Hans M. Amman

Computational approaches to economic problems

Section One:- Factor-GARCH Modeling of the Treasury Term Structure C.F. Baum, B. Bekdache. Analyzing a Small French ECM Model J.-L. Brillet. Wavelet Basis Selection for Regression by Cross-Validation S.A. Greenblatt. Computation and Inference in Semiparametric Efficient Estimation R.M. Adams, et al. Generating Random Numbers in Mathematica D.A. Belsley. Linked-Cone Profit Ratio Estimates of U.S. Total Factor Productivity Growth, Using DEA/AR Methods R.G. Thompson, et al. Several Algorithms to Determine Multipliers for Use in Cone-Ratio Envelopment Approaches to Efficiency Evaluations in DEA K. Tone. Section Two:- The Estimation of the Heath-Jarrow-Morton Model by Use of Kalman Filtering Techniques R. Bhar, C. Chiarella. Neural Networks for Contingent Claim Pricing via the Galerkin Method E. Barucci, et al. Asset Liability Management Diem Ho. An Efficient Parallel Implementation of a Lattice Pricing Model S.S. Nielsen, et al. Monitoring Active Portfolios Using Statistical Process Control E. Yashchin, et al. Section Three:- Ordering: Human Versus Computer A. Norman, et al. Strategic Uncertainty and the Genetic Algorithm Adaptation J. Arifovic. Fluctuating Benefits and Collective Action B.A. Huberman. A Trade Network Game with Endogenous Partner Selection L. Tesfatsion. Learning in a Computable Setting. Applications of Golds Inductive Inference Model F. Luna. Section Four:- The Range Process in Random Walks: Theoretical Results and Applications P. Vallois, C.S. Tapiero. Numerical Analysis of a Nonlinear Operator Equation Arising from a Monetary Model J. Li. A Numerical Procedure to Estimate Real Business Cycle Models Using Simulated Annealing W. Semmler, Gang Gong. Section Five:- The Internet: A Future Tragedy of the Commons? A. Gupta, et al. The DUALI/DUALPC Software for Optimal Control Models: Introduction H.M. Amman, D.A. Kendrick.

Nonconvexities in stochastic control models

Nonconvexities in the criterion function of adaptive control problems were first found about ten years ago with numerical methods. Recently they have been confirmed by B. Mizrach (1991) with analytical methods. He found that a source of the nonconvexity was the probing component of the cost-to-go. Mizrachs results have been extended in this paper. First, the probing function has been characterized and found to support the use of algorithms that exploit this character to find the global optimum. Secondly, a new source of nonconvexities has been found in the cautionary component of the cost-to-go. Copyright 1995 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Mitigation of the Lucas critique with stochastic control methods

Lucas (1976) pointed out, that when optimization is performed on a deterministic macro model, the resulting policy may not reflect the true optimal solution. Private agents may react to announced policies and consequently model parameters will start to drift. The aim of this paper is to develop a methodology for deriving an optimal policy in the presence of rational expectations and parameter drift. This drift is captured by a stochastic optimization framework with time varying parameters. The resulting optimal policy is capable of tracking changes in the parameters due to policy changes. A numerical example illustrates how the methodology provides a way to mitigate the effects of the Lucas critique.

Should Macroeconomic Policy Makers Consider Parameter Covariances

Many macroeconomic policy exercises consider the mean values of parameter estimates but do not use the variances and covariances. One can argue that the uncertainty of these parameter estimates is sufficiently small that it can safely be ignored. Or one can take the position that this kind of uncertainty cannot be avoided no matter what one does. Thus it is just as well to ignore it while making policy decisions. In this paper we address both of these positions in the presence of learning and find that they are unconvincing. To the contrary, we find evidence that the potential damage from ignoring the variances and covariances of the parameter estimates is substantial and that taking them into account can improve matters.

Programming Languages in Economics

Young economists sometimes ask which computer programming languages they should learn. This paper answers that question by suggesting that they begin with a high level language like GAUSS, GAMS, Mathematica, Maple or MATLAB depending on their field of specialization in economics. Then they should work down to one of the low level languages such as Fortran, Basic, C, C++ or Java depending on the planned areas of application. Finally, they should proceed to the languages which are used to develop graphical interfaces and internet applications, viz. Visual Basic, C, C++ or Java.

Linear Quadratic Optimization for Models with Rational Expectations

In this paper we present a method for using rational expectations in a linear-quadratic optimizationframework. Following the approach put forward by Sims, we solve the model through a QZdecomposition, which is generally easier to implement than the more widely used method of Blanchardand Kahn.

Numerical solutions of the algebraic matrix Riccati equation.

Abstract The linear-quadratic control model is one of the most widely used control models in both empirical and theoretical economic modeling. In order to obtain the equilibrium solution of this control model, the so-called algebraic matrix Riccati equation has to be solved. In this note we present a numerical solution method for solving this equation. Our method solves the Riccati equation as a multidimensional fixed-point problem. By establishing the analytical derivative of the Riccati equation we have been able to construct a very efficient Newton-type solution method with quadratic convergence properties. Our method is an extension for the Newton-Raphson method described in Kwakernaak and Sivan (1972) and does not require any special conditions on the transition matrix as in the nonrecursive method of Vaughan (1970).

Teaching Macroeconomics with GAMS

Our general goal in this paper is to show how to implement in GAMS standard deterministic nonlinear macro models, and stochastic linear macro models with rational expectations. We will also present basic concepts on solution methods and policy analysis for these kinds of models. As a practical illustration, we will use some well known teaching and experimental models in the macroeconomic literature.

Active learning Monte Carlo results

Abstract The revival of interest in learning in stochastic control models invites the examination of some unanswered issues from earlier work. Following in the tradition of Prescott and MacRae we consider Active Learning to examine the question of when these methods are superior to Passive Learning. In the earlier literature it was not possible to address this question, because computer speeds were not great enough. However, modern supercomputers make it possible to reopen this issue. In this paper we will examine two well-known stochastic control models, the MacRae model and the Abel model. Although no general conclusions can be drawn, the results from the MacRae model and the Abel model indicate that in certain cases Active Learning may be superior to Passive Learning and Certainty Equivalence. In the almost all cases we have analyzed so far, Active Learning is superior to Passive Learning which in turn is superior to Certainty Equivalence.

On social learning and robust evolutionary algorithm design in the cournot oligopoly game

Agent‐based computational economics (ACE) combines elements from economics and computer science. In this article, the focus is on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the genetic algorithm directly from the values of the economic model parameters.