Monte Carlo Investigation of Three Issues: Exact Monetary Aggregation under Risk, Tests for Nonlinearity and Chaos, and the AIM Model's Regularity Properties

如果存在相对于当期价格的风险厌恶，或 利率，跟踪所有统计指标的属性 数字，包括Divisia和最高级索引号， 未知 根据之前的NSF拨款，研究人员 证明了离散时间的跟踪能力 Divisia指数不受风险厌恶的严重影响。 然而，这项研究使用了经济产生的数据。 使用 来自经济的数据留下了许多问题没有答案。 为 例如，规范错误可能很大 可能恰好抵消了Divisia的跟踪误差 指数. 如果这是真的， 对风险厌恶的稳健性可能是错误的。 该项目使用了 蒙特卡罗方法生成模拟数据，其中只有一个 跟踪误差的可能来源仍然是：风险程度 厌恶 该项目的成果对货币政策具有重要意义。 因为货币总量是货币政策的基础， 当局跟踪经济的表现。 的 研究在方法上也很重要，因为它需要 对计算要求高的现有技术的复制 非线性理性预期模型

If there is risk aversion relative to current period prices or interest rates, the tracking properties of all statistical index numbers, including the Divisia and superlative index numbers, are unknown. Under the previous NSF grant, the investigator demonstrated that the tracking ability of the discrete time Divisia index is not seriously compromised by risk aversion. However, that study used data produced by the economy. The use of data from the economy leaves many problems unanswered. For example, it is possible that specification errors might be large and might coincidentally offset the tracking error in the Divisia index. If that were true, the conclusion that the Divisia index is robust to risk aversion might be wrong. This project uses a Monte Carlo approach to generate simulated data in which only one possible source of tracking error remains: the degree of risk aversion. The results of this project are important for monetary policy because monetary aggregates are used by monetary authorities to track the performance of the economy. The research is also methodologically significant because it requires computationally demanding replications of state of the art nonlinear rational expectations models.