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. 2
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 of the data. • The primary system of variables that interests us is one composed of 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. 3
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 orthogonal 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 technological opportunities that arise before these opportunities are actually productive in the market. 4
• This particular shock series cause standard business cycle co-movements (i.e., induce positive co-movement between consumption and investment) 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 simultaneously, 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 T F Pt, 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:
∆T F Pt ∆Xt
!
∆T F Pt ∆Xt
!
= Γ(L)
˜ (L) =Γ
1,t 2,t
!
˜ 1,t ˜ 2,t
!
,
(1)
,
(2)
- short run restriction: Γ0(1,2) = 0 (2 has no contemporaneous impact on T F P ) h
i P∞ ˜ (1)(1,2) = i=0 Γ ˜ i = 0 (˜ -long run restriction: Γ 2 has no long run
impact on T F P ) 8
• 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 2 and ˜ 1 be close to zero and that their associated impulses responses be different. • Therefore, we can evaluate the relevance of such a theory by examining 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, monetary shocks, pre-set wages and technological disturbances.
U = E0
∞ X t=0
j σ (Lt ) j t β log Ct − Λ
σ
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y=
zi = θt
!1 Z 1 ρ1 ρ1 zi di , o
!1 Z 1 ρ2 ρ2 , lj dj o
0 < ρ1 < 1
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 ∆T F Pt ∆SPt
!
=
1 0 1 (1 − L)
!
η1,t η2,t
!
(1)
• This model implies 1 = η1, 2 = η2, e1 = η1 and e2 = η2 • In particular, this type of model implies that 2 ⊥ e1.
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(b) A simple RBC model with technology and preference shocks • T F P is again a random walk "
• U = E0
P∞ t log C j − Λ β t t t=0
j (Lt )σ
σ
#
, Λt = η2 iid
• The model solution can be written as ∆T F Pt ∆pbt
!
=
1 1 −1 − (1 − γ) 1−γL
0 (1−L)(1−γ)2
σ(1−γL)
η1,t η2,t
!
(2)
• This model implies 1 = η1, 2 = η2, e1 = η1 and e2 = η2 • In particular, this type of model implies that 2 ⊥ e1. 12
(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 D t di νt
= = = =
Dt + νt P∞ i=0 diη1,t−i 1 − δ i, 0≥δ
