Diagnostic Tests Based on Quantile Residuals for Nonlinear Time Series Models

This thesis studies quantile residuals and uses different methodologies to develop test statistics that are applicable in evaluating linear and nonlinear time series models based on continuous distributions. Models based on mixtures of distributions are of special interest because it turns out that for those models traditional residuals, often referred to as Pearson's residuals, are not appropriate. As such models have become more and more popular in practice, especially with financial time series data there is a need for reliable diagnostic tools that can be used to evaluate them. The aim of the thesis is to show how such diagnostic tools can be obtained and used in model evaluation. The quantile residuals considered here are defined in such a way that, when the model is correctly specified and its parameters are consistently estimated, they are approximately independent with standard normal distribution. All the tests derived in the thesis are pure significance type tests and are theoretically sound in that they properly take the uncertainty caused by parameter estimation into account. -- In Chapter 2 a general framework based on the likelihood function and smooth functions of univariate quantile residuals is derived that can be used to obtain misspecification tests for various purposes. Three easy-to-use tests aimed at detecting non-normality, autocorrelation, and conditional heteroscedasticity in quantile residuals are formulated. It also turns out that these tests can be interpreted as Lagrange Multiplier or score tests so that they are asymptotically optimal against local alternatives. Chapter 3 extends the concept of quantile residuals to multivariate models. The framework of Chapter 2 is generalized and tests aimed at detecting non-normality, serial correlation, and conditional heteroscedasticity in multivariate quantile residuals are derived based on it. Score test interpretations are obtained for the serial correlation and conditional heteroscedasticity tests and in a rather restricted special case for the normality test. In Chapter 4 the tests are constructed using the empirical distribution function of quantile residuals. So-called Khmaladze s martingale transformation is applied in order to eliminate the uncertainty caused by parameter estimation. Various test statistics are considered so that critical bounds for histogram type plots as well as Quantile-Quantile and Probability-Probability type plots of quantile residuals are obtained. Chapters 2, 3, and 4 contain simulations and empirical examples which illustrate the finite sample size and power properties of the derived tests and also how the tests and related graphical tools based on residuals are applied in practice.

Studies on Binary Time Series Models with Applications to Empirical Macroeconomics and Finance

This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.

Search for Higgs bosons predicted in two-Higgs-doublet models via decays to tau lepton pairs in 1.96 TeV proton-antiproton collisions

We present the results of a search for Higgs bosons predicted in two-Higgs-doublet models, in the case where the Higgs bosons decay to tau lepton pairs, using 1.8 inverse fb of integrated luminosity of proton-antiproton collisions recorded by the CDF II experiment at the Fermilab Tevatron. Studying the observed mass distribution in events where one or both tau leptons decay leptonically, no evidence for a Higgs boson signal is observed. The result is used to infer exclusion limits in the two-dimensional parameter space of tan beta versus m(A).

Search for Higgs bosons predicted in two-Higgs-doublet models via decays to tau lepton pairs in 1.96 TeV proton-antiproton collisions

We present the results of a search for Higgs bosons predicted in two-Higgs-doublet models, in the case where the Higgs bosons decay to tau lepton pairs, using 1.8 inverse fb of integrated luminosity of proton-antiproton collisions recorded by the CDF II experiment at the Fermilab Tevatron. Studying the observed mass distribution in events where one or both tau leptons decay leptonically, no evidence for a Higgs boson signal is observed. The result is used to infer exclusion limits in the two-dimensional parameter space of tan beta versus m(A).

Combined Tevatron upper limit on gg->H->W+W- and constraints on the Higgs boson mass in fourth-generation fermion models

We combine results from searches by the CDF and D0 collaborations for a standard model Higgs boson (H) in the process gg->H->W+W- in p=pbar collisions at the Fermilab Tevatron Collider at sqrt{s}=1.96 TeV. With 4.8 fb-1 of integrated luminosity analyzed at CDF and 5.4 fb-1 at D0, the 95% Confidence Level upper limit on \sigma(gg->H) x B(H->W+W-) is 1.75 pb at m_H=120 GeV, 0.38 pb at m_H=165 GeV, and 0.83 pb at m_H=200 GeV. Assuming the presence of a fourth sequential generation of fermions with large masses, we exclude at the 95% Confidence Level a standard-model-like Higgs boson with a mass between 131 and 204 GeV.

Why Do Russians Support Censorship of the Media?

This article analyses support for censorship in Russia as part of the democratization process. Censorship has been an important part of Russian history and it was strengthened during the Soviet era. After the collapse of the Soviet system formal censorship was banned even though the reality has been different. Therefore it is not strange that many Russians would like to limit the freedom of the media and to censor certain topics. The views of Russians on censorship have been studied on the basis of a survey carried out in 2007. According to the results, three different dimensions of censorship were found. These dimensions include moral censorship, political censorship, and censorship of religious materials. Support for these dimensions varies on the basis of socio-demographic characteristics and media use. The article concludes that many Russians reject new phenomena, while support for the censorship of political criticism is not as high, but political censorship seems to enjoy more support among elites than among the common people.

Effective models of two-flavor QCD: finite $\mu$ and $m_q$-dependence

We study effective models of chiral fields and Polyakov loop expected to describe the dynamics responsible for the phase structure of two-flavor QCD at finite temperature and density. We consider chiral sector described either using linear sigma model or Nambu-Jona-Lasinio model and study the phase diagram and determine the location of the critical point as a function of the explicit chiral symmetry breaking (i.e. the bare quark mass $m_q$). We also discuss the possible emergence of the quarkyonic phase in this model.

Generalizations and Models in Ecology : Lawlikeness, Invariance, Stability, and Robustness

The question at issue in this dissertation is the epistemic role played by ecological generalizations and models. I investigate and analyze such properties of generalizations as lawlikeness, invariance, and stability, and I ask which of these properties are relevant in the context of scientific explanations. I will claim that there are generalizable and reliable causal explanations in ecology by generalizations, which are invariant and stable. An invariant generalization continues to hold or be valid under a special change called an intervention that changes the value of its variables. Whether a generalization remains invariant during its interventions is the criterion that determines whether it is explanatory. A generalization can be invariant and explanatory regardless of its lawlike status. Stability deals with a generality that has to do with holding of a generalization in possible background conditions. The more stable a generalization, the less dependent it is on background conditions to remain true. Although it is invariance rather than stability of generalizations that furnishes us with explanatory generalizations, there is an important function that stability has in this context of explanations, namely, stability furnishes us with extrapolability and reliability of scientific explanations. I also discuss non-empirical investigations of models that I call robustness and sensitivity analyses. I call sensitivity analyses investigations in which one model is studied with regard to its stability conditions by making changes and variations to the values of the model s parameters. As a general definition of robustness analyses I propose investigations of variations in modeling assumptions of different models of the same phenomenon in which the focus is on whether they produce similar or convergent results or not. Robustness and sensitivity analyses are powerful tools for studying the conditions and assumptions where models break down and they are especially powerful in pointing out reasons as to why they do this. They show which conditions or assumptions the results of models depend on. Key words: ecology, generalizations, invariance, lawlikeness, philosophy of science, robustness, explanation, models, stability

Ice reduction of winter discharge by watershed models

Yhteenveto: Talvivirtaamien redukointi vesistömallien avulla

Developing Peer-To-Peer Web Applications

As the virtual world grows more complex, finding a standard way for storing data becomes increasingly important. Ideally, each data item would be brought into the computer system only once. References for data items need to be cryptographically verifiable, so the data can maintain its identity while being passed around. This way there will be only one copy of the users family photo album, while the user can use multiple tools to show or manipulate the album. Copies of users data could be stored on some of his family members computer, some of his computers, but also at some online services which he uses. When all actors operate over one replicated copy of the data, the system automatically avoids a single point of failure. Thus the data will not disappear with one computer breaking, or one service provider going out of business. One shared copy also makes it possible to delete a piece of data from all systems at once, on users request. In our research we tried to find a model that would make data manageable to users, and make it possible to have the same data stored at various locations. We studied three systems, Persona, Freenet, and GNUnet, that suggest different models for protecting user data. The main application areas of the systems studied include securing online social networks, providing anonymous web, and preventing censorship in file-sharing. Each of the systems studied store user data on machines belonging to third parties. The systems differ in measures they take to protect their users from data loss, forged information, censorship, and being monitored. All of the systems use cryptography to secure names used for the content, and to protect the data from outsiders. Based on the gained knowledge, we built a prototype platform called Peerscape, which stores user data in a synchronized, protected database. Data items themselves are protected with cryptography against forgery, but not encrypted as the focus has been disseminating the data directly among family and friends instead of letting third parties store the information. We turned the synchronizing database into peer-to-peer web by revealing its contents through an integrated http server. The REST-like http API supports development of applications in javascript. To evaluate the platform’s suitability for application development we wrote some simple applications, including a public chat room, bittorrent site, and a flower growing game. During our early tests we came to the conclusion that using the platform for simple applications works well. As web standards develop further, writing applications for the platform should become easier. Any system this complex will have its problems, and we are not expecting our platform to replace the existing web, but are fairly impressed with the results and consider our work important from the perspective of managing user data.

Developing Peer-To-Peer Web Applications

As the virtual world grows more complex, finding a standard way for storing data becomes increasingly important. Ideally, each data item would be brought into the computer system only once. References for data items need to be cryptographically verifiable, so the data can maintain its identity while being passed around. This way there will be only one copy of the users family photo album, while the user can use multiple tools to show or manipulate the album. Copies of users data could be stored on some of his family members computer, some of his computers, but also at some online services which he uses. When all actors operate over one replicated copy of the data, the system automatically avoids a single point of failure. Thus the data will not disappear with one computer breaking, or one service provider going out of business. One shared copy also makes it possible to delete a piece of data from all systems at once, on users request. In our research we tried to find a model that would make data manageable to users, and make it possible to have the same data stored at various locations. We studied three systems, Persona, Freenet, and GNUnet, that suggest different models for protecting user data. The main application areas of the systems studied include securing online social networks, providing anonymous web, and preventing censorship in file-sharing. Each of the systems studied store user data on machines belonging to third parties. The systems differ in measures they take to protect their users from data loss, forged information, censorship, and being monitored. All of the systems use cryptography to secure names used for the content, and to protect the data from outsiders. Based on the gained knowledge, we built a prototype platform called Peerscape, which stores user data in a synchronized, protected database. Data items themselves are protected with cryptography against forgery, but not encrypted as the focus has been disseminating the data directly among family and friends instead of letting third parties store the information. We turned the synchronizing database into peer-to-peer web by revealing its contents through an integrated http server. The REST-like http API supports development of applications in javascript. To evaluate the platform s suitability for application development we wrote some simple applications, including a public chat room, bittorrent site, and a flower growing game. During our early tests we came to the conclusion that using the platform for simple applications works well. As web standards develop further, writing applications for the platform should become easier. Any system this complex will have its problems, and we are not expecting our platform to replace the existing web, but are fairly impressed with the results and consider our work important from the perspective of managing user data.

Inference on Cointegration in Vector Autoregressive Models (summary section only)

In the thesis we consider inference for cointegration in vector autoregressive (VAR) models. The thesis consists of an introduction and four papers. The first paper proposes a new test for cointegration in VAR models that is directly based on the eigenvalues of the least squares (LS) estimate of the autoregressive matrix. In the second paper we compare a small sample correction for the likelihood ratio (LR) test of cointegrating rank and the bootstrap. The simulation experiments show that the bootstrap works very well in practice and dominates the correction factor. The tests are applied to international stock prices data, and the .nite sample performance of the tests are investigated by simulating the data. The third paper studies the demand for money in Sweden 1970—2000 using the I(2) model. In the fourth paper we re-examine the evidence of cointegration between international stock prices. The paper shows that some of the previous empirical results can be explained by the small-sample bias and size distortion of Johansen’s LR tests for cointegration. In all papers we work with two data sets. The first data set is a Swedish money demand data set with observations on the money stock, the consumer price index, gross domestic product (GDP), the short-term interest rate and the long-term interest rate. The data are quarterly and the sample period is 1970(1)—2000(1). The second data set consists of month-end stock market index observations for Finland, France, Germany, Sweden, the United Kingdom and the United States from 1980(1) to 1997(2). Both data sets are typical of the sample sizes encountered in economic data, and the applications illustrate the usefulness of the models and tests discussed in the thesis.

Hedging Options with Different Time Units in the Pricing Models

This study examined the effects of the Greeks of the options and the trading results of delta hedging strategies, with three different time units or option-pricing models. These time units were calendar time, trading time and continuous time using discrete approximation (CTDA) time. The CTDA time model is a pricing model, that among others accounts for intraday and weekend, patterns in volatility. For the CTDA time model some additional theta measures, which were believed to be usable in trading, were developed. The study appears to verify that there were differences in the Greeks with different time units. It also revealed that these differences influence the delta hedging of options or portfolios. Although it is difficult to say anything about which is the most usable of the different time models, as this much depends on the traders view of the passing of time, different market conditions and different portfolios, the CTDA time model can be viewed as an attractive alternative.