Adaptive learning with a unit root: An application to the current account

Ronald B. Davies; Paul Shea

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Adaptive learning with a unit root: An application to the current account

机译：具有单位根的自适应学习：当前帐户的应用程序

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This paper develops a simple two-country, two-good model of international trade and borrowing that suppresses all previous sources of current account dynamics. Under rational expectations, international debt follows a random walk. Under adaptive learning, however, the model's unit root is eliminated and international debt is either a stationary or an explosive process, depending on agents' specific learning algorithm. Some stationary learning algorithms result in debt following an AR(1) process with an autoregressive coefficient less than 0.8. Because unit roots are a common and problematic feature of many international business cycle models, our results offer a new approach for generating stationarity.

机译：本文建立了一个简单的两国两商品国际贸易和借贷良好模型，该模型可以抑制以前所有经常账户动态的来源。在合理的预期下，国际债务会随机走动。但是，在自适应学习中，模型的单位根被消除，国际债务要么是平稳过程，要么是爆炸性过程，具体取决于代理商的特定学习算法。一些静态学习算法会导致AR（1）过程后的自回归系数小于0.8。由于单位根是许多国际商业周期模型的常见问题，因此我们的结果提供了一种产生平稳性的新方法。

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来源
《Journal of Economic Dynamics and Control》 |2010年第2期|179-190|共12页
作者
Ronald B. Davies; Paul Shea;
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作者单位

University College Dublin, G215 Newman Building, Belfield, Dublin 4, Ireland;

University of Kentucky, 335T Gatton Building, Lexington, KY 40536, USA;

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原文格式 PDF
正文语种 eng
中图分类
关键词
current account; international debt movements; expectations; adaptive learning;

机译：经常账户;国际债务运动;期望;适应性学习;

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Adaptive learning with a unit root: An application to the current account

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