A new ensemble method for cluster analysis is introduced, which can be interpreted in two different ways: As complexity-reducing preprocessing stage for hierarchical clustering and as combination procedure for several partitioning results. The basic idea is to locate and combine structurally stable cluster centers and/or prototypes. Random effects of the training set are reduced by repeatedly training on resampled sets (bootstrap samples). We discuss the algorithm both from a more theoretical and an applied point of view and demonstrate it on several data sets. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Generating random variates as generalisation of a given sample is an important task for stochastic simulations. The three main methods suggested in the literature are: fitting a standard distribution, constructing an empirical distribution that approximates the cumulative distribution function and generating variates from the kernel density estimate of the data. The last method is practically unknown in the simulation literature although it is as simple as the other two methods. The comparison of the theoretical performance of the methods and the results of three small simulation studies show that a variance corrected version of kernel density estimation performs best and should be used for generating variates directly from a sample. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

In this paper we examine, by Monte Carlo simulation, size and power of the Johansens trace test when the error covariance matrix is nonstationary, and we also investigate the properties of three different bootstrap cointegration tests. Earlier studies indicate that the Johansen trace test is not robust in presence of heteroscedasticity, and tests based on resampling methods have been proposed to solve the problem. The tests that are evaluated is the Johansen trace test, nonparametric bootstrap test and two different types of wild bootstrap tests. The wild bootstrap test is a resampling method that attempts to mimic the GARCH model by multiplying each residual by a stochastic variable with an expected value of zero and unit variance. The wild bootstrap tests proved to be superior to the other tests, but not as good as earlier indicated. The more the error terms differs from white noise, the worse these tests are doing. Although the wild bootstrap tests did not do a very bad job, the focus of further investigation should be to derive tests that does an even better job than the wild bootstrap tests examined here.

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This thesis involves three essays that explore the theory and application of econometric analysis to labour market interventions. One essay is methodological, and two essays are applications. The first essay contributes to the literature on inference with data sets containing within-cluster correlation. The essay highlights a problem with current practices when the number of clusters is 11 or fewer. Current practices can result in p-values that are not point identified but are instead p-value intervals. The chapter provides Monte Carlo evidence to support a proposed solution to this problem. The second essay analyzes a labour market intervention within Canada--the Youth Hires program--which aimed to reduce youth unemployment. We find evidence that the program was able to increase employment among the targeted group. However, the impacts are only present for males, and we find evidence of displacement effects amongst the non-targeted group. The third essay examines a set of Graduate Retention Programs that several Canadian provinces offer. These programs are aimed at mitigating future skill shortages. Once the solution proposed in the first essay is applied, I find little evidence of the effectiveness of these programs in attracting or retaining recent graduates. / Thesis (Ph.D, Economics) -- Queen's University, 2013-07-05 15:56:33.805

Šiame darbe yra nagrinejama baigtinė populiacija iš kurios išrinkta sluoksninė imtis su paprastąja atsitiktine imtimi sluoksniuose. Nagrinėjamas populiacijos parametras yra populiacijos dispersija. Pateikti trys jos įvertiniai ir parodyta, kad du iš jų yra nepaslinktieji, o vienas turi poslinkį ir šis poslinkis yra apskaičiuotas. Taip pat nagrinėjami penki savirankos metodai skirti dispersijos dispersijai vertinti: tradicinė saviranka, mastelio pakeitimo saviranka, veidrodinio sutapimo saviranka, negrąžintinė saviranka ir grąžintinė saviranka. Palyginimui yra naudojama apytikslės dispersijos įvertinio dispersijos formulė specialiai išvesta sluoksninėms imtims. Šio darbo tikslas palyginti savirankos metodais gautas dispersijos įvertinio dispersijos įvertinio reikšmes tarpusavyje ir su reikšmėmis gautomis skaičiuojant pagal apytikslę formulę. / Consider a finite population from which the stratified sample with simple random sample without replacement in each strata is drawn. The finite population parameter of interest is variance. There are viewed three estimators of variance and are shown that two estimators are unbiased and one estimator has a bias which is estimated. We studied five bootstrap methods, the naive bootstrap, the rescaling bootstrap, the mirror-match bootstrap, the without-replacement bootstrap and the with-replacement bootstrap for variance estimation of estimator of variance. In comparison, there is used approximation variance formula of variance which is derived special for stratified samples. Our purpose was to compare the result of the bootstrap methods with each other and with the result from the approximation variance formula.

In this dissertation, we propose a set of computationally efficient methods based on approximating/representing nonlinear processes by linear ones, so-called linearization. Firstly, a linearization method is introduced for estimating the multiple frequencies in sinusoidal processes. It utilizes a regularized autoregressive (AR) approximation, which can be regarded as a "large p - small n" approach in a time series context. An appealing property of regularized AR is that it avoids a model selection step and allows for an efficient updating of the frequency estimates whenever new observations are obtained. The theoretical analysis shows that the regularized AR frequency estimates are consistent and asymptotically normally distributed. Secondly, a sieve bootstrap scheme is proposed using the linear representation of generalized autoregressive conditional heteroscedastic (GARCH) models to construct prediction intervals (PIs) for the returns and volatilities. Our method is simple, fast and distribution-free, while providing sharp and well-calibrated PIs. A similar linear bootstrap scheme can also be used for diagnostic testing. Thirdly, we introduce a robust lagrange multiplier (LM) test, which utilizes either the bootstrap or permutation procedure to obtain critical values, for detecting GARCH effects. We justify that both bootstrap and permutation LM tests are consistent. Intensive numerical studies indicate that the proposed resampling algorithms significantly improve the size and power of the LM test in both skewed and heavy-tailed processes. Moreover, fourthly, we introduce a nonparametric trend test in the presence of GARCH effects (NT-GARCH) based on heteroscedastic ANOVA. Our empirical evidence show that NT-GARCH can effectively detect non-monotonic trends under GARCH, especially in the presence of irregular seasonal components. We suggest to apply the bootstrap procedure for both selecting the window length and finding critical values. The newly proposed methods are illustrated by applications to astronomical data, to foreign currency exchange rates as well as to water and air pollution data. Finally, the dissertation is concluded by an outlook on further extensions of linearization methods, e.g., in model order selection and change point detection.

This volume contains five essays in the field of time series econometrics. All five discuss properties of fractionally integrated processes and models. The first essay, entitled Do Long-Memory Models have Long Memory?, demonstrates that fractional integration can enhance the memory of ARMA processes enormously. This is however not true for all combinations of diffe-rencing, autoregressive and moving average parameters. The second essay, with the title On the Effects of Imposing or Ignoring Long-Memory when Forecasting, investigates how the choice between mo-delling stationary time series as ARMA or ARFIMA processes affect the accu-racy of forecasts. The results suggest that ignoring long-memory is worse than imposing it and that the maximum likelihood estimator for the ARFIMA model is to prefer. The third essay, Power and Bias of Likelihood Based Inference in the Cointegration Model under Fractional Cointegration, investigates the performance of the usual cointegration approach when the processes are fractionally cointegrated. Under these circumstances, it is shown that the maximum likelihood estimates of the long-run relationship are severely biased. The fourth and fifth essay, entitled respectively Bootstrap Testing for Fractional Integration and Robust Testing for Fractional Integration using the Bootstrap, propose and investigate the performance of some bootstrap testing procedures for fractional integration. The results suggest that the empirical size of a bootstrap test is (almost) always close to the nominal, and that a well-designed bootstrap test is quite robust to deviations from standard assumptions. / Diss. Stockholm : Handelshögsk. [7] s., s. x-xiv, s. 1-26: sammanfattning, s. 27-111, [4] s.: 5 uppsatser

Konstanz, Univ., Diss., 2008.

Thesis (Ph. D.)--Stockholm School of Economics, 1998. / Includes bibliographical references.