Bayesian estimation and entropy for economic dynamic stochastic models: An exploration of overconsumption
Argentiero, A., Bovi, M. and Cerqueti, R. (2016). Bayesian estimation and entropy for economic dynamic stochastic models: An exploration of overconsumption. Chaos, Solitons and Fractals. 88, pp. 143-157.
|Authors||Argentiero, A., Bovi, M. and Cerqueti, R.|
This paper examines psycho-induced overconsumption in a dynamic stochastic context. As emphasized by well-established psychological results, these psycho-distortions derive from a decision making based on simple rules-of-thumb, not on analytically sounded optimizations. To our end, we therefore compare two New Keynesian models. The first is populated by optimizing Muth-rational agents and acts as the normative benchmark. The other is a “psycho-perturbed” version of the benchmark that allows for the potential presence of overoptimism and, hence, of overconsumption. The parameters of these models are estimated through a Bayesian-type procedure, and performances are evaluated by employing an entropy measure. Such methodologies are particularly appropriate here since they take in full consideration the complexity generated by the randomness of the considered systems. In particular, they let to derive a not negligible information on the size and on the cyclical properties of the biases. In line with cognitive psychology suggestions our evidence shows that the overoptimism/overconsumption is: widespread—it is detected in nation-wide data; persistent—it emerges in full-sample estimations; it moves according to the expected cyclical behavior—larger in booms, and it disappears in crises. Moreover, by taking into account the effect of these psycho-biases, the model fits actual data better than the benchmark. All considered, then, enhancing the existing literature our findings: i) sustain the importance of inserting psychological distortions in macroeconomic models and ii) underline that system dynamics and psycho biases have statistically significant and economically important connections.
|Journal||Chaos, Solitons and Fractals|
|Journal citation||88, pp. 143-157|
|Digital Object Identifier (DOI)||doi:10.1016/j.chaos.2016.03.003|
|Online||21 Mar 2016|
|Publication process dates|
|Accepted||01 Mar 2016|
|Deposited||28 Feb 2020|
|Accepted author manuscript|
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