

21 0

2

ln( ) ln( )

100 ln( ) ln( )100

%100

y y y x x

x x

x

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1 2

2

ln( ) 0 500ln( )

500% 10 50

100 100

y x x

y x

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Slide A-40Principles of Econometrics, 3rd Edition