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Abdel-Salam, Abdel-Salam Gomaa. "Profile Monitoring with Fixed and Random Effects using Nonparametric and Semiparametric Methods." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/29387.
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Profile monitoring is a relatively new approach in quality control best used where the process data follow a profile (or curve) at each time period. The essential idea for profile monitoring is to model the profile via some parametric, nonparametric, and semiparametric methods and then monitor the fitted profiles or the estimated random effects over time to determine if there have been changes in the profiles. The majority of previous studies in profile monitoring focused on the parametric modeling of either linear or nonlinear profiles, with both fixed and random effects, under the assumption of correct model specification.
Our work considers those cases where the parametric model for the family of profiles is unknown or at least uncertain. Consequently, we consider monitoring profiles via two techniques, a nonparametric technique and a semiparametric procedure that combines both parametric and nonparametric profile fits, a procedure we refer to as model robust profile monitoring (MRPM). Also, we incorporate a mixed model approach to both the parametric and nonparametric model fits. For the mixed effects models, the MMRPM method is an extension of the MRPM method which incorporates a mixed model approach to both parametric and nonparametric model fits to account for the correlation within profiles and to deal with the collection of profiles as a random sample from a common population.
For each case, we formulated two Hotelling's T 2 statistics, one based on the estimated random effects and one based on the fitted values, and obtained the corresponding control limits. In addition,we used two different formulas for the estimated variancecovariance matrix: one based on the pooled sample variance-covariance matrix estimator and a second one based on the estimated variance-covariance matrix based on successive differences.
A Monte Carlo study was performed to compare the integrated mean square errors (IMSE) and the probability of signal of the parametric, nonparametric, and semiparametric approaches. Both correlated and uncorrelated errors structure scenarios were evaluated for varying amounts of model misspecification, number of profiles, number of observations per profile, shift location, and in- and out-of-control situations. The semiparametric (MMRPM) method for uncorrelated and correlated scenarios was competitive and, often, clearly superior with the parametric and nonparametric over all levels of misspecification. For a correctly specified model, the IMSE and the simulated probability of signal for the parametric and theMMRPM methods were identical (or nearly so).
For the severe modelmisspecification case, the nonparametric andMMRPM methods were identical (or nearly so). For the mild model misspecification case, the MMRPM method was superior to the parametric and nonparametric methods. Therefore, this simulation supports the claim that the MMRPM method is robust to model misspecification.
In addition, the MMRPM method performed better for data sets with correlated error structure. Also, the performances of the nonparametric and MMRPM methods improved as the number of observations per profile increases since more observations over the same range of X generally enables more knots to be used by the penalized spline method, resulting in greater flexibility and improved fits in the nonparametric curves and consequently, the semiparametric curves.
The parametric, nonparametric and semiparametric approaches were utilized for fitting the relationship between torque produced by an engine and engine speed in the automotive industry. Then, we used a Hotelling's T 2 statistic based on the estimated random effects to conduct Phase I studies to determine the outlying profiles. The parametric, nonparametric and seminonparametric methods showed that the process was stable. Despite the fact that all three methods reach the same conclusion regarding the –in-control– status of each profile, the nonparametric and MMRPM results provide a better description of the actual behavior of each profile. Thus, the nonparametric and MMRPM methods give the user greater ability to properly interpret the true relationship between engine speed and torque for this type of engine and an increased likelihood of detecting unusual engines in future production. Finally, we conclude that the nonparametric and semiparametric approaches performed better than the parametric approach when the user's model is misspecified. The case study demonstrates that, the proposed nonparametric and semiparametric methods are shown to be more efficient, flexible and robust to model misspecification for Phase I profile monitoring in a practical application.
Thus, our methods are robust to the common problem of model misspecification. We also found that both the nonparametric and the semiparametric methods result in charts with good abilities to detect changes in Phase I data, and in charts with easily calculated control limits. The proposed methods provide greater flexibility and efficiency than current parametric methods used in profile monitoring for Phase I that rely on correct model specification, an unrealistic situation in many practical problems in industrial applications.Ph. D.
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Li, Zhengrong. "Model-based Tests for Standards Evaluation and Biological Assessments." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/29108.
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Implementation of the Clean Water Act requires agencies to monitor aquatic sites on a regular basis and evaluate the quality of these sites. Sites are evaluated individually even though there may be numerous sites within a watershed. In some cases, sampling frequency is inadequate and the evaluation of site quality may have low reliability.
This dissertation evaluates testing procedures for determination of site quality based on modelbased procedures that allow for other sites to contribute information to the data from the test site. Test procedures are described for situations that involve multiple measurements from sites within a region and single measurements when stressor information is available or when covariates are used to account for individual site differences.
Tests based on analysis of variance methods are described for fixed effects and random effects models. The proposed model-based tests compare limits (tolerance limits or prediction limits) for the data with the known standard. When the sample size for the test site is small, using model-based tests improves the detection of impaired sites. The effects of sample size, heterogeneity of variance, and similarity between sites are discussed. Reference-based standards and corresponding evaluation of site quality are also considered. Regression-based tests provide methods for incorporating information from other sites when there is information on stressors or covariates.
Extension of some of the methods to multivariate biological observations and stressors is also discussed. Redundancy analysis is used as a graphical method for describing the relationship between biological metrics and stressors. A clustering method for finding stressor-response relationships is presented and illustrated using data from the Mid-Atlantic Highlands. Multivariate elliptical and univariate regions for assessment of site quality are discussed.Ph. D.
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Luna, Soledad. "Population Dynamics And Factors Affecting Spiny Lobster Small Scale Fisheries." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234815.
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This dissertation analyses the effects of current fisheries practices and management regulations of the green spiny lobster (Panulirus gracilis) in the Eastern Tropical Pacific Region (ETP). P. gracilis has reached a critical state in the ETP. Country-based studies report that between 60 and 98% of lobsters caught in the wild are under the minimum landing size (MLS). This means that spiny lobsters are being extracted before reproducing and contributing to the replenishment of interconnected populations. The recovery of green spiny lobster populations in the ETP and the future maintenance of a sustainable fishery will depend on effective management decisions and on taking in account environmental factors that influence the population dynamics of the lobsters.
In the first study (Chapter 2), the B52 Spiny Lobster individual based simulation model was used for conducting a population viability analysis to quantify the effect of current fishing practices and the effect of varying management regulations on minimum landing size (MLS) and fishing effort. The best suit of regulations to maintain the highest abundance, production of offspring and catch is to protect juveniles and egged females, and to establish a MLS that assures the reproduction of individuals before being extracted. This study revealed regional variations, however the patterns and the causes for variation were not yet clear. This led to the next chapters in this dissertation.
In Chapter 3, I used a meta-analysis to explore regional lobster variability by comparing published studies from the ETP. The objective was to identify patterns of variation related to geographic and environmental factors of the region that can inform the establishment and evaluation of coordinated regulations. Morphological relationships showed to be more variable at northern latitudes, where the mean annual sea surface temperatures are higher than at lower temperatures at the Equator. In terms of management, MLS regulations should be adapted accounting for the effect of sea surface temperature and its variation. Additionally, it was observed that monitoring methodologies are not standardized within the region and even in some cases, neither within countries. Furthermore, in most places monitoring of the spiny lobster fishery happens sporadically, only in Galapagos takes place every year. Identifying patterns of variations can improve the accuracy of prediction models which can help to explore, design, and apply more effective management measures, as well as promote regional coordination to support the recovery and maintenance of spiny lobsters.
In Chapter 4, I contrast current Ecuadorian minimum landing size (MLS) regulation to lobster empirical measurements within Ecuador in order to recognize potential pitfalls for management enforcement. I used linear regression and multiple regression models with the objective of identifying potential relative size variations of the individuals caught in the wild over time and in the different fishing areas in Ecuador, as well as to analyse the effect of locality, sex, age and mean SST on the tail length/total length ratio. Morphological relations were significantly different among sexes, in time and by all sites. Most importantly, this study shows that current minimum size regulations are not applicable to all sites. Additionally, I found that water temperature has a significant effect on morphological relationship variations. However, it was not the main site-specific variable responsible for explaining such variations. In general, this work emphasizes the need for length data collection standardization and the consideration of temporal and spatial variation implications in national and regional fishery management planning, enforcement and evaluation.
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Cau, Nicklasson Ronnie, and Simon Hansson. "Investment Companies’ Discount Fluctuation on the Swedish Market : A statistical analysis regarding different micro- and macroeconomic factors influence on Swedish closed-end funds’ discount." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Economics, Finance and Statistics, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-21250.
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Closed-end funds’ (CEF) discount and discount fluctuations have been puzzling researchers for decades. Up to date, there are no multidimensional or cross-sectional variables that have been proved to influence CEFs simultaneously. Fact is that, earlier research and theories on the subject are contradictious and several suggestions on the origin of the CEF’s discount and its fluctuations have been proposed. To mention a few, investor sentiments, taxation issues, dividend policies, agency costs and agency problems are considered to influence these discounts. The purpose of this report is to examine the relationship between micro- and macroeconomic variables fluctuations, and how these can explain the discount fluctuation of the Swedish CEFs. This report focuses upon the CEFs traded at NASDAQ OMX Stockholm, which have been selected through a comprehensive multistage selection process. 10 CEFs were selected. Monthly data for calculating micro- and macroeconomic variables was collected for the period March 2003 – February 2013, which resulted in approximately 1 200 observations. OLS regression analysis, Fixed- and Random Effect Models and Hausman tests were conducted. The findings conclude that some of this report’s chosen micro- and macro variables influence on the Swedish CEFs’ discount fluctuation, although these findings are conditioned. The CEFs’ individual characteristics or traits result in a significant impact on the fluctuation of CEFs’ discount. Hence, only by controlling these characteristics, multidimensional or cross-sectional micro- and macroeconomic variables can be proved to affect the CEFs’ discount fluctuation.
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Kamangar, Daniel, and Richard Sundin. "Management and CEO Stock Ownership and its Effect on Company Performance." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229670.
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This is a study on the effect of management and CEO stock ownership on company performance. A regression analysis is performed on panel data consisting of a sample of 30 companies listed on OMX Stockholm Mid Cap. A total of 210 and 2520 observations is considered on a yearly and monthly basis, respectively, for seven years (2010-2016). The Hausman test is applied for determining between the fixed effects and random effects regression models. Results show that management relative stock ownership has a significant positive effect on company net income growth and return on assets. The effect is not significant for CEO stock ownership, which is contrary to what commonly has been shown for large companies in previous research. Moreover, alternative methodology is discussed for the benefit of the future researcher. The authors illustrate how the selection of dummy variables can be vital for final model outcomes, and it is thus an important aspect to consider when performing panel data analysis.I den här studien undersöks hur aktieinnehav hos ledning och den verkställande direktören i ett företag påverkar företagsutvecklingen. Studien genomförs med regressionsanalys på paneldata som består av 30 företag, samtliga noterade på OMX Stockholm Mid Cap. Totalt samlas 210 och 2520 observationer på årsbasis respektive månadsbasis över sju år (2010-2016). Hausman-testet används för att bestämma vilken av fixed effects-modellen och random effects-modellen som ska användas i regressionen. Resultaten visar att relativt aktieinnehav hos ledningen har en positiv signifikant påverkan på ett företags nettoinkomstutveckling och avkastning på tillgångar. Den verkställande direktörens aktieinnehav visas inte vara signifikant, vilket är motsatt till det som generellt har visats för stora företag i tidigare forskning. Regressionerna genomförs även med alternativa metoder, vilka det resoneras kring i en diskussion som bör vara till gagn för vidare forskning. Författarna illustrerar hur val av dummy-variabler kan ha en avgörande betydelse för regressionsanalysen, och att det således är en viktig aspekt att ta hänsyn till när regressioner genomförs på paneldata.
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Fernando, Shasika. "Våldsbrott i Stockholms län och Uppsala län : En statistisk undersökning för att skatta effekten på våldsbrott med en multipel linjär regression genom studier av ungdomsbrottslighet i kombination med samhällsrelaterade faktorer." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-432998.
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This following report estimate the effect of violent crime with juvenile delinquency together with some societal factors with the help of a multiple regression of panel data. The results show that there are some variables that have a significant effect on violent crime. In my conclusion I discuss that the economic situation might contributes to violent crimes initially.
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Li, Guangjie. "Essays on economic and econometric applications of Bayesian estimation and model comparison." Thesis, University of Leicester, 2009. http://hdl.handle.net/2381/4792.
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This thesis consists of three chapters on economic and econometric applications of Bayesian parameter estimation and model comparison. The first two chapters study the incidental parameter problem mainly under a linear autoregressive (AR) panel data model with fixed effect. The first chapter investigates the problem from a model comparison perspective. The major finding in the first chapter is that consistency in parameter estimation and model selection are interrelated. The reparameterization of the fixed effect parameter proposed by Lancaster (2002) may not provide a valid solution to the incidental parameter problem if the wrong set of exogenous regressors are included. To estimate the model consistently and to measure its goodness of fit, the Bayes factor is found to be more preferable for model comparson than the Bayesian information criterion based on the biased maximum likelihood estimates. When the model uncertainty is substantial, Bayesian model averaging is recommended. The method is applied to study the relationship between financial development and economic growth. The second chapter proposes a correction function approach to solve the incidental parameter problem. It is discovered that the correction function exists for the linear AR panel model of order p when the model is stationary with strictly exogenous regressors. MCMC algorithms are developed for parameter estimation and to calculate the Bayes factor for model comparison. The last chapter studies how stock return's predictability and model uncertainty affect a rational buy-and-hold investor's decision to allocate her wealth for different lengths of investment horizons in the UK market. The FTSE All-Share Index is treated as the risky asset, and the UK Treasury bill as the riskless asset in forming the investor's portfolio. Bayesian methods are employed to identify the most powerful predictors by accounting for model uncertainty. It is found that though stock return predictability is weak, it can still affect the investor's optimal portfolio decisions over different investment horizons.
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Persson, Sanna, and Jerry Pettersson. "The connection between household savings ratio and human development index : Which factors affect the household savings ratio?" Thesis, Linnéuniversitetet, Institutionen för ekonomistyrning och logistik (ELO), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87939.
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This thesis investigates which factors affecting savings behavior by using a fixed effect regression model. To see what affects the household savings rate the following independent variables is considered: Natural logarithm of trend per capita income, natural logarithm of deviation from trend per capita income, growth of disposable income, real interest rate, inflation, wealth in relation to household disposable income, foreign savings in relation to disposable income, dependency ratio and human development index. To see whether changes of human development within a county impacts the household´s savings ratio this variables was included in a separate regression. To avoid possible biasedness from ordinary least square, a panel data technique called fixed effect regression model is used. The investigated time period is between year 1999 and 2016 and to make a restriction, variables from 25 developed countries were studied. The involved economic theories in this work are Keynesianism, permanent income hypothesis and the savings theory behind Maslow´s behavioral pyramid. The result made by using this study is that growth in income and foreign savings in relation to disposable income is insignificant and can´t be used in explaining the differences between household´s savings. Human development index within a country has a negative effect on the savings ratio but a conclusion regarding whether changes in HDI´s does affect savings can´t be made and more research within that field is needed.
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Waterman, Megan Janet Tuttle. "Linear Mixed Model Robust Regression." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/27708.
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Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. Model robust regression techniques predict mean response as a convex combination of a parametric and a nonparametric model fit to the data. It is a semiparametric method by which incompletely or incorrectly specified parametric models can be improved through adding an appropriate amount of a nonparametric fit. We apply this idea of model robustness in the framework of the linear mixed model. The mixed model robust regression (MMRR) predictions we propose are convex combinations of predictions obtained from a standard normal-theory linear mixed model, which serves as the parametric model component, and a locally weighted maximum likelihood fit which serves as the nonparametric component. An application of this technique with real data is provided.Ph. D.
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Mingolini, Riccardo. "Investimenti in lobby: Un modello per stimare il loro impatto sull'azienda." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13291/.
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In questo elaborato di tesi verrà analizzata l’attitudine di una azienda ad investire in lobbying, misurato attraverso varie variabili importanti per la stessa quali ad esempio il capitale, il reddito netto dell’impresa, il numero degli impiegati (et simila) e il loro impatto scoraggiante o incentivante rispetto alla nostra variabile dipendente.
Cercheremo infine di trovare un modello che approssima in modo sostanziale suddette dipendenze e variabili, in modo da tracciare un filo logico e matematico fra la nostra variabile dipendente Y ( investimento in lobbying) e le nostre variabili indipendenti X cioè gli indici e le variabili in valore monetario importanti per definire una azienda e il suo settore di appartenenza (SIC CODE).
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Pforr, Klaus [Verfasser], and Josef [Akademischer Betreuer] Brüderl. "femlogit : Implementation und Anwendung der multinominalen logistischen Regression mit "fixed effects" / Klaus Pforr ; Betreuer: Josef Brüderl." Mannheim : Universitätsbibliothek Mannheim, 2021. http://d-nb.info/1234389401/34.
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Fernández, Villegas Renzo. "A beta inflated mean regression model with mixed effects for fractional response variables." Master's thesis, Pontificia Universidad Católica del Perú, 2017. http://tesis.pucp.edu.pe/repositorio/handle/123456789/8847.
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In this article we propose a new mixed effects regression model for fractional bounded response variables. Our model allows us to incorporate covariates directly to the expected
value, so we can quantify exactly the influence of these covariates in the mean of the variable of interest rather than to the conditional mean. Estimation is carried out from a Bayesian perspective and due to the complexity of the augmented posterior distribution we use a Hamiltonian Monte Carlo algorithm, the No-U-Turn sampler, implemented using Stan software. A simulation study for comparison, in terms of bias and RMSE, was performed showing that our model has a better performance than other traditional longitudinal models for bounded variables. Finally, we applied our Beta Inflated mixed-effects regression model to real data which consists of utilization of credit lines in the peruvian financial system.En este artículo proponemos un nuevo modelo de regresión con efectos mixtos para variables acotadas fraccionarias. Este modelo nos permite incorporar covariables directamente al valor esperado, de manera que podemos cuantificar exactamente la influencia de estas covariables en la media de la variable de interés en vez de en la media condicional. La estimación se llevó a cabo desde una perspectiva bayesiana y debido a la complejidad de la distribución aumentada a posteriori usamos un algoritmo de Monte Carlo Hamiltoniano, el muestreador No-U-Turn, que se encuentra implementado en el software Stan. Se realizó un estudio de simulación que compara, en términos de sesgo y RMSE, el modelo propuesto con otros modelos tradicionales longitudinales para variables acotadas, resultando que el primero tiene un mejor desempeño. Finalmente, aplicamos nuestro modelo de regresión Beta Inflacionada con efectos mixtos a datos reales los cuales consistían en información de la utilización de las líneas de crédito en el sistema financiero peruano.
Tesis
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Chang, Wei-Te. "The analysis of random effects regression model for predicting the shelf-life of gun propellant." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA295246.
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Becerra-Cordoba, Nancy. "Modeling land-cover change in the Amazon using historical pathways of land cover change and Markov chains. A case study of Rondõnia, Brazil." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28322.
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The present dissertation research has three purposes: the first one is to predict anthropogenic deforestation caused by small farmers firstly using only pathways of past land cover change and secondly using demographic, socioeconomic and land cover data at the farm level. The second purpose is to compare the explanatory and predictive capacity of both approaches at identifying areas at high risk of deforestation among small farms in Rondõnia, Brazil. The third purpose is to test the assumptions of stationary probabilities and homogeneous subjects, both commonly used assumptions in predictive stochastic models applied to small farmers' deforestation decisions. This study uses the following data: household surveys, maps, satellite images and their land cover classification at the pixel level, and pathways of past land cover change for each farm. These data are available for a panel sample of farms in three municipios in Rondõnia, Brazil (Alto Paraiso, Nova União, and Rolim de Moura) and cover a ten-year period of study (1992-2002). Pathways of past land cover change are graphic representations in the form of flow charts that depict Land Cover Change (LCC) in each farm during the ten-year period of study. Pathways were constructed using satellite images, survey data and maps, and a set of interviews performed on a sub-sample of 70 farms. A panel data analysis of the estimated empirical probabilities was conducted to test for subject and time effects using a Fixed Group Effects Model (FGEM), specifically the Least Square Dummy Variable (LSDV1) fixed effects technique.
Finally, the two predictive modeling approaches are compared. The first modeling approach predicts future LCC using only past land cover change data in the form of empirical transitional probabilities of LCC obtained from pathways of past LCC. These empirical probabilities are used in a LSDV1 for fixed–group effects, a LSDV1 for fixed-time effects, and an Ordinary Least Square model (OLS) for the pooled sample. Results from these models are entered in a modified Markov chain model's matrix multiplication. The second modeling approach predicts future LCC using socio-demographic and economic survey variables at the household level. The survey data is used to perform a multinomial logit regression model to predict the LC class of each pixel. In order to compare the explanatory and predictive capacity of both modeling approaches, LCC predictions at the pixel level are summarized in terms of percentage of cells in which future LC was predicted correctly. Percentage of correct predicted land cover class is compared against actual pixel classification from satellite images. The presence of differences among farmers in the LSDV1-fixed group effect by farmer suggests that small farmers are not a homogeneous group in term of their probabilities of LCC and that further classification of farmers into homogeneous subgroups will depict better their LCC decisions. Changes in the total area of landholdings proved a stronger influence in farmer's LCC decisions in their main property (primary lot) when compared to changes in the area of the primary lot. Panel data analysis of the LCC empirical transition probabilities (LSDV1 fixed time effects model) does not find enough evidence to prefer the fixed time effects model when compared to a Ordinary Least Square (OLS) pooled version of the probabilities. When applying the results of the panel data analysis to a modified markov chain model the LSDV1-farmer model provided a slightly better accuracy (59.25% accuracy) than the LSDV1-time and the OLS-pooled models (57.54% and 57.18%, respectively). The main finding for policy and planning purposes is that owners type 1—with stable total landholdings over time—tend to preserve forest with a much higher probability (0.9033) than owner with subdividing or expanding properties (probs. of 0.0013 and 0.0030). The main implication for policy making and planning is to encourage primary forest preservation, given that the Markov chain analysis shows that primary forest changes into another land cover, it will never go back to this original land cover class. Policy and planning recommendations are provided to encourage owner type 1 to continue their pattern of high forest conservation rates. Some recommendations include: securing land titling, providing health care and alternative sources of income for the OT1's family members and elderly owners to remain in the lot. Future research is encouraged to explore spatial autocorrelation in the pixel's probabilities of land cover change, effects of local policies and macro-economic variables in the farmer's LCC decisions.Ph. D.
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Zhang, Dongquan. "Effects of model selection on the coverage probability of confidence intervals in binary-response logistic regression." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8538.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.Thesis research directed by: Dept. of Measurement, Statistics and Evaluation. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Metzger, Thomas Anthony. "Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/93226.
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Standard linear modeling approaches make potentially simplistic assumptions regarding the structure of categorical effects that may obfuscate more complex relationships governing data. For example, recent work focused on the two-way unreplicated layout has shown that hidden groupings among the levels of one categorical predictor frequently interact with the ungrouped factor. We extend the notion of a "latent grouping factor'' to linear models in general. The proposed work allows researchers to determine whether an apparent grouping of the levels of a categorical predictor reveals a plausible hidden structure given the observed data. Specifically, we offer Bayesian model selection-based approaches to reveal latent group-based heteroscedasticity, regression effects, and/or interactions. Failure to account for such structures can produce misleading conclusions. Since the presence of latent group structures is frequently unknown a priori to the researcher, we use fractional Bayes factor methods and mixture g-priors to overcome lack of prior information. We provide an R package, slgf, that implements our methodology in practice, and demonstrate its usage in practice.Doctor of Philosophy
Statistical models are a powerful tool for describing a broad range of phenomena in our world. However, many common statistical models may make assumptions that are overly simplistic and fail to account for key trends and patterns in data. Specifically, we search for hidden structures formed by partitioning a dataset into two groups. These two groups may have distinct variability, statistical effects, or other hidden effects that are missed by conventional approaches. We illustrate the ability of our method to detect these patterns through a variety of disciplines and data layouts, and provide software for researchers to implement this approach in practice.
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Luoma, Alem. "Tax competition among municipalities in the central part of Sweden : An empirical study: Does municipal taxation decisions depend on taxations in neighboring municipalities?" Thesis, Södertörns högskola, Institutionen för samhällsvetenskaper, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-26518.
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The primary task of this paper is to test the interactive relations between tax rates at municipality level. We include 96 municipalities between the years 2006 to 2013. The relations are estimated by panel data instrumental variable estimation method with fixed effect for overcoming the possible specific error of simultaneity. In addition, we choose a set of control variables to strength our analysis. The main findings of this study suggest, one percent tax cut in the neighboring municipality leads to a 0,62 percent decrease in the tax in the home municipality ceteris paribus. This result is in line with theory and is similar to findings in previous studies such as Edmark and Åhgren (2008).
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Hinz, Derik Michael. "The Effects of Part-Time Faculty on First Semester Freshmen Retention: A Predictive Model Using Logistic Regression." NCSU, 2005. http://www.lib.ncsu.edu/theses/available/etd-05192005-001811/.
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This study used data from five years of first-semester entering freshmen cohorts (1999-2003) from North Carolina State University to assess the effects of exposure to part-time faculty upon student retention. A logistic regression model containing high school gpa, total first year hours attempted, gender, SAT verbal score, SAT math score, and percent faculty exposure was found to be a significant (p<.0001) predictor of second year retention. While the percentage to which first-year students were exposed to part-time faculty instruction was significant (p<0.0253) in predicting retention, it had no real impact upon the odds ratio (0.996) of being retained.
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Erich, Roger Alan. "Regression Modeling of Time to Event Data Using the Ornstein-Uhlenbeck Process." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1342796812.
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Frey, Reik. "International and domestic Migration Patterns : International immigration effect on internal out-migration patterns in the German states between 1993 and 2016." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Nationalekonomi, 1994. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-44226.
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Internal migration has frequently been subject of empirical research. This study attempts to find a relationship between international immigration and internal out-migration in all German states, covering the time period between 1993 and 2016. The underlying theories were established by Card et al. (2008), Schlömer (2012), Florida (2002) and Chiswick and Miller (2015). These were used to develop a modified version of the gravity model. The dataset was received from the Federal Statistical Office of Germany (Statistisches Bundesamt). The regressions were executed using a fixed effects model and a pooled OLS as a robustness check. The empirical findings suggest no evidence of a statistically significant effect of international immigration on internal out-migration patterns in the covered period. Control variables suggest policymakers to focus on other factors when the effects of immigration policies on internal out-migration are being considered.
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Nordenberg, Caroline, and Sofia Ahlin. "Är ekonomisk tillväxt klimatets räddning? : En empirisk undersökning om sambandet mellan ekonomisk tillväxt och koldioxidutsläpp." Thesis, Uppsala universitet, Nationalekonomiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-419912.
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Denna studie har syftat till att undersöka om ekonomisk tillväxt kan sägas leda till minskade koldioxidutsläpp och därmed vara en lösning på miljöproblematiken. Genom att jämföra produktionsbaserade och konsumtionsbaserade koldioxidutsläpp ger studien en uppfattning om ekonomisk tillväxt leder till minskade utsläpp eller endast en omställning i ekonomin. Studiens resultat har estimerats med en fixed effects-modell och baserats på utsläpps-och inkomstdata för 105 länder under åren 1990 till 2016. Undersökningen har visat att ekonomisk tillväxt kan leda till minskade produktionsbaserade koldioxidutsläpp men att ett liknande samband för konsumtionsbaserade utsläpp inte kan konstateras. Resultaten implicerar att minskade produktionsbaserade utsläpp reflekterar en omställning i ekonomin, snarare än minskade totala utsläpp. Utifrån resultaten dras slutsatsen att ekonomisk tillväxt inte kan sägas leda till minskade totala koldioxidutsläpp.
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Book, Emil, and Linus Ekelöf. "A Multiple Linear Regression Model To Assess The Effects of Macroeconomic Factors On Small and Medium-Sized Enterprises." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254298.
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Small and medium-sized enterprises (SMEs) have long been considered the backbone in any country’s economy for their contribution to growth and prosperity. It is therefore of great importance that the government and legislators adopt policies that optimise the success of SMEs. Recent concerns of an impending recession has made this topic even more relevant since small companies will have greater difficulty withstanding such an event. This thesis will focus on the effects of macroeconomic factors on SMEs in Sweden, with the usage of multiple linear regression. Data was collected for a 10 year period, from 2009 to 2019 at a monthly interval. The end result was a five variable model with an coefficient of determination of 98%.Små- och medelstora företag (SMEs) har länge varit ansedda som en av de viktigaste komponenterna i ett lands ekonomi, främst för deras bidrag till tillväxt och framgång. Det är därför mycket viktigt att regeringar och lagstiftare för en politik som främjar SMEs optimala tillväxt. Flera år av högkonjunktur och oro över kommande lågkonjunktur har gjort detta ämne ytterst relevant då små företag är de som kommer att drabbas värst av en svårare ekonomisk tillvaro. Denna rapport använder multipel linjär regression för att utvärdera effekterna av olika makroekonomiska faktorer på SMEs i Sverige. Data har insamlats månadsvis för en 10 årsperiod mellan 2009 till 2010. Resultatet blev en modell med fem variabler och en förklaringsgrad på 98%.
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Loy, Jens-Peter, and Christoph Weiss. "Synchronisation in multi-product firms. Evidence from german grocery prices." Inst. für Volkswirtschaftstheorie und -politik, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/300/1/document.pdf.
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Using a unique panel data set for German grocery prices we find significant price synchronization within food retail chains as well as within individual food stores (between products). Price synchronization between chains appears to be less pronounced. Common shocks can only explain some synchronization, indicating that strategic motives as well as menu costs are of significant importance. (author's abstract)Series: Working Papers Series "Growth and Employment in Europe: Sustainability and Competitiveness"
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Walter, Benjamin. "Two essays on the market for Bitcoin mining and one essay on the fixed effects logit model with panel data." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLG002/document.
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Ma thèse se compose de deux parties indépendantes. La première traite decrypto-économie et la seconde d’économétrie théorique. Dans le premier chapitre, je présente un modèle qui prédit la puissance de calcul totale déployée par les mineurs en utilisant le taux de change bitcoin / dollar. Le deuxième chapitre s’appuie sur une version simplifiée du précédent modèle pour faire le constat de l’inefficacité du protocole Bitcoin actuel et proposer un moyen simple de réduire la consommation d’électricité engendrée par cette cryptomonnaie. Le troisième chapitre explique comment identifier et estimer les bornes exactes de la région d’identification de l’effet marginal moyen dans un modèle logit avec effets fixes sur données de panelMy dissertation concatenates two independent parts. The first one dealswith crypto-economics whereas the second one is about theoretical econometrics. In the first chapter, I present a model which predicts bitcoin miners’ total computing power using the bitcoin / dollar exchange rate. The second chapter builds on a simplified version of the preceeding model to show to which extent the current Bitcoin protocol is inefficient and suggest a simple solution to lower the cryptocurrency’s electricity consumption. The third chapter explains how to identify and estimate the sharp bounds of the average marginal effect’s identification region in a fixed effects logit model with panel data
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Gumprecht, Daniela. "R&D Spillovers: A Non-Spatial and a Spatial Examination." Austrian Statistical Society, c/o Bundesanstalt Statistik Austria, 2007. http://epub.wu.ac.at/5388/1/316%2D1053%2D1%2DSM.pdf.
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In recent years there were many debates and different opinions
whether R&D spillover effects exist or not. In 1995 Coe and Helpman published
a study about this phenomenon, based on a panel dataset, that supports
the position that such R&D spillover effects are existent. However, this
survey was criticized and many different suggestions for improvement came
from the scientific community. Some of them were selected and analysed and
finally led to a new model. And even though this new model is well compatible
with the data, it leads to different conclusions, namely that there does
not exist an R&D spillover effect. These different results were the motivation
to run a spatial analysis, which can be done by considering the countries as
regions and using an adequate spatial link matrix. The used methods from
the field of spatial econometrics are described briefly and quite general, and
finally the results from the spatial models (the ones which correspond to the
non-spatial ones) are compared with the results from the non-spatial analysis.
The preferred model supports the position that R&D spillover effects exist.
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Du, Chenguang. "How Well Can Two-Wave Models Recover the Three-Wave Second Order Latent Model Parameters?" Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103856.
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Although previous studies on structural equation modeling (SEM) have indicated that the
second-order latent growth model (SOLGM) is a more appropriate approach to
longitudinal intervention effects, its application still requires researchers to collect at least
three-wave data (e.g. randomized pretest, posttest, and follow-up design). However, in
some circumstances, researchers can only collect two-wave data for resource limitations.
With only two-wave data, the SOLGM can not be identified and researchers often choose
alternative SEM models to fit two-wave data. Recent studies show that the two-wave
longitudinal common factor model (2W-LCFM) and latent change score model
(2W-LCSM) can perform well for comparing latent change between groups. However, there
still lacks empirical evidence about how accurately these two-wave models can estimate the
group effects of latent change obtained by three-wave SOLGM (3W-SOLGM). The main
purpose of this dissertation, therefore, is trying to examine to what extent the fixed effects
of the tree-wave SOLGM can be recovered from the parameter estimates of the two-wave
LCFM and LCSM given different simulation conditions.
Fundamentally, the supplementary study (study 2) using three-wave LCFM was
established to help justify the logistics of different model comparisons in our main study
(study 1). The data generating model in both studies is 3W-SOLGM and there are in total
5 simulation factors (sample size, group differences in intercept and slope, the covariance
between the slope and intercept, size of time-specific residual, change the pattern of
time-specific residual). Three main types of evaluation indices were used to assess the
quality of estimation (bias/relative bias, standard error, and power/type I error rate). The
results in the supplementary study show that the performance of 3W-LCFM and
3W-LCSM are equivalent, which further justifies the different models' comparison in the
main study. The point estimates for the fixed effect parameters obtained from the two-wave
models are unbiased or identical to the ones from the three-wave model. However, using
two-wave models could reduce the estimation precision and statistical power when the
time-specific residual variance is large and changing pattern is heteroscedastic
(non-constant). Finally, two real datasets were used to illustrate the simulation resultsDoctor of Philosophy
To collect and analyze the longitudinal data is a very important approach to understand the phenomenon of development in the real world. Ideally, researchers who are interested in using a longitudinal framework would prefer collecting data at more than two points in time because it can provide a deeper understanding of the developmental processes. However, in real scenarios, data may only be collected at two-time points. With only two-wave data, the second-order latent growth model (SOLGM) could not be used. The current dissertation compared the performance of two-wave models (longitudinal common factor model and latent change score model) with the three-wave SOLGM in order to better understand how the estimation quality of two-wave models could be comparable to the tree-wave model. The results show that on average, the estimation from two-wave models is identical to the ones from the three-wave model. So in real data analysis with only one sample, the point estimate by two-wave models should be very closed to that of the three-wave model. But this estimation may not be as accurate as it is obtained by the three-wave model when the latent variable has large variability in the first or last time point. This latent variable is more likely to exist as a statelike construct in the real world. Therefore, the current study could provide a reference framework for substantial researchers who could only have access to two-wave data but are still interested in estimating the growth effect that supposed to obtain by three-wave SOLGM.
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Möbius, Thomas W. D. [Verfasser], Guido [Akademischer Betreuer] Knapp, and Katja [Gutachter] Ickstadt. "Parameter estimation in the random effects meta regression model / Thomas W. D. Möbius. Betreuer: Guido Knapp. Gutachter: Katja Ickstadt." Dortmund : Universitätsbibliothek Dortmund, 2014. http://d-nb.info/1105662705/34.
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Zharova, Alona. "Measures of University Research Output." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/18799.
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New Public Management unterstützt Universitäten und Forschungseinrichtungen dabei, in einem stark wettbewerbsorientierten Forschungsumfeld zu bestehen. Entscheidungen unter Unsicherheit, z.B. die Verteilung von Mitteln für den Forschungsbedarf und Forschungszwecke, erfordert von Politik und Hochschulmanagement, die Beziehungen zwischen den Dimensionen der Forschungsleistung und den resultierenden oder eingehenden Zuschüssen zu verstehen. Hierfür ist es wichtig, die Variablen der wissenschaftlichen Wissensproduktion auf der Ebene von Individuen, Forschungsgruppen und Universitäten zu untersuchen.
Das Kapitel 2 dieser Arbeit analysiert die Ebene der Individuen. Es verwendet die Beobachtungen der Forscherprofile von Handelsblatt (HB), Research Papers in Economics (RePEc, hier RP) und Google Scholar (GS) als meist verbreitete Ranking-Systeme in BWL und VWL im deutschsprachigen Raum.
Das Kapitel 3 liefert eine empirische Evidenz für die Ebene von Forschungsgruppen und verwendet die Daten eines Sonderforschungsbereichs (SFB) zu Finanzinputs und Forschungsoutput von 2005 bis 2016. Das Kapitel beginnt mit der Beschreibung passender Performanzindikatoren, gefolgt von einer innovativen visuellen Datenanalyse. Im Hauptteil des Kapitels untersucht die Arbeit mit Hilfe eines Zeit-Fixed-Effects-Panel- Modells und eines Fixed-Effects-Poisson-Modells den Zusammenhang zwischen finanziellen Inputs und Forschungsoutputs.
Das Kapitel 4 beschäftigt sich mit dem Niveau der Universitäten und untersucht die Interdependenzstruktur zwischen Drittmittelausgaben, Publikationen, Zitationen und akademischem Alter mit Hilfe eines PVARX-Modells, einer Impulsantwort und einer Zerlegung der Prognosefehlervarianz. Abschließend befasst sich das Kapitel mit den möglichen Implikationen für Politik und Entscheidungsfindung und schlägt Empfehlungen für das universitäre Forschungsmanagement vor.New Public Management helps universities and research institutions to perform in a highly competitive research environment. Decision making in the face of uncertainty, for example distribution of funds for research needs and purposes, urges research policy makers and university managers to understand the relationships between the dimensions of research performance and the resulting or incoming grants. Thus, it is important to accurately reflect the variables of scientific knowledge production on the level of individuals, research groups and universities. Chapter 2 of this thesis introduces an analysis on the level of individuals. The data are taken from the three widely-used ranking systems in the economic and business sciences among German-speaking countries: Handelsblatt (HB), Research Papers in Economics (RePEc, here RP) and Google Scholar (GS). It proposes a framework for collating ranking data for comparison purposes. Chapter 3 provides empirical evidence on the level of research groups using data from a Collaborative Research Center (CRC) on financial inputs and research output from 2005 to 2016. First, suitable performance indicators are discussed. Second, main properties of the data are described using visualization techniques. Finally, the time fixed effects panel data model and the fixed effects Poisson model are used to analyze an interdependency between financial inputs and research outputs. Chapter 4 examines the interdependence structure between third-party expenses (TPE), publications, citations and academic age using university data on individual performance in different scientific areas. A panel vector autoregressive model with exogenous variables (PVARX), impulse response functions and a forecast error variance decomposition help to capture the relationships in the system. To summarize, the chapter addresses the possible implications for policy and decision making and proposes recommendations for university research management.
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Dudokh, Dana. "What factors affect the destination choice of Jordanian tourists?A panel data analysis." Thesis, Högskolan Dalarna, Företagsekonomi, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3725.
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This paper investigates what factors affect the destination choice for Jordanian to 8 countries (Oman, Saudi Arabia, Syria, Tunisia, Yemen, Egypt, Lebanon and Bahrain) using panel data analysis. Number of outbound tourists is represented as dependent variable, which is regressed over five explanatory variables using fixed effect model. The finding of this paper is that tourists from Jordan have weak demand for outbound tourism; Jordanian decision of traveling abroad is determined by the cost of traveling to different places and choosing the cheapest alternative.
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Johnston, Jeremy M. "An activity-based non-linear regression model of Sopite syndrome and its effects on crew performance in high-speed vessel operations." Thesis, Monterey, Calif. : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Mar/09Mar%5FJohnston.pdf.
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Thesis (M.S. in Operations Research)--Naval Postgraduate School, March 2009.Thesis Advisor(s): McCauley, Michael E. "March 2009." Description based on title screen as viewed on April 23, 2009. Author(s) subject terms: Sopite syndrome, motion sickness, wrist actigraphy Includes bibliographical references (p. 77-82). Also available in print.
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Ohinata, Ren Verfasser], Stefan [Akademischer Betreuer] Sperlich, Stephan [Akademischer Betreuer] Klasen, and Thomas [Akademischer Betreuer] [Kneib. "Three Essays on Application of Semiparametric Regression: Partially Linear Mixed Effects Model and Index Model / Ren Ohinata. Gutachter: Stefan Sperlich ; Stephan Klasen ; Thomas Kneib. Betreuer: Stefan Sperlich." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://d-nb.info/1043515593/34.
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Ohinata, Ren [Verfasser], Stefan [Akademischer Betreuer] Sperlich, Stephan Akademischer Betreuer] Klasen, and Thomas [Akademischer Betreuer] [Kneib. "Three Essays on Application of Semiparametric Regression: Partially Linear Mixed Effects Model and Index Model / Ren Ohinata. Gutachter: Stefan Sperlich ; Stephan Klasen ; Thomas Kneib. Betreuer: Stefan Sperlich." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://d-nb.info/1043515593/34.
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Ohinata, Ren [Verfasser], Stefan [Akademischer Betreuer] Sperlich, Stephan [Akademischer Betreuer] Klasen, and Thomas [Akademischer Betreuer] Kneib. "Three Essays on Application of Semiparametric Regression: Partially Linear Mixed Effects Model and Index Model / Ren Ohinata. Gutachter: Stefan Sperlich ; Stephan Klasen ; Thomas Kneib. Betreuer: Stefan Sperlich." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://nbn-resolving.de/urn:nbn:de:gbv:7-webdoc-3831-5.
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Flaum, Shannon Kathleen. "Application of the Regression Discontinuity Technique to the Response to Intervention (RTI) Model of Service Delivery for Determining the Effects of Early Intervention in Reading." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1243532813.
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Akolly, Kokou S. "Looking in the Crystal Ball: Determinants of Excess Return." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/math_theses/87.
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This paper investigates the determinants of excess returns using dividend yields as a proxy in a cross-sectional setting. First, we find that types of industry and the current business cycle are determining factors of returns. Second, our results suggest that dividend yield serves a signaling mechanism indicating “healthiness” of a firm among prospective investors. Third we see that there is a positive relationship between dividend yield and risk, especially in the utility and financial sectors. And finally, using actual excess returns, instead of dividend yield in our model shows that all predictors of dividend yield were also significant predictors of excess returns. This connection between dividend yield and excess returns support our use of dividend yield as a proxy for excess returns.
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Holt, Sheldon D. "Ambient Temperature, Calf Intakes, and Weight Gains on Preweaned Dairy Calves." DigitalCommons@USU, 2014. https://digitalcommons.usu.edu/etd/2324.
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There has been little research conducted on the physiological response of calves to temperatures outside thermal neutrality and its effects on intake and weight gain. The effects of ambient temperature on Holstein dairy calves intakes and weight gain were evaluated over a 12-month period. Ambient temperature was monitored using a weather station located 1.3 kilometers from the Utah State University Caine Dairy. Calf health was monitored daily using the University of Wisconsin-Madison School of Veterinary Medicine scoring criteria. Calves were fed whole milk and free choice calf starter. Weight gain, hip height, starter intake, and weather data (temperature, wind speed, relative humidity, precipitation, and barometric pressure) were averaged for 7-day intervals beginning at birth through 13 weeks of age. A regression model was developed including starter intake, milk intake, hip and wither height, calf heath scores, and weather data with weight gain as the dependent variable for each of the 4 seasons of the year. The fall season (September, October, and November) had a negative impact on calf intake and weight gain (averaging 20 pounds (9.1 kilograms) less at 2 months) than other seasons. Calves raised in the winter months also ate significantly more starter, but had the same weight gain as other seasons. Environmental stress factors impact animal welfare and animal productivity, which in turn impacts the economics of the dairy operation and should also be used in determining husbandry practices.
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Janse, Sarah A. "INFERENCE USING BHATTACHARYYA DISTANCE TO MODEL INTERACTION EFFECTS WHEN THE NUMBER OF PREDICTORS FAR EXCEEDS THE SAMPLE SIZE." UKnowledge, 2017. https://uknowledge.uky.edu/statistics_etds/30.
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In recent years, statistical analyses, algorithms, and modeling of big data have been constrained due to computational complexity. Further, the added complexity of relationships among response and explanatory variables, such as higher-order interaction effects, make identifying predictors using standard statistical techniques difficult. These difficulties are only exacerbated in the case of small sample sizes in some studies. Recent analyses have targeted the identification of interaction effects in big data, but the development of methods to identify higher-order interaction effects has been limited by computational concerns. One recently studied method is the Feasible Solutions Algorithm (FSA), a fast, flexible method that aims to find a set of statistically optimal models via a stochastic search algorithm. Although FSA has shown promise, its current limits include that the user must choose the number of times to run the algorithm. Here, statistical guidance is provided for this number iterations by deriving a lower bound on the probability of obtaining the statistically optimal model in a number of iterations of FSA. Moreover, logistic regression is severely limited when two predictors can perfectly separate the two outcomes. In the case of small sample sizes, this occurs quite often by chance, especially in the case of a large number of predictors. Bhattacharyya distance is proposed as an alternative method to address this limitation. However, little is known about the theoretical properties or distribution of B-distance. Thus, properties and the distribution of this distance measure are derived here. A hypothesis test and confidence interval are developed and tested on both simulated and real data.
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Yaduma, Natina. "Essays on econometric analyses of economic development and effects on health, environmental damage and natural resource depletion." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/essays-on-econometric-analyses-of-economic-development-and-effects-on-health-environmental-damage-and-natural-resource-depletion(e9a56791-6ded-4a32-bac3-5132238d2876).html.
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The main part of this thesis is composed of three separate chapters, each using an innovative approach to analysing externalities from economic activity. The general introduction and overall conclusion sections complete the structure of the thesis. Chapter one examines the value of statistical life, an essential parameter used in ascribing monetary values to the mortality costs of air pollution in health risk analyses. This willingness to pay estimate is virtually non-existent for most developing countries. In the absence of local estimates, two major benefit transfer approaches lend themselves to the estimation of the value of statistical life: the value transfer method and the meta-regression analysis. Using Nigeria as a sample country, we find that the latter method is better tailored than the former for incorporating many characteristics that vary between study sites and policy sites into its benefit transfer application. It is therefore likely to provide more accurate value of statistical life predictions for very low-income countries. Employing the meta-regression method, we find Nigeria’s value of statistical life estimate to be $489,000. Combining this estimate with dose response functions from the epidemiological literature, it follows that if Nigeria had mitigated its 2006 particulate air pollution to the World Health Organisation standards, it could have avoided at least 58,000 premature deaths and recorded an avoided mortality related welfare loss of about $28 billion or 19 percent of the nation’s GDP for that year. The second chapter applies the quantile fixed effects technique in exploring the CO2 environmental Kuznets curve within two groups of economic development (OECD and Non-OECD countries) and six geographical regions – West, East Europe, Latin America, East Asia, West Asia and Africa. A comparison of the findings with those of the conventional fixed effects method reveals that the latter may depict a flawed summary of the prevailing income-emissions nexus depending on the conditional quantile examined. We also extend the Machado and Mata decomposition method to the Kuznets curve framework to explore the most important explanations for the carbon emissions gap between OECD and Non-OECD countries. We find a statistically significant OECD-Non-OECD emissions gap and this contracts as we ascend the emissions distribution. Also, had the Non-OECD group the incomes of the OECD group, the former would pollute 26 to 40 percent more than the latter ceteris paribus. The decomposition further reveals that there are non-income related factors working against the Non-OECD group’s greening. We tentatively conclude that deliberate and systematic mitigation of current CO2 emissions in the Non-OECD group is required. The final chapter employs the Arellano-Bond difference GMM method in investigating the oil curse in OECD and Non-OECD oil exporting countries. Empirical studies investigating the natural resource curse theory mostly employ cross-country and panel regression techniques subject to endogeneity bias. Also, most of these studies employ GDP in its aggregate or per-capita terms as the outcome variable in their analyses. However, the use of GDP measures of income for resource curse investigations does not portray the true incomes of resource intensive economies. Standard national accounts treat natural resource rents as a positive contribution to income without making a corresponding adjustment for the value of depleted natural resource stock. This treatment, inconsistent with green national accounting, leads to a positive bias in the national income computations of resource rich economies. Our paper deviates from most empirical studies in the literature by using the Arellano-Bond difference GMM method. We test the robustness of the curse in the predominantly used measures of national income, GDP, by investigating the theme in genuine income measures of economic output as well. We employ two alternative measures of resource intensity in our explorations: the share of oil rents in GDP and per-capita oil reserves. Our results provide evidence of the curse in Non-OECD countries employing aggregate and per-capita measures of genuine income. On the other hand, we find oil abundance to be a blessing rather than a curse to the OECD countries in our sample.
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Schanzer-Larsen, Arnold. "The effects of immigration on the income of native born workers: Evidence from Sweden." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54685.
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Abstract Course: NAA305 Bachelor Thesis in Economics 15 ECTS University: Mälardalen University, School of Business, Society and Engineering, Västerås Title: The effects of immigration on the income of native-born workers: Evidence from Sweden Author: Arnold Schanzer-Larsen Supervisor: Johan Lindén Problem: Sweden has experienced a lot of immigration, and the phenomenon has received a great deal of attention in the public and political debate. There is, among other things, fear that immigration could be harmful for the labor market outcome of the receiving country. Researchers from a variety of countries have tried to address this issue by estimating the effect of immigration on the native wage of the receiving country. The results have varied strongly and no universal conclusion can be drawn. For what can be said about Sweden, there is no paper (of our knowledge) that has done any similar estimates. For that reason, it is of great importance that there is some research which could bring empirical evidence and shed light on the debate. Purpose of the Research: The aim of the thesis is to quantitatively measure immigrations effect on the wage of native workers in Sweden. Methodology: Conducting a panel study, observation of the average native income from 290 municipalities over 2011-2019 was collected. The effect was estimated using OLS regression technique and a fixed effect model. Conclusion: From a 10% increase in the share of foreign-born within a municipality, led on average to a 2.89% increase in the native average income in that municipality. Keywords: Immigration, Income, Wage, Unemployment rate, Panel study, Fixed effect model, Native, Labor market, The equilibrium model, Human capital, Skill-composition
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Krueger, Kirk L. "Effects of Sampling Sufficiency and Model Selection on Predicting the Occurrence of Stream Fish Species at Large Spatial Extents." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/26214.
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Knowledge of species occurrence is a prerequisite for efficient and effective conservation and management. Unfortunately, knowledge of species occurrence is usually insufficient, so models that use environmental predictors and species occurrence records are used to predict species occurrence. Predicting the occurrence of stream fishes is often difficult because sampling data insufficiently describe species occurrence and important environmental conditions and predictive models insufficiently describe relations between species and environmental conditions. This dissertation 1) examines the sufficiency of fish species occurrence records at four spatial extents in Virginia, 2) compares modeling methods for predicting stream fish occurrence, and 3) assesses relations between species traits and model prediction characteristics.
The sufficiency of sampling is infrequently addressed at the large spatial extents at which many management and conservation actions take place. In the first chapter of this dissertation I examine factors that determine the sufficiency of sampling to describe stream fish species richness at four spatial extents across Virginia using sampling simulations. Few regions of Virginia are sufficiently sampled, portending difficulty in accurately predicting fish species occurrence in most regions. The sufficient number of samples is often large and varies among regions and spatial scales, but it can be substantially reduced by reducing errors of sampling omission and increasing the spatial coverage of samples.
Many methods are used to predict species occurrence. In the second chapter of this dissertation I compare the accuracy of the predictions of occurrence of seven species in each of three regions using linear discriminant function, generalized linear, classification tree, and artificial neural network statistical models. I also assess the efficacy of stream classification methods for predicting species occurrence. No modeling method proved distinctly superior. Species occurrence data and predictor data quality and quantity limited the success of predictions of stream fish occurrence for all methods. How predictive models are built and applied may be more important than the statistical method used.
The accuracy, generality (transferability), and resolution of predictions of species occurrence vary among species. The ability to anticipate and understand variation in prediction characteristics among species can facilitate the proper application of predictions of species occurrence. In the third chapter of this dissertation I describe some conservation implications of relations between predicted occurrence characteristics and species traits for fishes in the upper Tennessee River drainage. Usually weak relations and variation in the strength and direction of relations among families precludes the accurate prediction of predicted occurrence characteristics. Most predictions of species occurrence have insufficient accuracy and resolution to guide conservation decisions at fine spatial grains. Comparison of my results with alternative model predictions and the results of many models described in peer-reviewed journals suggests that this is a common problem. Predictions of species occurrence should be rigorously assessed and cautiously applied to conservation problems. Collectively, the three chapters of this dissertation demonstrate some important limitations of models that are used to predict species occurrence. Model predictions of species occurrence are often used in lieu of sufficient species occurrence data. However, regardless of the method used to predict species occurrence most predictions have relatively low accuracy, generality and resolution. Model predictions of species occurrence can facilitate management and conservation, but they should be rigorously assessed and applied cautiously.Ph. D.
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Sax, Kaijser Per. "Tobin’s Q theory and regional housing investment : Empirical analysis on Swedish data." Thesis, Uppsala universitet, Nationalekonomiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-226661.
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This thesis investigates the relationship between Tobin’s Q and regional housing investment in Sweden for the time period of 1998-2012. The relationship is tested through estimation of two models for time-series analysis, a vector error correction model (VECM) and an autoregressive distributed lag (ARDL) model. Depending on which model that is used, I find some evidence of positive correlation between Tobin’s Q and regional housing investment in the long run while the short run dynamics of investment does not seem to be explained by Tobin’s Q. By transforming the regional data into a panel data set and running a fixed effects model, I examine the gain in explanatory power of Tobin’s Q from using disaggregated data rather than aggregated. My findings suggest that using disaggregated data improves the explanatory power of Tobin’s Q on investment. However, the Granger Causality test indicates two-way causality between Tobin’s Q and investment, causing endogeneity problem in the estimated equations.
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Gallina, Andrea. "Entrepreneurship and election timing." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20906.
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Mestrado em FinançasO objetivo desta dissertação é examinar o impacto das eleições políticas na atividade empresarial. Para isso, coletamos dados de 16 países por um número variado de anos (dentro de uma faixa de 16 a 21 anos por país) para construir uma amostra de 3.056 observações. Os nossos dados vêm do Global Entrepreneurship Monitor Adult Population Survey e do Nordsieck, W. (1997) Parties and Elections in Europe, obtido em http://www.parties-and-elections.eu. Em seguida, estimamos um pooled Ordinary Least Squares (OLS) e um modelo de efeito fixo para ver como o momento das eleições afeta os níveis empresariais no país. Além disso, examinamos os efeitos de uma vitória inesperada. Os resultados obtidos sugerem que a Atividade Empreendedora Total aumenta durante um ano eleitoral e diminui no ano anterior, enquanto não há evidência estatística de qualquer efeito no ano seguinte.
Purpose of this dissertation is to examine the impact of political elections on entrepreneurial activity. In order to do so, we collect data from 17 countries for a varied number of years (within a range of 16 to 21 years per country) to build a sample of 3,056 observations. Our data comes from the Global Entrepreneurship Monitor Adult Population Survey and from Nordsieck, W. (1997) Parties and Elections in Europe retrieved from http://www.parties-and-elections.eu . We then estimate a pooled Ordinary Least Squares (OLS) and a fixed effect model to see how the elections timing affect entrepreneurial levels in the country. In addition, we examine the effects of an unexpected victory, Results obtained suggest that Total Entrepreneurial Activity increases during an election year and decreases the year before, while there is no statistical evidence of any effect in the year after.
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Lawal, Fadekemi. "Impact of Corruption on Economic Growth : A panel data study of selected African countries." Thesis, Högskolan Dalarna, Nationalekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:du-30218.
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African countries have over the last few decades, experienced a thorny path towards sustained economic growth. Quite a number of researchers have opined that a major factor responsible for their stunted growth path is the prevalence of corruption in the governments of many African countries. However, a group of scholars, called revisionists, have suggested that corruption actually acts as grease in the wheel that ensures the smooth running of an economy, by providing a mechanism to evade inefficient bureaucratic procedures and allow more equitable representation of minority members of the society. With the increasing exposure of African economies to the international community, there is a need to examine the obtainable evidence in relation to corruption and economic growth in African countries. This thesis, therefore, aims to establish the nature of the relationship between corruption and economic growth in the selected African countries. The growth rate of gross domestic product per capita is used to represent the variable, economic growth. The study employs the use of panel data fixed effects and random effect estimation techniques, across 18 countries, over the period of 1997 – 2016. The results show that corruption has a positive relationship with economic growth in the selected African countries. This is in line with the grease in the wheel argument for corruption proposed by revisionists. The results also indicate that corruption has a moderately significant impact on economic growth at 10% level of significance. The literature review suggests that corruption affects economic growth directly and indirectly through mechanisms such as investment (private and public), human capital, openness, and institutional mechanisms, among others.
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Veatch, William Curtis. "Quantifying the Effects of Forest Canopy Cover on Net Snow Accumulation at a Continental, Mid-Latitude Site, Valles Caldera National Preserve, NM, USA." Thesis, The University of Arizona, 2008. http://hdl.handle.net/10150/193352.
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Although forest properties are known to influence snowpack accumulation and spring runoff, the processes underlying the impacts of forest canopy cover on the input of snowmelt to the catchment remain poorly characterized. In this study I show that throughfall and canopy shading can combine to result in maximal snowpacks in forests of moderate canopy density. Snow depth and density data taken shortly before spring melt in the Jemez Mountains of New Mexico show strong correlation between forest canopy density and snow water equivalent, with maximal snow accumulation in forests with density between 25 and 45%. Forest edges are also shown to be highly influential on local snow depth variability, with shaded open areas holding significantly deeper snow than either unshaded open or deep forest areas. These results are broadly applicable in improving estimates of water resource availability, predicting the ecohydrological implications of vegetation change, and informing integrated water resources management.
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Bugana, Marco. "Mathematical Modeling to Investigate Antiarrhythmic Drug Side Effects: Rate-Dependence Role in Ionic Currents and Action Potentials Shape in the O’Hara Model." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3529/.
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Sudden cardiac death due to ventricular arrhythmia is one of the leading causes of mortality in the world. In the last decades, it has proven that anti-arrhythmic drugs, which prolong the refractory period by means of prolongation of the cardiac action potential duration (APD), play a good role in preventing of relevant human arrhythmias. However, it has long been observed that the “class III antiarrhythmic effect” diminish at faster heart rates and that this phenomenon represent a big weakness, since it is the precise situation when arrhythmias are most prone to occur.
It is well known that mathematical modeling is a useful tool for investigating cardiac cell behavior. In the last 60 years, a multitude of cardiac models has been created; from the pioneering work of Hodgkin and Huxley (1952), who first described the ionic currents of the squid giant axon quantitatively, mathematical modeling has made great strides. The O’Hara model, that I employed in this research work, is one of the modern computational models of ventricular myocyte, a new generation began in 1991 with ventricular cell model by Noble et al. Successful of these models is that you can generate novel predictions, suggest experiments and provide a quantitative understanding of underlying mechanism. Obviously, the drawback is that they remain simple models, they don’t represent the real system.
The overall goal of this research is to give an additional tool, through mathematical modeling, to understand the behavior of the main ionic currents involved during the action potential (AP), especially underlining the differences between slower and faster heart rates. In particular to evaluate the rate-dependence role on the action potential duration, to implement a new method for interpreting ionic currents behavior after a perturbation effect and to verify the validity of the work proposed by Antonio Zaza using an injected current as a perturbing effect.
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HE, Ran. "Carry-over and interaction effects of different hand-milking techniques and milkers on milk." Thesis, Uppsala universitet, Statistiska institutionen, 1986. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154641.
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The main idea of this thesis is studying the importance of the carry-over effects and interaction effects in statistical models. To investigate it, a hand-milking experiment in Burkina Faso was studied. In many no electricity access countries, such as Burkina Faso, the amount of milk and milk compositions are still highly relying on hand-milking techniques and milkers. Moreover, the time effects also plays a important role in stockbreeding system. Therefore, falling all effects, carry-over effects and interaction effects into a linear mixed effects model, it is concluded that the carry-over effects of milker and hand-milking techniques cannot be neglected, and the interaction effects among hand-milking techniques, different milkers, days and periods can be substantial.
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Abreu, Daniel Sebastião. "Threshold effects in the wage Phillips curve." Master's thesis, Instituto Superior de Economia e Gestão, 2018. http://hdl.handle.net/10400.5/16573.
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Mestrado em Econometria Aplicada e PrevisãoNeste trabalho, avaliamos a capacidade da curva de Phillips salarial Neo-Keynesiana (CPSNK) proposta por Galí (2011) para descrever a inflação dos salários nos EUA durante o período 1965-2018. De forma a estudar esta relação, empregamos um modelo de regressão de limiar que nos permite examinar a existência de não-linearidades. Os nossos resultados sugerem que a taxa de inflação salarial é bem descrita por um modelo de limiar com 3 regimes em que a variável de limiar é a taxa de desemprego. As estimativas para os parâmetros de limiar dividem a CPSNK em regimes consistentes com períodos de recessão profunda, de flutuações moderadas do ciclo económico e de crescimento prolongado. Encontramos evidência empírica consistente com a relação negativa entre a inflação salarial e a taxa de desemprego prevista pela CPSNK quando a taxa de desemprego está entre os limites de 5.69% e 7.63%. Quando a taxa de desemprego está fora deste intervalo, esta relação parece desaparecer. Para avaliar a robustez das nossas estimativas, incorporamos a possível endogeneidade dos regressores e da variável de limiar ao estimar o modelo de regressão limiar estrutural proposto por Kourtellos et al. (2016). Neste contexto, concluímos que os nossos resultados não são muito diferentes quando permitimos que os regressores sejam endógenos. Por outro lado, as estimativas dos coeficientes de limiar obtidas quando a variável de limiar é considerada como endógena implicam uma redução significativa do número de observações no segundo regime.
The main purpose of this work is to evaluate the ability of the New Keynesian wage Phillips curve (NKWPC), proposed by Galí (2011), to describe U.S. wage inflation dynamics over the 1965-2018 period. To study this relationship, a threshold regression model that allows assessing the existence of regime-switching nonlinearity is employed. Our results suggest that wage inflation dynamics are well described by a 3-regime threshold model where the best threshold variable is the current unemployment rate. The estimated thresholds split the NKWPC into regimes consistent with periods of deep recessions, moderate business cycle fluctuations and prolonged expansions. We find evidence that the negative relationship between wage inflation and unemployment implied by the NKWPC holds when unemployment is between the thresholds 5.69% and 7.63%; when unemployment is outside this band the relationship seems to break down. To assess the robustness of our estimates, we account for possible endogeneity of the regressors and the threshold variable by using the structural threshold model proposed by Kourtellos et al. (2016). In this setting, we conclude that our baseline results are not very sensitive to endogeneity affecting the regressors. In contrast, the threshold estimates obtained when the threshold variable is considered as endogenous yield a substantial reduction in the number of observations in the second regime.
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Berglund, Axel, and Carl Fransson. "Sovereign Credit Rating effects on equity markets: Applied on US Data." Thesis, Högskolan i Halmstad, Sektionen för hälsa och samhälle (HOS), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-18959.
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This paper is a study on how U.S stock market reacts on sovereign credit rating announcements, and if there is a significant difference between low or high debt firms. We have used an event study based on historical stock prices from 30 companies, 15 with high debt and 15 with low debt. All companies are taken from the S&P`s 500 index which we also use as a market index. We use a regression model with 10 % significance level to see if there is a significant impact on high debt firms. Our result shows that the market will be affected by the downgrade. We also conclude that there was a significant negative impact on the high debt firms.
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Margevicius, Seunghee P. "Modeling of High-Dimensional Clinical Longitudinal Oxygenation Data from Retinopathy of Prematurity." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1523022165691473.
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Fischer, Manfred M., Nico Pintar, and Benedikt Sargant. "Austrian Outbound Foreign Direct Investment in Europe: A spatial econometric study." The Romanian Regional Science Association, 2016. http://epub.wu.ac.at/5118/1/V1011.MMFischer.pdf.
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This paper focuses on Austrian outbound foreign direct investment (FDI, measured by sales of Austrian affiliates abroad) in Europe over the period 2009-2013, using a spatial Durbin panel data model specification with fixed effects, and a spatial weight matrix based on the first-order contiguity relationship of the countries and normalised by its largest eigenvalue. Third-country effects essentially enter the empirical analysis in two major ways: first, by the endogenous spatial lag on FDI (measured by FDI into markets nearby the host country), and, second, by including an exogenous market potential variable that measures the size of markets nearby the FDI host country in terms of gross domestic product. The question whether the empirical result is compatible with horizontal, vertical, export-platform or complex vertical FDI then depends on the sign and significance levels of both the coefficient of the spatial lag on FDI and the direct impact estimate of the market potential variable.
The paper yields robust results that provide significant empirical evidence for horizontal FDI as the main driver of Austrian outbound FDI in Europe. This result is strengthened by the indirect impact estimate of the mark
et potential variable indicating that spatial spillovers do not matter. (authors' abstract)