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1.1 <80><93>1.3, HGL 1.1 <80><93>1.5
Welcome to ECON 103. The goal of this course is to learn the core tool of empirical economics
Before the economics, a few logistics. Lectures meet Tuesday and Thursday, 3:15rlongmuir@g.ucla.edu with “ECON 103” in the subject line, and post questions to Campuswire (join code 6251), where the whole class benefits from the answer.
The main text is Principles of Econometrics by Hill, Griffiths & Lim (5th ed.); we cover roughly the first eight chapters, and the slides are posted as well. Stock & Watson is a useful second reference and is mapped chapter-by-chapter in the lecture notes. All the computing is done in R and RStudio, both free; install them before Lab 1 (campus and virtual desktops have them too). No prior coding experience is required.
The formal prerequisites are Econ 11 and Econ 41. We lean heavily on Econ 41
Your grade is a weighted average of homework, the midterm, and the final, and we take whichever weighting is better for you. There are about four homework assignments, submitted online and due Mondays at midnight; late work earns half credit absent an accommodation or prior arrangement. Every assignment includes some coding questions (R recommended). The exams are multiple choice, online, and closed-book / closed-internet.
grading <- data.frame(
Component = c("Homework", "Midterm", "Final"),
Option1 = c("20%", "30%", "50%"),
Option2 = c("20%", "40%", "40%")
)
knitr::kable(grading, col.names = c("Component", "Option 1", "Option 2"),
align = "lcc")| Component | Option 1 | Option 2 |
|---|---|---|
| Homework | 20% | 20% |
| Midterm | 30% | 40% |
| Final | 50% | 40% |
The midterm is Thursday, August 20 and covers Lectures 1
Ask a half-dozen econometricians what econometrics is and you get a half-dozen answers: the science of testing theories; a toolkit for forecasting; fitting models to data; making numerical policy recommendations. They are all right.
Econometrics is the science and art of using economic theory and statistical methods to analyze economic data
Econometrics bridges the gap between being “a student of economics” and being a practicing economist. Theory tells you that “a price increase lowers quantity demanded.” Econometrics answers the question theory cannot: by how much.
Theory gives you the direction of a relationship, but real decisions need a number. Consider:
Each question is about a parameter
Because the answer comes from data, it carries uncertainty: a different sample gives a different number. For that reason we never report a bare estimate
Economic theory writes relationships as exact functions. A demand equation, for instance, might say that quantity demanded depends on the good’s own price \(P\), the price of a substitute \(P^s\), the price of a complement \(P^c\), and income: \[
Q^d = \beta_1 + \beta_2 P + \beta_3 P^s + \beta_4 P^c + \beta_5 \text{INC}.
\] But real outcomes are not exact
This “systematic part \(+\) error” template is the skeleton of every model in the course. We meet it formally when we set up simple regression.
The same regression can serve different purposes
Estimate the effect of \(X\) on \(Y\), holding all else equal: “How much do test scores rise because of smaller classes?”
Use \(X\) to make a good guess of \(Y\). Causality is not required here: “What will GDP growth be next year?”
Plug the estimates into a decision: “How much should the Fed raise rates? How much should we advertise?”
For prediction, you only need a stable association
This is the central distinction of the course. Consider a regression of a student’s grade on the fraction of lectures they skip: \[ \text{GRADE} = \beta_1 + \beta_2\,\text{SKIP} + e . \] We expect \(\beta_2 < 0\): more skipping goes with lower grades. But does skipping cause low grades?
Maybe not. A demanding job, or low motivation, could drive both skipping and poor grades. In that case SKIP and GRADE are correlated
The lesson, repeated throughout the course: correlation is not causation. A good predictor need not be a cause. Umbrellas predict rain, but umbrellas don’t make it rain.
The gold standard for measuring a causal effect is a randomized experiment.
Split units into a treatment group and a control group by random assignment, then compare average outcomes between them.
Randomization makes the groups comparable in every other way, so the only systematic difference between them is the treatment. The causal effect is in fact defined as the difference such an experiment would reveal. Two classic illustrations: in agronomy, fertilize randomly chosen plots and compare yields; in education, Tennessee’s Project STAR randomly assigned students to small versus regular classes (we revisit it when we study treatment effects).
Real experiments are often unethical, infeasible, or too expensive. As a result most economic data are observational
When the data are observational, the danger is confounding: a third variable that drives both the treatment and the outcome.
Districts with small classes also tend to be wealthier. So a raw “small classes \(\to\) higher scores” comparison mixes the class-size effect with the income effect. Untangling the two is precisely the job of multiple regression in Part III.
Before modeling anything, it helps to know what kind of data we have. Economic data fall into three structural types:
Cutting across this classification, data are also experimental (generated by a designed experiment) or observational (surveys, administrative records)
Our running example throughout the course (from Hill, Griffiths & Lim) is a cross-section of 40 households, recording each household’s weekly income (in hundreds of dollars) and weekly food expenditure (in dollars). The natural first look at the relationship between two variables is a scatterplot, shown in Figure 6.3.
Two things stand out. Higher income tends to go with higher food spending
data(food)
fit <- lm(food_exp ~ income, food)
ggplot(food, aes(income, food_exp)) +
geom_point(color = ucla$darkblue, fill = ucla$blue,
shape = 21, size = 1.8) +
geom_abline(intercept = coef(fit)[1], slope = coef(fit)[2],
color = ucla$red, linewidth = 1) +
scale_x_continuous(limits = c(0, 35), breaks = c(0, 10, 20, 30)) +
scale_y_continuous(limits = c(0, 620), breaks = c(0, 200, 400, 600)) +
labs(x = "weekly income ($100s)", y = "weekly food exp. ($)")
The OLS fit through this cloud is food_exp \(= 83.42 + 10.21 \times\) income: each extra $100 of weekly income is associated with about $10.21 of additional weekly food spending. Simple regression and OLS estimation make the idea of “draw the best line through this cloud” precise
Econometrics uses economic theory together with statistics to turn data into numbers: estimating parameters, testing hypotheses, and predicting outcomes. We organize that work around three goals and a small vocabulary of data structures.
| Three goals | Data types | |
|---|---|---|
| Explain | causal effects, holding all else equal | cross-section, time series, panel |
| Predict | a good forecast; causality not required | experimental vs. observational |
| Optimize | plug estimates into a decision / policy | most economic data are observational |
The big ideas to carry forward: every model is a systematic part \(+\) a random error \(e\); correlation \(\neq\) causation; and randomized experiments define causal effects, while with observational data we must work to approximate them.
Next time: the language of uncertainty