Bell, William Paul (2009): Adaptive interactive expectations: dynamically modelling profit expectations.

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Abstract
This thesis aims to develop an alternative expectations model to the Rational Expectations Hypothesis (REH) and adaptiveexpectations models, which provides more accurate temporal predictive performance and more closely reflects recent advances in behavioural economics, the ‘science of complexity’ and network dynamics. The model the thesis develops is called Adaptive Interactive Expectations (AIE), a subjective dynamic model of the process of expectations formation. To REH, the AIE model provides both an alternative and a complement. AIE and REH complement one another in that they are diametrically opposite in the following five dimensions, agent intelligence, agent interaction, agent homogeneity, equilibrium assumptions and the rationalisation process. REH and AIE stress the importance of hyperintelligent agents interacting only via a price signal and near zerointelligent agents interacting via a network structure, respectively. The complementary nature of AIE and REH provide dual perspectives that enhance analysis.
The Dun & Bradstreet (D&B 2008) profit expectations survey is used in the thesis to calibrate AIE and make predictions. The predictive power of the AIE and REH models is compared. The thesis introduces the ‘pressure to change profit expectations index’, px. This index provides the ability to model unknowns within an adaptive dynamic process and combine the beliefs from interactiveexpectations, adaptiveexpectations and biases that include pessimism, optimism and ambivalence.
AIE uses networks to model the flow of interactiveexpectations between firms. To overcome the uncertainty over the structure of the interactive network, the thesis uses modelaveraging over 121 network topologies. These networks are defined by three variables regardless of their complexity. Unfortunately, the Bayesian technique’s use of the number of variables as a measure of complexity makes it unsuitable for modelaveraging over the network topologies. To overcome this limitation in the Bayesian technique, the thesis introduces two modelaveraging techniques, ‘runtimeweighted’ and ‘optimalcalibration’. These modelaveraging techniques are benchmarked against ‘Bayesfactor modelaveraging’ and ‘equalweighted modelaveraging’.
In addition to the aggregate called all–firms, the D&B (2008) survey has four divisions, manufacturing durables, manufacturing non–durables, wholesale and retail. To make use of the four divisions, the thesis introduces a ‘linkintensity matrix’ based upon an ‘inputoutput table’ to improve the calibration of the networks. The transpose of the table is also used in the thesis. The two ‘linkintensity matrices’ are benchmarked against the default, a ‘matrix of ones’. The aggregated and disaggregated versions of AIE are benchmarked against adaptiveexpectations to establish whether the interactiveexpectations component of AIE add value to the model.
The thesis finds that AIE has more predictive power than REH. ‘Optimalcalibration modelaveraging’ improves the predictive performance of the betterfitting versions of AIE, which are those versions that use the ‘inputoutput table’ and ‘matrix of ones’ linkintensity matrices. The ‘runtimeweighted modelaveraging’ improves the predictive performance of only the ‘inputoutput table’ version of AIE. The interactive component of the AIE model improves the predictive performance of all versions of the AIE over adaptiveexpectations. There is an ambiguous effect on prediction performance from introducing the ‘inputoutput table’. However, there is a clear reduction in the predictive performance from introducing its transpose.
AIE can inform the debate on government intervention by providing an AgentBased Model (ABM) perspective on the conflicting mathematical and narrative views proposed by the Greenwald–Stiglitz Theorem and Austrian school, respectively. Additionally, AIE can provide a complementary role to REH, which is descriptive/predictive and normative, respectively. The AIE network calibration uses an ‘inputoutput table’ to determine the linkintensity; this method could provide Computable General Equilibrium (CGE) and Dynamic Stochastic General Equilibrium (DSGE) with a way to improve their transmission mechanism. Furthermore, the AIE network calibration and prediction methodology may help overcome the validation concerns of practitioners when they implement ABM.
Item Type:  MPRA Paper 

Original Title:  Adaptive interactive expectations: dynamically modelling profit expectations 
Language:  English 
Keywords:  Small World Networks; Agent Based Model; Adaptive; Interactive; Profits; Expectations; Model Averaging; Survey; Australia; Business Cycle; Uncertainty 
Subjects:  D  Microeconomics > D0  General > D03  Behavioral Economics; Underlying Principles D  Microeconomics > D8  Information, Knowledge, and Uncertainty C  Mathematical and Quantitative Methods > C6  Mathematical Methods; Programming Models; Mathematical and Simulation Modeling > C61  Optimization Techniques; Programming Models; Dynamic Analysis 
Item ID:  38260 
Depositing User:  Dr William Paul Bell 
Date Deposited:  23. Apr 2012 02:36 
Last Modified:  12. Feb 2013 17:45 
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URI:  http://mpra.ub.unimuenchen.de/id/eprint/38260 