Syfte och frågeställningar: Syftet med studien var att i) undersöka validiteten i befolkningens skattning av stillasittande (SED) respektive måttlig till kraftig fysisk aktivitet (MVPA) samt ii) beskriva eventuella skillnader mellan de som under-, över- respektive korrekt skattar sitt beteende. Följande frågeställningar utformades: 1) Förekommer diskrepans mellan deltagarnas subjektiva skattning av SED och MVPA jämfört med objektivt mätt fysisk aktivitet med accelerometer? 2) Skiljer sig de deltagare som under-, över- respektive korrekt skattar sitt beteende avseende kön, socioekonomisk status, BMI, konditionsvärde respektive självskattad hälsa? Metod: Studien använder data från the Swedish CArdioPulmonary bioImage Study (SCAPIS) pilotstudie där deltagarna besvarade ett frågeformulär samt bar en accelerometer i sju på varandra följande dagar (N = 652). I denna studie har fyra frågor använts ur SCAPIS deltagarenkät för att mäta deltagarnas subjektiva nivå av fysiska aktivitet. Diskrepans av SED respektive MVPA räknades fram som subjektivt skattad tid (enkätfrågorna) minus accelerometerns registrerade tid. Data beskrivs deskriptivt där populationen har kategoriserats i kvintiler utefter medianskillnaden mellan subjektivt skattad och objektivt mätt tid. Resultat: Medianvärdet för diskrepans av SED och MVPA var -180,2 min/dag (under-) respektive 18,6 min/dag (överskattning). Avseende diskrepans av SED föreligger signifikanta skillnader mellan kvintilerna för diskrepans av SED (p = 0,000), accelerometermätt tid i SED (p = 0,000) samt totalt antal registrerade minuter (p = 0,000). Inga signifikanta skillnader ses för kön (p = 0,744), socioekonomisk status (p = 0,986), BMI (p = 0,806), konditionsvärde (p = 0,727) eller självskattad hälsa (p = 0,385). Avseende diskrepans av MVPA föreligger signifikanta skillnader för diskrepans av MVPA (p = 0,000), accelerometer-mätt tid i både SED (p = 0,000) och MVPA (p = 0,000), antal registrerade minuter totalt (p = 0,001), socioekonomisk status (p = 0,001) samt självskattad hälsa (p = 0,009). Slutsats: Resultaten visar att det är en stor diskrepans mellan hur svenska medelålders män och kvinnor skattar SED respektive MVPA om man jämför med objektivt accelerometermätt tid. Det verkar dock inte finnas en viss kategori människor som under- eller överskattar SED mer än andra avseende de undersökta variablerna. Beträffande diskrepans av MVPA ses dock en tendens till ökad diskrepans för de som bor i socioekonomiska högstatusområden samt att de med god självskattad hälsa överskattar mer. Studiens resultat tyder även på att mer registreringstid med accelerometern medför ökad diskrepans av SED respektive MVPA. Mer forskning kring hur olika registreringstider påverkar utfallen är därför önskvärt. / Aim: The aim of this study was to i) examine the validity of the population estimates of sedentary behaviour (SED) and moderate to vigorous physical activity (MVPA) and ii) describe possible differences between groups that are under-, over- and correctly classifying their behaviour. The following questions were designed to answer the above aim: 1. Are there discrepancies between the participants self-reported SED and MVPA compared to objective measurement by accelerometer? 2. Do the participants who under-, over- and correctly classify their behaviour differ with respect to gender, socioeconomic status, fitness factor and self-rated health? Method: This study used data from the Swedish CArdioPulmonary bioImage pilot Study  (SCAPIS) where participants answered a questionnaire and wore an accelerometer for seven subsequent days (N = 652). Four questions were used to measure the participants’ physical activity level. Discrepancy of SED and MVPA was calculated as the difference between subjectively and objectively measured time. The data is descriptively presented where misclassification has been categorized into quintiles and estimated as median differences. Results: The median time for discrepancy of SED was for the whole population -180,2 min/day (under-) and for MVPA 18,6 min/dag (overestimation). Regarding discrepancy of SED, there were significant differences between quintiles for discrepancy of SED (p = 0,000), accelerometer-registered time in SED (p = 0,000) and total number of minutes registered (p = 0,000). No significant differences existed for gender (p = 0,744), socioeconomic status (p = 0,986), BMI (p = 0,806), fitness factor (p = 0,727) or self-rated health (p = 0,385). Regarding discrepancy of MVPA there were significant differences for discrepancy of MVPA (p = 0,000), accelerometer-registered time in SED (p = 0,000) and MVPA (p = 0,000) and total number of minutes registered (p = 0,001). Significance also existed for socioeconomic status (p = 0,001) and self-rated health (p = 0,009). Conclusion: The results show that the population in this study misclassify SED and MVPA and the differences between the quintiles are large. However, there seems to be no particular category of people who misclassify SED more than others in regards to the examined variables. Concerning discrepancy of MVPA, the results suggest that the discrepancy is greater for those with high socioeconomic status and that those with good self-rated health tend to overestimate more. The results also suggest that more time registered with accelerometer entails greater discrepancy. More research examining how different recording times affect outcomes is warranted. / <p>Kursen Projektarbete.</p><p>SCAPIS-projektet</p>

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This paper examines the extent to which thin markets in conjunction with tax induced kinks in the budget constraint cause consumer demand to be skewed. To illustrate the principles I focus on the demand for owner-occupied housing. Housing units are indivisible and heterogeneous while tastes for housing are at least partly idiosyncratic, causing housing markets to be thin. In addition, prior to 1998, capital gains tax provisions introduced a sharp kink in the budget constraint of existing owner-occupiers in search of a new home: previous homeowners under age 55 paid no capital gains tax if they bought up, but were subject to capital gains tax if they bought down. I first characterize the economic conditions under which households err on the up or down side when choosing a home in the presence of a thin market and a kinked budget constraint. I then specify an empirical model that takes such effects into account. Results based on Monte Carlo experiments indicate that failing to allow for skewness in the demand for housing leads to biased estimates of the elasticities of demand when such skewness is actually present. In addition, estimates based on American Housing Survey data suggest that such bias is substantial: controlling for skewness reduces the price elasticity of demand among previous owner-occupiers from 1.6 to 0.3. Moreover, 58% of previous homeowners err on the up while only 42% err on the down side. Thus, housing demand is skewed. / Ph. D.

A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing those complex data in this dissertation. We have then provided several contributions to semiparametric and nonparametric methods for dealing with the following problems: the first is to propose a method for testing the significance of a functional association under the matched study; the second is to develop a method to simultaneously identify important variables and build a network in HDHC data; the third is to propose a multi-class dynamic model for recognizing a pattern in the time-trend analysis. For the first topic, we propose a semiparametric omnibus test for testing the significance of a functional association between the clustered binary outcomes and covariates with measurement error by taking into account the effect modification of matching covariates. We develop a flexible omnibus test for testing purposes without a specific alternative form of a hypothesis. The advantages of our omnibus test are demonstrated through simulation studies and 1-4 bidirectional matched data analyses from an epidemiology study. For the second topic, we propose a joint semiparametric kernel machine network approach to provide a connection between variable selection and network estimation. Our approach is a unified and integrated method that can simultaneously identify important variables and build a network among them. We develop our approach under a semiparametric kernel machine regression framework, which can allow for the possibility that each variable might be nonlinear and is likely to interact with each other in a complicated way. We demonstrate our approach using simulation studies and real application on genetic pathway analysis. Lastly, for the third project, we propose a Bayesian focal-area detection method for a multi-class dynamic model under a Bayesian hierarchical framework. Two-step Bayesian sequential procedures are developed to estimate patterns and detect focal intervals, which can be used for gas chromatography. We demonstrate the performance of our proposed method using a simulation study and real application on gas chromatography on Fast Odor Chromatographic Sniffer (FOX) system. / Doctor of Philosophy / A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing the following three types of data: (1) matched case-crossover data, (2) HCHD data, and (3) Time-series data. We contribute to the development of statistical methods to deal with such complex data. First, under the matched study, we discuss an idea about hypothesis testing to effectively determine the association between observed factors and risk of interested disease. Because, in practice, we do not know the specific form of the association, it might be challenging to set a specific alternative hypothesis. By reflecting the reality, we consider the possibility that some observations are measured with errors. By considering these measurement errors, we develop a testing procedure under the matched case-crossover framework. This testing procedure has the flexibility to make inferences on various hypothesis settings. Second, we consider the data where the number of variables is very large compared to the sample size, and the variables are correlated to each other. In this case, our goal is to identify important variables for outcome among a large amount of the variables and build their network. For example, identifying few genes among whole genomics associated with diabetes can be used to develop biomarkers. By our proposed approach in the second project, we can identify differentially expressed and important genes and their network structure with consideration for the outcome. Lastly, we consider the scenario of changing patterns of interest over time with application to gas chromatography. We propose an efficient detection method to effectively distinguish the patterns of multi-level subjects in time-trend analysis. We suggest that our proposed method can give precious information on efficient search for the distinguishable patterns so as to reduce the burden of examining all observations in the data.

Diese Dissertation umfasst drei Aufsätze zu verschiedenen Themen aus dem Bereich der Mikroökonometrie. Das erste Kapitel ist eine gemeinsame Arbeit mit Christoph Breunig und umfasst semi/nichtparametrische Regressionsmodelle, in denen die abhängige Variable einen nicht-klassischen Messfehler aufweist. Es werden Bedingungen erarbeitet, unter denen die Regressionsfunktion bis auf eine Normalisierung identifiziert werden kann. Zur Schätzung wird ein neuer Schätzer entwickelt, bei dem eine Rang-basierte Kriteriumsfunktion über einen sieve-Raum optimiert wird und dessen Konvergenzrate hergeleitet. Das zweite Kapitel beschäftigt sich mit der Schätzung von bedingten Dichtefunktionen von zufälligen Koeffizienten in linearen Regressionsmodellen. Es wird ein zweistufiges Schätzverfahren entwickelt, in dem zunächst eine Approximation der bedingten Dichte Koeffizienten hergeleitet wird. In einem weiteren Schritt können diese Funktionen mit generischen Methoden des maschinellen Lernens geschätzt werden. Des Weiteren wird auch die Konvergenzrate des Schätzers in der L2-Norm hergeleitet sowie dessen punktweise, asymptotische Normalität. Im dritten Kapitel wird ein neuer und einfach umsetzbarer Ansatz zur Schätzung semi(nicht)parametrischer diskreter Entscheidungsmodelle, unter Berücksichtigung von Restriktionen auf die funktionalen Parameter des Modells, vorgestellt. Die untersuchten Modelle weisen funktionale Parameter auf, die bestimmte funktionale Formen aufweisen. Zentraler Teil der Arbeit ist die Entwicklung eines GLS-Schätzers über einen geeigneten sieve-Raum, der aus I- und B-Spline Basisfunktionen unter geeigneten Restriktionen basiert. Es wird gezeigt, dass sich die Berücksichtigung der Restriktionen auf die funktionale Form positiv auf die Konvergenzrate des Schätzers in einer schwachen Norm auswirkt und so notwendige Bedingungen für die asymptotische Normalität semiparametrischer Schätzer einfacher erreichen lässt. / This dissertation comprises three individual papers on various topics in microeconometrics. In the first chapter, which is joint work with Christoph Breunig, we study a semi-/nonparametric regression model with a general form of nonclassical measurement error in the outcome variable. We provide conditions under which the regression function is identifiable under appropriate normalizations. We propose a novel sieve rank estimator for the regression function and establish its rate of convergence. The second chapter deals with the estimation of conditional random coefficient models. Here I propose a two-stage sieve estimation procedure. First, a closed-form sieve approximation of the conditional RC density is derived. Second, sieve coefficients are estimated with generic machine learning procedures and under appropriate sample splitting rules. I derive the $L_2$-convergence rate of the conditional RC-density estimator and also provide a result on pointwise asymptotic normality. The third chapter presents a novel and simple approach to estimating a class of semi(non)parametric discrete choice models imposing shape constraints on the infinite-dimensional and unknown link function parameter. I study multiple-index discrete choice models where the link function is known to be bounded between zero and one and is (partly) monotonic. In the paper I present an easy to implement and computationally efficient sieve GLS estimation approach using a sieve space of constrained I- and B-spline basis functions. The estimator is shown to be consistent and that imposing shape constraints speeds up the convergence rate of the estimator in a weak Fisher-like norm. The asymptotic normality of relevant smooth functionals of model parameters is derived and I illustrate that necessary assumptions are milder if shape constraints are imposed.

Thesis advisor: Henry I. Braun / Secondary analyses of international large-scale assessments (ILSA) commonly characterize relationships between variables of interest using correlations. However, the accuracy of correlation estimates is impaired by artefacts such as measurement error and clustering. Despite advancements in methodology, conventional correlation estimates or statistical models not addressing this problem are still commonly used when analyzing ILSA data. This dissertation examines the impact of both the clustered nature of the data and heterogeneous measurement error on the correlations reported between background data and proficiency scales across countries participating in ILSA. In this regard, the operating characteristics of competing modeling techniques are explored by means of applications to data from PISA 2012. Specifically, the estimates of correlations between math self-efficacy and math achievement across countries are the principal focus of this study. Sequentially employing four different statistical techniques, a step-wise model refinement approach is used. After each step, the changes in the within-country correlation estimates are examined in relation to (i) the heterogeneity of distributions, (ii) the amount of measurement error, (iii) the degree of clustering, and (iv) country-level math performance. The results show that correlation estimates gathered from two-dimensional IRT models are more similar across countries in comparison to conventional and multilevel linear modeling estimates. The strength of the relationship between math proficiency and math self-efficacy is moderated by country mean math proficiency and this was found to be consistent across all four models even when measurement error and clustering were taken into account. Multilevel multidimensional mixture IRT modeling results support the hypothesis that low-performing groups within countries have a lower correlation between math self-efficacy and math proficiency. A weaker association between math self-efficacy and math proficiency in lower achieving groups is consistently seen across countries. A multilevel mixture IRT modeling approach sheds light on how this pattern emerges from greater randomness in the responses of lower performing groups. The findings from this study demonstrate that advanced modeling techniques not only are more appropriate given the characteristics of the data, but also provide greater insight about the patterns of relationships across countries. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

In Paper [I] we use data on Swedish public procurement auctions for internal regularcleaning service contracts to provide novel empirical evidence regarding green publicprocurement (GPP) and its effect on the potential suppliers’ decision to submit a bid andtheir probability of being qualified for supplier selection. We find only a weak effect onsupplier behavior which suggests that GPP does not live up to its political expectations.However, several environmental criteria appear to be associated with increased complexity,as indicated by the reduced probability of a bid being qualified in the postqualificationprocess. As such, GPP appears to have limited or no potential to function as an environmentalpolicy instrument. In Paper [II] the observation is made that empirical evaluations of the effect of policiestransmitted through public procurements on bid sizes are made using linear regressionsor by more involved non-linear structural models. The aspiration is typically to determinea marginal effect. Here, I compare marginal effects generated under both types ofspecifications. I study how a political initiative to make firms less environmentally damagingimplemented through public procurement influences Swedish firms’ behavior. Thecollected evidence brings about a statistically as well as economically significant effect onfirms’ bids and costs. Paper [III] embarks by noting that auction theory suggests that as the number of bidders(competition) increases, the sizes of the participants’ bids decrease. An issue in theempirical literature on auctions is which measurement(s) of competition to use. Utilizinga dataset on public procurements containing measurements on both the actual and potentialnumber of bidders I find that a workhorse model of public procurements is bestfitted to data using only actual bidders as measurement for competition. Acknowledgingthat all measurements of competition may be erroneous, I propose an instrumental variableestimator that (given my data) brings about a competition effect bounded by thosegenerated by specifications using the actual and potential number of bidders, respectively.Also, some asymptotic results are provided for non-linear least squares estimatorsobtained from a dependent variable transformation model. Paper [VI] introduces a novel method to measure bidders’ costs (valuations) in descending(ascending) auctions. Based on two bounded rationality constraints bidders’costs (valuations) are given an imperfect measurements interpretation robust to behavioraldeviations from traditional rationality assumptions. Theory provides no guidanceas to the shape of the cost (valuation) distributions while empirical evidence suggeststhem to be positively skew. Consequently, a flexible distribution is employed in an imperfectmeasurements framework. An illustration of the proposed method on Swedishpublic procurement data is provided along with a comparison to a traditional BayesianNash Equilibrium approach.

With educational effectiveness studies, school-level aggregates of students' characteristics (e.g. achievement) are often used to assess the impact of school composition on students' outcomes – school compositional effects. Empirical findings on the magnitude and direction of school compositional effects have not been consistent. Relevant methodological studies raise the issue of under-specification at level 1 in compositional models - evident when the student-level indicator on which the aggregation is based is mis-measured. This phenomenon has been shown to bias compositional effect estimates, leading to misleading effects of the aggregated variables – phantom compositional effects. My thesis, consisted of three separate studies, presents an advanced methodological framework that can be used to investigate the effect of school composition net of measurement error bias. In Study 1, I quantify the impact of failing to account for measurement error on school compositional effects as used in value added models of educational effectiveness to explain relative school effects. Building on previous studies, multilevel structural equation models are incorporated to control for measurement error and/or sampling error. Study 1a, a large sample of English primary students in years one and four (9,059 students from 593 schools) reveals a small, significant and negative compositional effect on students' subsequent mathematics achievement that becomes more negative after controlling for measurement error. Study 1b, a large study of Cyprus primary students in year four (1694 students in 59 schools) shows a small, positive but statistically significant effect that becomes non-significant after controlling for measurement error. Further analyses with the English data (Study 2), demonstrates a negative compositional effect of school average mathematics achievement on subsequent mathematics self-concept – a Big Fish Little Pond Effect (BFLPE). Adjustments for measurement and sampling error result in more negative BFLPEs. The originality of Study 2 lies in verifying BFLPEs for students as young as five to eight/nine years old. Bridging the findings related to students' mathematics self-concept (Study 2) and the findings on students’ mathematics achievements (Study 1a), I demonstrate that the prevalence of BFLPEs with the English data partly explains the negative compositional effect of school average mathematics achievement on students' subsequent mathematics achievement. Lastly, in Study 3 I consider an alternative approach to school accountability to conventional value added models, namely the Regression Discontinuity approach. Specifically, I use the English TIMSS 1995 primary (years four and five) and secondary (years eight and nine) data to investigate the effect of one extra year of schooling on students' mathematics achievement and the variability across schools in their absolute effects. The extent to which school composition, as given by school average achievement, correlates with schools' added-year effects is addressed. Importantly the robustness of the RD estimates to measurement error bias is demonstrated. My findings have important methodological, substantive and theoretical implications for on-going debates on the school compositional effects on students' outcomes, because nearly all previous research has been based on traditional approaches to multilevel models, which are positively biased due to the failure to control for measurement error.

Estudamos o problema de obtenção de estimadores/preditores ótimos para combinações lineares de respostas coletadas de uma população finita por meio de amostragem aleatória simples. Nesse contexto, estendemos o modelo misto para populações finitas proposto por Stanek, Singer & Lencina (2004, Journal of Statistical Planning and Inference) para casos em que se incluem erros de medida (endógenos e exógenos) e informação auxiliar. Admitindo que as variâncias são conhecidas, mostramos que os estimadores/preditores propostos têm erro quadrático médio menor dentro da classe dos estimadores lineares não viciados. Por meio de estudos de simulação, comparamos o desempenho desses estimadores/preditores empíricos, i.e., obtidos com a substituição das componentes de variância por estimativas, com aquele de competidores tradicionais. Também, estendemos esses modelos para análise de estudos com estrutura do tipo pré-teste/pós-teste. Também por intermédio de simulação, comparamos o desempenho dos estimadores empíricos com o desempenho do estimador obtido por meio de técnicas clássicas de análise de medidas repetidas e com o desempenho do estimador obtido via análise de covariância por meio de mínimos quadrados, concluindo que os estimadores/ preditores empíricos apresentaram um menor erro quadrático médio e menor vício. Em geral, sugerimos o emprego dos estimadores/preditores empíricos propostos para dados com distribuição assimétrica ou amostras pequenas. / We consider optimal estimation of finite population parameters with data obtained via simple random samples. In this context, we extend a finite population mixed model proposed by Stanek, Singer & Lencina (2004, Journal of Statistical Planning and Inference) by including measurement errors (endogenous or exogenous) and auxiliary information. Assuming that variance components are known, we show that the proposed estimators/predictors have the smallest mean squared error in the class of unbiased estimators. Using simulation studies, we compare the performance of the empirical estimators/predictors obtained by replacing variance components with estimates with the performance of a traditional estimator. We also extend the finite population mixed model to data obtained via pretest-posttest designs. Through simulation studies, we compare the performance of the empirical estimator of the difference in gain between groups with the performance of the usual repeated measures estimator and with the performance of the usual analysis of covariance estimator obtained via ordinary least squares. The empirical estimator has smaller mean squared error and bias than the alternative estimators under consideration. In general, we recommend the use of the proposed estimators/ predictors for either asymmetric response distributions or small samples.