Predicting The Present

Predicting The Present



Delivered at Google AutoThink 2009, focused on trends and analysis by leading automotive consulting firm

Delivered at Google AutoThink 2009, focused on trends and analysis by leading automotive consulting firm



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Predicting The Present Predicting The Present Presentation Transcript

  • State of the Economy Hal Varian Chief Economist 22 April 2009 Google Confidential and Proprietary 1
  • 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 Proprietary 2
  • The good news Google Confidential and Proprietary 3
  • 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 •Goods Google Confidential and Proprietary 4
  • 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 hit •Government actions –Want to avoid downward spiral •Drop in demand … lay off workers … spending falls •Need to stabilize demand: consumption, govn't, investment –Trying a multipronged attack Google Confidential and Proprietary 5
  • Google Query Trends Google Confidential and Proprietary 6
  • Signs of hope • Good news? • Macroeconomics –Financial situation stabilizing •Particularly important for this recession –Market volatility coming down •VIX index – volatility index though back up again recently •Ted Spread – gap between LIBOR and T-bill rate –Keep a close eye on these metrics, as they are good leading indicators Google Confidential and Proprietary 7
  • 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 Proprietary 8
  • Implications for retail • Q1 has been slow, but not as bad as Q4 for economy –Impacted verticals –Real estate, auto, appliances, furniture, travel, luxury items –Less sensitive –Low end shopping, health, local spending •Areas to watch as leading indicators –Automotive, real estate –TED spread = 3 month Treasury bill rate – 3 month LIBOR –Watch the VIX! •Consumers are hunting for value –Classic, reliable, solid... Google Confidential and Proprietary 9
  • Everybody talks about the economy... • Can Google queries help forecast economy activity? •Government data released with a lag •Google data is real time •Appears to be correlated with current level of activity •May be helpful in “predicting the present” •This is still 4-6 weeks before official data release Google Confidential and Proprietary 10
  • Observing Query Growth with Google Trends Google Confidential and Proprietary 11
  • Observing Traffic with Google Trends DEMO Google Confidential and Proprietary 12
  • 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
  • Model with Panel Data Model: 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,t ei,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 Make ANOVA Table Df Sum Sq Mean Sq F value Pr(>F) trends1 1 7.48 7.48 333.8334 < 2.2e-16 *** trends2 1 1.71 1.71 76.2150 < 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) 26 2.11 0.08 3.6301 2.36e-09 *** Residuals 1535 34.40 0.02 14 Google Confidential and Proprietary 14
  • Actual vs. Fitted Sales (Top 9 Make by Sales) Google Confidential and Proprietary 15
  • Model with Univariate Time Series Model: log(Yi,t) = 3.0343 + 0.2054 * log(Yi,t-1) + 0.5396 * log(Yi,t-12) + 0.0034 * Xi,t,1 + ei,t ei,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 Make ANOVA Table Df Sum Sq Mean Sq F value Pr(>F) s1 1 0.23366 0.23366 21.151 2.603e-05 *** log(s1) 1 0.36614 0.36614 33.142 4.171e-07 *** log(s12) 1 0.30421 0.30421 27.537 2.651e-06 *** Residuals 54 0.59657 0.01105 16 Google Confidential and Proprietary 16
  • Toyota Sales 1st Week of Month Google Confidential and Proprietary 17
  • 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 Proprietary 18
  • Large differences in state patterns of unemployment claims Time Series Autocorrelation Function Google Confidential and Proprietary
  • Model Fit and Prediction Google Confidential and Proprietary
  • Useful sites • Economics blogs –Hall-Woodward (policy): –Mankiw (right): –Delong (left): –Thoma (mid): –Hamilton (tech): Google Confidential and Proprietary 21