Welcome to Research Methods: Applied Econometrics (ECO601M 2025 Version) Webpage!

Frontmatter

In a nutshell

This is the webpage for a graduate course called Research Methods: Applied Econometrics (ECO601M), specifically offered to students pursuing the degree Master of Applied Economics at De La Salle University - Manila.

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Information about using the materials

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

Lecture Materials on Research Methods: Applied Econometrics (ECO601M 2025 Version) © 2025 by Andrew Adrian Yu Pua is licensed under CC BY-NC-SA 4.0

To cite the material, please use

Pua, A. A. Y. (2025). Lecture Materials on Research Methods: Applied Econometrics (ECO601M 2025 Version). https://applied-metrics.neocities.org

Finding typos or unclear portions

If you find typos or unclear portions in the notes and materials, 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.

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

Course Diary

2025-01-25

2025-01-18

  1. Least squares algebra

    1. LS horizontal line, LS ray, LS line
    2. Properties of the residuals and the different LS fits
    3. Demonstration in gretl: point-and-click, command log, inputting commands not available from point-and-click
    4. Connection to conditional mean function: case of female/male dummy vs case of years of education
    5. Obtaining (2.13) and the connection to correlation
    6. How to interpret \(b\)? Is it the change in \(Y\) when \(X\) changes?
    7. Extend to the case of two regressors: demonstration in gretl, but how to interpret results?

  2. Move on to the distinction between sample and population quantities

    1. What features of the population can we learn using a sample? Recall our diagram involving the population and the many spreadsheets which you could get to observe.
    2. We tossed a fair coin using a computer for a few times, then a large number of times. Think about what can be learned by applying the sample average?
    3. This is preparation to answer: What do you learn from computing LS lines?

  3. To prepare for quizzes, make sure to work on

    1. The subsection on Useful algebra in Goldberger (1998) pages 16-17
    2. The details that lead to (2.15)
    3. Exercises 2.3, 2.5, 2.6, 2.7, 2.9, 2.10
    4. For Chapter 3, make sure you know how to work with discrete probability distributions, calculate expected values, and determine why these are of importance. The new part here relative to your previous statistics course is the section on the prediction problem. Work on all exercises of Chapter 3.

  4. To prepare for the next meeting in terms of the theory, you should do the following:

    1. For Chapter 4, make sure you know how to work with the bivariate generalization of univariate discrete probability distributions. The key object of study here is the conditional expectation function. Compare this to the conditional mean function. The section on prediction is also new relative toy our previous statistics course. Work on all exercises of Chapter 4.
    2. Chapter 5 is really a condensed version of the statistical inference part of your statistics course. There is nothing really new here and it serves as review. Perhaps some terms like unbiased estimator, consistent estimator, and asymptotic distirbution may be new.
    3. Bring a printed copy of Chapters 2 to 6. Mark the parts you have questions about and ASK THEM in class!

2025-01-11

  1. Introduction to the course, the instructor, and the students

  2. Worked on Chapter 1 of the main textbook, introduced a bit of gretl

  3. Started working on the main idea for Chapter 2

  4. To prepare for the next meeting in terms of gretl, you should do the following:

    1. Download gretl for whatever OS you are using.
    2. Keep a copy of the user guide.
    3. Keep a copy of Using gretl for Principles of Econometrics 5th edition, by Lee Adkins.
    4. Download the three datasets used in Chapter 1, namely CPS5, CAP3, PHL7. NOTE: Right click and save link as or save target as. Make sure to change the filetype to something other than HTML. These are supposed to be text files!
    5. Explore gretl by importing the CPS5 dataset (File, Open data, User file, select .txt as the filetype). Try reproducing Figure 1.1 (View, Graph specified vars, X-Y scatter, input things correctly here in the dialog box). To reproduce Figure 1.1 as faithfully as possible, locate and click on the icon with three lines on top of each other found in the window which produced the graph (Edit, change fitted line to none, and then apply).
    6. Figure 1.2 takes more effort to reproduce because you need to label the subgroup averages. I will do this in the next meeting so it is important you bring Table 1.1 with you.
    7. To reproduce Table 1.1 in gretl, you have to convert the years of education into a discrete variable (Highlight v1, right click on v1, Edit attributes, put a check mark on Treat this variable as discrete, then click ok). After that, go to View, Summary statistics, factorized, fill in the appropriate variables. It does not look exactly the same as Table 1.1 in terms of format, but the content should be very similar.
    8. If you can, explore how to change the axes names by highlighting and right-clicking on a variable and then Edit attributes.
    9. If you can, reproduce Figures 1.4 and 1.5 for yourself. Should you give the data a time series or panel interpretation?

  5. To prepare for the next meeting in terms of the theory, you should do the following:

    1. You should try working out all the mathematical results in Chapter 2! Work on Exercises 2.3, 2.5, 2.6, 2.7, 2.9, 2.10.
    2. For Chapter 3, make sure you know how to work with discrete probability distributions, calculate expected values, and determine why these are of importance. The new part here relative to your previous statistics course is the section on the prediction problem. Work on all exercises of Chapter 3.
    3. For Chapter 4, make sure you know how to work with the bivariate generalization of univariate discrete probability distributions. The key object of study here is the conditional expectation function. Compare this to the conditional mean function. The section on prediction is also new relative toy our previous statistics course. Work on all exercises of Chapter 4.
    4. Chapter 5 is really a condensed version of the statistical inference part of your statistics course. There is nothing really new here and it serves as review. Perhaps some terms like unbiased estimator, consistent estimator, and asymptotic distirbution may be new.
    5. Bring a printed copy of Chapters 2 to 6. Mark the parts you have questions about and ASK THEM in class!