advanced quantitative methods: mathematics (p)revie · 2012-01-14 · courseoutline matrixalgebra...

Course outlineMatrix algebra

DerivativesProbabilities and probability distributions

Expectations and variances

Advanced Quantitative Methods:Mathematics (p)review

Johan A. Elkink

University College Dublin

17 January 2012

Johan A. Elkink math (p)review




1 Course outline

2 Matrix algebra

3 Derivatives

4 Probabilities and probability distributions

5 Expectations and variances





Outline

1 Course outline

2 Matrix algebra

3 Derivatives







Introduction

Topic: advanced quantitative methods in political science.





Introduction


Or, alternatively: basic econometrics, as applied in political science.





Introduction


Or, alternatively: basic econometrics, as applied in political science.

Or, alternatively: linear regression and some non-linear extensions.





Readings

Peter Kennedy (2008), A guide to econometrics. 6th ed.,Malden: Blackwell.

Damodar N. Gujarati (2009), Basic econometrics. 5th ed.,Boston: McGraw-Hill.

Julian J. Faraway (2005), Linear models with R. Baco Raton:Chapman & Hill.





Readings

Peter Kennedy (2008), A guide to econometrics. 6th ed.,Malden: Blackwell.

Damodar N. Gujarati (2009), Basic econometrics. 5th ed.,Boston: McGraw-Hill.

Julian J. Faraway (2005), Linear models with R. Baco Raton:Chapman & Hill.

Andrew Gelman & Jennifer Hill (2007), Data analysis usingregression and multilevel/hierarchical models. Cambridge:Cambridge University Press.

William H. Greene (2003), Econometric analysis. 5th ed.,Upper Saddle River: Prentice Hall.





Topics

1 17/1 Mathematics review2 24/1 Statistical estimators3 31/1 Ordinary Least Squares4 7/2 Hypothesis testing5 14/2 Specification, multicollinearity and heteroscedasticity6 21/2 Autocorrelation7 28/2 Time-series analysis

Study break8 20/3 Maximum Likelihood9 27/3 Limited dependent variables10 3/4 Bootstrap and simulation11 10/4 Multilevel data12 17/4 Panel data





Frequentist vs Bayesian

Frequentist statistics interprets probability as the frequency ofoccurrance in (hypothetically) many repetitions. E.g. if we throwthis dice infinitely many times, what proportion of times would itbe heads? We can here also talk of conditional probabilities:what would this frequency be if ... and some condition follows.







Bayesian statistics interprets probability as a belief: if I throwthis dice, what do you think is the chance of getting heads? Wecan now talk of conditional probabilities in a different way: howwould your belief change given that ... and some condition follows.







Bayesian statistics interprets probability as a belief: if I throwthis dice, what do you think is the chance of getting heads? Wecan now talk of conditional probabilities in a different way: howwould your belief change given that ... and some condition follows.

This course is a course in the frequentist analysis of regressionmodels.





Homeworks

50 % Bi-weekly homeworksdue before class the following week

50 % Replication paperdue April 29, 2011, 5 pm

No exam





Homeworks

50 % Bi-weekly homeworksdue before class the following week

50 % Replication paperdue April 29, 2011, 5 pm

No exam

Working with others is a good idea





Grade conversions

Homeworks UCD TCD Homeworks UCD TCD

95-100% A+ A+ 45-49% E+ D91-94% A A 37-44% E D87-90% A- A 33-36% E- D83-86% B+ B+ 0-32% F F79-82% B B75-78% B- B70-74% C+ C+66-69% C C62-65% C- C58-61% D+ C54-57% D C50-53% D- C





Replication paper

Find replicable paper now and check whether appropriate

Contact authors asap if you need their data

See Gary King, “Publication, publication”





Syllabus and website

Website: http://www.joselkink.net/teaching

Syllabus downloadable there

Slides and notes on website

Data for exercises on website





Outline

1 Course outline

2 Matrix algebra

3 Derivatives







Vectors: examples

v =

3415

w =[

3.23 1.30 7.89 1.00]





Vectors: examples

v =

3415

w =[

3.23 1.30 7.89 1.00]

β =

β0β1β2β3β4





Vectors: examples

v =

3415

w =[

3.23 1.30 7.89 1.00]

β =

β0β1β2β3β4

v and β are column vectors, while w is a row vector. When notspecified, assume a column vector.





Vectors: transpose

v =

3415

v′ =[

3 4 1 5]





Vectors: summation

v =

35913

N∑

i

vi = 3 + 5 + 9 + 1 + 3

= 21





Vectors: addition

2316

+

6392

=

86108





Vectors: multiplication with scalar

v =

2316

3v =

69318





Vectors: inner product

v =

5313

w =

7681

v′w = 5 · 7 + 3 · 6 + 1 · 8 + 3 · 1 = 64





Vectors: outer product

v =

5313

w =

7681

vw′ =

35 30 40 521 18 24 37 6 8 121 18 24 3





Matrices: examples

M =

3 4 51 2 10 9 8

I =

1 0 0 00 1 0 00 0 1 00 0 0 1





Matrices: examples

M =

3 4 51 2 10 9 8

I =

1 0 0 00 1 0 00 0 1 00 0 0 1

The latter is called an identity matrix and is a special type ofdiagonal matrix.

Both are square matrices.





Matrices: transpose

X =

1 3 29 8 89 8 5

X′ =

1 9 93 8 82 8 5





Matrices: transpose

X =

1 3 29 8 89 8 5

X′ =

1 9 93 8 82 8 5

(A′)′ = A





Matrices: indexing

X4×3 =

x11 x12 x13x21 x22 x23x31 x32 x33x41 x42 x43





Matrices: indexing

X4×3 =

x11 x12 x13x21 x22 x23x31 x32 x33x41 x42 x43

Always rows first, columns second.





Matrices: indexing

X4×3 =

x11 x12 x13x21 x22 x23x31 x32 x33x41 x42 x43

Always rows first, columns second.

So Xn×k is a matrix with n rows and k columns. yn×1 is a columnvector with n elements.





Matrices: addition

4 2 1 36 5 3 2112 4 21

3 0

+

7 8 2 0−3 −2 3 11

24 2 1 1

=

11 10 3 33 3 6 31

2512 6 31

3 1





Matrices: addition

4 2 1 36 5 3 2112 4 21

3 0

+

7 8 2 0−3 −2 3 11

24 2 1 1

=

11 10 3 33 3 6 31

2512 6 31

3 1

(A+ B)′ = A′ + B′





Matrices: multiplication with scalar

X =

4 2 1 36 5 3 2112 4 21

3 0

4X =

16 8 4 1224 20 12 86 16 71

3 0





Matrices: multiplication

A =

[

6 41 3

]

B =

[

3 2 34 2 1

]

AB =

[

6 · 3 + 4 · 4 6 · 2 + 4 · 2 6 · 3 + 4 · 11 · 3 + 3 · 4 1 · 2 + 3 · 2 1 · 3 + 3 · 1

]

=

[

34 20 2215 8 6

]





Matrices: multiplication

A =

[

6 41 3

]

B =

[

3 2 34 2 1

]

AB =

[

6 · 3 + 4 · 4 6 · 2 + 4 · 2 6 · 3 + 4 · 11 · 3 + 3 · 4 1 · 2 + 3 · 2 1 · 3 + 3 · 1

]

=

[

34 20 2215 8 6

]

(ABC)′ = C′B′A′

(AB)C = A(BC)

A(B+ C) = AB+ AC

(A+ B)C = AC+ BC





Special matrices

Symmetric matrix A′ = A

Idempotent matrix A2 = A

Positive-definite matrix x′Ax > 0 ∀ x 6= 0Positive-semidefinite matrix x′Ax ≥ 0 ∀ x 6= 0





Matrix rank

The rank of a matrix is the maximum number of independentcolumns or rows in the matrix. Columns of a matrix X areindependent if for any v 6= 0, Xv 6= 0





Matrix rank

The rank of a matrix is the maximum number of independentcolumns or rows in the matrix. Columns of a matrix X areindependent if for any v 6= 0, Xv 6= 0

r(A) = r(A′) = r(A′A) = r(AA′)

r(AB) = min(r(A), r(B))





Matrix rank: example 1

A =

3 5 12 2 11 4 2

For matrix A, the three columns are independent and r(A) = 3.There is no v 6= 0 such that Av = 0 (of course, if v = 0,Av = A0 = 0).






B =

3 5 92 2 61 4 3

For matrix B the first and the last column vectors are linearcombinations of each other: b•1 =

13b•3. At most two columns

(b•1 and b•2 or b•2 and b•3) are independent, so r(B) = 2. Wecould construct a matrix v such that Bv = 0, namelyv =

[

1 0 −13

]′

(or any v =[

α 0 −13α

]′

).






C =

3 5 1112

2 2 71 4 5

r(C) = 2. In this case, one cannot express one of the columnvectors as a linear combination of another column vector, but onecan express any of the column vectors as a linear combination ofthe two other column vectors. For example, c•3 = 3c•1 +

12c•2 and

thus when v =[

3 12 −1

]

′

, Cv = 0.





Matrix inverse

The inverse of a matrix is the matrix one would have to multiplywith to get the identity matrix, i.e.:

A−1A = AA−1 = I





Matrix inverse

The inverse of a matrix is the matrix one would have to multiplywith to get the identity matrix, i.e.:

A−1A = AA−1 = I

(A−1)′ = (A′)−1

(AB)−1 = B−1A−1





Matrices: trace

The trace of a matrix is the sum of the diagonal elements.

A =

4 3 1 82 8 5 56 7 3 41 2 0 1

tr(A) = sum(diag(A)) = 4 + 8 + 3 + 1 = 16





Outline

1 Course outline

2 Matrix algebra

3 Derivatives







Derivatives of straight lines

0 1 2 3 4 5

01

23

45

A straight line

x

y

slope

y =1

2x + 2

dy

dx=

1

2





Derivatives of curves

0 1 2 3 4 5

01

23

45

A curve

x

y

y = x2 − 4x + 5






0 1 2 3 4 5

01

23

45

A curve

x

y

y = x2 − 4x + 5

d(axb)

dx= baxb−1

d(a+ b)

dx=

da

dx+

db

dx






0 1 2 3 4 5

01

23

45

A curve

x

y

y = x2 − 4x + 5

d(axb)

dx= baxb−1

d(a+ b)

dx=

da

dx+

db

dx

dy

dx= 2x − 4





Finding location of peaks and valleys

A maximum or minimum value of a curve can be found by settingthe derivative equal to zero.







y = x2 − 4x + 5

dy

dx= 2x − 4 = 0

2x = 4

x =4

2= 2







y = x2 − 4x + 5

dy

dx= 2x − 4 = 0

2x = 4

x =4

2= 2

So at x = 2 we will find a (local) maximum or minimum value -the plot shows that in this case it is a minimum.





Derivatives in regression

This concept of finding the minimum or maximum is crucial forregression analysis.

With ordinary least squares (OLS), we estimate theβ-coefficients by finding the minimum of the sum of squarederrors.

With maximum likelihood (ML), we estimate theβ-coefficients by finding the maximum point of the(log)likelihood function.





Derivatives of matrices

The derivative of a matrix consists of the matrix containing thederivatives of each element of the original matrix.






The derivative of a matrix consists of the matrix containing thederivatives of each element of the original matrix.

M =

x2 2x 32x + 4 3x3 2x

3 2 4x

dM

dx=

2x 2 02 9x2 20 0 4






d(v′x)

dx= v

d(x′Ax)

dx= (A+ A′)x

d(Bx)

dx= B

d2(x′Ax)

dxdx′= A+ A′





Outline

1 Course outline

2 Matrix algebra

3 Derivatives







Probabilities: definition

Frequentist definition:

P(x) = limn→∞

f (x)

n,

where P is the probability, n the number of trials and f thefrequency of event x occurring.





Probabilities: properties

For event E in sample space S :

0 ≤ P(E ) ≤ 1

P(S) = 1

P(¬E ) = 1− P(E )

P(E1 ∪ E2) = P(E1) + P(E2) if E1 and E2 are mutually exclusive

A sample space is the set of all possible outcomes, while an eventis a specific subset of the sample space.





Conditional probability

P(E1|E2) denotes the probability of event E1 happening, given thatevent E2 occurred.

If P(E1|E2) = P(E1) then E1 and E2 are independent events.





Venn diagram

E1

E2

S





Venn diagram

E1

E2

S

P(E1)





Venn diagram

E1

E2

E1

S

P(E1 ∪ E2)





Venn diagram

E1

E2

S

P(E1 ∩ E2)





Conditional probability

E1

E2

S

P(E1|E2) =P(E1∩E2)P(E2)





Bayes’ theorem

P(E1|E2) =P(E1 ∩ E2)

P(E2)

P(E2|E1) =P(E1 ∩ E2)

P(E1)

therefore:

P(E1 ∩ E2) = P(E1|E2) · P(E2)

P(E1 ∩ E2) = P(E2|E1) · P(E1)





Bayes’ theorem

P(E1|E2) =P(E1 ∩ E2)

P(E2)

=P(E2|E1) · P(E1)

P(E2)

=P(E2|E1) · P(E1)

P(E2|E1) · P(E1) + P(E2|¬E1) · P(¬E1)

Note that while Bayesian inference is based on Bayes’ theorem, thetheorem itself holds regardless of your definition of probability.





Probability distribution

1 2 3 4 5 6

Probability distribution of a dice

E

P(E

)

0.0

0.2

0.4

0.6

0.8

1.0

A probability distribution of adiscrete variable presents theprobabilities for each possibleoutcome.






1 2 3 4 5 6

Probability distribution of a dice

E

P(E

)

0.0

0.2

0.4

0.6

0.8

1.0

A probability distribution of adiscrete variable presents theprobabilities for each possibleoutcome.

Because P(S) = 1, the bars addup to 1.






0 500000 1000000 1500000 2000000 2500000

0e+

002e

−07

4e−

076e

−07

8e−

07

Probability density of a vote

FF vote

dens

ity o

f P

A probability distribution of acontinuous variable presents theprobability density for eachpossible outcome. The surfaceunder the plot represents theprobability of the outcome beingwithin a particular range.






0 500000 1000000 1500000 2000000 2500000

0e+

002e

−07

4e−

076e

−07

8e−

07

Probability density of a vote

FF vote

dens

ity o

f P

A probability distribution of acontinuous variable presents theprobability density for eachpossible outcome. The surfaceunder the plot represents theprobability of the outcome beingwithin a particular range.

Because P(S) = 1, the surfaceunder the entire plot is 1.





Outline

1 Course outline

2 Matrix algebra

3 Derivatives







Expected value

The expected value of a random variable x is:

E (x) =N∑

i

P(xi ) · xi ,

whereby N is the total number of possible outcomes in S .





Expected value

The expected value of a random variable x is:

E (x) =N∑

i

P(xi ) · xi ,

whereby N is the total number of possible outcomes in S .

The expected value is the mean (µ) of a random variable (not tobe confused with the mean of a particular set of observations).





Moments

moments are E (x)n, n = 1, 2, ...

So µx is the first moment of x

central moments are E (x − E (x))n, n = 1, 2, ...

absolute moments are E (|x |)n, n = 1, 2, ...

absolute central moments are E (|x − E (x)|)n, n = 1, 2, ...





Variance

The second central moment is called the variance, so:

var(x) = E (x − E (x))2 = E (x − µx)2

=

N∑

i

P(xi ) · (xi − E (x))2





Variance

The second central moment is called the variance, so:

var(x) = E (x − E (x))2 = E (x − µx)2

=

N∑

i

P(xi ) · (xi − E (x))2

The standard deviation is the square root of the variance:

sd(x) =√

var(x) =√

E (x − E (x))2





Covariance

The covariance of two random variables x and y is defined as:

cov(x , y) = E [(x − E (x))(y − E (y))]





Covariance

The covariance of two random variables x and y is defined as:

cov(x , y) = E [(x − E (x))(y − E (y))]

If x and y are independent of each other, cov(x , y) = 0.





Variance of a sum

var(x + y) = var(x) + var(y) + 2cov(x , y)

var(x − y) = var(x) + var(y)− 2cov(x , y)


advanced quantitative methods: mathematics (p)revie · 2012-01-14 · courseoutline matrixalgebra...

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