Computational Economics
http://lists.repec.orgmailman/listinfo/nep-cmp
Computational Economics
2015-11-15
Beyond Equilibrium: Revisiting Two-Sided Markets from an Agent-Based Modeling Perspective
http://d.repec.org/n?u=RePEc:pra:mprapa:67860&r=cmp
Two-sided markets are an important aspect of today's economies. Yet, the attention they have received in economic theory is limited, mainly due to methodological constraints of conventional approaches: two-sided markets quickly lead to non-trivial dynamics that would require a computational approach, as analytical models quickly become intractable. One approach to this problem is to opt for models that operate on an aggregated level, abstracting from most of the (micro-level) causes of these non-trivial dynamics. Here we revisit a well known equilibrium model by Rochet and Tirole of two-sided markets that has taken this approach. Analyzing the model from an agent-based perspective, however, reveals several inconsistencies and implicit assumptions of the original model. This, together with the highly implausible assumptions that are required to make the model analytically tractable, limits its explanatory power significantly and motivates an alternative approach. The agent-based model we propose allows us to study the phenomenon of two-sided markets in a more realistic and adequate manner: Not only are we able to compare different decision making rules for the providers, we are also able to study situations with more than two providers.%We find that Thus, our model represents a first step towards a more realistic and policy-relevant study of two-sided markets.
Heinrich, Torsten
GrÃ¤bner, Claudius
Two-sided markets; Network externalities; Agent-based modeling; Simulation; Heuristic decision making; Reinforcement learning; Satisficing; Differential evolution; Evolutionary economics; Market structure; IT economics; Equilibrium dynamics
2015-11-13
Envelope Condition Method with an Application to Default Risk Models
http://d.repec.org/n?u=RePEc:red:sed015:1239&r=cmp
We develop an envelope condition method (ECM) for dynamic programming problems -- a tractable alternative to expensive conventional value function iteration. ECM has two novel features: First, to reduce the cost, ECM replaces expensive backward iteration on Bellman equation with relatively cheap forward iteration on an envelope condition. Second, to increase the accuracy of solutions, ECM solves for derivatives of a value function jointly with a value function itself. We complement ECM with other computational techniques that are suitable for high-dimensional problems, such as simulation-based grids, monomial integration rules and derivative-free solvers. The resulting value-iterative ECM method can accurately solve models with at least up to 20 state variables and can successfully compete in accuracy and speed with state-of-the-art Euler equation methods. We also use ECM to solve a challenging default risk model with a kink in value and policy functions, and we find it to be fast, accurate and reliable.
Viktor Tsyrennikov
Serguei Maliar
Lilia Maliar
Cristina Arellano
2015
Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies
http://d.repec.org/n?u=RePEc:tin:wpaper:20150125&r=cmp
This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models, logistic smooth transition regressions models, threshold autoregressive models, nonlinear autoregressive models, and additive nonlinear autoregressive models, plus Neural Network models.The results suggest that there is no dominating class of time series models, and the different currency pairs relationships with the US dollar are captured best by neural net regression models, over the ten year sample of daily exchange rate returns data, from August 2005 to August 2015.
David E. Allen
Michael McAleer
Shelton Peiris
Abhay K. Singh
Non linear models; time series; non-parametric; smooth-transition regression models; neural networks; GMDH shell
2015-11-06