Welcome to the course webpage of ECONSTA (Term 1 AY 2024-2025 version)!

Frontmatter

In a nutshell

This is a course webpage for an undergraduate major course called Economic Statistics (ECONSTA), or perhaps better called Statistical Methods for Economists. Materials for a similar course I have taught before could be found here.

Information about using the materials

If you want to use my slides or the materials in this webpage, please abide by the license:

Lecture Slides on Statistics for Economists (2024 Version) © 2024 by Andrew Adrian Yu Pua is licensed under CC BY-NC-SA 4.0

To cite the slides, please use

Pua, A. A. Y. (2024). Lecture Slides for Statistics for Economists [Quarto slides]. https://econsta.neocities.org

Finding typos or unclear portions

If you find typos or unclear portions in the notes, please let me know. I will be monitoring your contributions during the term and I will acknowledge you in these notes. If you make substantial contributions, I will treat you to some non-alcoholic drinks at Auro Cafe located near the Brother Andrew Gonzalez Hall of De La Salle University.

Resources on time management, learning to learn, and the illusion of learning

I would ask you to take an opportunity to reexamine how you learn and study things. It does not matter if your motivation is only to pass the exam or something greater. It would be good for society if you study for something much greater. I have found the following resources to be helpful to students I have taught in the past. Of course, I am not sure if it would work for you, but do keep an open mind.

Main Body

Textbook examples and exercises for practice

Some of these examples and exercises were discussed in classes or in pre-recorded videos. There are also duplicated exercises across the references. Finally, most of these already have readily accessible solutions: they could be found as part of the chapters, at the back of the book, or as a separate file of solutions legitimately provided by the authors.

  • Related to Lecture 5 (as of 2024-10-09)

    • Wasserman: Examples 2.2, 2.4, 2.6, 2.10
    • Wasserman: Exercises 2, 6
    • Dekking et al: Quick Exercises 4.1 to 4.5
    • Dekking et al: Exercises 4.2, 4.3, 4.4, 4.6 to 4.10
    • Evans and Rosenthal: Examples 2.1.1 to 2.1.10, 2.2.1 to 2.2.3, 2.3.3, 2.5.1, 2.6.1, 2.6.2
    • Evans and Rosenthal: Exercises 2.1.1 to 2.1.9, 2.2.1 to 2.2.10, 2.3.1 to 2.3.5, 2.3.7, 2.3.11, 2.3.14, 2.3.22, 2.5.1, 2.5.2

  • Related to Lecture 4

    • Wasserman: Examples 1.1 to 1.3, 1.7, 1.10, 1.11, 1.13, 1.15, 1.19
    • Wasserman: Exercises 5, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23
    • Dekking et al: All Quick Exercises and Exercises of Chapters 2 and 3
    • Evans and Rosenthal: Examples 1.2.1 to 1.2.5, 1.4.1 to 1.4.9 (skip the examples about bridge), 1.5.1 to 1.5.3
    • Evans and Rosenthal: Exercises 1.2.1 to 1.2.12 (except 1.2.5), 1.3.1 to 1.3.9, 1.4.1 to 1.4.21 (except 1.4.5, 1.4.18), 1.5.1 to 1.5.18
    • Arias-Castro: Examples 1.8, 1.9, 1.12, 1.15, 1.16, 1.17, 1.33, 1.48
    • Arias-Castro: Problems 1.10, 1.11, 1.13, 1.18, 1.32, 1.35, 1.36, 1.37, 1.38, 1.39, 1.40, 1.41, 1.45, 1.46

Course Diary

Lecture 5d, 2024-10-21

Exam 1, 2024-10-19

Lecture 5c, 2024-10-14 and 2024-10-17

Mostly focused on the construction of a quantile function, simulating a quantile function in R, and seeing a business application

Lecture 5b, 2024-10-10

  1. R commands related to binomial distributions
  2. More details and examples regarding discrete random variables
  3. Working on some exercises by hand and using R

Lecture 5a, 2024-10-07

  1. Random variables, distributions, cumulative distribution functions
  2. Connection to past lectures
  3. Quiz 02

Lecture 4d, 2024-10-03, asyncrhonous

  1. Wrap up Monty Hall problem, also do a simulation
  2. Conditional probabilities and their uses
  3. Independence
  4. Exercises and more practice with simulations

Lecture 4c, 2024-09-30

  1. Using the infrastructure for calculating probabilities systematically

  2. Using R again to estimate probabilities, but for the birthday problem

  3. We cannot just assign probabilities to events of an infinite sample space, willy-nilly. The idea of equally likely outcomes in this context does not make sense.

  4. Thinking through the Monty Hall problem

  5. Below you will find some code we used in class to break apart some of the commands and understand what they are doing.

group.size <- 2:30
# Approximation of the probability of at least 2 in a group sharing the same birthday
1-exp(-group.size^2/730)
# Figure out which entries will have more than 50% probability
which(1-exp(-group.size^2/730))
# Making sense of unique(b) through examples
# In the end, what matters is whether k-length(unique(b)) is equal to zero
b <- c(1, 2, 3, 4, 5)
unique(b)
5-length(unique(b))
b <- c(1, 1, 3, 4, 5)
unique(b)
5-length(unique(b))
b <- c(1, 1, 1, 4, 5)
unique(b)
5-length(unique(b))
# Making sense of sum(diff(sort(b))==0)
# In the end, what matters is whether sum(diff(sort(b))==0) is equal to zero
b <- c(1, 2, 3, 4, 5)
diff(sort(b))
diff(sort(b))==0
sum(diff(sort(b))==0)
b <- c(1, 1, 3, 4, 5)
diff(sort(b))
diff(sort(b))==0
sum(diff(sort(b))==0)
b <- c(1, 1, 1, 4, 5)
diff(sort(b))
diff(sort(b))==0
sum(diff(sort(b))==0)

Lecture 4b, 2024-09-26, asynchronous

  1. Definition of probability distributions and its implications
  2. Demonstration of the constraints on probability assignments imposed by the definition of probability distributions
  3. Probability assignments based on the equally likely outcomes or equiprobable outcomes
  4. Mistakes made in the past arising from assuming equally likely outcomes but incorrectly specifying the sample space
  5. Digression into counting outcomes for subsets of a finite sample space
  6. Using R to estimate probabilities
  7. Making sense of the birthday problem and developing an approximation for the probability that a room with \(C\) people would have at least 2 people sharing the same birthday

Lecture 4a, 2024-09-23, moved online

  1. Transportation strike pushed this lecture online.
  2. Class administration: What classroom activities and discussion boards we have so far
  3. Online quiz: Explain how quizzes work, explain how the “scoring” for feedback works
  4. Actual quiz: Copy of quiz questions along with your answers sent to your email. Solutions already sent.
  5. Classroom activity on matching numbers
  6. Set-theoretic language and building the infrastructure

Lecture 3b, 2024-09-19, asynchronous

  1. Wrap up the slides of Lecture 03.

  2. Give a preview of Lecture 04.

    • RSS article on statistical literacy of MPs here
    • Ingredients which make up the definition of a probability function

Lecture 3a, 2024-09-16

  1. Recap and clarifying aspects of Lecture 02

  2. Card prediction activity

    • Why do you think this activity was conducted? What do you think is the phenomenon we are trying to investigate?
    • You collected data on whether you were able to predict correctly the card (suit and number) which will be drawn from a deck of 52 cards.
    • Go through the ingredients of a hypothesis test. Match it to the card activity you did.
    • Calculate a \(p\)-value. Depending on your observed data (specific to your case), it may not be necessary to use the computer to find this \(p\)-value (Why?).
    • But it would be useful to try modifying the code you have so far and use R to calculate a \(p\)-value.

Make-up for suspended classes, asynchronous

Classes were suspended on 2024-09-02 and 2024-09-05. We lost 3 hours of contact time.

  1. (about 2 hours) Introductory R tutorials using M&M data have been recorded. Links to these recordings are available at Canvas/Animospace. Exercises for you to try are part of the recordings.
  2. (about 1 hour) Continuation of Lecture 02

Lecture 2, 2024-09-12, asynchronous

  1. More details about the course syllabus
  2. Introductory R for simulation purposes
  3. Elaborating more on evaluating claims in a statistical manner

Lecture 1, 2024-09-09

  1. M&M activity
  2. What the course is going to be about
  3. Setting some expectations