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Introduction ECN 102: Analysis of Economic Data Winter, 2011 J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 1 / 51 Contact Information Instructor: John Parman Email: jmparman@ucdavis.edu Office: 1125 SSH (NW

  1. Introduction ECN 102: Analysis of Economic Data Winter, 2011 J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 1 / 51

  2. Contact Information Instructor: John Parman Email: jmparman@ucdavis.edu Office: 1125 SSH (NW entrance to building) Office hours: Monday and Thursday, 2pm - 4pm TAs: Kuk Mo Jung (kmjung@ucdavis.edu) Danielle Sandler (dhsandler@ucdavis.edu) Yi Chen (yiychen@ucdavis.edu) J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 2 / 51

  3. Course Website We will have a course website on Smartsite: smartsite.ucdavis.edu The syllabus, problem sets, past exams, solutions, data files and grades will all be posted there Lecture slides will be posted, typically about 30 minutes before lecture If you are open campus or auditing the course, let me know and I will give you access to the Smartsite page J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 3 / 51

  4. Textbook The required text is Analysis of Economic Data by Colin Cameron. It is available as a course reader from Davis Textbooks (3rd and A). You can use older versions of the reader. There will be a copy on reserve in the library. J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 4 / 51

  5. Waitlist, PTA numbers, etc. The course is currently full. The only way to get into the course is through the waitlist, no PTA numbers will be given. For open campus students, you can’t be enrolled until after the drop/add period is over. In the meantime, send me an email and I will give you access to the Smartsite page so that you can keep up with the course. J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 5 / 51

  6. Grading Grades will be based on problem sets, two midterms and a final exam, weighted as follows: Problem Sets: 10% Midterm 1: 25% Midterm 2: 25% Final: 40% Grades for the course will be curved such that the average GPA for the course is a 2.4. Although the curve will be based on the distribution of overall course grades at the end of the quarter I will give you a rough idea after each exam of what letter grades correspond to different ranges of the uncurved numerical scores. J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 6 / 51

  7. Schedule Week of Tuesday Thursday January 3 lecture lecture January 10 lecture lecture January 17 lecture lecture January 24 lecture Midterm 1 January 31 lecture lecture February 7 lecture lecture February 14 lecture lecture February 21 lecture Midterm 2 February 28 lecture lecture March 7 lecture lecture Final Exam: Thursday March 17, 10:30am-12:30pm J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 7 / 51

  8. Exams All exams will be cumulative but will place greater emphasis on new topics (I will go over what that means closer to the exams). For each exam, you will need to bring a scantron sheet (UCD 2000), something to write with and a non-graphing calculator. You have one week after any graded material is returned to raise any grading issues. You must submit regrade requests in writing and include an explanation of why a regrade is warranted. J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 8 / 51

  9. Problem Sets Problem sets will be posted online and announced in class. Four of the problem sets will be collected and graded. It will state on the problem set whether or not it will be graded. Grading will be on a check plus, check, check minus scale. You may work in groups on problem sets but each person must write up and submit his or her own problem set. This includes creating your own tables, graphs, etc. Problem sets will typically involve a fair amount of work in Excel (learning how to use the ’set print area’ function will be very useful). J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 9 / 51

  10. Excel, data, etc. You will often have to use Excel and data provided on the course website for problem sets. Excel 2007 will be used in class and in sections to demonstrate how to work with data. You may use other versions of Excel or other programs (OpenOffice, Stata, etc.) to do the homework. Datasets will be provided in a generic format so that it can be used in whichever program you choose. Excel 2007 and Stata are available on the lab computers in Hutchison. Helpful handhouts on using Excel can be found on Professor Cameron’s website: http://cameron.econ.ucdavis.edu/excel/excel.html J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 10 / 51

  11. Uses of Economic Data To describe the economic “landscape” Examples: What is the annual growth rate of GDP? Has unemployment risen over the past year? Do people with higher levels of education tend to have greater earnings? Do democracies have greater growth rates than dictatorships? Descriptive statistics motivate economic theory To test or attempt to distinguish between economic theories To help guide policy and expectations about the future J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 11 / 51

  12. Types of Data There are a variety of different types of data that you will encounter in economics. The ways in which we categorize types of data include the following: Value: numerical data, categorical data Unit of observation: cross-section data, time series data, panel data Number of variables: univariate data, bivariate data, multivariate data J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 12 / 51

  13. Types of Data: Numerical Data Numerical data are data that are naturally recorded and interpreted as numbers. They can be continuous or discrete. Examples of numerical data include: Annual income (continuous) Hours worked (discrete) Annual GDP (continuous) Number of times a person has moved (discrete) J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 13 / 51

  14. Types of Data: Categorical Data Categorical data are data that are recorded as belonging to one or more groups. They can be recorded as numbers but these numbers have no inherent meaning. Examples of categorical data include: Gender Birthplace Religion J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 14 / 51

  15. Types of Data: Cross-section Data Cross-section data are data on different individuals collected at a common point in time. Notation: x i , i = 1 , ..., n i specifies a particular individual for an observation n is the total number of individuals observed (typically called the sample size) x is the value of whatever variable we are observing Examples: a single year of census data, GDP by country for a particular year, unemployment rates by state for a particular year J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 15 / 51

  16. Types of Data: Time-Series Data Time-series data are data on a particular phenomenon collected at different points in time. Notation: x t , t = 1 , ..., T t specifies the time period of an observation T is the total number of time periods x is the value of whatever variable we are observing Examples: GDP over time, daily averages of the S & P 500, monthly unemployment rates J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 16 / 51

  17. Types of Data: Panel Data Panel data are data on different individuals with each individual observed at multiple points in time. Notation: x i , t , i = 1 , ..., n ; t = 1 , ..., T Panel data is a mixture of cross-section and time series data Examples: Earnings of Davis graduates over time, life expectancy by country over time J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 17 / 51

  18. Types of Data: Univariate Data Univariate data is a single data series containing observations of only one variables. Notation: x i for cross-section data, x t for time series data Examples: Earnings of high school graduates in 2008, inflation rate from 1950 to 2008 J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 18 / 51

  19. Types of Data: Bivariate Data Bivariate data is composed of two potentially related data series. Notation: ( x i , y i ) (cross-section data), ( x t , y t ) (time series data) We’re often interested in the relationship between x and y Examples: education and earnings for high school graduates, inflation and unemployment rates over time J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 19 / 51

  20. Types of Data: Multivariate Data Multivariate data is composed of three or more potentially related data series. Notation: ( x 1 , i , x 2 , i , ..., x K , i , y i ) (cross-section data), ( x 1 , t , x 2 , t , ..., x K , t , y t ) (time series data) We’re often interested in how x 1 , ..., x K are related to y Examples: inputs, outputs and profits for a firm over time; education, gender and income for a cross-section of individuals J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 20 / 51

  21. What do we do with economic data? The basic steps of data analysis: 1 Data summary 2 Statistical inference 3 Interpretation J. Parman (UC-Davis) Analysis of Economic Data, Winter 2011 January 4, 2011 21 / 51

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