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<p> This dissertation focuses on two aspects of factor models, testing and forecasting. For testing, we investigate a more general high-dimensional testing problem, with an emphasis on panel data models. Specifically, we propose a novel technique to boost the power of testing a high-dimensional vector against sparse alternatives. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers, whereas more powerful tests such as thresholding and extreme-value tests require either stringent conditions or bootstrap to derive the null distribution, and often suffer from size distortions. Based on a screening technique, we introduce a ''power enhancement component", which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. As a byproduct, the power enhancement component also consistently identifies the elements that violate the null hypothesis. </p><p> Next, we consider forecasting a single time series using many predictors when nonliearity is present. We develop a new methodology called sufficient forecasting, by connecting sliced inverse regression with factor models. The sufficient forecasting correctly estimates projections of the underlying factors and provides multiple predictive indices for further investigation. We derive asymptotic results for the estimate of the central space spanned by these projection directions. Our method allows the number of predictors larger than the sample size, and therefore extends the applicability of inverse regression. Numerical experiments demonstrate that the proposed method improves upon a linear forecasting model. Our results are further illustrated in an empirical study of macroeconomic variables, where sufficient forecasting is found to deliver additional predictive power over conventional methods.</p>

<p> Objective: The focus of this study is a broad examination of household energy consumption for appliance use, space heating, space cooling, and water heating in United States over the period 2001-2009 using Residential Energy Consumption Survey (RECS) from the years 2001 and 2009. Methods: Linear Regression Analysis is used to identfy determinants of household energy consumption for each of the end uses. Regression based decomposition analysis is used to identify trends in residential energy consumption for each of the end uses. Results: The study identified current determinants of household energy consumption for each of the end uses. These determinants are employed in the study to predict trends in household energy consumption for each of the end uses. Based on the results policy interventions at local and federal level for energy conservation are suggested.</p>

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 68-02, Section: A, page: 0656. Adviser: Thomas Ulen. Includes bibliographical references (leaves 174-191) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. / Source: Dissertation Abstracts International, Volume: 68-11, Section: A, page: 4888. Adviser: Geoffrey D. J. Hewings. Includes bibliographical references (leaves 106-111) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--Indiana University, Kelley School of Business, 2008. / Title from PDF t.p. (viewed on May 11, 2009). Source: Dissertation Abstracts International, Volume: 69-08, Section: A, page: 3233. Advisers: Shibo Li; Rockney G. Walters.

Thesis (Ph.D.)--Indiana University, Dept. of Telecommunications, 2009. / Title from PDF t.p. (viewed on Jul 7, 2010). Source: Dissertation Abstracts International, Volume: 70-10, Section: A, page: 3688. Adviser: David Waterman.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-11, Section: A, page: 4276. Adviser: Werner Baer. Includes bibliographical references (leaves 195-207) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-07, Section: A, page: 2779. Adviser: Geoffrey J. D. Hewings. Includes bibliographical references (leaves 175-182) Available on microfilm from Pro Quest Information and Learning.

My dissertation explores macro-level questions in Political Economy. Using the toolbox of Economics, I find a new reason for international conflict: cultural similarity. Two culturally similar nations may have very different political regimes (e.g. the two Koreas). The cultural similarity encourages citizens to compare the different political regimes, which in turn threatens a dictator. I formalize this process of political contagion in an infinitely-repeated bargaining model and show that more cultural similarity gives a politically-threatened dictator greater incentive to start a war against a democracy. The leader wants to ensure that his citizens see the other nation as an enemy rather than a role model. I test the implications of my model using cross-national statistical analysis, historical case studies, and text analysis. My cross-national statistical data set combines cultural similarity measures of up to 200x200 country pairs with data on wars among these nations between 1816 and 2008. In panel regressions which include country-pair fixed effects, I find that when two countries share culture (measured by religion, race, and civilization), but differ in their political institutions, they are up to 80 % more likely to fight a war. My results are stronger between physically distant country-pairs, which suggests that cultural affinity is not mismeasured physical proximity but a distinct factor in wars. I complement my analysis by considering an extension of my theory to domestic conflict, and by exploring the implications of wars creating shared identity. / Political Economy and Government