Predicting Interest Rate Decisions for Economic Forcast Models
Modelling Monetary Policy Using Novel Engineering System Identification
This project is an exploration in applying engineering system identification and control theory to forecasting models in macroeconomics. This is a multi-disciplinary project that combines the knowledge from two very different fields.
Interest rate is decided upon twice per quarter by the Federal Open Market Committee (FOMC). This decision is not based on a rule, but rather left to the discretion of the FOMC board of directors. Unfortunately, most forecast models require a way to mimic the FOMC decision making process in order to function. While decision making based on numerical data can usually be modeled by a set of equations, priorities within monetary policy change between decades, Presidents, and board chairs. This makes the development of a single rule difficult, if not impossible.
This thesis aims to use breakthroughs in engineering in the fields of system identification and robust control design to revisit those problems to produce improved models for forecasting use.
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