This thesis consists of four essays, three in the field of random effects models and one in the field of GARCH. The first essay in this thesis, ''Maximum likelihood based inference in the two-way random effects model with serially correlated time effects'', considers maximum likelihood estimation and inference in the two-way random effects model with serial correlation. We derive a straightforward maximum likelihood estimator when the time-specific component follow an AR(1) or MA(1) process. The estimator is also easily generalized to allow for arbitrary stationary and strictly invertible ARMA processes. In addition we consider the model selection problem and derive tests of the null hypothesis of no serial correlation as well as tests for discriminating between the AR(1) and MA(1) specifications. A Monte-Carlo experiment evaluates the finite-sample properties of the estimators, test-statistics and model selection procedures. The second essay, ''Asymptotic properties of the maximum likelihood estimator of random effects models with serial correlation'', considers the large sample behavior of the maximum likelihood estimator of random effects models with serial correlation in the form of AR(1) for the idiosyncratic or time-specific error component. Consistent estimation and asymptotic normality is established for a comprehensive specification which nests these models as well as all commonly used random effects models. The third essay, ''Specification and estimation of random effects models with serial correlation of general form'', is also concerned with maximum likelihood based inference in random effects models with serial correlation. Allowing for individual effects we introduce serial correlation of general form in the time effects as well as the idiosyncratic errors. A straightforward maximum likelihood estimator is derived and a coherent model selection strategy is suggested for determining the orders of serial correlation as well as the importance of time or individual effects. The methods are applied to the estimation of a production function using a sample of 72 Japanese chemical firms observed during 1968-1987. The fourth essay, entitled ''A simple efficient GMM estimator of GARCH models'', considers efficient GMM based estimation of GARCH models. Sufficient conditions for the estimator to be consistent and asymptotically normal are established for the GARCH(1,1) conditional variance process. In addition efficiency results are obtained for a GARCH(1,1) model where the conditional variance is allowed to enter the mean as well. That is, the GARCH(1,1)-M model. An application to the returns to the SP500 index illustrates. / <p>Diss. Stockholm : Handelshögskolan, 2001</p>

This volume consists of four papers, which in principle could be read in any order. The common denominator is that they deal with contingent claims models of a firm's securities or related derivatives. A Framework for Valuing Corporate Securities Early applications of contingent claims analysis to the pricing of corporate liabilities tend to restrict themselves to situations where debt is perpetual or where financial distress can only occur at debt maturity. This paper relaxes these restrictions and provides an exposition of how most corporate liabilities can be valued as packages of two fundamental barrier contingent claims: a down-and-out call and a binary option. Furthermore, it is shown how the comparative statics of the resulting pricing formulae can be derived.A New Compound Option Pricing ModelThis paper extends the Geske (1979) compound option pricing model to the case where the security on which the option is written is a down-and-out call as opposed to a standard Black and Scholes call. Furthermore, we develop a general and flexible framework for valuing options on more complex packages of contingent claims - any claim that can be valued using the ideas in chapter 1. This allows us to study the interaction between the detailed characteristics of a firm's capital structure and the prices of for example stock options.Implementing Firm Value Based ModelsThis paper evaluates an implementation procedure for contingent claims models suggested by Duan (1994). Duan's idea is to use time series data of traded securities such as shares of common stock in order to estimate the dynamics of the firm's asset value. Furthermore, we provide an argument which allows us to relax the (common) assumption that the firm's assets may be continuously traded. It is sufficient to assume that the firm's assets are traded at one particular point in time.Asset Substitution, Debt Pricing, Optimal Leverage and MaturityChapters 1-3 have focused on the problem of pricing corporate securities.They have thus abstracted strategic aspects of corporate finance theory. This paper is an attempt to combine the contingent claims literature with the non-dynamic corporate finance literature. I allow the management of the firm to alter its investment policy strategically. This yields a model which allows us to examine the relationship between bond prices, agency costs, optimal leverage and maturity. / Diss. Stockholm : Handelshögsk.

When using visual and proprioceptive information to plan a reach, it has been proposed that the brain combines these cues to estimate the object and/or limb’s location. Specifically, according to the maximum-likelihood estimation (MLE) model, more reliable sensory inputs are assigned a greater weight (Ernst & Banks, 2002). In this research we examined if the brain is able to adjust which sensory cue it weights the most. Specifically, we asked if the brain changes how it weights sensory information when the availability of a visual cue is manipulated. Twenty-four healthy subjects reached to visual (V), proprioceptive (P), or visual + proprioceptive (VP) targets under different visual delay conditions (e.g. on V and VP trials, the visual target was available for the entire reach, it was removed with the go-signal or it was removed 1, 2 or 5 seconds before the go-signal). Subjects completed 5 blocks of trials, with 90 trials per block. For 12 subjects, the visual delay was kept consistent within a block of trials, while for the other 12 subjects, different visual delays were intermixed within a block of trials. To establish which sensory cue subjects weighted the most, we compared endpoint positions achieved on V and P reaches to VP reaches. Results indicated that all subjects weighted sensory cues in accordance with the MLE model across all delay conditions and that these weights were similar regardless of the visual delay. Moreover, while errors increased with longer visual delays, there was no change in reaching variance. Thus, manipulating the visual environment was not enough to change subjects’ weighting strategy, further i

Wireless communication systems such as multipoint communication systems (MCS) are becoming attractive as cost-effective means for providing network access in sparsely populated, rugged, or developing areas of the world. Since the radio spectrum is limited, it is desirable to use spectrally efficient modulation methods such as quadrature amplitude modulation (QAM) for high data rate channels. Many MCS employ time division multiple access (TDMA) and/or time division duplexing (TDD) techniques, in which transmissions operate in bursts. In many cases, a preamble of known symbols is appended to the beginning of each burst for carrier and symbol timing recovery (symbol timing is assumed known in this thesis). Preamble symbols consume bandwidth and power and are not used to convey information. In order for burst-mode communications to provide efficient data throughput, the synchronization time must be short compared to the user data portion of the burst. <p> Traditional methods of communication system synchronization such as phase-locked loops (PLLs) have demonstrated reduced performance when operated in burst-mode systems. In this thesis, a feedforward (FF) digital carrier recovery technique to achieve rapid carrier synchronization is proposed. The estimation algorithms for determining carrier offsets in carrier acquisition and tracking in a linear channel environment corrupted by additive white Gaussian noise (AWGN) are described. The estimation algorithms are derived based on the theory of maximum likelihood (ML) parameter estimation. The estimations include data-aided (DA) carrier frequency and phase estimations in acquisition and non-data-aided (NDA) carrier phase estimation in tracking. The DA carrier frequency and phase estimation algorithms are based on oversampling of a known preamble. The NDA carrier phase estimation makes use of symbol timing knowledge and estimates are extracted from the random data portion of the burst. The algorithms have been simulated and tested using Matlab® to verify their functionalities. The performance of these estimators is also evaluated in the burst-mode operations for 16-QAM and compared in the presence of non-ideal conditions (frequency offset, phase offset, and AWGN). The simulation results show that the carrier recovery techniques presented in this thesis proved to be applicable to the modulation schemes of 16-QAM. The simulations demonstrate that the techniques provide a fast carrier acquisition using a short preamble (about 111 symbols) and are suitable for burst-mode communication systems.

Climate factors like precipitation and temperature, being closely intertwined with agriculture, make a changing climate a big concern for the entire human race and its basic survival. Adaptation to climate is a long-running characteristic of agriculture evidenced by the varying types and forms of agricultural enterprises associated with differing climatic conditions. Nevertheless climate change poses a substantial, additional adaptation challenge for agriculture. Mitigation encompasses efforts to reduce the current and future extent of climate change. Biofuels production, for instance, expands agriculture’s role in climate change mitigation. This dissertation encompasses adaptation and mitigation strategies as a response to climate change in the U.S. by examining comprehensively scientific findings on agricultural adaptation to climate change; developing information on the costs and benefits of select adaptations to examine what adaptations are most desirable, for which society can further devote its resources; and studying how ethanol prices are interrelated across, and transmitted within the U.S., and the markets that play an important role in these dynamics. Quantitative analysis using the Forestry and Agricultural Sector Optimization Model (FASOM) shows adaptation to be highly beneficial to agriculture. On-farm varietal and other adaptations contributions outweigh a mix shift northwards significantly, implying progressive technical change and significant returns to adaptation research and investment focused on farm management and varietal adaptations could be quite beneficial over time. Northward shift of corn-acre weighted centroids observed indicates that substantial production potential may shift across regions with the possibility of less production in the South, and more in the North, and thereby, potential redistribution of income. Time series techniques employed to study ethanol price dynamics show that the markets studied are co-integrated and strongly related, with the observable high levels of interaction between all nine cities. Information is transmitted rapidly between these markets. Price seems to be discovered (where shocks originate from) in regions of high demand and perhaps shortages, like Los Angeles and Chicago (metropolitan population centers). The Maximum Likelihood approach following Spiller and Huang’s model however shows cities may not belong to the same economic market and the possibility of arbitrage does not exist between all markets.

Wireless communication systems such as multipoint communication systems (MCS) are becoming attractive as cost-effective means for providing network access in sparsely populated, rugged, or developing areas of the world. Since the radio spectrum is limited, it is desirable to use spectrally efficient modulation methods such as quadrature amplitude modulation (QAM) for high data rate channels. Many MCS employ time division multiple access (TDMA) and/or time division duplexing (TDD) techniques, in which transmissions operate in bursts. In many cases, a preamble of known symbols is appended to the beginning of each burst for carrier and symbol timing recovery (symbol timing is assumed known in this thesis). Preamble symbols consume bandwidth and power and are not used to convey information. In order for burst-mode communications to provide efficient data throughput, the synchronization time must be short compared to the user data portion of the burst. <p> Traditional methods of communication system synchronization such as phase-locked loops (PLLs) have demonstrated reduced performance when operated in burst-mode systems. In this thesis, a feedforward (FF) digital carrier recovery technique to achieve rapid carrier synchronization is proposed. The estimation algorithms for determining carrier offsets in carrier acquisition and tracking in a linear channel environment corrupted by additive white Gaussian noise (AWGN) are described. The estimation algorithms are derived based on the theory of maximum likelihood (ML) parameter estimation. The estimations include data-aided (DA) carrier frequency and phase estimations in acquisition and non-data-aided (NDA) carrier phase estimation in tracking. The DA carrier frequency and phase estimation algorithms are based on oversampling of a known preamble. The NDA carrier phase estimation makes use of symbol timing knowledge and estimates are extracted from the random data portion of the burst. The algorithms have been simulated and tested using Matlab® to verify their functionalities. The performance of these estimators is also evaluated in the burst-mode operations for 16-QAM and compared in the presence of non-ideal conditions (frequency offset, phase offset, and AWGN). The simulation results show that the carrier recovery techniques presented in this thesis proved to be applicable to the modulation schemes of 16-QAM. The simulations demonstrate that the techniques provide a fast carrier acquisition using a short preamble (about 111 symbols) and are suitable for burst-mode communication systems.

In this thesis, pairwise multiple comparisons and multiple comparisons with a control are studied when the observations have short-tailed symmetric distributions. Under non-normality, the testing procedure is given and Huber estimators, trimmed mean with winsorized standard deviation, modified maximum likelihood estimators and ordinary sample mean and sample variance used in this procedure are reviewed. Finally, robustness properties of the stated estimators are compared with each other and it is shown that the test based on the modified maximum likelihood estimators has better robustness properties under short-tailed symmetric distribution.

Longitudinal data arise when subjects are followed over time. This type of data is typically dependent, due to including repeated observations and this type of dependence is termed as within-subject dependence. Often the scientific interest is on multiple longitudinal measurements which introduce two additional types of associations, between-response and cross-response temporal dependencies. Only the statistical methods which take these association structures might yield reliable and valid statistical inferences. Although the methods for univariate longitudinal data have been mostly studied, multivariate longitudinal data still needs more work. In this thesis, although we mainly focus on multivariate longitudinal binary data models, we also consider other types of response families when necessary. We extend a work on multivariate marginal models, namely multivariate marginal models with response specific parameters (MMM1), and propose multivariate marginal models with shared regression parameters (MMM2). Both of these models are generalized estimating equation (GEE) based, and are valid for several response families such as Binomial, Gaussian, Poisson, and Gamma. Two different R packages, mmm and mmm2 are proposed to fit them, respectively. We further develop a marginalized multilevel model, namely probit normal marginalized transition random effects models (PNMTREM) for multivariate longitudinal binary response. By this model, implicit function theorem is introduced to explicitly link the levels of marginalized multilevel models with transition structures for the first time. An R package, bf pnmtrem is proposed to fit the model. PNMTREM is applied to data collected through Iowa Youth and Families Project (IYFP). Five different models, including univariate and multivariate ones, are considered to forecast multivariate longitudinal binary data. A comparative simulation study, which includes a model-independent data simulation process, is considered for this purpose. Forecasting independent variables are taken into account as well. To assess the forecasts, several accuracy measures, such as expected proportion of correct prediction (ePCP), area under the receiver operating characteristic (AUROC) curve, mean absolute scaled error (MASE) are considered. Mother&#039 / s Stress and Children&#039 / s Morbidity (MSCM) data are used to illustrate this comparison in real life. Results show that marginalized models yield better forecasting results compared to marginal models. Simulation results are in agreement with these results as well.

Dissertation

This paper uses Maximum likelihood estimation method to estimate the common correlation coefficients for multivariate datasets. We discuss a graphical tool, Q-Q plot, to test equality of the common intraclass correlation coefficients. Kolmogorov-Smirnov test and Cramér-von Mises test are used to check if the intraclass correlation coefficients are the same among populations. Bootstrap and empirical likelihood methods are applied to construct the confidence interval of the common intraclass correlation coefficients.