Macro-Econometric Modeling After the Crisis

1. Macro-Econometric Modeling After the Crisis Peter Pauly University of Toronto September 2010

2. What is the issue? • Global financial crisis as prompted re-examination of many of the tenets of what was seen as modern (accepted) economic theory. Why did we (most of us) miss (most of) the structural defects of core assumptions? • By implication, empirical analysis of interdependent economic structures (macro-econometric modeling) faces the same questions • Particular relevance to global economic models since many of the problems appear to be the result of neglected interdependencies • Review pre-crisis state-of-the-art. Then focus on various areas that require advances in theory/implementation to advance consensus models

3. Why models ? • Models as representions/simplifications are used in many fields • Structural econometric models and time-series models provide ‘maps’ to economy [ ‘metaphors’ ], but are not complete ‘automatons’ • DSGE models seek to uncover ‘deep structural parameters’ governing agents’ behavior • Type of ‘maps’ (detail/content) depends on particular use (normative vs. positive) and need for information • Size of models depends on information requirements and problem complexity [ ‘economic size’ vs. ‘numbers of equations’ ]

4. Econometric Models • How do we accumulate useful, quantitative, empirical knowledge of relevance to macroeconomic theorizing and policy making? • Macro-econometric modeling involves the following dichotomy: first, evidence needs to be assembled and summarized in a convenient and meaningful way while, in the second, effort is devoted to interpreting the evidence through a set of principles or theories. (Pagan, 2000) • To achieve these purposes, model must have sufficient structural detail to serve as an approximation of the economy and its multiple interactions. Good model imposes discipline on argumentation. • Model should provide sufficient detail to generate forecast of interest and include relevant policy variables and their transmission channels

5. Modeling Alternatives (1) • Traditional Macro-Models • Generally based on (ad hoc) decision rules, i.e. subject to Lucas Critique • Mostly demand side models • Either explicitly fixed-price (no supply side), or • Flex-price , on unspecified price inflexibility based on market imperfections (neo-Keynesian), but no full specification of supply side • VAR Critique : unbelievable a priori exclusion restrictions (“too much theory”) • GE Critique : data mining (“too little theory”)

6. Modeling Alternatives (2) • VAR/SVARs • Purely data-determined dynamic structures • No well-defined equilibrium • Limited number of variables (BVARs) • Conditional analysis difficult [‘Structural VARs’] • No contemporaneous feedback ; a priori causal ordering

7. Modeling Alternatives (3) • DSGEs • Strong equilibrium structure with explicit supply side • Flex-price equilibrium solution; some allowance for nominal rigidities and informational asymmetries • Seek to identify ‘deep structural’ parameters, with long-run focus • Estimation vs. Calibration. Calibration maps model behavior into observable moments of the data. But, lack of statistical theory makes it difficult to ascertain ‘data coherency’ and coefficient sets are not unique • Dominant in the academic community pre-crisis • Practical implementation in central banks and at IMF

8. Modeling Alternatives (4) • Large-scale modeling practice has seen remarkable convergence, helped by econometrics (‘cointegration’). Modern macro-econometric models combine elements from different traditions : • Well-defined long-run equilibrium solution • Explicit supply side • Strong theoretical priors. Issue is : ‘how much theory ?’ • Data-driven short-run disequilibrium dynamics compatible with market imperfections • Flexible expectations formation • Long-run AND short-run applicability

9. What went wrong (from a modeling perspective)? • Expectations Formation • Monetary Policy (1): Credit Channel • Monetary Policy (2): Institutional Structure • Representative Agents • Systemic Risk • International Monetary Linkages

10. Expectations formation • Models differ according to the specification of the information set Ω in : tYet+i = Et ( Yt+i| Ωt ) • Adaptive/Extrapolative Ωt = { Yt-1 , Yt-2 , … ) • Rational/Model consistent Ωt = { Yt-1 , Yt-2 , … Xt-1 , Xt-2 , … Xt+i, … , Φ ) • Weak (Bounded) Rationality Ωt = { Yt-1 , Yt-2 , … X’t-1 , X’t-2 , …, Φ’ )

11. Weak Rationality (1) • In forming their expectations, agents’ information set contains not the complete model, but only a subset • Linear model with expectation formation : Y = A X + B Ye + ɛ Ye = D X Y = A X + B D X + ɛ Requires the a priori choice of an information set. Modeler estimates D and amends the model by a model of expectations formation

12. Weak Rationality (2) • Model can be modified further by allowing agents’ learning about the structure, e.g. by way of a Kalman filter : D(t) = ơD(t-1) + v(t) • With increased number of variables and with increased sample size the algorithm will converge to RE. • Model can incorporate alternative inter-temporal utility assumptions and known behavioral biases, e.g. hyperbolic discounting, hindsight biases, and anchoring.

13. Weak Rationality (3) • Model is a natural generalization of RE • Avoids unrealistic extreme assumptions of RE • Allows for agents’ short-run mistakes and behavioral biases • But, requires significant a priori assumptions: specification of approximate information set and/or learning behavior, utility functions • Avoids undesirable RE forecast properties, e.g. instantaneous jumps to saddle paths that do not occur in reality or endpoint constraints that load all forecast errors into initial periods

14. Monetary Sector (1) • Significant changes are seen necessary in our understanding (and modeling) of monetary transmission channels • Mainstream models have paid insufficient attention to financial intermediation and credit channels • No ‘bank lending channel’ in mainstream transmission channels; models have tended to miss the changing roles of banks (e.g. new sources of funding other than deposits) • Traditional models link a representative real interest rate to levels of output through a simple aggregate analysis of monetary sector (‘Taylor rule’) • Actual interactions between multiple suppliers and demanders of funds lead to multiple interest rates

15. Monetary Sector (2) • Models need to explicitly incorporate credit demand and supplies • Interest rates paid to savers differs from rates at which intermediaries are able to fund themselves; spread between the two is an important indicator of systemic liquidity risk • Shadow banking system complicates borrower/lender interactions; role of derivative markets and hedge funds • Zero interest bound ignored; models do not allow for role for ‘quantitative easing’

16. Representative Agent • DSGEs (and most consensus structural models) explicitly or implicitly rely on representative agent (or dynasty) assumption • Combined with RE leads to unrealistic optimization; all-purpose decision maker cannot sufficiently explain macro-economic disequilibria. • Microeconomic ontology downplays aggregation and distribution issues. Has normative value but little positive validity. • By definition cannot capture multi-agent interactions. • In principle, agent-based model better suited. Still in its infancy. Computational requirements daunting.

17. Systemic Risk • Neglect of systemic risk flows from representative agent assumption • Example: value-at-risk models make sophisticated assumptions about fat-tailed residuals (macro-risk), but ignore counter-party risk • Little research into sectoral interactions, i.e. bankruptcy cascades • Efficient market assumptions disregarded information asymmetries. Too little attention to the possibility of rational bubbles.

18. International Monetary Linkages • Over-reliance on traditional transmission channels (trade) tragically insufficient for short-run analysis • Despite magnitude of short-term capital flows, little consistent information available on cross-country linkages • Channels of Contagion. ‘Unholy Trinity’: Capital Inflows, Surprises, and Common Creditors • Lack of international flow-of-funds data obscured composition of international debt linkages • Insufficient information on off-shore leverage

19. Research agenda (1) • Modeling is not fundamentally flawed, neither as ‘economic theory’ nor as ‘econometric modeling’ • But it has relied on simplifying assumptions that limit model applicability. A new macroeconomic paradigm is required. • Agents’ behavior requires modeling of interactions as well as behavioral assumptions (‘behavioral economics’) • Expectations formation needs fundamental rethink of informational assumptions to be positively useful.

20. Research agenda (2) • Monetary sector modeling needs to be richer to incorporate non-interest rate transmission channels and properly reflect the multi-faceted role of market participants (‘role of banks’) • Analysis of short-run international linkages has to focus on monetary interdependencies (‘contagion’). • Much richer data sets are required on bilateral international monetary linkages. • International capital flow models need to match national financial sector modeling in sophistication.