The International Propagation of News Shocks Paul Beaudry, Martial - - PowerPoint PPT Presentation

The International Propagation of News Shocks

Paul Beaudry, Martial Dupaigne & Franck Portier University of British Columbia & Universit´ e de Toulouse SED Meeting, 06.28-30.2007 Prague

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• 1. Motivation
• News shocks:

data : Beaudry & Portier [2006, Aer; 2005, Jjie], Haertel & Lucke [2007], models : [Beaudry & Portier [2004, Jme; 2007, Jet], Christiano, Rostagno & Motto [2005], Jaimovich & Rebelo [2006], Den Haan & Kaltenbrunner [2006], Beaudry, Portier & Collard [2007]

• Technological News Shocks: Short run demand shock, Long run

supply shock

• A source of international fluctuations?

Small Open Economy: Jaimovich & Rebelo [2007]; Two-country economies: this paper

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1.1. Business cycle comovements

• Y , C, I, H are positively correlated with each other within developed

countries, at business cycle frequencies National Business Cycle (NBC)

• Y , C, I, H are pairwise positively correlated among developed coun-

tries, at business cycle frequencies International Business Cycle (IBC)

• Which combination(s) of impulses and propagation mechanisms can

help understand these business cycle co-movements?

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1.2. The effects of technological shocks

• The international RBC literature faces huge difficulties to account

for international comovements.

• If countries experience different technology shocks, mobile inputs

reallocate to the most productive economy, and the returns to immo- bile inputs lower. Extremely correlated technology shocks are required to match the

• bserved correlations of inputs.
• “Demand” shocks might help. Wen [2006, Jecd]

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1.3. The nature of technological shocks

• The usual assumption is that technology shocks are surprises.
• Beaudry & Portier [2006, Aer] show that (permanent) technology

improvements diffuse slowly over time, and are forecastable to a large extent.

• In the short–run, these news shock stimulate the demand for in-

vestment goods, and might not trigger reallocation.

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Outline of the Talk

• 1. Motivation
• 2. The Propagation of News Shocks : Facts
• 3. NBC and IBC in a canonical model
• 4. NBC and IBC in an extended model

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• 2. The Propagation of News Shocks: Facts

2.1. Conditional moments

• If technological change diffuses slowly over time, ‘forward’ variables

may react faster than usual indicators of technology.

• We identify news shock using TFP (corrected for utilization) and

stock market capitalization (SP)

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• BP 2006:
• ∆TFPi,t

∆SPi,t

• = A(L)
• ε1,t

ε2,t

• =
• I + +∞

k=0 AkLk

ε1,t ε2,t

• .
• the news shock ε2,t has no impact on TFP in country i;
• Here

  

∆TFPi,t ∆SPi,t Xj,t

   = ˜

A(L)

  

ε1,t ε2,t ε3,t

   =

• I + +∞

k=0 ˜

AkLk

  

ε1,t ε2,t ε3,t

  

with ˜ Ak =

  

Ak × × ×

   .

• the news shock ε2,t has no impact on TFP in country i;
• the third shock ε3,t has no impact on TFP and stock prices in

country i.

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2.2. US news shocks and their propagation

• (Corrected TFP, SP) VECM with 5 lags.
• The US news shock has a significant long–run effect on US TFP

and explains a large share of the forecast error.

• It has almost no impact on US TFP during the first five years

⇒ this is not a TFP surprise. Response to a news shock, USA

5 10 15 20 25 30 −0.4 −0.2 0.2 0.4 0.6 0.8 1 CTFP 50 100 150 200 0.1 0.2 0.3 0.4 0.5 0.6 0.7 CTFP

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Response to a news shock, USA

10 20 0.2 0.4 0.6 0.8 1 1.2 1.4 C 10 20 0.5 1 1.5 2 2.5 3 3.5 I 10 20 −0.2 0.2 0.4 0.6 0.8 N 10 20 −0.5 0.5 1 1.5 Y 10 20 0.2 0.4 0.6 0.8 1 1.2 1.4 C+I+X−M 10 20 −0.15 −0.1 −0.05 0.05 (X−M)/Y

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• A news shock triggers an expansion in Canada as well as in the US.

Response of Canadian aggregates to a news on US TFP

10 20 −0.4 −0.2 0.2 0.4 0.6 0.8 1 10 20 −1 −0.5 0.5 1 1.5 2 2.5 10 20 −0.4 −0.2 0.2 0.4 0.6 0.8 1 10 20 −0.5 0.5 1 1.5 10 20 −0.5 0.5 1 1.5 10 20 −0.4 −0.2 0.2 0.4 0.6 C I N Y C+I+X−M (X−M)/Y

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2.3. German news shocks and their propagation

• German data are from Haertel & Lucke [2006]. (Corrected TFP,

SP) VECM with 2 lags.

• The permanent improvement in TFP takes place after 4 years.

Response to a news shock, Germany

5 10 15 20 25 30 −0.4 −0.2 0.2 0.4 0.6 0.8 1 CTFP 50 100 150 200 0.1 0.2 0.3 0.4 0.5 CTFP

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Response to a news shock, Germany

10 20 −0.5 0.5 1 1.5 2 10 20 −0.5 0.5 1 1.5 2 10 20 −0.4 −0.2 0.2 0.4 0.6 10 20 −0.5 0.5 1 1.5 2 10 20 −0.5 0.5 1 1.5 2 10 20 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 C I N Y C+I+X−M (X−M)/Y

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Response of Austrian aggregates to a News on German TFP

10 20 −1 1 2 3 C 10 20 −1 1 2 I 10 20 −0.4 −0.2 0.2 0.4 N 10 20 −0.5 0.5 1 1.5 Y 10 20 −1 1 2 C+I+X−M 10 20 −0.4 −0.2 0.2 0.4 (X−M)/Y

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Response of French aggregates to a News on German TFP

10 20 −0.5 0.5 1 1.5 10 20 −1 1 2 3 4 5 10 20 −0.2 0.2 0.4 0.6 0.8 1 1.2 10 20 −0.2 0.2 0.4 0.6 0.8 1 1.2 10 20 −0.5 0.5 1 1.5 2 10 20 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 C I N Y C+I+X−M (X−M)/Y

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Response of Bristish aggregates to a News on German TFP

10 20 −0.5 0.5 1 1.5 2 10 20 −1 1 2 3 4 10 20 −0.2 0.2 0.4 0.6 0.8 1 1.2 10 20 −0.2 0.2 0.4 0.6 0.8 1 1.2 10 20 −0.5 0.5 1 1.5 10 20 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 C I N Y C+I+X−M (X−M)/Y

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Response of Italian aggregates to a News on German TFP

10 20 −0.5 0.5 1 1.5 10 20 −0.5 0.5 1 1.5 2 2.5 3 10 20 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 10 20 −0.5 0.5 1 1.5 10 20 −0.5 0.5 1 1.5 10 20 −0.2 −0.1 0.1 0.2 0.3 C I N Y C+I+X−M (X−M)/Y

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2.4. What have we learned?

• Conditional on news to future TFP, main macro aggregates display

strong comovements across countries.

• We now try to account for these findings.

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• 3. NBC and IBC in a canonical model
• Here we show that in a canonical model, news shocks is a IBC

driving force

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3.1. The model

• A 2-country, 1-good economy. The economy is hit by technology

shocks θA,t and θB,t. Capital quantity and location are predetermined.

• Choose
• Cj,t, Hj,t, Ij,t, Kj,t+1
• j=A,B in order to

max E0

+∞

• t=0

βt U

• CA,t, 1 − HA,t
• + U
• CB,t, 1 − HB,t
• subject to

                

KA,t+1 ≤ (1 − δ) KA,t + IA,t KB,t+1 ≤ (1 − δ) KB,t + IB,t CA,t + CB,t + IA,t + IB,t ≤ F

• KA,t, HA,t; θA,t
• YA,t

+ F

• KB,t, HB,t; θB,t
• YB,t

KA,0 = KB,0 given .

• We make the further simplifying assumption that preferences are

separable in consumption and leisure (U12 = 0).

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3.2. Some Propositions

• Some propositions can be proved, that show the respective role of

local/global/surprises/news in creating NBC and IBC.

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Result 1 In response to global surprises (θA,t = θB,t ∀t), equilibrium allocations are symmetrical. The model displays IBC. Under functional and parameters restrictions, the model also displays NBC.

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World Technological Surprise

2 4 6 8 10 0.5 1 1.5 2 4 6 8 10 0.35 0.4 0.45 0.5 2 4 6 8 10 5 10 15 2 4 6 8 10 0.5 1 1.5 2 4 6 8 10 −0.5 0.5 1 ΘA ΘB CA CB IA IB YA YB HA HB

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Result 2 If technology shocks are local and surprises (dθA,t > 0, dθB,t = 0 for some t), then hours worked are not perfectly corre- lated across countries. For realistic settings, hours and investments are negatively correlated. There is therefore no IBC and no NBC in the foreign country.

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Local Technological Surprise

2 4 6 8 10 0.5 1 1.5 ΘA ΘB 2 4 6 8 10 0.2 0.25 0.3 CA CB 2 4 6 8 10 −4000 −2000 2000 4000 IA IB 2 4 6 8 10 −100 −50 50 100 YA YB 2 4 6 8 10 −100 −50 50 100 HA HB

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Result 3 If technology shocks are announced/forecastable N periods in advance, then allocations are symmetrical in the N −1 first periods

• f the interim period, for both world and local news ⇒ IBC.

In the interim period, consumption and hours always move in opposite directions ⇒ no NBC.

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(World) Technological News

2 4 6 8 10 0.5 1 1.5 2 4 6 8 0.02 0.04 0.06 0.08 2 4 6 8 −10 −8 −6 −4 −2 2 4 6 8 −0.8 −0.6 −0.4 −0.2 2 4 6 8 −1 −0.8 −0.6 −0.4 −0.2 ΘA ΘB CA CB IA IB YA YB HA HB

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(Local) Technological News

2 4 6 8 10 0.5 1 1.5 ΘA ΘB 2 4 6 8 0.01 0.02 0.03 0.04 0.05 CA CB 2 4 6 8 −4000 −2000 2000 4000 IA IB 2 4 6 8 −0.8 −0.6 −0.4 −0.2 YA YB 2 4 6 8 −0.6 −0.5 −0.4 −0.3 −0.2 HA HB

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• 4. An Extended Model
• We build on Beaudry & Portier [2004, jme] “Pigou model”
• Building blocks are :
• 1. Two sectors in each countries (Consumption and Investment (struc-

tures))

• 2. Capital and Labor are complementary in the consumption good

sector

• 3. There are static gains to trade (Armington aggregators for con-

sumption and investment goods) + Investment is produced with labor only, with DRS

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4.1. Model Choose

• Cj,t, Hj,t, ˜

Hj,t, Ij,t, Kj,t+1

• j=A,B in order to

max E0

+∞

• t=0

βt ln CA,t − χ

• HA,t + ˜

HA,t

• + ln CB,t − χ
• HB,t + ˜

HB,t

• s.t.

                                  

KA,t+1 ≤ (1 − δ) KA,t + IA,t XA,t ≤

• ΘA,t ˜

HαX

A,t

ZA,t ≤

• a
• ΘA,tHϕ

A,t

• + Kν

A,t

1

ν

CA,t ≤

• bZνC

AA,t + (1 − b)ZνC BA,t

1

νC

IA,t ≤

• bXνI

AA,t + (1 − b)XνI BA,t

1

νI

idem country B KA,0 = KB,0 given.

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4.2. Result: Local Technological News to ΘA

2 4 6 8 10 0.5 1 1.5 %

ΘA ΘB

2 4 6 8 10 0.5 1 %

CA CB

2 4 6 8 10 0.5 1 %

HA HB

2 4 6 8 10 2 4 6 %

IA IB

2 4 6 8 10 0.5 1 %

YA YB 31

4.3. To Sum Up

• News shocks are observed to create NBC and IBC
• One can design almost standard models to account for this
• In progress: Reproduce VARs conditional responses with simulated

data

• In progress: Check for unconditional moments

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