Economics Theses and Dissertations

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Browsing Economics Theses and Dissertations by Subject "Adaptive learning"

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    Essays in Regime Switching Policy and Adaptive Learning in Dynamic Stochastic General Equilibrium
    (University of Oregon, 2018-09-06) McClung, Nigel; Evans, George
    This dissertation studies monetary-fiscal policy interactions and adaptive learning applications in regime-switching DSGE models. A common thread through my research is understanding how policymakers may be affected by the interaction of policy regime change and agents' beliefs about past, current or future policy in general equilibrium. The work I present in this dissertation shows that conventional and unconventional policy outcomes, as well as the existence, uniqueness and expectational stability of rational expectations solutions, depend heavily on the expectational effects of time-varying policy. These findings suggest that uncertainty over future fiscal policy may curb the effectiveness of monetary policy, or otherwise constrain the actions of central bankers. In carrying out this research agenda, my work also examines the relationship between determinacy and expectational stability in a general class of Markov-switching DSGE models.
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    Incorporating High Dimensional Data Vectors into Structural Macroeconomic Models
    (University of Oregon, 2016-10-27) Gelfer, Sacha; Piger, Jeremy
    In this dissertation I incorporate high dimensional data vectors in estimated Dynamic Stochastic General Equilibrium (DSGE) models, evaluating the labor market dynamics incorporated inside such data vectors, out-of-sample forecasting performance of many models estimated with such data vectors and analytically examining the reduction of macroeconomic volatility that can occur when such data vectors are used in the formation of expectations about the future. The second chapter investigates the extent to which modern DSGE models can produce labor market dynamics in response to a financial crisis that are consistent with the experience of the Great Recession. I estimate two New-Keynesian models, one with and one without financial frictions, in a data-rich environment. I find that negative financial shocks are associated with longer recoveries in real investment, capital-intensive sectors of the labor market and average unemployment duration. I also find the model with a financial accelerator is equipped with better tools to identify the dynamics associated with the Great Recession and its recovery in regard to many labor and financial metrics. The third chapter compares the out-of-sample forecasting performance of the two DSGE models of Chapter II when they are estimated both out of and in a data-rich environment. This chapter finds that many financial time series variance decomposition are significantly better explained using the structural set-up of the New-Keynesian model with financial frictions. DSGE models estimated with high dimensional data vectors significantly out forecast their regularly estimated counterpart in regard to output, investment and consumption growth. Lastly, the use of real-time optimal pool model weighting significantly out-forecasts traditional macroeconomic models as well as an equally weighted weighting scheme in terms of many macroeconomic variables. The fourth chapter examines the role forecasts derived by high dimensional data vectors can have on lowering macroeconomic volatility. Bounded rational agents are introduced into the Chapter II DSGE model with financial frictions and are given the option to use or ignore professionally generated forecasts from a dynamic factor model in their perceived forecasting model. In simulations, I find that professionally generated forecasts can significantly lower the volatility of many macroeconomic variables including inflation and hours worked.
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    Macroeconomic models with endogenous learning
    (University of Oregon, 2010-06) Gaus, Eric
    The behavior of the macroeconomy and monetary policy is heavily influenced by expectations. Recent research has explored how minor changes in expectation formation can change the stability properties of a model. One common way to alter expectation formation involves agents' use of econometrics to form forecasting equations. Agents update their forecasts based on new information that arises as the economy progresses through time. In this way agents "learn" about the economy. Previous learning literature mostly focuses on agents using a fixed data size or increasing the amount of data they use. My research explores how agents might endogenously change the amount of data they use to update their forecast equations. My first chapter explores how an established endogenous learning algorithm, proposed by Marcet and Nicolini, may influence monetary policy decisions. Under rational expectations (RE) determinacy serves as the main criterion for favoring a model or monetary policy rule. A determinant model need not result in stability under an alternative expectation formation process called learning. Researchers appeal to stability under learning as a criterion for monetary policy rule selection. This chapter provides a cautionary tale for policy makers and reinforces the importance of the role of expectations. Simulations appear stable for a prolonged interval of time but may suddenly deviate from the RE solution. This exotic behavior exhibits significantly higher volatility relative to RE yet over long simulations remains true to the RE equilibrium. In the second chapter I address the effectiveness of endogenous gain learning algorithms in the presence of occasional structural breaks. Marcet and Nicolini's algorithm relies on agents reacting to forecast errors. I propose an alternative, which relies on agents using statistical information. The third chapter uses standard macroeconomic data to find out whether a model that has non-rational expectations can outperform RE. I answer this question affirmatively and explore what learning means to the economy. In addition, I conduct a Monte Carlo exercise to investigate whether a simple learning model does, empirically, imbed an RE model. While theoretically a very small constant gain implies RE, empirically learning creates bias in coefficient estimates.
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    The Effects of News Shocks and Bounded Rationality on Macroeconomic Volatility
    (University of Oregon, 2017-09-06) Dombeck, Brian; McGough, Bruce
    This dissertation studies the impact embedding boundedly rational agents in real business cycle-type news-shock models may have on a variety of model predictions, from simulated moments to structural parameter estimates. In particular, I analyze the qualitative and quantitative effects of assuming agents are boundedly rational in a class of DSGE models which attempt to explain the observed volatility and comovements in key aggregate measures of U.S. economic performance as the result of endogenous responses to information in the form of ``news shocks''. The first chapter explores the theoretical feasibility of relaxing the rational expectations hypothesis in a three-sector real business cycle (RBC) model which generates boom-bust cycles as a result of periods of optimism and pessimism on the part of households. The second chapter determines whether agents forming linear forecasts of shadow prices in a nonlinear framework can lead to behavior approximately consistent with fully informed individuals in a one-sector real business cycle model. The third chapter analyzes whether empirical estimates of the relative importance of anticipated shocks may be biased by assuming rational expectations. By merging the two hitherto separate but complementary strands of literature related to bounded rationality and news shocks I am able to conduct in-depth analysis of the importance of both the information agents have and what they choose to do with it. At its core, the study of news in macroeconomics is a study of the specific role alternative information sets play in generating macroeconomic volatility. Adaptive learning on the other hand is concerned with the behavior of agents given an information set. Taken together, these fields jointly describe the input and the ``black box'' which produce model predictions from DSGE models. While previous research has been conducted on the effects of bounded rationality or news shocks in isolation, this dissertation marks the first set of research explicitly focused on the interaction of these two model features.
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    Three essays on adaptive learning, institutions and multiple equilibria
    (University of Oregon, 2009-06) Steiger, Laura Christina, 1977-
    This dissertation examines the role that institutions play in the existence of multiple equilibria in models of economic development. In addition, it examines the dynamics of transition between such equilibria. In the first chapter of this dissertation, I build a dynamic model of institutional choice, wherein the government invests in the legal infrastructure in response to the need for the protection of output from appropriation. A unique equilibrium exists only under commitment, not under discretion. This would suggest that a measure of institutional quality must not only consider the extent to which current policies protect property rights but also include the ability of the government to commit to reform in the long run. The second chapter of this dissertation examines the effect of adaptive learning on stability and transitional dynamics between multiple equilibria in a growth model with human capital externalities. I find that there are two equilibria, one a poverty trap with no education. Only the poverty trap is locally stable under learning. However, productivity shocks are not sufficient to generate transitions between the equilibria. Indeed, productivity shocks must lie below a threshold in order for the economy to escape the poverty trap. These escape paths do not allow the economy to transition to the upper steady state. I propose instead the use of shocks to expectations to permit such a transition. The third chapter of this dissertation presents an empirical test for the role that human capital and institutions may play in transitions between equilibria by estimating a Markov-switching regression. This methodology allows me to characterize both distinct growth regimes and transitions between them. I explore the effects of time-varying institutional measures and human capital on transition probabilities. I find that political and economic institutions are similar in their effects on transitions arid that the time variation in the institutional measure increases the probability of identifying both miracle growth and stagnation regimes. Furthermore, human capital has a significant effect on switches between miracle growth, stable growth and stagnation.