predicting the present

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  1. 1. State of the EconomyHal Varian Chief Economist22 April 2009 Google Confidential and Proprietary 1
  2. 2. The bad news Homes: U.S. home prices have fallen 27% since peak. Pending sales fell 7.7% in January (though up slightly in west.) 2008 CPI: Full-year changes of +0.1% overall, +1.8% core. Dramatic deceleration Q4 due to falling aggregate demand. March unemployment rate now at 8.5%, Manufacturing-Activity Index: Currently at 28-year low. Stock market: Down by 45% from peak. Bottom Line: The financial crisis contributed to an already weak U.S. economy that officially entered a recession in 12/07. As of April, it is now the the longest U.S. recession since the Great Depression.2 Google Confidential and Proprietary2
  3. 3. The good news Google Confidential and Proprietary3
  4. 4. No, really... Asset prices are low Houses pending sales up 2.1% in February. Mortgages conventional loans to qualified borrowers available Stocks up 20% since March low Stimulus plan started in April Payroll tax cut started April 1 (up to $400 per person) Tax refunds larger Home buyer, auto purchase credit Some accelerated depreciation now available Inventories are being depleted, albeit slowly Housing GoodsGoogle Confidential and Proprietary4
  5. 5. What happens in a recession? Delay everything that can be delayed Business investment State and local spending (due to tax receipts) Consumer durable purchase However, consumer staples usually see much smaller hitGovernment actions Want to avoid downward spiralDrop in demand lay off workers spending fallsNeed to stabilize demand: consumption, govn't, investment Trying a multipronged attack Google Confidential and Proprietary 5
  6. 6. Google Query Trends Google Confidential and Proprietary6
  7. 7. Signs of hope Good news? Macroeconomics Financial situation stabilizingParticularly important for this recession Market volatility coming downVIX index volatility index though back up again recentlyTed Spread gap between LIBOR and T-bill rate Keep a close eye on these metrics,as they are good leading indicators Google Confidential and Proprietary7
  8. 8. Two sectors to watch: Real Estate and Autos Mortgage money available Auto loans to follow Real estate shows signs of stabilizing Queries showing usual seasonal uplift May see further activity in Spring Automotive sector is depressed Expect to see very attractive terms offered Also typical seasonal uplift Google Confidential and Proprietary8
  9. 9. Implications for retail Q1 has been slow, but not as bad as Q4 for economyImpacted verticalsReal estate, auto, appliances, furniture, travel, luxury itemsLess sensitiveLow end shopping, health, local spendingAreas to watch as leading indicatorsAutomotive, real estateTED spread = 3 month Treasury bill rate 3 month LIBORWatch the VIX! Consumers are hunting for valueClassic, reliable, solid... Google Confidential and Proprietary 9
  10. 10. Everybody talks about the economy... Can Google queries help forecast economy activity? Government data released with alag Google data is real time Appears to be correlated withcurrent level of activity May be helpful in predicting thepresent This is still 4-6 weeks beforeofficial data releaseGoogle Confidential and Proprietary 10
  11. 11. Observing Query Growth with Google Trends Google Confidential and Proprietary 11
  12. 12. Observing Traffic with Google Trends DEMOGoogle Confidential and Proprietary 12
  13. 13. Google Categories under Vehicle Brands NOTE: Area represents the queries volume from first half year 2008 and the color represents queries yearly growth rate Google Confidential and Proprietary 13
  14. 14. Model with Panel DataModel: log(Yi,t) = 1.681 + 0.3618 * log(Yi,t-1) + 0.4621 * log(Yi,t-12)+ 0.0014 * Xi,t,2 + 0.0020 * Xi,t,2 + ai * Makei + ei,tei,t ~ N(0, 0.14972) , Adjusted R2 = 0.9791 Yi,t = Auto Sales of i-th Make at month t Xi,t,1 = Google Trend Search at 1st week of month t and from i-th make Xi,t,2 = Google Trend Search at 2nd week of month t and from i-th make Makei = Dummy variable to indicate Auto Make ai = Coefficient to capture the mean level of Auto Sales by MakeANOVA TableDf Sum Sq Mean Sq F value Pr(>F) trends1 17.487.48 333.8334 < 2.2e-16 *** trends2 11.711.7176.2150 < 2.2e-16 *** log(s1) 1 1609.52 1609.52 71826.7401 < 2.2e-16 *** log(s12)1 20.24 20.24 903.2351 < 2.2e-16 *** as.factor(brand) 262.110.08 3.6301 2.36e-09*** Residuals1535 34.400.0214Google Confidential and Proprietary 14
  15. 15. Actual vs. Fitted Sales (Top 9 Make by Sales) Google Confidential and Proprietary 15
  16. 16. Model with Univariate Time SeriesModel: log(Yi,t) = 3.0343 + 0.2054 * log(Yi,t-1) + 0.5396 * log(Yi,t-12) + 0.0034 * Xi,t,1 + ei,tei,t ~ N(0, 0.10512) , Adjusted R2 = 0.5804 Yi,t = Auto Sales of i-th Make at month t Xi,t,1 = Google Trend Search at 1st week of month t and from i-th country Makei = Dummy variable to indicate Auto MakeANOVA Table DfSum SqMean Sq F value Pr(>F)s1 1 0.23366 0.2336621.1512.603e-05 ***log(s1)1 0.36614 0.3661433.1424.171e-07 ***log(s12) 1 0.30421 0.3042127.5372.651e-06 ***Residuals 54 0.59657 0.01105 16Google Confidential and Proprietary 16
  17. 17. Toyota Sales1st Week of MonthGoogle Confidential and Proprietary 17
  18. 18. Other interesting things Government statistics automobile sales home sales retail sales travel Can look at state and city level data Geographic variation is often quite striking Great viz: 18 Google Confidential and Proprietary18
  19. 19. Large differences in state patterns of unemployment claimsTime SeriesAutocorrelation Function Google Confidential and Proprietary
  20. 20. Model Fit and PredictionGoogle Confidential and Proprietary
  21. 21. Useful sites Economics blogsHall-Woodward (policy): (right): (left): (mid): (tech): Confidential and Proprietary 21


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