123449855 introductory econometrics chapter 1 ppt

8/10/2019 123449855 Introductory Econometrics Chapter 1 Ppt

http://slidepdf.com/reader/full/123449855-introductory-econometrics-chapter-1-ppt 1/14

1

Welcome to Econometrics

Introduction



2

Why study Econometrics?

An empirical analysis uses data to test atheory or to estimate a relationship

A formal model can be tested

Theory may be ambiguous as to the effectof some policy change – can useeconometrics to evaluate the program



3

Steps in Empirical Analysis

Careful formulation of the question ofinterestEconomic Model or Informal Economic

Intuition or ReasoningTurn an Economic Model into anEconometric ModelState Hypothesis of Interest

DataEstimationTesting of Hypothesis



4

Types of Data – Cross Sectional

Cross-sectional data is a random sample

Each observation is a new individual, firm,

etc. with information at a point in time

If the data is not a random sample, we have

a sample-selection problem



5




6




7

Types of Data – Time Series

Time series data has a separate observation

for each time period – e.g. stock prices

Since not a random sample, different

problems to consider

Trends and seasonality will be important



8

Types of Data – Time Series



9

Types of Data – Pooled Cross-

sections and Panel

Can pool random cross sections and treat

similar to a normal cross section – known as

pooled cross-sections data. Will just needto account for time differences.

Can follow the same random individualobservations over time – known as panel

data



Types of Data – Pooled Cross-

sections

10



Types of Data – Panel

11



12

The Question of Causality

Simply establishing a relationship between

variables is rarely sufficient

Want to the effect to be considered causal

If we’ve truly controlled for enough other

variables, then the estimated ceteris paribus

effect can often be considered to be causalCan be difficult to establish causality



13

Example: Returns to Education

A model of human capital investment implies

getting more education should lead to higher

earnings

In the simplest case, this implies an equation like

ueducation Earnings 10



14

Example: (continued)

The estimate of 1, is the return to

education, but can it be considered causal?

While the error term, u, includes otherfactors affecting earnings, want to control

for as much as possible

Some things are still unobserved, whichcan be problematic

123449855 introductory econometrics chapter 1 ppt

Documents