Lecture 1 · Week 1 · Özata slides & Gujarati ch. 1 · about 35 min

The Nature and Scope of Econometrics

What econometrics actually is, the three disciplines it draws from, the eight-step methodology every empirical study follows, and the four types of economic data you will meet throughout this course.

By the end you can

Define econometrics and name the three disciplines it combines
List the eight steps of the econometric methodology, using the Keynesian consumption function as a running example
Distinguish time series, cross-sectional, pooled and panel data, and give an example of each
Explain why correlation is not the same as causality

What is econometrics?

Econometrics sits at the intersection of three disciplines, and it is the combination — not any one alone — that defines it.

The three disciplinesKey ideaFormula sheet →

\text{Econometrics} = \text{Economic theory} \cap \text{Mathematics} \cap \text{Statistics}

Use it when a question asks you to justify why econometrics is not simply 'applied statistics' or 'mathematical economics.'

Economic theory identifies the variables and predicts the direction of relationships (a demand curve slopes down), but rarely says how much quantity changes for a given price change.
Mathematics supplies the language to express those relationships precisely and manipulate them.
Statistics supplies the tools to collect data and estimate the actual numerical values, plus a way to judge how reliable those estimates are.

Pairwise overlaps of these three disciplines have their own names: economic theory ∩ mathematics is mathematical economics; economic theory ∩ statistics is economic statistics; mathematics ∩ statistics is mathematical statistics. Econometrics is the overlap of all three at once.

The three aims of econometrics

Aims of econometricsKey listFormula sheet →

1.\ \text{Formulate econometric models} \qquad 2.\ \text{Estimate and test them with data} \qquad 3.\ \text{Use them for prediction and policy}

Use it when you are asked to summarise, in one line, what econometric research is ultimately for.

These aims translate directly into real decisions: a restaurant assessing an ad campaign’s effect on sales, a government estimating the price elasticity of demand before a tax change, a private university projecting how a tuition increase of 10,000 TL affects enrolment, or a firm like Arçelik forecasting refrigerator demand a decade out. Each of these is an economic question that theory alone cannot answer numerically — it requires econometrics.

The methodology of econometrics: eight steps

Steps of an econometric analysisKey procedureFormula sheet →

\begin{aligned} &1.\ \text{Statement of theory or hypothesis} \\ &2.\ \text{Specification of the mathematical model} \\ &3.\ \text{Specification of the statistical (econometric) model} \\ &4.\ \text{Obtaining data} \\ &5.\ \text{Estimation of the econometric model} \\ &6.\ \text{Hypothesis testing} \\ &7.\ \text{Forecasting or prediction} \\ &8.\ \text{Use of the model for policy purposes} \end{aligned}

Use it when an exam asks you to list, or apply, the standard stages of an empirical econometric study — walk through each with the running example below.

Keynesian consumption function (mathematical model)Formula sheet →

Y = \beta_0 + \beta_1 X

$Y$: consumption
$X$: income (here, GDP)
$\beta_0$: the intercept (constant term)
$\beta_1$: the slope, the marginal propensity to consume (MPC), 0 < \beta_1 < 1

Use it when the mathematical (deterministic) stage of the model, step 2, before a disturbance term is added.

Keynesian consumption function (econometric model)Step 3Formula sheet →

Y = \beta_0 + \beta_1 X + u

$u$: the disturbance (error) term, a random variable capturing everything the deterministic line leaves out

Use it when the model is now stochastic rather than exact — the standard starting point for every regression in this course.

The Keynesian multiplier: a policy application

Keynesian multiplierPolicy use (step 8)Formula sheet →

\text{Multiplier} = \frac{1}{1-\widehat{MPC}}

$\widehat{MPC}$: the estimated marginal propensity to consume, \hat\beta_1 from the fitted consumption function

Use it when a fitted consumption function's slope is used to estimate the impact of a change in government spending on national income — a direct step-8 application.

Four types of economic data

Types of economic dataKey classificationFormula sheet →

\text{Time series} \quad \text{Cross-sectional} \quad \text{Pooled (repeated cross sections)} \quad \text{Panel (longitudinal)}

Use it when you are given a dataset's description and need to classify it — a very common short-answer question.

Time series: observations on one or more variables over time, at regular intervals (denoted $Y_t$ , $t=1,\dots,T$ ). Example: Türkiye’s quarterly GDP from 2010 to 2023.
Cross-sectional: observations on one or more variables at a single point in time, across different units. Example: GDP of every OECD member country in 2023.
Pooled (repeated cross sections): different cross-sectional samples collected at different points in time and combined — but not necessarily the same units each time. Example: a survey of (a different sample of) 150 houses sold in 2018 and 180 different houses sold in 2021, combined into one dataset.
Panel (longitudinal): the same cross-sectional units followed over multiple time periods. Example: GDP per capita for the same three named countries, observed every year from 2017 to 2020.

Sources of economic data

Economic data comes from government agencies (TÜİK, the Central Bank of the Republic of Türkiye), international organisations (World Bank, IMF, OECD), private sector organisations (research firms, banks), academic institutions, surveys and polls, and market data (stock prices, exchange rates).

Causality and correlation

Correlation vs regressionKey distinctionFormula sheet →

\text{Correlation: symmetric, measures linear association strength} \qquad \text{Regression: asymmetric, one variable predicts/explains another}

Use it when a question asks you to explain why a regression result does not, by itself, prove causation.

Correlation measures the strength and direction of a linear association between two variables (a coefficient between −1 and +1), treating both variables symmetrically — the correlation between X and Y is identical to the correlation between Y and X. Regression is asymmetric: it treats one variable as dependent (explained, stochastic) and the other as independent (explanatory), and it aims to predict or explain one from the other, not merely measure association.

Exam practice

Summary and review

Econometrics = economic theory ∩ mathematics ∩ statistics; it supplies numerical content that theory alone cannot.
Three aims: formulate models, estimate and test them, use them for prediction and policy.
Eight-step methodology: theory → mathematical model → statistical model (add $u$ ) → data → estimation → hypothesis testing → forecasting → policy use.
$u$ exists because of omitted factors, measurement error, and human indeterminacy.
Four data types: time series (one unit, many periods), cross-sectional (many units, one period), pooled (different units, many periods), panel (same units, many periods).
Regression is asymmetric (explains Y from X) while correlation is symmetric; neither, by itself, proves causation — that requires a theoretical argument from outside the statistics.