Download Paper`s Main Question - Columbia Business School

Discussion of
“Intertemporal Disturbances”
By G. Primiceri, E. Schaumburg,
and A. Tambalotti
Marc Giannoni
Columbia University
NY Area Workshop on Monetary Policy
FRB New York, November 2005
Paper’s Main Question
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What drives business cycle fluctuations?
z
Intratemporal disturbances
• shocks to FOC that relate MRS to MRT within a period
z
Intertemporal disturbances
• shocks to FOCs that relate MRS to MRT across periods
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Short answers:
z
z
Hall (1997), CKM (2005): Intratemporal
This paper: Intertemporal
1
What are these shocks?
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PST offer 3 interpretations:
z
z
z
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Genuine shifts in technology, preferences, …
Reduced-form representation of frictions (wedges,
CKM)
“convenient” representation of model
misspecification (shocks = model failure)
But adopt the last one:
z “intertemporal disturbances, i.e., the empirical
failures of the intertemporal optimization conditions”
Why is it important to know
which shocks matter?
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|
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Important to understand business cycles
Policy response may vary depending on shocks
If shocks are interpreted as indicating model failure:
provide directions for future economic research
z
Hall (1997): “The finding that the [intratemporal]
preference shift bears almost all of the burden of
explaining recessions should be the starting point for
research… more in areas of atemporal analysis than
in intertemporal analysis”
z
PST: “The finding that intertemporal disturbances are
paramount leads us to conclude that more effort
should be directed towards understanding agents’
intertemporal choices”
2
Using shocks as diagnostic for
where to conduct research?
What is dangerous with this logic?
z
Shocks may provide insights, but they
remain conditional on model adopted!
z
Changing one mechanism in model may
change all shocks!
Importance of intertemporal
disturbances (b): Basic intuition
Euler Equation: 1= E{[β(Ct+1/Ct)-1 (bt+1/bt)] (1+rt+1)}
b = small with macro data (rk) but large with financial data
return stocks (NYSE value weighted)
return 3m T-bill
rk-d = a (Y/K)-d
1.2
1.15
1.1
1.05
1
0.95
0.9
0.85
Q3:03
Q4:00
Q1:98
Q2:95
Q3:92
Q4:89
Q1:87
Q2:84
Q3:81
Q4:78
Q1:76
Q2:73
Q3:70
Q4:67
Q1:65
Q2:62
Q3:59
0.8
Q4:56
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If we had really believed that in 1997: we
would all have focused on improving
intratemporal conditions, and would have
never seen the model in PST!
Q1:54
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3
Basic intuition (2)
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Hall, CKM:
z use rk = α(Y/K) as real return
z Very smooth, as is Ct+1/Ct
z EE “fits”: small intertemporal shocks
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PST:
z Use financial data: real 3month T-Bill rate
z More volatile
z EE does not “fit” well: need large intertemporal shocks
z Conclude:
Finding of small intertemporal shocks is an artifact of
omitting information from asset prices
|
Important lesson:
z Be careful not omitting relevant information in
estimating model! (get back later)
Return on Capital
A self-interested question: Where can I invest in this capital stock and
earn rk (with almost no risk)?
1954:12004:3
Mean
Std. dev.
|
z
z
rk-δ
7.89
0.76
capital can be rented each period
or can invest in capital and resell on economy-wide market next quarter
Realistic?
What if capital is firm-specific? rk more volatile?
z
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Real return
on stocks
8.40
28.20
rk: presumes:
z
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3m Tbill
(real)
1.66
2.36
If so, EE may not fit even with rk … but rk would be consistent with
financial data
Firm-specific capital => important intertemporal disturbances?
4
Empirical analysis
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Estimate state-of-the-art model w/ many
frictions, shocks (CEE, Smets-Wouters)
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Look at special cases
z
z
z
z
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NK model: sticky prices, flexible wages, fixed
capital
RBC model (replicate Hall, CKM)
RBC model w/ real frictions
RBC model + price rigidities + Add interest rate
as observable variable
Potentially interesting: allows us to understand
what ingredients are responsible results
Empirical analysis: Quibbles
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Problems:
Change the set of shocks for each
model
z Change set of observable variables for
each model
z Introduce financial market data only
jointly w/ price rigidities
z
• Why not use real returns from financial
markets to estimate RBC model?
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Too many things change at same time!
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Quibbles: NK Model
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4 shocks: b, technology, MP, markup
…but no intratemporal taste shock (unfortunate!)
(not separately identified from markup)
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NKPC:
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Problem: 100% variance in πt due to markup!
Either k≈0 or mc≈0
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πt = k mct + β Etπt+1 + markupt
Taste shock would have a larger effect on output
than markup:
z
z
affects directly mc => employment => output
and/or mc => inflation (if k>0) => output
Quibble with Conclusion
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Full Model
z
z
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Inter. pref. shock accounts for 48% of ΔlogC …. but only 1% of ΔlogY
Other intertemporal shock (Invest.-specific tech. shock) accounts for 40%
of ΔlogY and 57% of ΔlogL: huge!
But when remove rigidities on consumption, investment, wage (Table 8):
z
z
z
Inter. pref. shock accounts for 5% of ΔlogY
Invest.-specific tech. shock accounts for 4% of ΔlogY and 2% of ΔlogL
Without these rigidities:
• “Variability of output remains an intratemporal phenonemon”
• Consistent with Hall, CKM despite the fact that include financial data!
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To assess sources of business cycles:
z
z
need to allow for a large class of possible shocks, frictions and data
series.
Not only data!
6
On the dangers of omitting
information
PST show that it is important to use
financial data to assess sources of
business cycles
| Full model estimated with 7 series:
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one for each concept: Y, C, I, L, W/P, π, R
z Observation equation
Xt = Ft
z
• Xt = data (e.g. Δlog(GDP defl.))
• Ft = model concepts (e.g. π)
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Is that enough?
Inflation: Which series?
Quarterly inflation (demeaned)
2.5
G D P d e fl.
P C E d e fl.
C P I a ll
2
1.5
1
0.5
0
-0 . 5
-1
-1 . 5
-2
1965
1970
1975
1980
1985
1990
1995
2000
7
One proposal: Estimate DSGE
model in data-rich environment
Boivin-Giannoni (2005)
| Generalize observation equation to:
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Xt = ΛSt + et
z
z
z
Xt = potentially large vector of data series [e.g.
Δlog(GDP defl.), Δlog(PCE defl.) ]
St = latent state vector of model economy (satisfies
restrictions imposed by model)
et = series-specific component not related to model
concept
Inflation: Which series?
Quarterly inflation (demeaned)
2.5
G D P d e fl.
P C E d e fl.
C P I a ll
E s t . in fla t io n
2
1.5
1
0.5
0
-0 . 5
-1
-1 . 5
-2
1965
1970
1975
1980
1985
1990
1995
2000
8
Estimate a similar DSGE model
in data-rich environment
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Results (Boivin-Giannoni, 2005):
z
Sources of BC fluctuations depend a lot on
data set considered
• E.g., if Δlog(GDP defl.) = π
•
π largely explained by markup shocks
• If π is estimated from several indicators
• markup shocks irrelevant
• Investment shocks matter even more when
consider large data set
• But labor supply shocks matter too
Conclusion
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PST: Very nice paper
Emphasizes role of intertemporal disturbances as sources of
BC fluctuations
z Are important when combined with rigidities and when model
confronted with financial data
Main contribution:
z Explain where relevance of intertemporal shocks comes from
z Explain differences with Hall and CKM
Suggestion:
z In comparing models, do not change as many things at the
same time
Important message (in my view)
z Be careful not to omit relevant information in estimating
model!
9