%0 Thesis %1 noauthororeditor %A Lester, Chandler %D 2021 %K Hamilton-Jacobi-Bellman Kalman_filter economics optimization review %T Adaptive learning in continuous-time: techniques and theory %X How we model individual’s expectations and predictions in economic models plays an essential role in economic outcomes. We can assume that individuals are well informed and developed nuanced views on the economy, meaning they understand and have detailed knowledge of economic parameters and economic models, or we can suppose individuals are observant and develop perceptions of the economy and make decisions based on available data. One method of including this level of realistic behavior in economic models is adaptive learning. In adaptive learning models, agents use simple forecasting rules to make predictions about future values of economic variables or the state of the economy. The work presented in this dissertation builds a framework for examining these dynamics in a high-frequency setting. It is important to extend these behavioral modeling techniques to this setting because increasing data are available at higher frequencies. This work combines existing continuous-time modeling techniques with emerging research from economics to develop modelings in which an agent can respond to high-frequency information. This dissertation demonstrates that complex high-frequency learning is possible and has potential benefits and improvements over discrete-time counterparts. The ivdominant theme of this work is defining and mathematically developing a framework for examining bounded rationality in continuous-time models. In chapter two, basic exogenous adaptive rules are explored in a simple Ramsey Model setting. Chapter three introduces shadow-price learning and more complicated endogenous learning rules, including a derivation of continuous-time recursive least squares and the definition of a continuous-time mapping between an agent’s perceptions and actuality. Chapter four builds on the dynamics defined in chapter three by applying them to a linearized Real Business cycle model. We find that the continuous-time learning dynamics offer some improvements to the volatility of predictions.

@phdthesis{noauthororeditor, abstract = {How we model individual’s expectations and predictions in economic models plays an essential role in economic outcomes. We can assume that individuals are well informed and developed nuanced views on the economy, meaning they understand and have detailed knowledge of economic parameters and economic models, or we can suppose individuals are observant and develop perceptions of the economy and make decisions based on available data. One method of including this level of realistic behavior in economic models is adaptive learning. In adaptive learning models, agents use simple forecasting rules to make predictions about future values of economic variables or the state of the economy. The work presented in this dissertation builds a framework for examining these dynamics in a high-frequency setting. It is important to extend these behavioral modeling techniques to this setting because increasing data are available at higher frequencies. This work combines existing continuous-time modeling techniques with emerging research from economics to develop modelings in which an agent can respond to high-frequency information. This dissertation demonstrates that complex high-frequency learning is possible and has potential benefits and improvements over discrete-time counterparts. The ivdominant theme of this work is defining and mathematically developing a framework for examining bounded rationality in continuous-time models. In chapter two, basic exogenous adaptive rules are explored in a simple Ramsey Model setting. Chapter three introduces shadow-price learning and more complicated endogenous learning rules, including a derivation of continuous-time recursive least squares and the definition of a continuous-time mapping between an agent’s perceptions and actuality. Chapter four builds on the dynamics defined in chapter three by applying them to a linearized Real Business cycle model. We find that the continuous-time learning dynamics offer some improvements to the volatility of predictions.}, added-at = {2021-05-28T19:47:13.000+0200}, author = {Lester, Chandler}, biburl = {https://www.bibsonomy.org/bibtex/2c4d02a178746909935cb727d5bb14651/peter.ralph}, interhash = {686210ee34ba4436a96b8326668edc58}, intrahash = {c4d02a178746909935cb727d5bb14651}, keywords = {Hamilton-Jacobi-Bellman Kalman_filter economics optimization review}, school = {University of Oregon}, timestamp = {2021-05-28T19:47:13.000+0200}, title = {Adaptive learning in continuous-time: techniques and theory}, type = {PhD thesis}, year = 2021 }