By Herbert Dawid, Willi Semmler
This quantity is situated round the factor of industry layout and ensuing industry dynamics. the industrial concern of 2007-2009 has once more highlighted the significance of a formal layout of industry protocols and institutional info for monetary dynamics and macroeconomics. Papers during this quantity catch institutional information of specific markets, behavioral info of brokers' determination making in addition to spillovers among markets and results to the macroeconomy. Computational tools are used to copy and comprehend marketplace dynamics rising from interplay of heterogeneous brokers, and to increase types that experience predictive strength for advanced industry dynamics. eventually remedies of overlapping generations versions and differential video games with heterogeneous actors are provided.
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Extra resources for Computational Methods in Economic Dynamics
1 Introduction PowerWeb is a computer program that allows you to use your skills in economic decision-making to test an electricity market. You will have the opportunity to earn money through your actions in this test. Any money that you earn will be yours to keep, and you should try to make as much money as possible. Other people in the test will be competing directly against you in the market. Please do not communicate with any of the other participants. It is important to us that you understand these instructions.
Therefore, when the exact size of the market is not known, the choice of the parameter under γ -resampling is more robust. This superiority carries over when traders’ protection is ancillary to allocative efficiency. The left-hand side of Fig. 8 superimposes the usual graphs of the mean values for AE and TP under γ -resampling for n = 200. The equivalent representation for π -resampling is on the right-hand side of Fig. 4. In general, γ -resampling 26 M. LiCalzi et al. Fig. 8 Traders’ protection and allocative efficiency for π - and γ -resampling delivers a higher traders’ protection than π -resampling for any given level of allocative efficiency.
Table 5 compares the performance of π - and γ -resampling in thin, thick, and crowded markets. Similarly to Table 3, we list the highest mean values obtained for each combination of n and resampling rule. For n = 200 or n = 1000, the differences between the mean values are statistically significant. ) Conditional on choosing the right parameter, traders’ protection is higher using γ -resampling. Note also that the optimal values of π and γ are decreasing in the thickness of the market, but this effect is much stronger for π resampling.