Learning and Expectations in Macroeconomics

Evans, George W.; Honkapohja, Seppo

doi:10.1515/9781400824267

Cited by 1,466 publications

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“…Model Risk & Optimal Execution. Problems in which agents make their decisions based upon misspecified models are well-studied in the economics and behavioral finance communities ( [35], [38], [63], [25], [57], [20], [91], [56]). To the best of our knowledge, such an approach has not been directly pursued in the financial mathematics literature on optimal execution.…”

Section: Background and Contributionsmentioning

confidence: 99%

Mini-Flash Crashes, Model Risk, and Optimal Execution

Bayraktar

Munk

2017

SSRN Journal

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Abstract. Oft-cited causes of mini-flash crashes include human errors, endogenous feedback loops, the nature of modern liquidity provision, fundamental value shocks, and market fragmentation. We develop a mathematical model which captures aspects of the first three explanations. Empirical features of recent mini-flash crashes are present in our framework. For example, there are periods when no such events will occur. If they do, even just before their onset, market participants may not know with certainty that a disruption will unfold. Our mini-flash crashes can materialize in both low and high trading volume environments and may be accompanied by a partial synchronization in order submission.Instead of adopting a classically-inspired equilibrium approach, we borrow ideas from the optimal execution literature. Each of our agents begins with beliefs about how his own trades impact prices and how prices would move in his absence. They, along with other market participants, then submit orders which are executed at a common venue. Naturally, this leads us to explicitly distinguish between how prices actually evolve and our agents' opinions. In particular, every agent's beliefs will be expressly incorrect.As far as we know, this setup suggests both a new paradigm for modeling heterogeneous agent systems and a novel blueprint for understanding model misspecification risks in the context of optimal execution.

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Section: Background and Contributionsmentioning

confidence: 99%

Mini-Flash Crashes, Model Risk, and Optimal Execution

Bayraktar

Munk

2017

SSRN Journal

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“…However, recent research suggests that adaptive expectations about in ‡ation might be much more tenable in many settings than the idea that in ‡ation expectations are formed rationally by voters, especially when the true data generating process is unknown (e.g., Suzuki 1991;Haller and Norpoth 1994;Hey 1994;Sargent 1999;Evans and Honkapohja 2001;Agliari, Chiarella, and Gardini 2006). Muth (1960) demonstrated that adaptive expectations and rational expectations are the same if the data generating process follows a random walk.…”

Section: Optimization Problem and Solutionsmentioning

confidence: 99%

Hyperbolic Memory Discounting and the Political Business Cycle

Findley

2014

SSRN Journal

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The vintage political business cycle framework of Nordhaus (1975) represents the idea that the macroeconomic business cycle is manipulated opportunistically by an incumbent government to achieve re-election. A key assumption in this prototypical framework is that voters discount their memories about unemployment and in ‡ation at a constant rate. Yet starting with Ebbinghaus (1885) and Jost (1897), a large body of research in psychology documents an empirical regularity that has come to be known as Jost's Second Law of Forgetting-individuals discount recent memories at a higher rate compared to the rate at which they discount older memories. I …nd that incorporating this insight from psychology (i.e., hyperbolic memory discounting) into the benchmark framework moderates the amplitude of the predicted political business cycle.JEL classi…cation: D03; D72

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“…For the Lucas-type macro model the parameter restriction is 0    1, so that there is positive feedback from expectations to outcomes. See Evans and Honkapohja (2001), Chapter 2, Sections 2.2 and 2.3 for more details on the Muth and Lucas models.…”

Section: Muth Model With Bayesian Learningmentioning

confidence: 99%

“…More precisely, we study a setting in which the pair of models used by 1 See Evans and Honkapohja (2001) for the earlier literature; for recent critical overviews see Sargent (2008) and Evans and Honkapohja (2009).…”

Section: Introductionmentioning

confidence: 99%

“…4 In particular, as above we assume that agents also employ a time-varying parameter (TVP) model as a second available forecasting model. The analysis is carried out using a standard general set-up, discussed, e.g., in chapter 2 of Evans and Honkapohja (2001). It is known that for this model there is convergence of usual least-squares (LS) learning to a unique REE unless the expectations feedback is positive and more than one-to-one.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Model Averaging, Learning, and Model Selection*

Evans¹,

Honkapohja²,

Sargent³

et al. 2013

Macroeconomics at the Service of Public Policy

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Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.

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Learning and Expectations in Macroeconomics

Cited by 1,466 publications

References 0 publications

Mini-Flash Crashes, Model Risk, and Optimal Execution

Mini-Flash Crashes, Model Risk, and Optimal Execution

Hyperbolic Memory Discounting and the Political Business Cycle

Bayesian Model Averaging, Learning, and Model Selection*

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