Quantitative methods for economic policy: limits and new directions

1. Quantitative methods for economic policy: limits and new directions Ignazio Visco Banca d’Italia Philadelphia, 25 October 2014

2. 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

3. 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)

4. Before the outbreak of the global financial crisis Source: Banca d’Italia staff calculations *Obtained using a (non-centered) 10-year moving window

5. Before the outbreak of the global financial crisis

6. 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

7. Before the outbreak of the global financial crisis

8. 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)

9. 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

10. Limits unveiled • Real-financial linkages • Non-linearities • Increased interconnectedness

11. Limit #1: Real-financial linkages

12. 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

13. 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

14. 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

15. 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)

16. 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

17. 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?

18. 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?

19. Current challenges for macroeconomic policy • Taking advantage of large datasets • Modeling inflation expectations • Identifying structural vs. cyclical developments • Macroprudential policy

20. Challenge #1: Taking advantage of large datasets

21. 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

22. Challenge #1: Taking advantage of large datasets

23. 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

24. 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?

25. 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

26. 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

27. 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…

28. 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

29. 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?

30. 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?

31. Conclusion (I)

32. Conclusion (II)

33. Thanks