lecture 1 applied econometrics and economic modeling

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lecture 1 Applied Econometrics and Economic Modeling

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lecture 1 applied econometrics and economic modeling

  1. 1. Introduction
  2. 2. What is Econometrics <ul><li>Application of statistical methods to economics. </li></ul><ul><li>It is distinguished from economic statistics (statistical data) by the unification of economic theory, mathematical tools, and statistical methodology. </li></ul><ul><li>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 . </li></ul>
  3. 3. Estimating Economic Relationships <ul><li>Examples include: </li></ul><ul><ul><li>d/s of various products and services </li></ul></ul><ul><ul><li>firms wishes to estimate the effect of advertising on sales and profits </li></ul></ul><ul><ul><li>relate stock price to characteristics of the firm </li></ul></ul><ul><ul><li>macro policy, federal, state, and local tax revenue forecasts </li></ul></ul>
  4. 4. Testing Hypotheses <ul><li>Examples include: </li></ul><ul><ul><li>Has an advertising campaign been successful in increasing sales? </li></ul></ul><ul><ul><li>Is demand elastic or inelastic with respect to price-important for competition policy and tax incidence, among other things. </li></ul></ul><ul><ul><li>Effectiveness of government policies on macro policy. </li></ul></ul><ul><ul><li>Have criminal policies been effective in reducing crime? </li></ul></ul>
  5. 5. Forecasting <ul><li>Examples include: </li></ul><ul><ul><li>Firms forecast sales, profits, cots of production, inventory requirements </li></ul></ul><ul><ul><li>Utilities project demand for energy. Sometimes, these forecasts aren’t very good, such as what is currently happening in California. </li></ul></ul><ul><ul><li>Federal government projects revenues, expenditures, inflation, unemployment, and budget and trade deficits </li></ul></ul><ul><ul><li>Municipalities forecast local growth. </li></ul></ul>
  6. 6. Uncertainty in These Three Steps <ul><li>The reason is that we generally base these steps on sample data rather than a complete census. </li></ul><ul><li>Therefore, estimated relationships are not precise. </li></ul><ul><li>Conclusions from hypothesis tests may accept a false hypothesis or reject a true one. </li></ul><ul><li>Forecasts are not on target. </li></ul>
  7. 7. CODING.XLS <ul><li>Represents responses from a questionnaire concerning the president's environmental policies. </li></ul><ul><li>The data set includes data on 30 people who responded to the questionnaire. </li></ul><ul><li>The data is organized in rows and columns. </li></ul>
  8. 9. Observations <ul><li>An observation is a member of the population or sample. Alternative terms for observations are cases and records . </li></ul><ul><li>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. </li></ul><ul><li>In this data set, each person represents an observation. </li></ul>
  9. 10. Variables <ul><li>Each column represents a variable . An alternative term for variable that is commonly used in database packages is field . </li></ul><ul><li>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. </li></ul>
  10. 11. Variables -- continued <ul><li>The number of variables can vary widely from one data set to another. </li></ul><ul><li>It is customary to include a row that gives variable names. </li></ul><ul><li>Variable names should obviously be meaningful - and no longer than necessary. </li></ul>
  11. 12. Type of Data <ul><li>There are several ways to categorize data. </li></ul><ul><ul><li>Numerical versus categorical </li></ul></ul><ul><ul><li>Cross-sectional versus time series </li></ul></ul><ul><li>Using this example we can look at the various types of data. </li></ul><ul><li>On the next slide is an alternate way to represent the data set. </li></ul>
  12. 14. Numerical versus Categorical <ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul>
  13. 15. Numerical versus Categorical -- continued <ul><li>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. </li></ul><ul><li>The Opinion variable is best treated as categorical. </li></ul><ul><li>In the case of the Opinion variable there is a general ordering of categories that does not exist in the Gender and State variables. </li></ul>
  14. 16. Numerical versus Categorical -- continued <ul><li>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 . </li></ul><ul><li>Both ordinal and nominal variables are categorical. </li></ul><ul><li>Categorical variable can be coded numerically or left in uncoded form. This option is largely a matter of taste. </li></ul><ul><li>Coding a truly categorical variable doesn’t make it numerical and open to arithmetic operations. </li></ul>
  15. 17. Numerical versus Categorical -- continued <ul><li>Some options for this example are to: </li></ul><ul><ul><li>code Gender (1 for male and 2 for female) </li></ul></ul><ul><ul><li>uncode Opinion variable </li></ul></ul><ul><ul><li>categorize the Age variable as young (34 or younger), middle aged (from 35-59) and elderly (60 or older). </li></ul></ul><ul><li>The one performing the study often dictates if variables should be treated numerically or categorically; there is no right or wrong way. </li></ul>
  16. 18. Numerical versus Categorical -- continued <ul><li>Numerical variables can be subdivided into two types - discrete and continuous . </li></ul><ul><li>The basic distinction between the two is whether the data arises from counts or continuous measurements. </li></ul><ul><li>The Children variable is clearly discrete whereas Salary is best treated as continuous. </li></ul><ul><li>This distinction is sometimes important because it dictates the type of analysis that is most natural. </li></ul>
  17. 19. Cross-sectional versus Time Series <ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>Very different type of analysis are appropriate for cross-sectional and time series data. </li></ul>

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