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Summary lecture notes, Q and A's and practice assignment Empirical Methods in Finance $8.64
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  5. Empirical Methods in Finance (323063M6)

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Summary lecture notes, Q and A's and practice assignment Empirical Methods in Finance

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  • Course
  • Empirical Methods in Finance (323063M6)
  • Institution
  • Tilburg University (UVT)
  • Book
  • Introductory Econometrics

Document is full of lecture notes, summarizing teacher's comment as well as Q and A contents (also polls, etc.). The summary is quite long due to screenshots from slides from examples.

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  • No
  • Certain theories
  • October 13, 2020
  • 125
  • 2020/2021
  • Summary

Subjects

  • emf
  • empirical methods in finance
  • empirical methods in finance summary
  • empirical methods in finance lecture notes
  • empirical methods in finance q and a

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  • Tilburg University (UVT)
  • Master Finance
  • Empirical Methods in Finance (323063M6)
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MSc Finance: Empirical Methods in Finance


Inhoudsopgave

MSc Finance: Empirical Methods in Finance ................................................................................................... 1
Block 0: Introduction .......................................................................................................................................... 3
Block 1: Maths and Stats Review ....................................................................................................................... 3
Block 2: Bivariate CLRM ................................................................................................................................... 10
Block 3: Hypothesis testing .............................................................................................................................. 19
Block 4: Multivariate CLRM .............................................................................................................................. 25
Block 5: Causal inferences ................................................................................................................................ 47
Block 6: Discrete choice models ....................................................................................................................... 66
Block 7: Machine learning ................................................................................................................................ 77

Stata Q&A Lectures ...................................................................................................................................... 82
Q&A 1 ............................................................................................................................................................... 82
Q&A 2 ............................................................................................................................................................... 83
Q&A 3 ............................................................................................................................................................... 85
Q&A 4 ............................................................................................................................................................... 88
Q&A 5 ............................................................................................................................................................... 90
Q&A 6 ............................................................................................................................................................... 95
Q&A 7 ............................................................................................................................................................. 101
Q&A 8 assignment ......................................................................................................................................... 102
Q&A 9 ............................................................................................................................................................. 105




1

, ........................... 108
Q&A 10 ........................................................................................................................................................... 108

Stata Web Lectures .................................................................................................................................... 112
Video part 1 .................................................................................................................................................... 112
Video part 2 .................................................................................................................................................... 112
Video part 3 .................................................................................................................................................... 115

Book and literature .................................................................................................................................... 121
Readings Empirical Methods Lecture 1 .......................................................................................................... 121
Notes Chapter 2, 3, 5, 6 (for lecture 2) ........................................................................................................... 122




2

,Block 0: Introduction

The world’s most valuable resource is no longer oil, but data.

Examples of how econometrics can help us with problems in finance:
Suppose you work for a central bank and you are asked to forecast how interest rates,
inflation rates, or GDP will evolve in the next quarter or year
- Collecting past data on these variables, econometrics gives you the statistical tools
necessary to forecast them

Econometrics aims at determining relationships between different variables.

Main uses of financial econometrics:
- Testing theories in finance
- Forecasting future values of financial variables
- Determining the relationship between financial variables

Steps financial econometrics:
- Formulate a clear question of interest
- Construct an economic/financial model
o Model: mathematical equation that describes the relationship between y
(output variable) and x (input variable).
- Find data for the variables that you need
- Turn your economic/financial model into an econometric model, this means
specifying the form of the function f(x).
o 𝑦 = ∝ + 𝛽𝑥 + 𝑢
§ ∝ 𝑎𝑛𝑑 𝛽 – parameters that describe sign and magnitude of the effect
of x on y.
§ U is the error term, including all unobserved factors other than x that
affect y.

Three types of data analysis of financial problems:
1. Cross-sectional data: data on one or more variables collected at a single point in
time.
a. Example: CEO’s salaries and past performance from measured on single point
in time.
2. Time series data: data on one/more variables collected at many points in time.
a. Example: weekly % return on the NYSE composite index between year 1900
and 2000.
3. Panel data: time series for each cross-sectional member of the dataset.
a. Example: housing rental prices and town population across US college towns
in 1980 and 1990.


Block 1: Maths and Stats Review




3

, Random variables: one that can take on any value from a given set, and value is at least in
part determined by chance.
3 types:
1. Bernoulli/binary: can only take the value 0 or 1.
a. E.g. tossing a coin (head or tail)
2. Discrete: takes only a finite number of values.
a. E.g. outcomes of rolling a dice (1-6)
3. Continuous: takes infinitely many values.
a. E.g. price change of a stock, which is technically a discrete random variable,
but it can take so many values that it can be seen as continuous.

E.g. Sum of rolling two dice (2-12)
We can calculate the probability of each possible score occurring, called P(X).
2 and 12 will occur with a probability of 1/6*1/6 = 0.028
Hence, P(X=2) = 0.028 and P(X=12) = 0.028
3 and 11 will occur with probability of 1/6*1/6 + 1/6*1/6 = 0.056
You have either 1+2 or 2+1 and 5+6 or 6+5.

Probability distribution function: diagram showing the probability of each possible score.
pj = P(X = j), where j=2,…., 12

If we increase the number of dice towards infinity, X converges towards a continuous
random variable.

PDF: summarizes the information on the possible outcomes of X and corresponding
probabilities.
f(x) = P (X = x)
Often, however, we do not want to know the exact probability, but in the probability that
the probability of a random variable that lies below or above a certain value.
- E.g. probability below 4?
o P2 + p3 = 0.028 + 0.025 = 0.084
- F(x) = P(x £ X)
Joint distribution is a joint PDF of (X,Y).
𝑓-,/ (𝑥, 𝑦) = 𝑃 (𝑋 = 𝑥, 𝑌 = 𝑦) = 𝑃 (𝑋 = 𝑥)𝑃(𝑌 = 𝑦)

In the two dice case, the joint probability of each dice giving a score of 1 = 1/6* 1/6 = 0.028.

Two variables are independent if knowing the outcome of X doesn’t change the probabilities
of the possible outcomes of Y, and vice versa.
- This means, the formula above is valid.

If two random variables are dependent, we can study how X affects Y by looking at the
conditional distribution Y given X, described by their conditional PDF.

𝑓-,/ (𝑥, 𝑦)
𝑓-| / (𝑦|𝑥) = = 𝑃 (𝑌 = 𝑦|𝑋 = 𝑥)
𝑓𝑥(𝑥)



4

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