Econ 299 Quantitative Methods in Economics

1. Econ 299Quantitative Methods in Economics Economic Data Calculus and Economics Basics of Economic Models Advanced Calculus and Economics Statistics and Economics Econometric Introduction Lorne Priemaza, M.A. Lorne.priemaza@ualberta.ca

2. 1. Data Description, Presentation, and Manipulation 1.1 Data Types and Presentations 1.2 Real and Nominal Variables 1.3 Price Indexes 1.4 Growth Rates and Inflation 1.5 Interest Rates 1.6 Aggregating Data: Stocks and Flows 1.7 Seasonal Adjustment Appendix 1.1 Exponentials and Logarithms

3. Why do economists need data? • 1) Describe Economy • Current and past data, as well as increases and decreases • This information can influence decisions ie: GDP, interest rate, unemployment, price, debt, etc. • 2) Test Theory • Data is needed to test a hypothesis that one aspect of the economy impacts another ie: Smokers and the cost to healthcare ie: Married couples and health

4. 1 Data Types Data is essential for economists. Data can be categorized by: 1) How it is collected: • time series data • cross-sectional data • panel data 2) How it is measured: • nominal data • real data

5. Time Series Data -Collects data on one economic agent (city/person/firm/etc.) over time -Frequency can vary (yearly/monthly/ quarterly/weekly/daily/etc.) -ie: Canadian GDP, GMC stock value, your height, U of A tuition, world population

6. Alberta’s Tuition – Time Series

7. Final Fantasy Quality - Time Series Data Source: www.thefinalfantasy.com Source: the truth Time Series: One Agent Many Time Periods

8. Cross Sectional Data -Collects data on multiple economic agents (locations/persons/firms/etc) at one time -Taken at one specific point in time (September report, January report, etc.) -ie: current stock portfolio, hockey player stats, provincial GDP comparison, last year’s grades

9. 99/00 Tuition – Cross Sectional

10. Timothy A. Student’s Weekly Time Spent Studying for Midterms - Cross Sectional Data Cross Sectional: Many Agents One Time Period

11. Panel Data -Combination of Time Series and Cross-sectional Data -Many economic agents -Many time periods -More difficult to use -Often required due to data restrictions -also referred to as pooled data

12. Pooled Tuition

13. 1.1 Data Types • Exercise: What kind of data is: 1) Election Predictions 10 days before an election? 2) MacLean’s University Rankings? 3) Yearly bank account summary? 4) University Transcript after your 4th year?

14. 1.2 Real and Nominal Variables • 1. Nominal variables • Measured using current prices • Provides a measure of current value Ie: a movie today costs $12.

15. 1.2 Real and Nominal Variables • 2. Real variables • Measured using base year prices • Provides a measure of quantity (removing the effects of price change over time) Ie: a movie today costs $4.00 in 1970 dollars

16. A Movie in 1970 In 1970, a movie cost $0.50 BUT $0.50 then was a lot more than $0.50 now. Nominal Comparison: Movie prices have increased by a factor of 24 ($0.50 -> $12) Real Comparison: Movie prices have increased by a factor of 8 ($0.50 -> $4)

17. GDP example • Gross Domestic Product -Monetary value of all goods and services produced in an economy How do nominal and real GDP differ?

18. Nominal GDP -Current monetary value of all goods produced: ∑ quantities X prices -changes when prices change -changes when quantities change

19. The Problem with Nominal GDP Assume: prices quadruple (x4) production is cut in half (x 1/2) Nominal GDP (year 1) = 1 X 1 = 1 Nominal GDP (year 2) = 0.5 X 4 = 2 -although production has been devastated, GDP reflects extreme growth

20. Real GDP -Base year value of all goods currently produced: ∑ quantities X prices baseyear -doesn’t change when prices change -changes when quantities change

21. The Solution of Real GDP Assume: prices quadruple (x4) production is cut in half (x 1/2) Real GDP (year 1) = 1 X 1 = 1 Real GDP (year 2) = 0.5 X 1 = 0.5 -real GDP accurately reflects the economy

22. Price Indexes (Indices) -Used to convert between real and nominal terms -different indexes for different variables or groups of variables Ie: GDP Deflator 2002 = 100 (base year) 2010 = 125 (World Bank) The “price” of GDP has risen 25% between 2002 and 2010

23. GDP – Converting Between Real and Nominal

24. General Conversion Equations

25. Example: Tuition

26. 1.3 How to Calculate Price Indexes -simple price index -weighted sum of individual prices of a good or group of goods Price index = ∑price X weight

27. Example #1 John is constructing a price index to reflect his entertainment spending John values two activities equally: seeing movies and eating hot dogs The prices of movies and hot dogs have moved as follows:

28. Example #1 To construct a price index, simply sum the products of the prices and weights Price index = ∑price X weight Exercise: If John valued hot dogs three times as much as movies, what would the price indexes become?

29. 1.3.1 Normalizing Price Indexes -price indexes themselves are meaningless “The price of GDP was 78.9 this year” -price indexes help us: • Compare between years • Convert between real and nominal -to compare more easily, we normalize to make the index equal 100 in the base year

30. Normalizing the price index: = 100 in base year For example, if GDP was 310 in 1982, dividing every year’s GDP by 310 and then multiplying by 100 normalizes GDP to be 100 in 1982.

31. Example #1a - Normalized Take 2000 as the base year: Does the base year chosen affect the outcome?

32. Example #1b - Normalized Take 2002 as the base year: Note:Raw and normalized PI’s WORK the same, normalized PI’s are just easier to visually interpret

33. Example #2 – Tuition If instead of using inflation for our tuition deflator, we use the education deflator, we can first normalize it to 1999/2000: *Cansim series V735564, January Data

34. Example: Tuition

35. 1.3.1.1 Changing Base Years -base years can be changed using the same formula learned earlier -in the formula, always use the price indexes from the SAME SERIES (same base year)

36. 1.3.2 Common Price Indexes -Up until this point, price index weights have been arbitrary -Arbitrary weights leads to bias, difficulty in recreating data, and difficulty in interpreting and comparing data -Two common universal price indexes are the Paasche and the Laspeyres Price Indexes

37. 1.3.2 Laspeyres Price Index -uses base year quantities as weights -still = 100 in base year LPIt = ∑ pricest X quantitiesbase year ---------------------------------- X 100 ∑ pricesbase year X quantitiesbase year -tracks cost of buying a fixed (base year) basket of goods (ie: CPI)

38. 1.3.2 Paasche Price Index -uses current year quantities as weights -still = 100 in base year PPIt = ∑ pricest X quantitiest ---------------------------------- X 100 ∑ pricesbase year X quantitiest -compares cost of current basket now to cost of current basket in base year

39. Example: Movies and Karaoke

40. Example: Laspeyres (Base year 1)

41. Example: Paasche (Base year 1)

42. Comparing Paasche and Laspeyres

43. Choosing Paasche or Laspeyres Current year weights = Paasche Base year weights = Laspeyres

44. 2 Price Index Calculation Methods • Using individual prices and quantities -Same as before 2) Using basket costs PaQb Price of basket b in year a P2012Q1997 Price in 2012 of what was bought in 1997

45. Method 1 – Individual Prices and Quantities

46. Method 2 – Basket Costs

47. Method 2 Example Every year, Lillian Pigeau likes to travel. The first year, she went to Maraket, the second year to Ohm, and the third year to Moose Jaw. The costs of those trips are as follows:

48. Method 2 –Paasche(Year 1 Base Year)

49. Method 2 –Paasche(Year 1 Base Year)

50. Method 2 –Paasche(Year 1 Base Year)