News, Stock Prices and Economic Fluctuations Paul Beaudry & - - PowerPoint PPT Presentation

News, Stock Prices and Economic Fluctuations

Paul Beaudry & Franck Portier University of British Columbia & Universit´ e de Toulouse March 2004 Oxford Anglo-French Meeting

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Introduction

• What drives business cycle fluctuations?
• What is the relative importance of demand versus supply shocks?
• Can we identify the class of models most capable of explaining the

reaction of the economy to such shocks?

• We present evidence suggesting that business cycle fluctuations may

be primarily (or at least largely) driven by a shock which is neither a traditional demand or technology shock, but is instead a type of hybrid which admits a simple structural interpretation as a news shock.

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Empirical Strategy

• We perform two different orthogonalization schemes as a means of

identifying properties of the data, that can then be used to evaluate different theories of business cycles.

• We impose sequentially, not simultaneously, either impact or long

run restrictions on the orthogonalized moving average representation

• f the data.
• The primary system of variables that interests us is one composed
• f measured total factor productivity (TFP) and an index of stock

market value (SP).

• Stock prices are likely to be a good variable for capturing any

changes in agents expectations about future economic growth.

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Main Results

• Data on TFP and stock market value have properties that run

counter to the demand and supply type dichotomy inherent to most New Keynesian and RBC models.

• The innovation in stock prices which is contemporaneously orthog-
• nal to TFP is actually extremely correlated with the shock that

explains long run movements in TFP.

• The observed pattern is easily understood as the result of news/diffusion

shocks, that is, innovations in agents expectations of future techno- logical opportunities that arise before these opportunities are actually productive in the market.

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• This particular shock series cause standard business cycle co-movements

(i.e., induce positive co-movement between consumption and invest- ment) and explains a large fraction of business cycle fluctuations.

Plan of the talk

• 1. Using impact and long-run restriction sequentially to learn about

macroeconomic fluctuations

• 2. Data and Specification Issues
• 3. Results in bi-variate system
• 4. Higher Dimension Systems

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1. Using impact and long-run restriction sequentially to learn about macroeconomic fluctuations 1.2. Main Idea

• Using short run and long run restrictions in VARs, not simulta-

neously, but instead sequentially as a means of evaluating different classes of economic models.

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• Simple bi-variate system.
• Measured total factor productivity TFPt, and a forward looking

economic decision variable Xt.

• The only characteristic of Xt that is important for our argument is

that it be a jump variable, that is, a variable that can immediately react to changes in information without lag (stock price, interest rate, even consumption).

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• We consider two alternative representations of the Bivariate VAR

with orthogonalized errors:

• ∆TFPt

∆Xt

• = Γ(L)
• ǫ1,t

ǫ2,t

• ,

(1)

• ∆TFPt

∆Xt

• = ˜

Γ(L)

• ˜

ǫ1,t ˜ ǫ2,t

• ,

(2)

• short run restriction: Γ0(1,2) = 0 (ǫ2 has no contemporaneous im-

pact on TFP)

• long run restriction: ˜

Γ(1)(1,2)

• = ∞

i=0 ˜

Γi

• = 0 (˜

ǫ2 has no long run impact on TFP)

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• Our idea now is to use these two different ways of organizing the

data to help evaluate different classes of economic models.

• For example, a particular theory may imply that the correlation

between the resulting errors ǫ2 and ˜ ǫ1 be close to zero and that their associated impulses responses be different.

• Therefore, we can evaluate the relevance of such a theory by ex-

amining the validity of its implications along such a dimension.

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1.2. The predictions of three simple models (a) A New Keynesian model

• The model is driven by monetary shocks and surprise changes in

technology

• It is an economy with no capital, monopolistic competition, mone-

tary shocks, pre-set wages and technological disturbances. U = E0

∞

• t=0

βt

 log Cj

t − Λ(Lj t)σ

σ

 

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y =

1

• zρ1

i di

1

ρ1 ,

0 < ρ1 < 1 zi = θt

1

• lρ2

j dj

1

ρ2 ,

0 < ρ2 < 1

• θ is a random walk
• Firms have a value because there is some monopoly power

• The model solution can be written as
• ∆TFPt

∆SPt

• =
• 1

1 (1 − L)

• η1,t

η2,t

• (1)
• This model implies ǫ1 = η1, ǫ2 = η2,

ǫ1 = η1 and ǫ2 = η2

• In particular, this type of model implies that ǫ2 ⊥

ǫ1.

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(b) A simple RBC model with technology and preference shocks

• TFP is again a random walk
• U = E0

∞

t=0 βt

• log Cj

t − Λt (Lj

t)σ

σ

• , Λt = η2 iid
• The model solution can be written as
• ∆TFPt

∆pb

t

• =

 

1 (1 − γ)

1 1−γL − 1

−(1−L)(1−γ)2

σ(1−γL)

 

• η1,t

η2,t

• (2)
• This model implies ǫ1 = η1, ǫ2 = η2,

ǫ1 = η1 and ǫ2 = η2

• In particular, this type of model implies that ǫ2 ⊥

ǫ1.

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(c) A model with delayed response of innovation on productivity

• Measured TFP, denoted θ, is composed of two components:

a non-stationary component Dt and a stationary component νt.

        

θt = Dt + νt Dt =

∞

i=0 diη1,t−i

di = 1 − δi, 0 ≥ δ < 1 νt = ρνt−1 + η2,t, 0 ≤ ρ < 1

• We call Dt a diffusion process since an innovation η1 is restricted

to have no immediate impact on productive capacity (d0 = 0), the effect of the technological innovation on productivity is assumed to grow over time (di ≤ di+1) and the long run effect is normalized to 1.

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• To derive the implied structural moving average for ∆TFP and

∆SP, we consider a simple Lucas’ tree type of model

• The ownership of the unique tree of the economy is tradable and

where it pays dividend θt.

• Households consume and trade firms shares.
• U = E0

∞

t=0 βtC1−σ

t

1−σ , σ = 0

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• The model solution can be written as
• ∆TFPt

∆SPt

• =

  

(1 − δ) ∞

i=1 δi−1Li (1−L) (1−ρL) β(1−δ) 1−βδ

∞

i=0 δiLi βρ 1−βρ 1−L 1−ρL

  

• η1,t

η2,t

• 15

• the impact matrix on levels of TFP and SP is of the form:

 

1

β(1−δ) 1−βδ βρ 1−βρ

 

(3) And the long run matrix for the levels of TFP and SP is of the form

• 1

β 1−βδ

• (4)
• This model implies ǫ1 = η1, ǫ2 = η2,

ǫ1 = η1 and ǫ2 = η2

• In particular, this type of model implies that ǫ2 ⊥

ǫ1.

• This model implies ǫ1 = η2, ǫ2 = η1,

ǫ1 = η1 and ǫ2 = η2

• In particular, we have that ǫ2 is co-linear to

ǫ1.

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Discussion

• The important aspect of the first model is that it implies that

business cycle fluctuations that can be decomposed into structurally meaningful supply driven and demand driven components.

• The non technological disturbance – which we can refer to as the

demand disturbance – should be contemporaneously orthogonal to innovations in TFP and should not cause long run movements in TFP.

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• The news/diffusion model is different.
• Even before technological opportunities have actually expanded an

economy’s production possibility set, forward looking variables already incorporate this possibility (implementation cycle models also do the job)

• Such a model does not validate a decomposition in terms of demand

versus supply effect.

• In the short run, an anticipated technological improvement (a news

shock) looks like a demand effect, while in the long run it looks like a supply effect.

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• 2. Data and Specification Issues

2.1. Data The data we use are U.S. 1948Q1-2000Q4 non farm PBS, per capita

• Stock Price: S&P500, deflated by GDP deflator
• TFP: Non farm private business sector, computed with GDP, hours,

a constant labor share α which is the average over 1948-98, as com- puted by the BLS, capital services (BLS): log TFPt = log

 

GDPt

Hoursα

t × Capital services(1−α) t

 

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• We check for robustness to various refinements of the TFP measure

(variable capital utilization, varying α)

• Consumption is personal consumption of non durable goods and

services.

• Investment is fixed private domestic investment plus personal con-

sumption of durable goods.

1960 1980 2000 50 100 150 200 250 TFP % deviation from 1948 1960 1980 2000 50 100 150 200 250 Stock Prices % deviation from 1948 1960 1980 2000 50 100 150 200 250 Consumption % deviation from 1948 1960 1980 2000 50 100 150 200 250 Investment % deviation from 1948

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2.2. VAR Specification

• Main idea: impose as less structure as possible on the data 6

lags, levels or n − 1 cointegrating relations

• 3. Bi-variate VAR
• We model the joint behavior of TFP and the stock price index.

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ǫ2 Against ǫ1 in the (TFP, SP) VAR, baseline specification

−4 −3 −2 −1 1 2 3 4 −4 −3 −2 −1 1 2 3 4 ε2 ε1 tilde

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Impulse Responses to ǫ2 (plain lines)) and ǫ1 (circles) in the (TFP, SP) VAR

5 10 15 20 −0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 TFP quarters % deviation 5 10 15 20 5 6 7 8 9 10 11 Stock prices quarters % deviation 23

Robustness to Cointegration: Impulse Responses to ǫ2 (upper panels) and ǫ1 (lower panels) in the (TFP, SP) VAR

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5 10 15 20 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 TFP quarters % deviation

VECM Levels

5 10 15 20 5 6 7 8 9 10 11 Stock Prices quarters % deviation

VECM Levels

5 10 15 20 −0.6 −0.4 −0.2 0.2 0.4 0.6 0.8 1 TFP quarters % deviation

VECM Levels

5 10 15 20 4 5 6 7 8 9 10 11 Stock Prices quarters % deviation

VECM Levels

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Robustness to the Lag Structure: Impulse Responses to ǫ2 (upper panels) and ǫ1 (lower panels) in the (TFP, SP) VAR

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5 10 15 20 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 TFP quarters % deviation

5 lags 2 lags

5 10 15 20 5 6 7 8 9 10 11 Stock Prices quarters % deviation

5 lags 2 lags

5 10 15 20 −0.6 −0.4 −0.2 0.2 0.4 0.6 0.8 1 TFP quarters % deviation

5 lags 2 lags

5 10 15 20 4 5 6 7 8 9 10 11 Stock Prices quarters % deviation

5 lags 2 lags

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Impulse Responses to ǫ2 in the (TFP, SP) VAR

2 4 6 8 10 12 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 quarters % deviation Consumption 2 4 6 8 10 12 0.5 1 1.5 2 2.5 3 quarters % deviation Investment

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• Results are robust to the measure of TFP
• Let’s look at a (annual) “clean” measure of Basu, Kimball & Fernald

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ǫ2 Against ǫ1 in the (TFP, SP) VAR, baseline specification, using Basu, Fernald and Kimball (1999) measure of TFP (annual, 1949-1989)

−4 −3 −2 −1 1 2 3 4 −4 −3 −2 −1 1 2 3 4 ε2 ε1 tilde 30

Impulse Responses to shocks ǫ2 and ǫ1 in the (TFP, SP) VAR, using Basu, Fernald and Kimball (1999) measure of TFP (annual, 1949-1989)

5 10 15 20 −1 1 2 3 4 5 6 TFP years % deviation 5 10 15 20 5 10 15 20 25 30 35 40 45 Stock prices years % deviation

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• We cannot do as well on our quarterly data.
• We simply correct for variable capital utilization at the aggregate

level

• Likely to overcorrect, as u is measured in the industry only
• But results are again robust

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ǫ2 Against ǫ1 in the (TFP, SP) VAR, Using Annual Observations (1948-2000), with-

• ut Adjusting TFP for Capacity Utilization (left panel) or with TFP Adjustment

−4 −3 −2 −1 1 2 3 4 −4 −3 −2 −1 1 2 3 4 ε2 ε1 tilde −4 −3 −2 −1 1 2 3 4 −4 −3 −2 −1 1 2 3 4 ε2 ε1 tilde 33

Impulse Responses to ǫ2 in the (TFP, SP) VAR, Quarterly Data, with or without Correction for Variable Capacity Utilization

5 10 15 20 25 30 35 40 −0.4 −0.2 0.2 0.4 0.6 0.8 1 TFP quarters % deviation 5 10 15 20 25 30 35 40 3 4 5 6 7 8 9 10 Stock prices quarters % deviation 34

Summary

• The permanent innovation to TFP does not move TFP on im-

pact, while other macroeconomic aggregates (consumption, invest- ment, hours) respond positively on impact.

• This shock is observed to be collinear to a shock obtained by as-

suming zero impact on TFP.

• An interpretation for this shock is that it brings some news about

permanent level of TFP, before this new level is effectively reached.

• This view that a third shock (which is neither “demand” nor “sup-

ply”) may be an important source of short run fluctuations needs to be pushed further.

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• To do so, we study now larger dimension systems, in which we

explicitly allow for the presence of traditional demand and supply shocks.

• 3. Higher Dimension Systems

3.1. A (TFP, SP, C) VAR

• Short run identification:
• the 1,2 element of the impact matrix be equal to zero, and recu-

perate the associate shock ǫ2.

• no restrictions related to the shock ǫ1 as to let it potentially represent

an unanticipated technology shock.

• As for the shock ˜

ǫ3, we impose that it have no long run effect on either TFP or Consumption, and therefore it could potentially capture traditional demand effects.

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• Long run identification: easier, we simply impose that lower trian-

gular long run impact matrix, so that ǫ1 is the only permanent shock to TFP

• Our previous results are strikingly robust

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ǫ2 Against ǫ1 in the (TFP, SP, C) VAR, without (left panel) or with (right panel) Adjusting TFP for Capacity Utilization

−4 −3 −2 −1 1 2 3 4 −4 −3 −2 −1 1 2 3 4 ε2 ε1 tilde −4 −3 −2 −1 1 2 3 4 −4 −3 −2 −1 1 2 3 4 ε2 ε1 tilde 38

Impulse Responses to ǫ3 (upper panels) and ǫ1 (lower panels) in the (TFP, SP, C) VAR

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5 10 15 20 25 30 35 40 −0.6 −0.4 −0.2 0.2 0.4 0.6 0.8 TFP quarters % deviation 5 10 15 20 25 30 35 40 −1 1 2 3 4 5 6 7 8 Stock prices quarters % deviation 5 10 15 20 25 30 35 40 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 Consumption quarters % deviation 5 10 15 20 25 30 35 40 0.2 0.4 0.6 0.8 1 1.2 1.4 TFP quarters % deviation 5 10 15 20 25 30 35 40 −8 −6 −4 −2 2 4 6 Stock prices quarters % deviation 5 10 15 20 25 30 35 40 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 Consumption quarters % deviation

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3.2. 4-variables Systems

• We add I , C + I or H to the 3-VAR.
• The long run identification is as before: “long run Choleski”

ǫ1 is the only permanent shock to TFP

• Short run: As before + ǫ4 is a 4th variable specific shock.
• Again, we find very similar results

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ǫ2 Against ǫ1 in the (TFP, SP, C, I) VAR (left panel), in the (TFP, SP, C, C +I) VAR (center panel) and in the (TFP, SP, C, H) VAR (right panel), baseline specification

−4 −3 −2 −1 1 2 3 4 −4 −3 −2 −1 1 2 3 4 ε2 ε1 tilde −4 −3 −2 −1 1 2 3 4 −4 −3 −2 −1 1 2 3 4 ε2 ε1 tilde −4 −3 −2 −1 1 2 3 4 −4 −3 −2 −1 1 2 3 4 ε2 ε1 tilde

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Share of the Forecast Error Variance of Consumption (C), Investment I, Output (C + I) and hours (H) attributable to ǫ2 (left panel) and to ǫ1 (right panel) in 4-variables VARs, with non adjusted TFP (top panels) or adjusted TFP (bottom panels)

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5 10 15 20 25 30 0.2 0.4 0.6 0.8 1 quarters share of F.E.V.

C I C+I H

5 10 15 20 25 30 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 quarters share of F.E.V.

C I C+I H

5 10 15 20 25 30 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 quarters share of F.E.V.

C I C+I H

5 10 15 20 25 30 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 quarters share of F.E.V.

C I C+I H

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Conclusion

• We have examined the correlation between the innovations that

drive the long run movements in TFP and the innovation which is contemporaneously orthogonal to TFP.

• This correlation to be very positive and almost equal to 1.
• This observed positive correlation runs counter to that predicted by

many of the currently popular macroeconomic models

• This type of pattern appear consistent with a view that emphasizes

the role of expectations about future technological change (news) as a main driving force behind macroeconomic fluctuations.

• We believe that this later view deserves more attention.

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• The existence and prevalence of such a shock in the business cycle

has some important theoretical implications.

• We show in a companion paper that many of the models we use in

applied macroeconomic cannot display a aggregate boom following a shock to expectations, unless non convexities or rigid prices are assumed.

• In that paper, we derive a set of necessary and sufficient condi-

tions for expectationally driven fluctuations to exhibit positive co- movements of consumption, investment and hours.

• The simple interpretation of these conditions is that some com-

plementarity between input factors in multi-sectoral models (keeping convexity) is needed.

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