TAC方法用于拟合指数自回归模型及其他模型：在经济和金融中的应用
TAC Method for Fitting Exponential Autoregressive Models and Others: Applications in Economy and Finance
AI总结：
本文提出TAC算法，无需初始猜测即可用指数函数逼近数据，并成功应用于需求曲线和非线性时间序列，验证了其鲁棒性和广泛适用性。
摘要：
本文有两个目的：研究指数函数逼近问题，并展示其与经济科学的相关性。我们提出的结果完全解决了用指数函数进行最佳逼近的问题，并能确定何种类型的数据适合被拟合。数据将使用TAC（在R包nlstac中实现）进行逼近，这是一种由作者设计的数值算法，无需初始猜测即可用指数模式拟合数据。我们再次检验了该算法的鲁棒性，成功将其应用于经济学的两个相距甚远的领域：需求曲线和非线性时间序列。这展示了TAC的实用性，并凸显了该算法可被应用的范围之广。
英文摘要：
There are a couple of purposes in this paper: to study a problem of approximation with exponential functions and to show its relevance for the economic science. We present results that completely solve the problem of the best approximation by means of exponential functions and we will be able to determine what kind of data is suitable to be fitted. Data will be approximated using TAC (implemented in the R-package nlstac), a numerical algorithm for fitting data by exponential patterns without initial guess designed by the authors. We check one more time the robustness of this algorithm by successfully applying it to two very distant areas of economy: demand curves and nonlinear time series. This shows TAC's utility and highlights how far this algorithm could be used.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
