Learning and the Planning Horizon: Applications to Economic Fluctuations, Asset Prices and Policy

University Of Oregon Eugene, Eugene OR

Investigators

Abstract

LEARNING AND THE PLANNING HORIZON: APPLICATIONS TO ECONOMIC FLUCTUATIONS, ASSET PRICES AND POLICY Intellectual Merit. A central feature of modern economic theory is forward-looking decision making by firms and households. While the rational expectations approach provides the benchmark theory of expectation formation, adding learning dynamics has yielded many novel insights. The proposed research will advance this research conceptually and examine several prominent issues in macroeconomics. Theoretical issues will focus on incorporation of structural knowledge, implications of the planning horizon, and eductive stability in infinite-horizon models. Applications will include monetary and fiscal policy in deep recessions, the impact of anticipated future changes in fiscal policy, and the tendency for asset prices to exhibit bubbles and crashes. Unifying themes are that learning impacts both the stability of the system and the propagation mechanisms for shocks. The research will focus on five interconnected lines of research: (i) Macroeconomic policy in New Keynesian models. Even when private agents use long-horizon decision rules, a large pessimistic expectations shock can push the economy along a trajectory of falling output and deflation. This research will study alternative monetary and fiscal policies for avoiding deep recession and deflation, and which return the economy to equilibrium. The implications of heterogeneous expectations for monetary policy will also be studied. (ii) Anticipated policy and learning. This research will show how private agents can combine econometric learning with structural knowledge about future policy changes. Examples include the impact on hours and investment of anticipated future changes in government spending and taxes. The scope of validity of Ricardian equivalence under learning will receive particular attention. (iii) Learning to optimize and the planning horizon. This project proposes a natural model of bounded optimality for long-horizon agents. The research will show how solving and updating suitable two-period decision problems, using adaptive learning rules, can converge to fully optimal decisions. The methodology will be extended to cover real business cycle (RBC) type models. (iv) Financial markets. One application will examine the impact of learning about risk and return on stock price dynamics, and determine the conditions in which bubbles and crashes are likely to emerge. A second application will look at the possibility of financial collapse when adaptive learning is introduced into a model of financial intermediation with long-term relationships. (v) Eductive stability in RBC models. This research will examine whether mental reasoning based on common knowledge can lead agents to coordinate on rational expectations in RBC models. Eductive learning combined with long-horizon decision-making appears prone to cyclical dynamics and even instability. Methods for combining eductive and adaptive learning will also be explored. Broader Impacts. The broader aim of the project is to inform policymakers of the need to take into account learning and bounded rationality by private agents and policymakers themselves. The importance of learning, expectations and model uncertainty, for monetary and fiscal policy, is increasingly being recognized by policymakers. In the last five years the PI has been a visiting scholar at the Cleveland and St. Louis Federal Reserve Banks and made presentations at the Board of Governors, the Kansas City and San Francisco FRBs, the Bank of Japan, the Bank of England, the Banque de France, the Central Bank of Chile, the ECB, the IMF and the IMF Institute. The PI has also co-authored surveys, including one aimed at policymakers. The research from the proposed project will be disseminated widely in seminars, workshops and conferences, at universities and central banks. Research papers from the project will be made available on the web. The project will also support the research of graduate students. Past NSF grants by the PI have supported research on related topics and led to PhD theses and academic appointments at research universities.