The theoretical debate over the ability of parties and leaders in the House of Representatives to influence legislative decision-making is at the center of much of the literature on Congress. On the one hand, the Procedural Cartel perspective argues that while the tools used by the majority party leadership to assure the triumph of its preferences may vary depending on the institutional context, the basic ability of the leadership to impact legislative outcomes remains consistent. In contrast, Conditional Party Government (CPG) theory posits that any power the majority party and its leadership possesses over legislative decision-making is directly conditioned upon the amount of agreement within the majority party caucus as to collective goals, as well as the amount of ideological polarization that exists between the majority and minority parties. This thesis provides an original test of these two theoretical perspectives by evaluating their comparative ability to account for the proposal and passage of limitation riders on the House floor during the annual appropriations process since the 1980s. Limitation riders provide a good vehicle to test theories of congressional voting as they often have important policy implications in areas of significant controversy. In addition, the extent to which the individual members or legislative parties are able to successfully utilize limitation riders as a means of making substantive policy is indicative of larger patterns of committee or party domination of the floor process. After reviewing the relevant literature on congressional decision-making, this analysis proceeds to outline the theoretical predictions that the Procedural Cartel and CPG perspectives make regarding limitation riders. An original dataset comprised of over 800 limitation riders from the 97th through the 110th Congresses is analyzed both with respect to overall proposal and passage rates as well their party of origin. This study finds that while the CPG perspective is best able to account for what occurs during periods of low polarization and cohesion, Procedural Cartel provides the most accurate prediction of what occurs when polarization and cohesion are high. These findings suggest that, although these theories both have some ability to account for congressional decision-making on the House floor, both of these frameworks need to be revisited so that they can accurately account for what occurs during floor phase of the legislative process.

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<p>This dissertation presents the construction procedure of &ldquo;high-frequency&rdquo; daily measure of changes in housing valuations, and analyzes its return dynamics, as well as investigates its relationship to capital markets. The dissertation consists of three chapters. The first chapter introduces the house price index methodologies and housing transaction data, and reviews the related literature. The second chapter shows the construction and modeling of daily house price indexes and highlights the informational advantage of the daily indexes. The final chapter provides detailed empirical and theoretical investigations of housing index return volatilities. </p><p>Chapter 2 discusses the relationship of the housing market with the other markets, such as consumer good market and financial markets. Different housing price indexes and their construction methodologies are introduced, with emphases on the repeat sales model and S&P/Case Shiller Home Price Index. A detailed description of the housing transaction data I use in the dissertation is also provided in this chapter.</p><p>Chapter 3 is co-authored with Professor Tim Bollerslev and Professor Andrew Patton. We construct daily house price indexes for ten major U.S. metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the procedure used in the construction of the popular monthly Case-Shiller house price indexes. Our new daily house price indexes exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity. The correlations across house price index returns are low at the daily frequency, but rise monotonically with the return horizon, and are commensurate with existing empirical evidence for existing monthly and quarterly house price series. Timely and accurate measures of house prices are important in a variety of applications, and are particularly valuable during times of turbulence, such as the recent housing crisis. To quantify the informational advantage of our daily index, we show that a relatively simple multivariate time series model for the daily house price index returns, explicitly allowing for commonalities across cities and GARCH effects, produces forecasts of monthly house price changes that are superior to various alternative forecast procedures based on lower frequency data.</p><p>Chapter 4 investigates the properties of housing index return volatilities. Similar to stock market volatility, housing volatilities are found to respond asymmetrically to negative and positive returns. A direct test of volatility on changes in loan-to-value ratio suggests that the observed volatility asymmetry does not stem from changes in degree of housing financial leverage, but could result from the risk premium carried by housing volatility, which is supported by a consumption-based asset pricing model with housing. Moreover, housing and stock volatilities are found to be positively correlated from a set of predictive regressions based on realized variances of housing and stock markets, in which higher (lower) volatility in one market will be followed by higher (lower) volatility in the other. Finally, housing and stock cross-sectional return dispersions are shown to contain useful information in predicting both within-market and cross-market realized volatilities.</p> / Dissertation

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