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首页> 外文期刊>Journal of Econometrics >A flexible approach to parametric inference in nonlinear and time varying time series models
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A flexible approach to parametric inference in nonlinear and time varying time series models

机译：非线性和时变时间序列模型中参数推断的灵活方法

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Many structural break and regime-switching models have been used with macroeconomic and financial data. In this paper, we develop an extremely flexible modeling approach which can accommodate virtually any of these specifications. We build on earlierwork showing the relationship between flexible functional forms and random variation in parameters. Our contribution is based around the use of priors on the time variation that is developed from considering a hypothetical reordering of the data and distance between neighboring (reordered) observations. The range of priors produced in this way can accommodate a wide variety of nonlinear time series models, including those with regime-switching and structural breaks. By allowing the amount of random variation in parameters to depend on the distance between (reordered) observations, the parameters can evolve in a wide variety of ways, allowing for everything from models exhibiting abrupt change (e.g. threshold autoregressive models or standard structuralbreak models) to those which allow for a gradual evolution of parameters (e.g. smooth transition autoregressive models or time varying parameter models). Bayesian econometric methods for inference are developed for estimating the distance function and types of hypothetical reordering. Conditional on a hypothetical reordering and distance function, a simple reordering of the actual data allows us to estimate our models with standard state space methods by a simple adjustment to the measurement equation.We use artificial data to show the advantages of our approach, before providing two empirical illustrations involving the modeling of real GDP growth.
机译：许多结构性突破和政权转换模型已用于宏观经济和金融数据。在本文中，我们开发了一种非常灵活的建模方法，该方法几乎可以适应所有这些规范。我们以早期工作为基础，展示了灵活的功能形式与参数的随机变化之间的关系。我们的贡献是基于对时间变化的先验使用，该时间变化是通过考虑对数据的假设重新排序以及相邻（重新排序）观测值之间的距离而得出的。以这种方式产生的先验范围可以适应各种非线性时间序列模型，包括那些具有状态切换和结构中断的模型。通过允许参数的随机变化量取决于（重新排序的）观测值之间的距离，参数可以以多种方式演化，从而允许从表现出突变的模型（例如阈值自回归模型或标准结构破坏模型）到所有模型允许参数逐步演化的模型（例如平滑过渡自回归模型或时变参数模型）。贝叶斯计量经济学的推论方法是为估计距离函数和假设的重排类型而开发的。在假设的重新排序和距离函数的条件下，对实际数据进行简单的重新排序使我们能够通过对测量方程的简单调整来使用标准状态空间方法来估计模型。在提供之前，我们使用人工数据来展示我们方法的优势涉及对实际GDP增长进行建模的两个经验例证。

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