Unfulfilled Expectations: One Economist’s History

Foley, Duncan K.

doi:10.1007/978-3-030-41357-6_1

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…Parameter

—expected rate of fundamental price increase of a house—provides useful information about the expectations of economic agents. If

, it means that expectations of individuals about the fundamental house price increase do not coincide with the actual average price increase of the whole market signaling “unfulfilled" expectations of the individuals [ 33 ].…”

Section: Model Inference and Resultsmentioning

confidence: 99%

“…The period of crash with a declining house prices in most of the regions shows itself as a negatively skewed frequency distribution of house price changes in Figure 5 and Figure 6 . Since the values of houses kept depreciating with declining prices, the negative skewness could be interpreted as the “market punishment for the sellers [ 33 ]” during the crisis.…”

Section: Discussionmentioning

confidence: 99%

“…Traditional economic theory assumes that decision makers have complete and transitive preferences over outcomes represented by a utility function [ 30 , 31 , 32 ]. According to this approach, when utility maximizing rational individuals face with a decision to buy/sell a house in a particular market based on their payoff functions (i.e., the difference between the rate of increase in prices in the region and their estimate of the fundamental rate of price increase of the house), they would almost never sell (buy) the house when prices in the region rise slower (faster) than their expected fundamental rate of price increase of the house, and would wait until their expectations about house price increase coincide with actual price increase in the market (See Foley [ 33 ] for more methodological and theoretical details).…”

Section: A Quantal Response Statistical Equilibrium Model Of the Hmentioning

confidence: 99%

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Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach

Ömer

2018

Entropy

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In this article, we demonstrate that a quantal response statistical equilibrium approach to the US housing market with the help of the maximum entropy method of modeling is a powerful way of revealing different characteristics of the housing market behavior before, during and after the recent housing market crash in the US. In this line, a maximum entropy approach to quantal response statistical equilibrium model (QRSE) is employed in order to model housing market dynamics in different phases of the most recent housing market cycle using the S&P Case Shiller housing price index for 20 largest- Metropolitan Regions, and Freddie Mac housing price index (FMHPI) for 367 Metropolitan Cities for the US between 2000 and 2015. Estimated model parameters provide an alternative way to understand and explain the behaviors of economic agents, and market dynamics by questioning the traditional economic theory, which takes assumption for the behavior of rational utility maximizing representative agent with self-fulfilled expectations as given.

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“…Parameter

—expected rate of fundamental price increase of a house—provides useful information about the expectations of economic agents. If

Section: Model Inference and Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: A Quantal Response Statistical Equilibrium Model Of the Hmentioning

confidence: 99%

See 1 more Smart Citation

Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach

Ömer

2018

Entropy

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“…Earlier, Dragulescu and Yakovenko [12] showed how in a closed economic system, the probability distribution of money should follow the Boltzmann-Gibbs law [13]. Foley [14] discusses Rational expectations and boundedly rational behaviour in economics. Harré [15] gives an overview of information-theoretic decision-theory and applications in economics, and Foley [16] analyses information-theory and results on economic behaviour.…”

Section: Background and Motivationmentioning

confidence: 99%

A Maximum Entropy Model of Bounded Rational Decision-Making with Prior Beliefs and Market Feedback

Evans

Prokopenko

2021

Entropy

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Bounded rationality is an important consideration stemming from the fact that agents often have limits on their processing abilities, making the assumption of perfect rationality inapplicable to many real tasks. We propose an information-theoretic approach to the inference of agent decisions under Smithian competition. The model explicitly captures the boundedness of agents (limited in their information-processing capacity) as the cost of information acquisition for expanding their prior beliefs. The expansion is measured as the Kullblack–Leibler divergence between posterior decisions and prior beliefs. When information acquisition is free, the homo economicus agent is recovered, while in cases when information acquisition becomes costly, agents instead revert to their prior beliefs. The maximum entropy principle is used to infer least biased decisions based upon the notion of Smithian competition formalised within the Quantal Response Statistical Equilibrium framework. The incorporation of prior beliefs into such a framework allowed us to systematically explore the effects of prior beliefs on decision-making in the presence of market feedback, as well as importantly adding a temporal interpretation to the framework. We verified the proposed model using Australian housing market data, showing how the incorporation of prior knowledge alters the resulting agent decisions. Specifically, it allowed for the separation of past beliefs and utility maximisation behaviour of the agent as well as the analysis into the evolution of agent beliefs.

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“…As detailed in Foley (2020b) and Scharfenaker (2020) the asymmetric effects may also emerge from differences in agents' expectations,

\mu

, and the central tendency of the market rate,

\xi

. Setting

\phi =0

asymmetries emerge only through the interaction of

\mu

and

\xi

.…”

Section: Macroscopic Approachesmentioning

confidence: 99%

Statistical Equilibrium Methods in Analytical Political Economy

Scharfenaker

2020

Journal of Economic Surveys

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Economic systems produce robust statistical patterns in key sate variables including prices and incomes. Statistical equilibrium methods explain the distributional properties of state variables as arising from specific institutional, environmental, and behavioral postulates. Two broad traditions have developed in political economy with the complementary aim of conceptualizing economic processes as irreducibly statistical phenomena but differ in their methodologies and interpretations of statistical explanation. These conceptual differences broadly mirror the methodological divisions in statistical mechanics, but also emerge in distinct ways when considered in the context of social sciences. This paper surveys the use of statistical equilibrium methods in analytical political economy and identifies the leading methodological and philosophical questions in this growing field of research.

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Unfulfilled Expectations: One Economist’s History

Cited by 7 publications

References 12 publications

Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach

Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach

A Maximum Entropy Model of Bounded Rational Decision-Making with Prior Beliefs and Market Feedback

Statistical Equilibrium Methods in Analytical Political Economy

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