We consider the use of a sieve bootstrap based on moving average (MA) and autoregressive moving average (ARMA) approximations to test the unit root hypothesis when the true Data Generating Process (DGP) is a general linear process. We provide invariance principles for these bootstrap DGPs and we prove that the resulting ADF tests are asymptotically valid. Our simulations indicate that these tests sometimes outperform those based on the usual autoregressive (AR) sieve bootstrap. We study the reasons for the failure of the AR sieve bootstrap tests and propose some solutions, including a modified version of the fast double bootstrap. / We also argue that using biased estimators to build bootstrap DGPs may result in less accurate inference. Some simulations confirm this in the case of ADF tests. We show that one can use the GLS transformation matrix to obtain equations that can be used to estimate bias in general ARMA(p,q) models. We compare the resulting bias reduced estimator to a widely used bootstrap based bias corrected estimator. Our simulations indicate that the former has better finite sample properties then the latter in the case of MA models. Finally, our simulations show that using bias corrected or bias reduced estimators to build bootstrap DGP sometimes provides accuracy gains.

Vector autoregression models have become widely used research tools in the analysis of macroeconomic time series. Cointegrated techniques are an essential part of empirical macroeconomic research. They infer causal long-run relationships between nonstationary variables. In this study, six information criteria were reviewed and compared. The methods focused on determining the optimum information criteria for detecting the correct lag structure of a two-variable cointegrated process.

No description available.

published_or_final_version / abstract / toc / Physics / Doctoral / Doctor of Philosophy

Statistical quality control (SQC) is one technique companies are using in the development of a Total Quality Management (TQM) culture. Shewhart control charts, a widely used SQC tool, rely on an underlying normal distribution of the data. Often data are skewed. The inverse Gaussian distribution is a probability distribution that is wellsuited to handling skewed data. This analysis develops models and a set of tools usable by practitioners for the constrained economic statistical design of control charts for inverse Gaussian distribution process centrality and process dispersion. The use of this methodology is illustrated by the design of an x-bar chart and a V chart for an inverse Gaussian distributed process.

Ma Ning. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 49-57). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.5 / Chapter 2 --- Literature Review --- p.11 / Chapter 2.1 --- Problems about Using Sibling Samples --- p.19 / Chapter 2.2 --- Difficulties with the Within-twin-pair Studies --- p.20 / Chapter 3 --- Method --- p.21 / Chapter 3.1 --- Omitted Variable Bias (Selection Effect) --- p.21 / Chapter 3.1.1 --- OLS Model --- p.21 / Chapter 3.1.2 --- Fixed-Effect Model --- p.23 / Chapter 3.1.3 --- GLS Model --- p.23 / Chapter 3.2 --- Measurement Error --- p.24 / Chapter 4 --- Data --- p.26 / Chapter 5 --- Results --- p.29 / Chapter 5.1 --- "OLS, Fixed-Effect, GLS and IV estimates" --- p.29 / Chapter 5.2 --- Important findings --- p.34 / Chapter 5.3 --- Further Results --- p.35 / Chapter 5.3.1 --- Consistency of Fixed-Effect Estimate --- p.35 / Chapter 5.3.2 --- Smoking as an Instrument for Education --- p.39 / Chapter 5.3.3 --- Symmetry Test --- p.41 / Chapter 5.3.4 --- Hausman Test --- p.44 / Chapter 5.3.5 --- Selection Bias --- p.45 / Chapter 6 --- Conclusions --- p.48 / Chapter 7 --- Bibliography --- p.49

This thesis focuses on learning from revealed preferences and their implications across operations management problems through an Inverse Problem perspective. For the first part of the thesis, we focus on decentralized platforms facilitating many-to-many matches between two sides of a marketplace. In the absence of direct matching, inefficiency in market outcomes can easily arise. For instance, popular supply agents may garner many units from the demand side, while other supply units may not receive any match. A central question for the platform is how to manage congestion and improve market outcomes. In Chapter One, we study the impact of a detail-free lever: the disclosure of information to agents on current competition levels. How large are the effects of this lever, and how do they affect overall market outcomes? We answer this question empirically. We partner with the largest service marketplace in Latin America, which sells non-exclusive labor market leads to workers. The key innovation in our approach is the proposal of a structural model that allows agents (workers) to respond to competitors through beliefs about competition at the lead level, which in turn implies an equilibrium at the platform level under the assumption of rational expectations. In this problem, we observe agents' best responses (actions), and from that, we need to infer their structural parameters. Identification follows from an exogenous intervention that changes agents' contextual information and the platform equilibrium. We then conduct counterfactual analyses to study the impact of signaling competition on workers' lead purchasing decisions, the platform's revenue, and the expected number of matches. We find that signaling competition is a powerful lever for the platform to reduce congestion, redirect demand, and ultimately improve the expected number of matches for the markets we analyze. For the second part of the thesis, we discuss both parametric and modelling approaches in Inverse Problems. In Chapter Two, we focus on Inverse Optimization Problems in a single-agent setting. Specifically, we study offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after-the-fact, the optimal action an oracle with full knowledge of the objective function would have taken. We aim to minimize regret, which is defined as the difference between our losses and the ones incurred by an all-knowing oracle. In the offline setting, the decision-maker has information available from past periods and needs to make one decision, while in the online setting, the decision-maker optimizes decisions dynamically over time based on a new set of feasible actions and contextual functions in each period. For the offline setting, we characterize the optimal minimax policy, establishing the performance that can be achieved as a function of the underlying geometry of the information induced by the data. In the online setting, we leverage this geometric characterization to optimize the cumulative regret. We develop an algorithm that yields the first regret bound for this problem, which is logarithmic in the time horizon. Furthermore, we show via simulation that our proposed algorithms outperform previous methods from the literature. Finally, in Chapter Three, we consider data-driven methods for general Inverse Problem formulations under a statistical framework (Statistical Inverse Problem-SIP) and demonstrate how Stochastic Gradient Descent (SGD) algorithms can be used to solve linear SIP. We provide consistency and finite sample bounds for the excess risk. We exemplify the algorithm in the Functional Linear Regression setting with an empirical application in predicting illegal activity from bitcoin wallets. We also discuss additional applications and extensions.

This thesis focuses on the renewable energy policies for the case study countries (European Union, United States, United Kingdom, Turkey, and Nigeria) with using quantitative and qualitative analysis. The thesis adopts a three -pronged approach to address three main issues: The first paper investigates a 1990-2008 panel dataset to conduct an econometric analysis of policy instruments, such as; feed-in tariffs, quotas, tenders, and tax incentives, in promoting renewable energy deployment in 27 EU countries and 50 US states. The results suggest that renewable energy policy instruments play a significant role in encouraging renewable energy sources. Using data from 1990 to 2012 with the vector auto regression (VAR) approach for three case study countries, namely United Kingdom, Turkey, and Nigeria, the second paper focuses on how renewable energy consumption as part of total electricity consumption is affected by economic growth and electricity prices. The findings from the VAR model illustrate that the relationship between case study countries’ economic growth and renewable energy consumption is positive and economic growth in case study countries respond positively and significantly. The third paper focuses on the relationship between renewable energy policies and investment in renewables in the countries of United Kingdom and Turkey. The third paper builds upon current knowledge of renewable energy investment and develops a new conceptual framework to guide analyses of policies to support renewables. Past and current trends in the field of renewable energy investment are investigated by reviewing the literature on renewable energy investment linkage with policies, which identifies patterns and similarities in RE investment. This also includes the interview analysis with investors focusing on policies for renewable energy investment. The results from the interview and conceptual analysis show that renewable policies play a crucial role in determining investment in renewable energy sources. The findings from this thesis demonstrate that renewable energy policies increase with a growth of the renewable energy investment in the sector. Finally, the outcomes of this thesis also contribute to the energy economics literature, especially for academic and subsequent research purposes.

The Soviet economy experienced a marked decline in the rate of growth of output in the mid 1970s. Research was conducted for Soviet postwar industry in order to try and identify when the shift was strongest, and in which industrial branches. A statistical technique known as the "Chow Test" was used to test for a "break" year -- the year when the production function most dramatically changed. Regression results showed that two types of industry -- that which was closely associated with military production, and industry responsible for producing consumer goods, showed little or no shift in the mid 1970s. The remaining sectors, which were primarily resource intensive, did show a significant shift in 1974. A description of the investigation, including input data and regression results, is included. / M.A.