Adaptive interactive expectations: dynamically modelling profit expectations

This thesis aims to develop an alternative expectations model to the Rational Expectations Hypothesis (REH) and adaptive-expectations 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 hyper-intelligent agents interacting only via a price signal and near zero-intelligent 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 interactive-expectations, adaptive-expectations and biases that include pessimism, optimism and ambivalence. AIE uses networks to model the flow of interactive-expectations between firms. To overcome the uncertainty over the structure of the interactive network, the thesis uses model-averaging 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 model-averaging over the network topologies. To overcome this limitation in the Bayesian technique, the thesis introduces two model-averaging techniques, ‘runtime-weighted’ and ‘optimal-calibration’. These model-averaging techniques are benchmarked against ‘Bayes-factor model-averaging’ and ‘equal-weighted model-averaging’. 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 ‘link-intensity matrix’ based upon an ‘input-output table’ to improve the calibration of the networks. The transpose of the table is also used in the thesis. The two ‘link-intensity matrices’ are benchmarked against the default, a ‘matrix of ones’. The aggregated and disaggregated versions of AIE are benchmarked against adaptive-expectations to establish whether the interactive-expectations component of AIE add value to the model. The thesis finds that AIE has more predictive power than REH. ‘Optimal-calibration model-averaging’ improves the predictive performance of the better-fitting versions of AIE, which are those versions that use the ‘input-output table’ and ‘matrix of ones’ link-intensity matrices. The ‘runtime-weighted model-averaging’ improves the predictive performance of only the ‘input-output table’ version of AIE. The interactive component of the AIE model improves the predictive performance of all versions of the AIE over adaptive-expectations. There is an ambiguous effect on prediction performance from introducing the ‘input-output 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 Agent-Based 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 ‘input-output table’ to determine the link-intensity; 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.