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Quantitative methods for economic policy: limits and new directions. Ignazio Visco Banca d ’ Italia Philadelphia, 25 October 2014. Outline. Before the outbreak of the global financial crisis Limits unveiled Real-financial linkages Non- linearities Increased interconnectedness

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quantitative methods for economic policy limits and new directions

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
  • Limits unveiled
      • Real-financial linkages
      • Non-linearities
      • 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-scalemacroeconometric models to more structural, medium-size “microfounded” DSGE models
  • Policy analysis framework in central banks: New Keynesian (NK) DSGE models
    • Rational expectations (RE), representative agent, real/nominal rigidities
    • Structural interpretation, complement to VAR analysis, positive and normative use
  • Forecasting: large-scale models
    • Flexibility, role of judgment
    • 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

Financial resources

collected by private sector

(percentage of GDP)

OTC and exchange-traded

derivatives in US

(notional value, trillion of USD)

Source: Banca d’Italia staff calculations

Source: Banca d’Italia staff calculations


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

  • Yet, explaining the dynamics of the crisis is crucial.
  • Analytical toolbox for macroeconomic policy must be repaired and updated

Limits unveiled

  • Real-financial linkages
  • Non-linearities
  • Increased interconnectedness

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. Medium-scale NK models enriched along several dimensions:
    • inclusion of financial intermediation and liquidity
    • private-sector leverage over the cycle and role of institutions
    • modelling unconventionalmonetary 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

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 outcomes
  • Problems with existing models:
    • Not enough information within historical data about shocks of such size and nature (“dummying out” of rare events)
    • Linear dynamics cannot properly account for shock transmission and propagation

Limit #2: Nonlinearities

Advancesin non-linear macroeconomic modeling

  • Models with time-varying parameters and stochastic volatility
    • Flexible, although structural interpretation may become tricky if all parameters are allowed to change
    • Large shocks and non-Gaussian (tail) dependence: Can macro borrow from financial econometrics?
  • Regime-switching models
    • Good in-sample fit. Less clear performance in out-of-sample forecasting
  • Nonlinear methods in NK models
      • 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 beyondtrade linkages and account for foreign asset exposure, global banks
  • Methods: Global VAR,Panel-VAR
    • Exploit cross-section data, static and dynamic links
    • Can account for changes in parametersthatcapture cross-country linkages and spillovers
    • Applications of network theoryto studyinterconnectedness
    • Modeling issues: common shocks or contagion?

Current challenges for macroeconomic policy

  • Taking advantage of large datasets
  • Modeling inflation expectations
  • Identifying structural vs. cyclical developments
  • Macroprudential policy

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
    • 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 sourcesto produce good “nowcasts”
    • bridge models and MIDAS
    • large Bayesian VARs
    • 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

€-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)
  • Machinelearning techniques: useful to cope with data of such size; can be applied to detect patterns and regularities, but… what role for economic theory?

Challenge #2: Modeling inflation expectations

  • At the zero lower bound, repeated downward revisions in inflation expectations may trigger a self-fulfilling deflationary spiral
  • 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:
    • Learning mechanisms (example)
    • Rationalinattention
    • Behaviouraleconomics

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 exception)
  • 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)
    • changes in “natural” rates
    • unemployment hysteresis effects
  • Large uncertainty surrounds global growth prospects
    • “Secular stagnation”
    • “Second Machine Age”
  • How to design appropriate macroeconomicpolicies? E.g. fiscal policy…

Challenge #3: Structural vs. cyclical developments

  • With the global financial crisis, public debt has reached record peacetime levels in many advanced economies
  • 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 sourcesof financial cycles?
    • Financial shocks, news shocks, risk/uncertainty shocks
  • What are the sourcesof systemic risk?
    • Pecuniary externalities, endogenous risk
  • What are the boundaries of the financial system?
    • Regulatory arbitrage, shadow banking system
  • How to assess conflicts and complementarity between monetary, micro and macroprudential policy?

Challenge #4: Macroprudential policy

  • Monitoring financial instability
    • Density forecasts and tail events
    • Early warning: which models/variables?
  • Data: effort in identifying data needs (G20 Data Gaps Initiative)
  • Empirical evidence on macroprudential policy effectiveness:
    • So far mostly on EMEs (evidence not clear-cut)
    • Identification issues: macroprudential used in conjunction with other policies
  • Methods
    • Event studies, stress tests, panel regressions, micro-data analysis, regime-switching, “microfounded ”.
    • Suite of models?