La congiuntura e le prospettive

Quantitative methods for
economic policy:
limits and new directions
Ignazio Visco
Banca d’Italia
Philadelphia, 25 October 2014
Before the outbreak of the global financial crisis
II. Limits unveiled
1. Real-financial linkages
2. Non-linearities
3. Increased interconnectedness
III. Quantitative challenges for macroeconomic policy
Taking advantage of large datasets
Modeling inflation expectations
Identifying structural vs. cyclical developments
Macroprudential policy
Before the outbreak of the global financial crisis
 Policymaking tools: from large-scale macroeconometric models
to more structural, medium-size “microfounded” DSGE
 Policy analysis framework in central banks: New Keynesian
(NK) DSGE models
o Rational expectations (RE), representative agent, real/nominal
o Structural interpretation, complement to VAR analysis,
positive and normative use
 Forecasting: large-scale models
o Flexibility, role of judgment
o Provide detailed description of the economy (pros and cons)
Before the outbreak of the global financial crisis
Source: Banca d’Italia staff calculations
*Obtained using a (non-centered) 10-year moving window
Before the outbreak of the global financial crisis
“macroeconomics in this original sense has succeeded: its central
problem of depression prevention has been solved, for all
practical purposes, and has in fact been solved for many decades”
Robert Lucas, 2003
“the state of macro is good”
Olivier Blanchard, 2008
Before the outbreak of the global financial crisis
Financial resources
collected by private sector
OTC and exchange-traded
derivatives in US
(percentage of GDP)
(notional value, trillion of USD)
Source: Banca d’Italia staff calculations
Source: Banca d’Italia staff calculations
Before the outbreak of the global financial crisis
“Philosophically, I do not believe that the market system, in even
its purest form, provides adequate self-regulatory responses. The
economy definitely needs guidance – even leadership – and it is
up to professional economists to provide public policy makers
with the right information to deliver such leadership. As for the
methods of doing this, I see no alternative to the quantitative
approach of econometrics, but I do realize that all policy issues
are not quantitative and measurable. At times, subjective
decisions must also be made.”
Lawrence Klein (1992)
The outbreak of the global financial crisis
FRB/US Assessment of the Likelihood of Recent Events:
History Versus 2007Q4 Model Projection
Source: Chung, Laforte, Reifschneider, Williams (2012)
The outbreak of the global financial crisis
“One thing we are not going to have, now or ever, is a set of
models that forecasts sudden falls in the value of financial assets,
like the declines that followed the failure of Lehman Brothers”
Robert E. Lucas (2009)
“The crisis has made it clear that this view was wrong and that
there is a need for a deep reassessment.”
Olivier Blanchard (2014)
 Yet, explaining the dynamics of the crisis is crucial.
 Analytical toolbox for macroeconomic policy must be repaired
and updated
Limits unveiled
1. Real-financial linkages
2. Non-linearities
3. Increased interconnectedness
Limit #1: Real-financial linkages
“If the real sector of the economy does not function so well, for
instance, if it is dynamically unstable under some circumstances
[…] then the need for stabilization policies is hard to deny, and
with it the need to model financial and monetary sectors of
the economy”
Albert Ando (1979)
Limit #1: Real-financial linkages
 No financial sector in pre-crisis, workhorse NK models
used for policy analysis: one interest rate enough to track
cyclical dynamics and support normative analysis
 Why? Efficient Markets Hypothesis (EMH) behind the scenes:
market clearing and RE guarantee that all information is
efficiently used. No need to explicitly model financial sector…
 …nonetheless, significant work on financial factors in
pre-crisis NK models (e.g. financial accelerator)
 Important (overlooked) contributions in macroeconomic
literature: e.g. debt deflation, financial crises
Limit #1: Real-financial linkages
 The crisis has ignited promising research in this area. Mediumscale NK models enriched along several dimensions:
o inclusion of financial intermediation and liquidity
o private-sector leverage over the cycle and role of
o modelling unconventional monetary policy. Which
channels? Liquidity, credit, expectations
 Departures from representative agent framework
 More attention to country-specific institutional features:
shadow banking, sovereign risk, sovereign-banking linkages
 Risk and uncertainty: rediscovery of Knightian uncertainty
Limit #1: Real-financial linkages
 Large-scale macroeconometric models also shared the
absence of significant real-financial interactions
 However, they have historically proved to be flexible tools,
open to non-mechanical use of external information (with
“tender loving care”), especially in the occasion of unexpected
breaks in empirical regularities
 E.g. Klein (first oil shock, 1973): embed external information
in the Wharton and LINK model to account for
unprecedentedly large shock on oil prices, that no model
could handle
 In a similar vein today: role of credit in Bank of Italy model 14
Limit #1: Real-financial linkages
External information on loan supply restrictions
Effect on current-year GDP forecast error in 2008-2009 and 2011-2012 recessions
Source: Rodano, Siviero and Visco (2014)
Limit #2: Nonlinearities
 Pre-crisis empirical models were best suited to deal with
“regular” business cycles
 The crisis marked a huge discontinuity with the past…
 …in non-stationary environments, predictions based on past
probability distributions can differ persistently from actual
 Problems with existing models:
o Not enough information within historical data about shocks
of such size and nature (“dummying out” of rare events)
o Linear dynamics cannot properly account for shock
transmission and propagation
Limit #2: Nonlinearities
Advances in non-linear macroeconomic modeling
 Models with time-varying parameters and stochastic volatility
o Flexible, although structural interpretation may become
tricky if all parameters are allowed to change
o Large shocks and non-Gaussian (tail) dependence: Can
macro borrow from financial econometrics?
 Regime-switching models
o Good in-sample fit. Less clear performance in out-of-sample
 Nonlinear methods in NK models
o Global methods account for occasionally binding constraints,
uncertainty and to go beyond “small” shocks. Which/how
many nonlinearities?
Limit #3: Increased interconnectedness
 Trade linkages: (non-LINK) model forecasts typically rely on
assumptions about world demand, commodity prices,
exchange rates (all exogenous variables). Open-economy
dimension often contributes to large part of forecast errors,
especially during crisis
 Cross-border financial integration has markedly increased:
need to go beyond trade linkages and account for foreign
asset exposure, global banks
 Methods: Global VAR, Panel-VAR
o Exploit cross-section data, static and dynamic links
o Can account for changes in parameters that capture crosscountry linkages and spillovers
 Applications of network theory to study interconnectedness
 Modeling issues: common shocks or contagion?
Current challenges for macroeconomic policy
1. Taking advantage of large datasets
2. Modeling inflation expectations
3. Identifying structural vs. cyclical developments
4. Macroprudential policy
Challenge #1: Taking advantage of large datasets
“My present approach is to construct simple time-series models
of high frequency data based on latest information, by days or
weeks or months - for use in somewhat lower frequency
macromodels […] I am a proponent of combining different
sources of information, and the information source in this
case is cross-section data from survey investigations. They
should be integrated within macromodels.”
Lawrence Klein (1987)
Challenge #1: Taking advantage of large datasets
 In times of crisis, the availability of accurate data is more
crucial for policy analysis than it is in “normal” times
o The more timely, accurate and relevant the data, the better
our assessment of the current state of economic activity
 Various econometric instruments exploit data of different
types and sources to produce good “nowcasts”
o bridge models and MIDAS
o large Bayesian VARs
o factor models (Banca d’Italia: €-Coin)
 Combining evidence from models based on various datasets
and assumptions (‘thick modeling’: Granger) as a way to
account for growing uncertainty
Challenge #1: Taking advantage of large datasets
“Good predictions have two requisites that are often hard to
come by. First they require either a theoretical understanding
of the phenomena to be predicted, as a basis for the prediction
model, or phenomena that are sufficiently regular that they
can be simply extrapolated. Since the latter condition is seldom
satisfied by data about human affairs (or even by the weather),
our predictions will generally be only as good as our theories.
The second requisite for prediction is having reliable data
about the initial conditions – the starting point from which
the extrapolation is to be made.”
Herbert Simon (1981)
Challenge #1: Taking advantage of large datasets
€-coin indicator
Source: Bank of Italy. For details see: Altissimo, F., Bassanetti, A., Cristadoro, R., Forni, M., Hallin, M., Lippi, M., Reichlin, L. and Veronese, G. (2001). A
real Time Coincident Indicator for the euro area Business Cycle. CEPR Discussion Paper No. 3108; Altissimo, F., Cristadoro, R., Forni, M., Lippi, M.,
Veronese, G., New Eurocoin: Tracking economic growth in real time. The Review of Economics and Statistics, 2010
Challenge #1: Taking advantage of large datasets
 Nowcasting of many indicators can also benefit from use of
‘Big Data’: e.g. Google-based queries of unemployment
benefits claims, car and housing sales, loan modification, etc.
 Technological advances have made available a massive
quantity of data, which offer potentially useful information
for statistical and economic analysis (back, now and forecast)
 Machine learning techniques: useful to cope with data of
such size; can be applied to detect patterns and regularities,
but… what role for economic theory?
“deep statistical theory with much stochastic structure in the
analysis, […] is no substitute for economic theory. […]
Without theory and other a priori information, we are lost”
Lawrence Klein (1977)
Challenge #2: Modeling inflation expectations
 At the zero lower bound, repeated downward revisions in
inflation expectations may trigger a self-fulfilling deflationary
 Persistent differences in actual and expected inflation question
the validity of the RE assumption in policy models
 It is unlikely that households and firms can completely discount
the effects of current and future policies in their demand and
pricing decisions
 Macromodels for policy analysis have largely ignored research on:
o Learning mechanisms (example)
o Rational inattention
o Behavioural economics
Challenge #2: Modeling inflation expectations
Inflation expectations and price stability in the euro area
Rational expectations vs. adaptive learning
Source: Banca d’Italia; simulation of Clarida, Galí and Gertler 1999
Challenge #3: Structural vs. cyclical developments
 Financial crises are typically followed by a much slower
recovery than “normal” recessions (the current one is no
 For policy analysis it is imperative to disentangle the
structural and cyclical effects of the Great Recession
(although the two tend to be intimately related)
o changes in “natural” rates
o unemployment hysteresis effects
 Large uncertainty surrounds global growth prospects
o “Secular stagnation”
o “Second Machine Age”
 How to design appropriate macroeconomic policies? E.g. fiscal
Challenge #3: Structural vs. cyclical developments
 With the global financial crisis, public debt has reached record
peacetime levels in many advanced economies
“There is nothing in the Keynesian prescriptions to support
highly unbalanced policies or excessive reliance on monetary
policy to provide economic stabilization”
Lawrence Klein (1992)
 High levels of public debt are a source of vulnerability and
possible nonlinearities. How to measure fiscal sustainability and
model its effects on sovereign risk?
 Success of consolidation depends on credibility as well as on
long-run structural measures to increase potential output
 Models must account for both long and short-term factors
Challenge #4: Macroprudential policy
 Macroprudential policies: maintain stability of financial system
through containing systemic risks by increasing the resilience of
the system and leaning against build-up of financial imbalances
 What are the sources of financial cycles?
o Financial shocks, news shocks, risk/uncertainty shocks
 What are the sources of systemic risk?
o Pecuniary externalities, endogenous risk
 What are the boundaries of the financial system?
o Regulatory arbitrage, shadow banking system
 How to assess conflicts and complementarity between
monetary, micro and macroprudential policy?
Challenge #4: Macroprudential policy
 Monitoring financial instability
o Density forecasts and tail events
o Early warning: which models/variables?
 Data: effort in identifying data needs (G20 Data Gaps Initiative)
 Empirical evidence on macroprudential policy effectiveness:
o So far mostly on EMEs (evidence not clear-cut)
o Identification issues: macroprudential used in conjunction
with other policies
 Methods
o Event studies, stress tests, panel regressions, micro-data
analysis, regime-switching, “microfounded ”.
o Suite of models?
Conclusion (I)
“The history of science, like the history of all human ideas, is
a history of irresponsible dreams, of obstinacy, and of error.
But science is one of the very few human activities — perhaps
the only one — in which errors are systematically criticized
and fairly often, in time, corrected. This is why we can say that,
in science, we often learn from our mistakes, and why we can
speak clearly and sensibly about making progress there.”
Karl Popper (1963)
Conclusion (II)
“It is my firm belief that the only satisfactory test of
economics is the ability to predict, and in crucial predictive
situations such as reconversion after World War II, the
settlement of the Korean War, the settlement of the Vietnam
War, the abrupt economic policy switch of the Nixon
Administration in August 1972, the oil shock of 1973 (forecast
of a world-wide succession by LINK), the recession of 1990.
In these crucial periods, econometric models outperformed
other approaches, yet there is considerable room for
improvement, and that is precisely what is being examined in
development of high-frequency models that aim to forecast
the economy, every week, every fortnight, or every month,
depending on the degree of fineness of the information flow.”
Lawrence Klein (2005)

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