Transcript Introduction

Introduction
What is Econometrics

Application of statistical methods to economics.

It is distinguished from economic statistics (statistical
data) by the unification of economic theory,
mathematical tools, and statistical methodology.

It is concerned with (1) estimating economic
relationships (2) confronting economic theory with
facts and testing hypotheses about economic
behavior, and (3) forecasting economic variables .
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Estimating Economic
Relationships

Examples include:
– d/s of various products and services
– firms wishes to estimate the effect of advertising on sales
and profits
– relate stock price to characteristics of the firm
– macro policy, federal, state, and local tax revenue forecasts
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Testing Hypotheses

Examples include:
– Has an advertising campaign been successful in increasing
sales?
– Is demand elastic or inelastic with respect to price-important for
competition policy and tax incidence, among other things.
– Effectiveness of government policies on macro policy.
– Have criminal policies been effective in reducing crime?
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Forecasting

Examples include:
– Firms forecast sales, profits, cots of production, inventory
requirements
– Utilities project demand for energy. Sometimes, these
forecasts aren’t very good, such as what is currently
happening in California.
– Federal government projects revenues, expenditures,
inflation, unemployment, and budget and trade deficits
– Municipalities forecast local growth.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Uncertainty in These Three Steps

The reason is that we generally base these steps on
sample data rather than a complete census.

Therefore, estimated relationships are not precise.

Conclusions from hypothesis tests may accept a
false hypothesis or reject a true one.

Forecasts are not on target.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
CODING.XLS

Represents responses from a questionnaire
concerning the president's environmental policies.

The data set includes data on 30 people who
responded to the questionnaire.

The data is organized in rows and columns.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Observations

An observation is a member of the population or
sample. Alternative terms for observations are cases
and records.

Each row corresponds to an observation. The
number of observations vary widely from one data set
to another, but they can all be put in this format.

In this data set, each person represents an
observation.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Variables

Each column represents a variable. An alternative
term for variable that is commonly used in database
packages is field.

In this data set, each piece of information about a
person represents a variable. The six variables are
person’s age, gender, state of residence, number of
children, annual salary and opinion of the president’s
environmental policies.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Variables -- continued

The number of variables can vary widely from one
data set to another.

It is customary to include a row that gives variable
names.

Variable names should obviously be meaningful - and
no longer than necessary.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Type of Data

There are several ways to categorize data.
– Numerical versus categorical
– Cross-sectional versus time series

Using this example we can look at the various types
of data.

On the next slide is an alternate way to represent the
data set.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Numerical versus Categorical

The basic distinction between the two is whether you
intend to do any arithmetic on the the data. It makes
sense to do arithmetic on numerical data.

Clearly, the Gender and State variables are
categorical and the Children and Salary variables are
numerical. Age and opinion variables are more
difficult to categorize.

Age is expressed numerically, and we might want to
perform some arithmetic on age such as the average
age of respondents. However, age could be treated
as categorical.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Numerical versus Categorical -continued

The Opinion variable is expressed numerically on a
1-5 Likert scale. These numbers are only codes for
the categories strongly disagree, disagree, neutral,
agree, and strongly agree. It is not intended for
arithmetic to be performed on these numbers; in fact,
it is not appropriate to do so.

The Opinion variable is best treated as categorical.

In the case of the Opinion variable there is a general
ordering of categories that does not exist in the
Gender and State variables.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Numerical versus Categorical -continued

We classify these types of variables as ordinal. If
there is no natural ordering , as with the Gender and
State variables, we classify the variables as nominal.

Both ordinal and nominal variables are categorical.

Categorical variable can be coded numerically or left
in uncoded form. This option is largely a matter of
taste.

Coding a truly categorical variable doesn’t make it
numerical and open to arithmetic operations.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Numerical versus Categorical -continued

Some options for this example are to:
– code Gender (1 for male and 2 for female)
– uncode Opinion variable
– categorize the Age variable as young (34 or younger),
middle aged (from 35-59) and elderly (60 or older).

The one performing the study often dictates if
variables should be treated numerically or
categorically; there is no right or wrong way.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Numerical versus Categorical -continued

Numerical variables can be subdivided into two types
- discrete and continuous.

The basic distinction between the two is whether the
data arises from counts or continuous
measurements.

The Children variable is clearly discrete whereas
Salary is best treated as continuous.

This distinction is sometimes important because it
dictates the type of analysis that is most natural.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12
Cross-sectional versus Time
Series

Data can be categorized as cross-sectional or time
series.The Opinion data is Example 2.1 is crosssectional. A pollster sampled a cross section of
people at one particular point in time.

In contrast, time series data occurs when we track
one or more variables through time. An example
would be the series of daily closing values of the Dow
Jones Index.

Very different type of analysis are appropriate for
cross-sectional and time series data.
2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.9 | 2.10 | 2.11 | 2.12