Predicting The Present

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
2

The good news

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

5

Google Query Trends

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

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

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...

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

10

Observing Query Growth with Google Trends

Google Confidential and Proprietary 11

Observing Traffic with Google Trends DEMO


Google Categories under Vehicle Brands

NOTE: Area represents the queries volume from first half year 2008 and the color represents queries yearly growth rate

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

Actual vs. Fitted Sales (Top 9 Make by Sales)


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

Toyota Sales

1st Week of Month


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: http://www.slate.com/id/2216238/

18
18

Large differences in state patterns of unemployment claims

Time Series Autocorrelation Function


Model Fit and Prediction


Useful sites

• Economics blogs
–Hall-Woodward (policy): http://woodwardhall.wordpress.com/
–Mankiw (right): http://gregmankiw.blogspot.com/
–Delong (left): http://delong.typepad.com/
–Thoma (mid): http://economistsview.typepad.com/
–Hamilton (tech): http://www.econbrowser.com/

21

Predicting The Present

Recomendados

Recomendados

Mais conteúdo relacionado

Semelhante a Predicting The Present

Semelhante a Predicting The Present (20)

Mais de Ralph Paglia

Mais de Ralph Paglia (20)

Último

Último (20)

Predicting The Present