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.

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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 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.