This thesis contains two essays that use state-of-the-art econometric methods to estimate the implicit prices of various housing and vehicle attributes in hedonic price analysis. The additive nonparametric regression proposed by Hastie and Tibshirani (1990) is applied to capture a series of nonlinearities relating prices to their attributes that cannot be captured by conventional parametric approach. Due to its additive structure, the additive nonparametric regression retains an important interpretative feature of the linear model and avoids the drawbacks of a fully nonparametric design such as slow rates of convergence and the "curse of dimensionality." The "benchmark" parametric specification for the hedonic price function is carefully chosen via the estimation of the Box and Cox (1964) and Wooldridge (1992) transformations. The additive nonparametric model provides smaller price prediction errors than the benchmark parametric specification in standard goodness of fit measures. The first study examines the effects on housing prices of the structural and environmental attributes using residential sales data from Portland, Oregon. The overall estimation results verify that most housing attributes that are generally linked to the perception of quality, such as larger total structure square footage and higher elevation, have significant positive implicit prices. Attributes that reduce house quality, such as age of house and distance to environmental amenities, discount the value of a house. Complex price effects of various housing attributes are revealed by the additive nonparametric regression. The second study uses a hedonic price approach to estimate the effects on used car prices of vehicle emission attributes, such as hydrocarbon and carbon monoxide emissions, using data from the Vehicle Inspection Program in Portland, Oregon. The estimation results show that used car value is on average higher for vehicles with lower hydrocarbon and carbon monoxide emissions, ceteris paribus. This empirical finding is consistent with recent reports from the U.S. Environmental Protection Agency, which indicate that used vehicles failing to pass required emission tests face potentially high repair costs and frequent smog-check requirements. More cylinders and larger engine size are highly valued. Higher mileage receives relatively little discount compared to age of vehicle. / Graduation date: 2000

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 32-33). Also available on the World Wide Web.

This thesis considers two important problems in finance, namely, correlation stress testing and multivariate volatility modeling. Correlation stress testing refers to the correlation matrix adjustment to evaluate potential impact of the changes in correlations under financial crises. Very often, some correlations are explicitly adjusted (core correlations), with the remainder left unspecified (peripheral correlations), although it would be more natural for both core correlations and peripheral correlations to vary. However, most existing methods ignored the potential change in peripheral correlations. Inspiring from this idea, two methods are proposed in which the stress impact on the core correlations is transmitted to the peripheral correlations through the dependence structure of the empirical correlations. The first method is based on a Bayesian framework in which a prior for a population correlation matrix is proposed that gives flexibility in specifying the dependence structure of correlations. In order to increase the rate of convergence, the algorithm of posterior simulation is extended so that two correlations can be updated in one Gibbs sampler step. To achieve this, an algorithm is developed to find the region of two correlations keeping the correlation matrix positive definite given that all other correlations are held fixed. The second method is a Black-Litterman approach applied to correlation matrices. A new correlation matrix is constructed by maximizing the posterior density. The proposed method can be viewed as a two-step procedure: first constructing a target matrix in a data-driven manner, and then regularizing the target matrix by minimizing a matrix norm that reasonably reflects the dependence structure of the empirical correlations. Multivariate volatility modeling is important in finance since variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high frequency returns as the ex-post realization of the covariance matrix of low frequency returns. For the analysis of dynamics of RCOV matrices, the generalized conditional autoregressive Wishart model is proposed. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The proposed model is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments and stationarity are discussed. The forecasting performance of the proposed model is compared with the existing models. Outliers exist in the series of realized volatility which is often decomposed into continuous and jump components. The vector multiplicative error model is a natural choice to jointly model these two non-negative components of the realized volatility, which is also a popular multivariate time series model for other non-negative volatility measures. Diagnostic checking of such models is considered by deriving the asymptotic distribution of residual autocorrelations. A multivariate portmanteau test is then devised. Simulation experiments are carried out to investigate the performance of the asymptotic result in finite samples. / published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

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published_or_final_version / abstract / Business / Master / Master of Philosophy

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This dissertation is concerned with the examination of some widely employed nonlinear exchange rate models. In particular, its aim is to assess how well non-linear statistical models accommodate the theoretical implications contained in economic models and how well they are able to capture the empirical properties of the data. Chapter 2 gives a brief background to the concept of PPP and discusses the role of transaction costs in economic models, making it necessary to model exchange rates within a non-linear framework. Parametric as well as non-parametric statistical techniques are applied to a long time-series data set to give an indication of the empirical validity of non-linearity in real exchange rates. Wide threshold bands are found to be a common characteristic of real exchange rate data. Chapter 3 studies the fitness of the ESTAR model for real exchange rate modelling. It is shown that wide threshold bands in the empirical data necessitate a small transition function parameter in the exponential regime weighting function, leading to difficulties in the meaningful interpretation of regimes. When this occurs, it is also shown that the ESTAR model is weakly identified over the range of the sample data that one generally works with. These results are illustrated on an empirical data set by replicating the often cited study of Taylor et al. (2001). In Chapter 4 and Chapter 5 a number of non-linear models are evaluated. Simulation experiments indicate that LM style tests that are commonly employed in the literature to test for ESTAR non-linearity have a very low probability of rejecting the false null hypothesis of linearity when the true data generating process is in fact the ESTAR model of Taylor et al. (2001). It is further shown that, contrary to the claims of the recent study by Rapach and Wohar (2006), long-horizon forecasts from the ESTAR model converge to the unconditional mean of the series, so that there is no gain in utilising the ESTAR model for long-horizon forecasts. Studying the Markov switching model of Bergman and Hansson (2005) reveals that the model does not generate any non-linearity as predicted from economic models.

Thesis (M.B.A.)--Texas State University-San Marcos, 2008. / Vita. Includes bibliographical references (leaf 66). Also available on microfilm.