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Discussion of
“General equilibrium model of Bank
Indonesia ((GEMBI)) 2007”
Feng Zhu
Bank for International Settlements
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Overview of the paper
z BI experience:
p
practical
p
DSGE modelling
g
¾ Oriented towards policy issues: theory & reality
¾ Emerging
g g economy
y features: complexity
y
¾ Large & frequent changes in economic structure
¾ Transition dynamics vs. steady state
z Great efforts & progress
¾ Well-conceived & developed
p
¾ Impulse responses to important shocks
¾ Very interesting findings
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DSGE Modeling in Central Banks
z Canonical models
•
•
•
Christiano, Eichenbaum & Evans (2005)
Laxton & Pesenti (2003)
S t & Wouter
Smets
W t (2003)
z Why DSGEs at central banks?
•
•
Better policy analysis
Better forecasts, or both
z Some examples
•
•
BEQM, MAS, NEMO, RAMSES, ToTEM
Many CBs have DSGE(s), some use a DSGE as
benchmark
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GEMBI: features I
z Six sectors ((complex
p
GEMBI))
¾ Household; Firms (intermediate/importers & final):
agriculture; Banking; Government (FA & BI);
External sector (ROW)
¾ Complex: 33 equations, 43 parameters
z Rich & interesting features
¾ Optimisation-based model, hybrid expectations
¾ General equilibrium with clear transmission
¾ Heterogeneous households (complex version)
¾ Agriculture; Commodity producers
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GEMBI: features II
z Core inflation
¾ Unconventional definition: interpreted as PPI
¾ But really: less volatile component here
¾ Permanent-transitory decomposition
z Separate budget constraints for CB & FA
¾ CB b
balance
l
sheet
h t concerns can llead
d tto iindeterminacy
d t
i
or instability
¾ Issuing CB bills without fiscal backup can lead to
balance sheet constraints on policy: BG≠ BCB
¾ Definition of fiscal rule (spending): taxation
¾ Analysis of zone of indeterminacy & instability
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Rigor-fit tradeoff: DSGE free lunch?
z Theoretical rigor & goodness of fit
¾ Theoretical consistency: compelling story?
¾ Data coherency: good fit?
¾ Parsimony (theory) & complexity (reality): good compromise?
z Theoretical consistency?
¾ Completely specified, economy-wide, probabilistic model for
observed time series
z Good fit to data & superior forecasts?
¾ Smets and Wouters (2003), Edge, Kiley & Laforte (2006),
Adolfson, Andersson, Lindé, Villani & Vredin (2006), Adolfson,
Lindé & Villani (2007);
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Structural GEMBI?
z Is GEMBI model structural?
¾ Immune from Lucas Critique?
z Shocks & frictions: “wedgeology”
g
gy
¾ Examples: habit formation, adjustment costs, CalvoYun price & wage devices, judgment
¾ Shocks “structural” or statistical necessity? Microfounded frictions? Why 1st-order AR process? Why
independent source of variations in DSGE?
¾ Wedgeology (Hansen & Sargent 2008): adjustment
for risk or uncertainty?
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Taking GEMBI to data I
z Is GEMBI model data-consistent?
¾ Beyond calibration: estimated GEMBI
¾ Immune from Sims Critique?
z Shocks & frictions: add factors
¾ Remedy for empirical failures of baseline DSGE?
more interesting dynamics? Better fit?
¾ Sims (2008): “add enough sources of friction and
inertia … so that it can fit the complex
p
behavior we
see in data” but this “strains the credibility of the
claims to complete behavioral interpretation”
¾ Does the model detect structural changes?
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Taking GEMBI to data II
z Bayesian estimation of GEMBI
¾ Posterior too close to prior
z Usefulness - focus
¾ More on medium-size estimable GEMBI
¾ Less on model forecasts
¾ DSGE advantage: understanding the mechanism
¾ Weak identification & small samples, data quality
¾ Great uncertainties with model, parameter estimates
& data: other models & data analysis
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Taking GEMBI to reality I
z A changing world?
¾ Large, persistent shocks to commodity prices
¾ Shocks from some advanced industrial economies
¾ Possibility of stagflation & frequent crises, 1970s style
z LPHI events & uncertainty: avoid large errors
¾ Uncertainty typical in EMEs (model, data, parameter)
¾ Weak financial-real linkages in models
¾ Pervasive nonlinearity: linearisation insufficient
¾ Robust monetary policy at turning points: direction of
monetary policy?
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Taking GEMBI to reality II
z Assessing GEMBI as a FPAS
¾ Impulse responses (vs. VAR empirics?)
¾ Statistical fit (& forecasting performance)
¾ Assessment of current state of economy
¾ Usefulness for policy decisions
z GEMBI and policymaking
¾ IT & clarity in monetary transmission
¾ Shaping expectations
¾ Financial stability as a goal?
¾ Policymaking under uncertainty
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Making GEMBI work
z Do we trust our model?
¾ Too much is missing in any model (incl GEMBI)
¾ Assuming
g stationarity
y ((detrending)?
g) DSGEs often not
trusted in lowest frequencies & misbehave in very
high frequencies
¾ Credible & practical model
z Use of GEMBI & communications
¾ How to form and substantiate model forecasts?
¾ Is the model too complex for decision makers?
¾ How to communicate model output to public?
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