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Nobel Prize - Economics

Nobel Prize - Economics. Three Amigos. Financial Economics. American. Eugene Fama - U. Chicago Lars Peter Hansen - U. Chicago Robert Shiller - Yale. Case-Shiller Housing Index. Chap 3 - Index Numbers. Statistics Canada - “The Daily” - Online. Index Numbers - Outline.

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Nobel Prize - Economics

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  1. Nobel Prize - Economics Three Amigos Financial Economics American Eugene Fama - U. Chicago Lars Peter Hansen - U. Chicago Robert Shiller - Yale

  2. Case-Shiller Housing Index

  3. Chap 3 - Index Numbers Statistics Canada - “The Daily” - Online

  4. Index Numbers - Outline Constructing an Index - 3 Issues Price Relatives – an example Weighting Schemes Simple average - Geometric average Laspeyres index Paasche index Consumer Price Index Diewert article – other issues in building the CPI

  5. Commodity Research Bureau - Spot Index 22 Commodities 1967 = 100

  6. Commodity Research Bureau - Foodstuffs Index 1967 = 100

  7. Commodity Research Bureau - Metals Index 1967 = 100

  8. Napoleanic Wars WW I Fall 2011 – Gold $1800/oz, wheat $330/tonne = 0.18 oz/tonne

  9. Building an Index: Three Issues to Consider • Which commodities to include? Fundamental conflict (cost – benefit) • Reflect population of interest • Data availability • Proxy data - high correlation • Price level or price changes?

  10. Building an Index: Three Issues Simple average or weighted average ? • e.g. egg price index Weights reflect relative importance (sales, volume) 2. Weighting prices

  11. Building an Index: Three Issues Weighted average - index formed as a weighted sum of prices Geometric - when price changes expressed as a product 2. Weighting prices Weighted or Geometric Average? E.g. stock price up 10% up 20% Down 30% How are you doing?

  12. Building an Index: Three Issues Index reflects level relative to the level some time in the past (Base year) to the level now Base year is arbitrary e.g. index of agricultural output 3. Choice of Base Year

  13. Example: Price Relatives • Objective: • build indices to measure proportional price variation during a trading day • For each commodity, + for the group • 3 commodities (wheat, corn, beans) - $/bu • Data: high & low prices and volume of trade for September 15, 2003

  14. Alternative Weights - Price IndicesTwo well known + popular indices Laspeyres • Beginning year expenditure weights Paasche • Ending year expenditure weights Current prices expressed relative to base year (base = 100) Prices weighted in relation to proportion of expenditure Weights are static

  15. Alternative Weights - Price Indices

  16. Consumer Price Index Uses of CPI • compare changes in real wages and income • adjust expenditure data for price changes => estimate changes in quantities http://www.statcan.gc.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=2301&lang=en&db=imdb&adm=8&dis=2 (2013 active) Laspeyres index • calculated each month • national sample of retail prices (600 goods) Weights - past (base period) expenditure shares (fixed) Weights reflect expenditure patterns of national sample of households

  17. Statistics Canada: CANSIM II SERIES V735320 TABLE NUMBER: 3260001 CANSIM I Series Number: P100001

  18. Stat Can., The Daily September 21, 2011

  19. Nominal and Real (CPI deflated) Butter Prices in Ontario 1985 – 1997 (monthly data - 1986 base)Source: Statistics Canada $/dozen Nominal Price

  20. Erwin Diewert: Index Number Issues JEP (1988) Objective: • Problems related to measuring price changes, based on the Laspeyers index • Differences between Laspeyres & other cost of living indexes

  21. 1995 Boskin Commission Mandate from US Senate CPI overestimated price changes by 1.1% per year Consequences: If CPI indicated 3%, while true inflation was 2%, over 12 years inflate national budget by 1 $ TRILLION Boskin Budget = $25,000

  22. Some History: Cost of Living (COL) Index • individual or society • A. Konus (1939) - True Cost of Living Index • (individual or family) • min cost to achieve U0 (base period) relative to subsequent period - given a price increase • R. Pollak (1981) generalized the concept to a social cost of living index – society as a whole • concept the same, practically very difficult • Not the same as Laspeyres or Paache indices

  23. U0 BEER U1 TCOLI = I1/I0 * * * I1 I0 PIZZA True Cost of Living IndexMeasure impact of Increase in Pizza Price

  24. Laspeyres Index used to construct the CPI over estimates impact of rising prices on welfare product substitutions Paache Index under-estimates the impact of price changes Diewert (1983) Pollak-Konus true COL index somewhere in between not observable

  25. Alternative to Konus-Pollack • Some average of LI + PI • Diewert argues for Irving Fisher’s (1922) Index geometric mean of LI & PI vs arithmetic mean satisfies many desirable properties superlative index • index increases if prices increase? • lays somewhere between the LI and the PI • if all prices increase by 10%, index increases by 10% (CRTS) • it is exact when preferences are homothetic

  26. BEER MRS = MPP/MPB MRS Pizza Homothetic Preferences

  27. Prices for each outlet collected (k prices gathered for commodity j for outlet 1 for example) Calculate Unit value price for each outlet - k prices combined for each outlet i - n outlet prices for commodity j Combine n outlet prices to create and index for commodity j, using the Laspeyers Index or other method “Elementary Level Index” Combine m commodity indices into the final index using the Laspeyers Index “Commodity Level Index” Mechanics of Building the Index Number

  28. Biases - use of the LI for the CPI 1 Substitution Biases –relative to Fisher Index 1.1 - elementary index level aggregating prices across outlets using LI substitution effects neglected 1.2 - commodity level aggregating commodity prices into an index substitution effects neglected 1.3 - between outlets discount operators with significant market share discount share neglected

  29. Elementary Level Bias Substitution (commodity) Bias Calculation is the same as elementary bias Example: Diewert provides and example where he assumes that: V() = 0.005 i = 2 percent total bias in the index of about 0.5 percentage points

  30. Outlet Substitution Bias s = market share of discounters d = percent discount • For conservative assumptions, he estimates this bias at about 0.4 percentage points

  31. 2 Quality Bias • Goods disappear, no longer sold, quality improved • Disappearance about 20%/year • Agencies “link in” improved product s = market share of new product e = percent increase in efficiency of improved product

  32. 3 New Goods Bias how to deal with new goods? “linked in” after some time initial high price that falls later – not captured s = market share of new product d = decline in new good price

  33. WHAT TO DO ? • Use of new index formula's • Scanner data - construct better indices at the elementary level • Hedonic methods (regression) to adjust for quality changes – value of product attributes • New goods bias - introduce these goods more quickly

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