lecture 1 applied econometrics and economic modeling

Introduction

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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 .

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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

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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?

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Cross-sectional versus Time Series Data can be categorized as cross-sectional or time

series.The Opinion data is Example 2.1 is cross-sectional. 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.