This study has twofold purpose. First, it intends to show that optimal control theory can provide an important tool for analyzing and understanding the dynamic properties of a macroeconomic model, for formulating stabilization policies based on that model, and for better understanding in a quantitative way the trade-offs that the economic policy maker faces. Second, it intends to examine the value of optimal control as an aid in the determination of policy in developing countries. / The first objective is met with the development of a small quarterly macroeconomic model of the U.S. economy. The specification and estimation of the model are given in this study, and the model's dynamic properties are explored. The second purpose is met with the application of optimal control theory to a small annual model of the Yugoslav economy. / Several experiments are run under different sets of assumptions about economic goals and policy commitments. As usual in optimal control analyses, these conditions are represented by a cost function that specifies the relative penalities for deviations of economic targets and control variables from their desired paths. / The results indicate that the value of optimal control as an aid in the determination of policy depends largely (if not entirely) on the robustness, accuracy, and dynamic nature of the estimated econometric model. They also show that, given such a model, an optimal-control approach is probably the best one not only for short-term policy planning but also for a better comprehension of the dynamic trade-offs that a policy maker will eventually face in the course of macroeconomic planning. / Source: Dissertation Abstracts International, Volume: 51-12, Section: A, page: 4214. / Major Professor: James H. Gapinski. / Thesis (Ph.D.)--The Florida State University, 1991.

This study analyzes business cycles from a sectoral perspective. Multi-sectoral models are able to explain some elements of economic fluctuations that cannot be examined in a single-consumer single-producer model. Two problems are examined in this research work: (i) are distortions in the allocation of capital inputs across sectors able to explain fluctuations in output?; (ii) are economic fluctuations the result of sectoral or aggregate shocks? / A theoretical sectoral model where capital allocative disturbances produce deviations of output from its long-run trend is presented. This model shows that small sectoral shocks may play an important role in the business cycle phenomenon. / To provide empirical evidence for the first issue, a proxy variable for capital allocative disturbances is constructed. The empirical analysis consists of estimating of a four variable VAR model comprised by industrial production, an ex-ante real interest rate, the Solow residual, and the variable for capital allocative disturbances. The analysis shows that capital allocative disturbances can explain some of the fluctuations in industrial production. / To empirically examine the second issue the Solow residual for nine industrial groups is used as proxy variables for technological advance. To extract the common and specific shocks, a factor analysis was used. The common and specific factors are then incorporated into a VAR model, where industrial production is used as the economic activity indicator. It is found that, although common shocks explain a large proportion of the forecast error variance of industrial production, the sectoral shocks explain around one third of this variance. / Source: Dissertation Abstracts International, Volume: 51-04, Section: A, page: 1322. / Major Professor: Milton Marquis. / Thesis (Ph.D.)--The Florida State University, 1990.

Source: Dissertation Abstracts International, Volume: 30-05, Section: A, page: 1737. / Thesis (Ph.D.)--The Florida State University, 1968.

Thesis (Ph.D.)--Indiana University, Dept. of Economics, 2009. / Title from PDF t.p. (viewed on Feb. 8, 2010). Source: Dissertation Abstracts International, Volume: 70-05, Section: A, page: 1743. Adviser: Eric M. Leeper.

Thesis (Ph.D.)--Indiana University, Dept. of Economics, 2009. / Title from PDF t.p. (viewed on Feb. 8, 2010). Source: Dissertation Abstracts International, Volume: 70-05, Section: A, page: 1733. Adviser: Hugh E.M. Kelley.

This thesis examines several issues that arise in restructured electricity markets. These issues include production, scheduling and forward contract decision making, capital investment decisions in uncertain environments, and equilibrium bidding in wholesale electricity auctions. In the chapter, "Supply Function Equilibria with Pivotal Suppliers", we study the impact of pivotal suppliers in supply function bidding settings. Observers of these markets have noted the important role that pivotal suppliers, those who can substantially raise the market price by unilaterally withholding generation output, sometimes play. However the literature on SFE has not considered the potential impact of pivotal suppliers on equilibrium predictions. This is a potentially important deficiency of applications of SFE to electricity markets, given the large role that pivotal suppliers sometimes play in these markets. We formulate a model in which generation capacity constraints can cause some suppliers to be pivotal. In symmetric and asymmetric versions of the model we show that when pivotal suppliers are present, the set of SFE is reduced relative to when no suppliers are pivotal. In the chapter, "Dynamic Oligopolistic Games Under Uncertainty: A Stochastic Programming Approach", we study several stochastic programming formulations of dynamic oligopolistic games under uncertainty. It is well known that if the number of state variables increases, dynamic programming becomes computationally intractable. For such games, we show that under certain symmetry assumptions, players earn greater expected profits as demand volatility increases. The key to our approach is the "scenario formulation" of stochastic programming. The examples presented in this paper illustrate that this approach can address dynamic games that are clearly out of reach for dynamic programming, the common approach in the literature on dynamic games. In the chapter, "Scenario-based Electricity-Gas Forward and Spot Pricing and Load Formulations", we propose load models and price and return formulations in specific energy markets. Existing energy models do not consider inter-relations between the trio: spot price, derivative price and electric load. Also these models, which are in the spirit of the models proposed in financial and commodity markets, ignore special characteristics of electricity, which may make the proposed models useless. In our formulations we consider these characteristics and correlations between these variables. Simulation results that we run support our modeling approaches.

An attempt is made to create a model of exchange rates that explains the short term, daily levels of the foreign exchange spot market. The model is a monetary type that focuses on the eurocurrency markets and the current account. It has a liquidity preference form and employs daily data. The futures rate, the euro interest rate, the eurocurrency money stocks and a current account variable are the individual variables of the model. The futures rate and the euro interest rates are from the assumed Fisher's 'Covered Interest Rate Paradigm'. The eurocurrency money stock variable's justification is based on the real world structure of the spot market where the foreign exchange desks of the major world commercial banks are the dominant players. The current account variable, which is motivated by a desire to improve on the short run performance of the Purchasing Power Parity variable of other monetary models, is justified by trade theory. The liquidity preference form of the model is in keeping with current monetary models. The econometric results show that the model is better than the random walk model. However the results of the individual variables are mixed. The futures rate accounts for the vast majority of the model's success. Although the eurocurrency variable is as statistically significant as the interest rate differentials from the widely accepted Fisher's Covered Interest Rate Parity paradigm, neither was as significant as the futures rate. The current account variable results are not statistically significant. Thus, the current account variable may be discarded while the eurocurrency interest rates and euromoney variables warrant further study. As a result of the dominance of the futures rate variable, models that cry to capture rational expectations such as the News or Chaos Models are appealing. This rational expectations characteristic of the market combined with the dominance of speculation over economic fundamentals also points toward game theory as a good candidate for further study.

Chapter one of this dissertation provides an experimental test of a joint hypothesis implied by the constant relative risk averse model of first price sealed bid auction theory and standard risk preference theory. This test will be used to determine whether observed risk attitude is solely a characteristic of the individual (as conventional economic theory has it) or jointly a characteristic of the individual and the environment (as some cognitive psychologists maintain). It also presents a simple adjustment to first price auction experiments which causes "throw away" bidding behavior to be dramatically decreased and makes empirical estimation more precise. Standard theoretical search models assume that agents behave as if they search optimally. The second chapter of this dissertation reports the experimental results of a test of whether agents maximize their payouts by optimally searching for the best of n candidates in what has been called the "Secretary Problem." The novelty of this design is that the optimal search rule is invariant to agents' risk attitudes. This is made possible by implementing a binary payout schedule in which stopping at the best candidate pays a positive amount and stopping at any other candidate pays nothing. On average, subjects chose the best candidate slightly less often than an optimal searcher, while the El-Gamal and Grether (1995) estimation procedure suggests that subjects were either using a sub-optimal rule or just randomly guessing.

This dissertation uses samples of Initial Public Offerings (IPOs) to examine the separate effects that a capital gains tax and investor interest have on trading volume and returns. Chapter one looks at how different tax rates for long-term and short-term capital gains and losses affect trading in IPOs. Prior to the Tax Reform Act of 1986 (TRA '86), long-term capital gains were taxed at a lower rate than short-term gains, presenting investors with an opportunity to increase their after-tax return by delaying the sale of appreciated assets until after they qualified for long-term status and selling depreciated assets prior to long-term qualification. Using a sample of Initial Public Offering, I find that stocks that appreciated prior to long-term qualification exhibit increased trading volume and decreased returns just after their qualification date, while stocks that depreciated prior to long-term qualification exhibit these effects just prior to their qualification date. Chapter two explores how the previously undefined variable "investor interest" affects an IPO's trading activity. The level of investor interest in an IPO prior to its issue influences its offer price, its initial return and its initial trading volume. After issue, this interest level impacts the stock's long-term trading volume, leading to a positive relationship between an IPO's initial return and its trading volume for more than three years after issuance. Using newspaper references as a proxy for the level of interest in a firm, I find that investor interest is positively related to initial return, initial trading volume and long-term trading volume.

Each chapter of this dissertation focuses on a different aspect of strategic behavior. The first chapter presents research in which humans play against a computer decision maker that follows either a reinforcement learning algorithm or an Experience Weighted Attraction algorithm. The algorithms are more sensitive than humans to exploitable opponent play. Further, learning algorithms respond to calculated opportunities systematically; however, the magnitudes of these responses are too weak to improve the algorithm's payoffs. Additionally, humans and current models of their behavior differ in that humans do not adjust payoff assessments by smooth transition functions but when humans do detect exploitable play they are more likely to choose the best response to this belief. The second chapter reports research designed to directly reveal the information used by subjects in a game. Human play is often classified as adhering to reinforcement learning or belief learning. This is typically due to using subjects' observed action choices to estimate the learning models' parameters. We use a different, more direct approach: an experiment in which subjects choose which kind of information they see--either the information required for reinforcement learning, or the information required for belief learning. Results suggest that while neither kind of information is chosen exclusively, subjects most often choose information that is consistent with belief learning and inconsistent with reinforcement learning. The third chapter discusses the Groves-Ledyard mechanism. In economics we typically rely on continuous analysis, however doing so may not lead to an accurate assessment of a discrete environment. The Groves-Ledyard mechanism is such a case that demonstrates a drastic divergence of results between continuous and discrete analysis. This chapter shows that given quasi-linear preferences, a discrete strategy space will not necessarily yield a single Pareto optimal Nash equilibrium, but typically many Nash equilibria, not all of which are necessarily Pareto optimal. Further, the value of the mechanism's single free parameter determines the number of Nash equilibria and the proportion of Pareto optimal Nash equilibria.