Empirical Similarity: estimation, multivariate extensions, and applications

经验相似性：估计、多元扩展和应用

• 批准号: 415503985
• 负责人: Professor Vasyl Golosnoy
• 金额: --
• 依托单位: Lehrstuhl für Statistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/415503985?language=en
• 关键词: Empirical; Similarity; estimation; multivariate; extensions

Economists aim to describe reality with formal decision rules which postulate specific causal relationships validated empirically by econometric analysis. However, sometimes there is no appropriate decision rule at hand, but only some experienced cases defined as a set of conditions, acts and their outcomes. Then a case-based decision should resemble successful decisions in those experienced cases which are similar to the current case. The empirical similarity (ES) approach provides econometric framework for case-based decisions and presumes that the variable of interest is a sum of historical outcomes weighted by similarities of current and previous cases. Hence, the ES weights are time-varying and determined by nonlinearly by exogenous variables which allows to reveal the principles of case-based decision making directly from the data. Its application has already provided useful insights in experimental economics, modeling real estate prices, predicting volatilities on financial markets or evaluating legal juridical decisions. However, there are still some unresolved econometric issues as well as interesting applications which we will address in this project.In the theoretical part of the project we are going to analyse, first, which properties of empirical data are suitable for applying ES technique and what happens if the underlying assumptions are violated. Second, estimation instability of existing ES models hinders their application in many important problem settings. Our objective is to propose regularization techniques for stabilizing ES estimation procedure as well as to provide results about variable selection and dimension reduction issues. Third, the current theoretical results focus on univariate data whereas multivariate ES (MES) models are required for decisions characterized by vectors of variables. We aim to develop MES models with a particular attention paid to model selection, inferences and robust estimation.Further, in the empirical part of the project we are going to apply the ES concept to research problems in economics and finance. We plan to investigate whether monetary policy of the US Federal reserve implemented by adjustments of the nominal interest rate is driven by formal rules or primarily by experience relying on case-based arguments. Then, we are going to consider technical analysis as a nonparametric approach to asset pricing from the ES perspective. We focus on recognition of price patterns and measuring similarities between different patterns in order to get information about future prices. Next, we will consider various portfolio selection strategies and determine the weights of different strategy by means of ES approach.Summarizing, the project will cover a set of theoretical problems and potential applications of the promising ES approach which is the econometric setting for case-based decisions contrasted to formal rule-based modeling.

经济学家的目标是用正式的决策规则来描述现实，这些规则假设了通过计量经济学分析实证验证的特定因果关系。然而，有时候手头并没有合适的决策规则，而只有一些被定义为一组条件、行为及其结果的经验案例。然后，基于案例的决策应该类似于与当前案例相似的经验案例中的成功决策。经验相似性（ES）方法为基于案例的决策提供了计量经济学框架，并假设感兴趣的变量是由当前和以前案例的相似性加权的历史结果的总和。因此，ES权重是时变的，并由外生变量非线性地确定，这允许直接从数据中揭示基于案例的决策的原则。它的应用已经为实验经济学、真实的房地产价格建模、预测金融市场波动或评估法律的司法决定提供了有用的见解。然而，仍然有一些未解决的计量经济学问题，以及有趣的应用，我们将在这个项目中address.In项目的理论部分，我们要分析，首先，哪些属性的经验数据是适合应用ES技术和会发生什么，如果基本的假设被违反。其次，现有ES模型的估计不稳定性阻碍了它们在许多重要问题设置中的应用。我们的目标是提出稳定ES估计过程的正则化技术，以及提供有关变量选择和降维问题的结果。第三，目前的理论结果集中在单变量数据，而多变量ES（MES）模型所需的变量向量为特征的决策。我们的目标是开发MES模型，特别注意模型选择，推理和稳健估计。此外，在项目的实证部分，我们将把ES概念应用于经济和金融问题的研究。我们计划调查美国联邦储备银行通过调整名义利率实施的货币政策是由正式规则驱动，还是主要依靠基于案例的论据的经验。然后，我们将从ES的角度考虑技术分析作为资产定价的非参数方法。我们专注于识别价格模式和测量不同模式之间的相似性，以获得有关未来价格的信息。接下来，我们将考虑各种投资组合选择策略，并通过ES方法确定不同策略的权重。总而言之，该项目将涵盖一系列理论问题和有前途的ES方法的潜在应用，ES方法是基于案例决策的计量经济学环境，而不是正式的基于规则的建模。