Computational suite of models with heterogeneous agents II: Multi-country real business cycle models

This paper describes the second model considered in the computational suite project that compares the performance of different numerical algorithms. It is a multi-country model in which countries face different productivity shocks. Solving such models is a challenging numerical problem unless the number of countries is small. The solutions are functions of a large set of arguments and the functional forms are unknown. Moreover, the solution procedures have to deal with high-dimensional integration problems.     

Comparison of solutions to the multi-country Real Business Cycle model

We compare the performance of perturbation, projection, and stochastic simulation algorithms for solving the multi-country RBC model described in Den Haan et al. (this issue). The main challenge of solving this model comes from its large number of continuous-valued state variables, ranging between four and 20 in the specifications we consider. The algorithms differ substantially in terms of speed and accuracy, and a clear trade-off exists between the two. Perturbation methods are very fast but invoke large approximation errors except at points close to the steady state; the projection methods considered are accurate on a large area of the state space but are very slow for specifications with many state variables; stochastic simulation methods have lower accuracy than projection methods, but their computational cost increases only moderately with the state-space dimension. Simulated series generated by different methods can differ noticeably, but only small differences are found in unconditional moments of simulated variables. On the basis of our comparison, we identify the factors that account for differences in accuracy and speed across methods, and we suggest directions for further improvement of some approaches.     

Preemptive behavior in sequential-move tournaments with heterogeneous agents

Rank-order tournaments are usually modeled simultaneously. However, real tournaments are often sequential. We show that agents' strategic behavior in sequential-move tournaments significantly differ from the one in simultaneous-move tournaments: In a sequential-move tournament with heterogeneous agents, the first acting agent may choose a preemptively high effort so that the following agent gives up. The principal is able to prevent preemptive behavior in equilibrium by choosing a sufficiently small spread between winner and loser prize. Received: January 2003, Accepted: November 2004 JEL Classification: J3, M12, M5 We would like to thank two anonymous referees and the editor Kai Konrad for helpful comments. Financial support by the Deutsche Forschungsgemeinschaft (DFG), projects no. KR 2077/2-1 and SFB/TR 15 ("Governance and the Efficiency of Economic Systems"), is gratefully acknowledged.     

Learning in linear models with expectational leads

Models with expectational leads typically admit multiple rational expectations solutions. Based on the ordinary least-squares algorithm, this paper provides an adaptive learning scheme which allows a forecasting agent to select a particular solution on economic grounds. Conditions are given under which this scheme converges to rational expectations solutions globally for all initial conditions. We strengthen convergence results in relaxing standard assumptions and in providing conditions ensuring algorithm convergence which are easier to verify and to interpret than those previously known.     

Optimal money growth in a limited participation model with heterogeneous agents

This paper studies optimal money growth in a cash-in-advance production economy with heterogeneity in patience levels and know-how. We show that the rate of deflation suggested by the Friedman rule is limited by the subjective discount rate of the most patient agent in the economy. The output distortion due to cash-in-advance constraints on firms can completely be eliminated by means of the Friedman rule if and only if firms are run by the most patient agents.Received: 16 August 2003, Accepted: 22 March 2005, JEL Classification: D52, D90, E21, E41, E52Parts of this paper were completed while the first and second authors were visiting the University of York and Princeton University respectively. We are thankful for the hospitality of these institutions. The second author also acknowledges the hospitality and support of Bilkent University that he is currently visiting, the support from the Turkish Academy of Sciences in the framework of the ‘Distinguished Young Scientist Award Program’ (TUBA-GEBIP/2004), the grant awarded by the Scientific and Technical Research Council of Turkey (TUBITAK) under the NATO Science Fellowship Program as well as the support from the Center for Economic Design of Bogaziçi University. This paper was presented in the ERC/METU Second International Conference on Economics, Ankara, September 1998, and at seminars in Bilkent, Bogaziçi and Ohio State Universities. We thank, in particular, Emre Alper, Sumru Altug, Huw Dixon, Tatsuro Ichiishi, Ivan Pastine, Murat Sertel and Alan Sutherland for useful comments. We also thank two anonymous referees for comments and suggestions. The views expressed in this paper are those of the authors and do not necessarily reflect that of the Central Bank of Republic of Turkey.     

Assessing the importance of measurement error in capital investment models

New developments in the economics of capital investment emphasize the role of financial variables. Econometric evidence on these hypotheses is potentially compromised by measurement error due to accounting conventions. The paper reviews new capital investment models and considers ways in which accounting procedures might lead to measurement error biases. Advances in errors-in-variables econometric models are employed to gauge the impact of measurement error on estimates of financial influences on capital investment. Cash-flow models appear to be especially susceptible to measurement error but q models seem fairly insensitive to measurement problems.     

Assessing value at risk with CARE, the Conditional Autoregressive Expectile models

In this paper we propose a downside risk measure, the expectile-based Value at Risk (EVaR), which is more sensitive to the magnitude of extreme losses than the conventional quantile-based VaR (QVaR). The index θ of an EVaR is the relative cost of the expected margin shortfall and hence reflects the level of prudentiality. It is also shown that a given expectile corresponds to the quantiles with distinct tail probabilities under different distributions. Thus, an EVaR may be interpreted as a flexible QVaR, in the sense that its tail probability is determined by the underlying distribution. We further consider conditional EVaR and propose various Conditional AutoRegressive Expectile models that can accommodate some stylized facts in financial time series. For model estimation, we employ the method of asymmetric least squares proposed by Newey and Powell [Newey, W.K., Powell, J.L., 1987. Asymmetric least squares estimation and testing. Econometrica 55, 819–847] and extend their asymptotic results to allow for stationary and weakly dependent data. We also derive an encompassing test for non-nested expectile models. As an illustration, we apply the proposed modeling approach to evaluate the EVaR of stock market indices.     

Validation and forecasting accuracy in models of climate change

Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster’s perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.     

Equilibrium dynamics in an aggregative model of capital accumulation with heterogeneous agents and elastic labor

The paper extends the canonical representative agent Ramsey model to include heterogeneous agents and elastic labor supply. The welfare maximization problem is analyzed and shown to be equivalent to a non-stationary reduced form model. An iterative procedure is exploited to prove the supermodularity of the indirect utility function. Supermodularity is subsequently used to establish the convergence of optimal paths.     

On the accuracy of bootstrap confidence intervals for efficiency levels in stochastic frontier models with panel data

We study the construction of confidence intervals for efficiency levels of individual firms in stochastic frontier models with panel data. The focus is on bootstrapping and related methods. We start with a survey of various versions of the bootstrap. We also propose a simple parametric alternative in which one acts as if the␣identity of the best firm is known. Monte Carlo simulations indicate that the parametric method works better than the␣percentile bootstrap, but not as well as bootstrap methods that make bias corrections. All of these methods are valid␣only for large time-series sample size (T), and correspondingly none of the methods yields very accurate confidence intervals except when T is large enough that the identity of the best firm is clear. We also present empirical results for two well-known data sets.      

Matching models with a conservation law: The existence and global structure of the set of stationary equilibria

We study random matching models where there is a set of infinitely lived agents, and in each period agents are pairwise matched to each other and play a stage game. We investigate the basic structure of equilibria in such models: the existence of equilibria and the global structure of the set of equilibria. Specifically, we focus on models with a conservation law, which typically holds in economies having some assets, such as money. In such models, under certain regularity conditions the set of equilibria is one-dimensional and each connected component of it is a piecewise smooth one-dimensional manifold being homeomorphic to either the unit circle or the unit interval. Moreover, in an endpoint of an interval all agents have the same amount of assets.     

A note on the use of aggregate data in individual choice models: Discrete consumer choice among alternative fuels for residential appliances

In order to exploit the usefulness of qualitative models of individual choice and the availability of aggregate energy demand data, this paper develops a maximum likelihood technique for incorporating aggregate data into individual choice models. The paper treats the use of aggregate data as a measurement error problem; it develops consistent estimators of individual taste by correcting for the implied measurement error. Using the maximum likelihood technique, a number of individual choice models are estimated for residential demand for space heaters, water heaters, ranges and clothes dryers. The extent of asymptotic bias generated by the uncorrected use of aggregate data is documented.     

浅析我国在国际贸易中发展电子商务的对策

20世纪90年代以来,随着互联网的应用,电子商务应运而生,成了国际贸易的通行证,给国际贸易带来了新的运作模式。自问世以来,电子商务在全球内的蓬勃发展,对世界经济格局和贸易体制的变化产生了深远的影响,其使国际贸易产生了传统国际贸易所无可比拟的优势,是未来国际贸易的必然趋势。     

Mitigation of the Lucas critique with stochastic control methods

Lucas (In: Brunner, K., Meltzer, A.H. (Eds.), The Phillips Curve and the Labor Markets, Supplementary Series to the Journal of Monetary Economics, 1976, pp. 19–46) 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.     

浅谈变电运行中存在的安全隐患及解决措施

本文就机电设备日常运行中出现的故障进行分析,探讨故障排除方法以及关键技术的应用,进一步促进电网的平稳运行。     

基于数据融合的自来水浊度检测方法

为了提高流动电流仪(SCM)对自来水浊度检测的精度,文章提出了一种基于神经网络数据融合技术的数据处理方法。该方法可以消除测量中的原水流量、供电电源波动、絮凝剂浓度、温度等干扰量对流动电流仪的影响,提高了测量的精度。     

基于数据融合的自来水浊度检测方法

为了提高流动电流仪(SCM)对自来水浊度检到的精度，文章提出了一种基于神经网络数据融合技术的数据处理方法。该方法可以消除到量中的原水流量、供电电源波动、絮凝剂浓度、温度等干扰量对流动电流仪的影响，提高了测量的精度。