Ambiguity, Business Cycle Dynamics and Optimal Policy

模糊性、经济周期动态和最优政策

• 批准号: 1261014
• 负责人: Cosmin Ilut
• 金额: $ 17.5万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2015-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1261014&HistoricalAwards=false
• 关键词: Ambiguity; Business; Cycle; Dynamics; Optimal

Abstract Proposal Title: Ambiguity, Business Cycle Dynamics and Optimal PolicyProposal Number: SES ? 1261014Principal Investigator: llut, Cosmin This proposal starts from two observations. On the one hand, a large body of evidence suggests that humans perceive ambiguous situations (that is, uncertain situations where probabilities are not known) differently from risky situations (where probabilities are known). Arguably the majority of decisions made on a daily basis by investors and businesses are made under ambiguity. On the other hand, there is a large literature in economics that attempts to understand the quantitative implications of decision makers' attitudes towards uncertainty for asset prices and the business cycle. This literature has almost exclusively assumed that decision makers behave as if probabilities are known. It has so far met with limited success: a number of "puzzles" show that standard models cannot reconcile the volatility of business cycle quantities with the level and volatility of asset prices and investors' asset positions. This casts doubt on how useful these models are for evaluating policy in uncertain situations. This proposal describes a new approach to building macroeconomic models with ambiguity-averse decision makers. In terms of methodology, the proposal provides a simple way to build, solve and estimate such models. In particular, the approach accommodates time variation in the ambiguity perceived by agents about different sources of uncertainty, such as productivity or policy parameters. The approach also suggests how to connect uncertainty measured by an econometrician to the uncertainty perceived by economic agents in a model. The proposed research analyzes how to use the new tools to show that ambiguity is a promising model ingredient for understanding business cycle dynamics, asset price volatility and optimal policy.To model agents? attitude towards ambiguity, the proposed approach builds on developed decision-theoretical foundations. In particular the approach uses the multiple priors utility model, according to which agents act as if they evaluate plans using a worst case probability drawn from a set of multiple beliefs. If agents have less confidence in probability assessments on the uncertain events then the set of beliefs is larger. In a dynamic model, one reason for a change in the size of the belief set is the arrival of information. For example, a loss of confidence could be triggered by worrisome information about the future. Conversely, an increase in confidence -- captured by shrinkage of the set of beliefs -- could occur as agents might learn reassuring information that moves them closer toward thinking in terms of probabilities. In either case, agents respond to a change in confidence if the worst case probability used to evaluate actions also changes. In a macroeconomic model, uncertainty comes from a variety of sources, summarized by a number of exogenous shocks. Examples include contemporaneous changes to technology or the policy regime, but also news about those issues in the future. The proposed approach to constructing belief sets for agents with multiple priors utility is the following. At every date, the set of beliefs about a shock next period is parameterized by an interval of means centered around zero. A loss of confidence is then captured by an increase in the width of the interval; in particular, the "worst case" mean becomes worse. Conversely, an increase in confidence is captured by a narrowing of the interval and thereby a better worst case mean. Since agents take actions based on the worst case mean, a change in confidence works like news shock: an agent who gains (losses) confidence responds as if he had received good (bad) news about the future. The analysis of confidence shocks is particularly tractable in economies that are essentially linear, that is, the worst case means supporting agents' equilibrium choices can be written as a linear function of the state variables. This property implies that equilibria can be accurately characterized using first order approximations. In particular, the proposed research can study agents' responses to changes in uncertainty, as well as time variation in uncertainty premia on assets, without resorting to higher order approximations. This is in sharp contrast to the case of changes in risk, where higher order solutions are critical.An important goal of the proposed research is to quantify the effects of confidence shocks in driving the US business cycle and asset prices. In particular, one application is to incorporate agents? ambiguity about productivity into a standard quantitative DSGE model and allow their confidence to vary over time, a type of "uncertainty shock". Even though uncertainty shocks are present, standard linearization methods can be used to solve and estimate the model. Preliminary results suggest that such time-varying ambiguity can emerge as a major source of business cycle fluctuations. The proposed research also aims to make progress in understanding movements in asset prices. Here the important insight is that time variation in ambiguity leads econometricians to measure time varying premia in asset markets. Indeed, when investors evaluate an asset as if the mean payoff is low, then they are willing to pay only a low price for it. To an econometrician, the return on the asset -- actual payoff minus price -- will then look unusually high. The more ambiguity investors perceive, the lower is the price and the higher is the subsequent return. The proposal aims to address issues such as the magnitude of average asset premia, which are puzzlingly low in rational expectations models, and time-variation in excess returns in a quantitative model that can be fit to the data. This research will have a broad impact, in part, because it brings diverse areas of economic research into greater contact with one another. Specifically, the proposal links advances in decision theory to areas in macroeconomics and finance such as business cycles, asset pricing, and optimal policy. So the project should enhance synergies across sub-disciplines. The proposal should also lead to progress in understanding environments in which agents? confidence matters for the economy, which is important for positive and normative analysis. The project has the potential of having a significant impact on how policy makers analyze such complex environments, including stabilization policies, the functioning of financial markets, booms and crashes in asset prices and real activity.

提案题目：歧义、商业周期动态与最优政策[1261014]首席研究员：llut， Cosmin这个建议从两个观察开始。一方面，大量证据表明，人类对模棱两可情况（即概率未知的不确定情况）的感知与对危险情况（概率已知的情况）的感知不同。可以说，投资者和企业每天做出的大多数决定都是在模棱两可的情况下做出的。另一方面，有大量的经济学文献试图理解决策者对资产价格和商业周期不确定性的态度的定量含义。这些文献几乎完全假设决策者的行为好像概率是已知的。迄今为止，它取得了有限的成功：许多“谜题”表明，标准模型无法将商业周期数量的波动性与资产价格的水平和波动性以及投资者的资产头寸调和起来。这让人怀疑这些模型在不确定情况下评估政策的用处。这一建议描述了一种新的方法来建立宏观经济模型与模糊性厌恶的决策者。在方法学方面，该建议提供了一种简单的方法来构建、求解和估计这些模型。特别是，该方法适应了代理对不同不确定性来源（如生产率或政策参数）所感知的模糊性的时间变化。该方法还建议如何将计量经济学家测量的不确定性与模型中经济主体感知的不确定性联系起来。本研究分析了如何使用这些新工具来表明模糊性是理解商业周期动态、资产价格波动和最优政策的一个有希望的模型成分。去模拟代理人？对歧义的态度，提出的方法建立在发达的决策理论基础上。特别是，该方法使用了多先验实用模型，根据该模型，智能体的行为就好像是使用从一组多个信念中得出的最坏情况概率来评估计划。如果主体对不确定事件的概率评估信心较弱，则信念集较大。在动态模型中，信念集大小变化的一个原因是信息的到来。例如，对未来的担忧信息可能会引发信心的丧失。相反，信心的增加——通过信念集合的收缩——可能会发生，因为代理可能会学习到令人放心的信息，使他们更接近于从概率的角度思考。在任何一种情况下，如果用于评估行为的最坏情况概率也发生变化，代理都会对置信度的变化做出反应。在宏观经济模型中，不确定性来自多种来源，归纳起来就是一些外生冲击。例子包括同期技术或政策制度的变化，也包括未来有关这些问题的新闻。构造具有多先验效用的智能体的信念集的方法如下。在每个日期，关于下一个周期的冲击的信念集由以0为中心的均值区间参数化。然后通过增加区间宽度来弥补信心的损失；特别是，“最坏情况”意味着变得更糟。相反，置信度的增加体现在区间的缩小，从而得到更好的最坏情况均值。由于行为主体根据最坏情况均值采取行动，信心的变化就像消息冲击一样起作用：获得（失去）信心的行为主体的反应就好像他收到了关于未来的好（坏）消息。对信心冲击的分析在本质上是线性的经济体中特别容易处理，也就是说，最坏的情况意味着支持主体的均衡选择可以写成状态变量的线性函数。这个性质意味着平衡可以用一阶近似精确地表征。尤其值得一提的是，本文的研究可以研究agent对不确定性变化的反应，以及资产不确定性溢价的时间变化，而无需采用高阶近似。这与风险变化的情况形成鲜明对比，在风险变化中，高阶解决方案至关重要。拟议研究的一个重要目标是量化信心冲击在推动美国商业周期和资产价格方面的影响。特别地，一个应用程序是合并代理？将生产率的模糊性纳入标准的量化DSGE模型，并允许他们的信心随时间变化，这是一种“不确定性冲击”。即使存在不确定性冲击，也可以使用标准线性化方法来求解和估计模型。初步结果表明，这种时变的模糊性可能成为商业周期波动的主要来源。拟议中的研究还旨在在理解资产价格变动方面取得进展。这里的重要见解是，模糊性的时间变化导致计量经济学家测量资产市场中的时变溢价。事实上，当投资者评估一项资产时，如果平均收益很低，那么他们只愿意为它支付较低的价格。对计量经济学家来说，资产的回报——实际收益减去价格——将显得异常高。投资者感知的模糊性越强，价格就越低，随后的回报就越高。该提案旨在解决一些问题，比如平均资产溢价的幅度，在理性预期模型中，平均资产溢价的幅度低得令人费解，以及在一个可以与数据拟合的定量模型中，超额回报的时间变化。这项研究将产生广泛的影响，部分原因是它使不同的经济研究领域彼此之间有了更大的联系。具体来说，该建议将决策理论的进展与宏观经济和金融领域（如商业周期、资产定价和最优政策）联系起来。因此，该项目应加强跨学科的协同作用。这一提议还将导致对环境的理解取得进展，在这些环境中，代理人？信心对经济至关重要，这对积极和规范的分析很重要。该项目有可能对政策制定者如何分析这些复杂的环境产生重大影响，包括稳定政策、金融市场的运作、资产价格的繁荣和崩溃以及实际活动。