Quantitative methods for economic policy limits and new directions
Download
1 / 33

Quantitative methods for economic policy: limits and new directions - PowerPoint PPT Presentation


  • 100 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Quantitative methods for economic policy: limits and new directions' - kyle-jensen


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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


Outline

  • 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


    Before the outbreak of the global financial crisis


    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


    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


    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


    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?





    ad