Longitudinal Data Analysis and Time Series Commons^™

Using Spatiotemporal Methods To Fill Gaps In Energy Usage Interval Data, Kristin K. Graves

Theses and Dissertations

Researchers analyzing spatiotemporal or panel data, which varies both in location and over time, often find that their data has holes or gaps. This thesis explores alternative methods for filling those gaps and also suggests a set of techniques for evaluating those gap-filling methods to determine which works best.

The Effects Of Quantitative Easing In The United States: Implications For Future Central Bank Policy Makers, Matthew Q. Rubino

Senior Honors Projects, 2010-2019

The purpose of this thesis is to examine the effects of the Federal Reserve’s recent bond buying programs, specifically Quantitative Easing 1, Quantitative Easing 2, Operation Twist (or the Fed’s Maturity Extension Program), and Quantitative Easing 3. In this study, I provide a picture of the economic landscape leading up to the deployment of the programs, an overview of quantitative easing including each program’s respective objectives, and how and why the Fed decided to implement the programs. Using empirical analysis, I measure each program’s effectiveness by applying four models including a yield curve model, an inflation model, a money supply …

A New Approach To Modeling Multivariate Time Series On Multiple Temporal Scales, Tucker Zeleny

Department of Statistics: Dissertations, Theses, and Student Research

In certain situations, observations are collected on a multivariate time series at a certain temporal scale. However, there may also exist underlying time series behavior on a larger temporal scale that is of interest. Often times, identifying the behavior of the data over the course of the larger scale is the key objective. Because this large scale trend is not being directly observed, describing the trends of the data on this scale can be more difficult. To further complicate matters, the observed data on the smaller time scale may be unevenly spaced from one larger scale time point to the …

Estimation Of Heterogeneous Panels With Structural Breaks, Badi Baltagi

Center for Policy Research

This paper extends Pesaran's (2006) work on common correlated effects (CCE) estimators for large heterogeneous panels with a general multifactor error structure by allowing for unknown common structural breaks. Structural breaks due to new policy implementation or major technological shocks, are more likely to occur over a longer time span. Consequently, ignoring structural breaks may lead to inconsistent estimation and invalid inference. We propose a general framework that includes heterogeneous panel data models and structural break models as special cases. The least squares method proposed by Bai (1997a, 2010) is applied to estimate the common change points, and the consistency …

Key Factors Driving Personnel Downsizing In Multinational Military Organizations, Ilksen Gorkem, Resit Unal, Pilar Pazos

Engineering Management & Systems Engineering Faculty Publications

Although downsizing has long been a topic of research in traditional organizations, there are very few studies of this phenomenon in military contexts. As a result, we have little understanding of the key factors that drive personnel downsizing in military setting. This study contributes to our understanding of key factors that drive personnel downsizing in military organizations and whether those factors may differ across NATO nations’ cultural clusters. The theoretical framework for this study was built from studies in non-military contexts and adapted to fit the military environment.

This research relies on historical data from one of the largest multinational …

Nonlinear Hierarchical Models For Longitudinal Experimental Infection Studies, Michael David Singleton

Theses and Dissertations--Epidemiology and Biostatistics

Experimental infection (EI) studies, involving the intentional inoculation of animal or human subjects with an infectious agent under controlled conditions, have a long history in infectious disease research. Longitudinal infection response data often arise in EI studies designed to demonstrate vaccine efficacy, explore disease etiology, pathogenesis and transmission, or understand the host immune response to infection. Viral loads, antibody titers, symptom scores and body temperature are a few of the outcome variables commonly studied. Longitudinal EI data are inherently nonlinear, often with single-peaked response trajectories with a common pre- and post-infection baseline. Such data are frequently analyzed with statistical methods …

Using Time Series Models For Defect Prediction In Software Release Planning, James W. Tunnell

All Master's Theses

To produce a high-quality software release, sufficient time should be allowed for testing and fixing defects. Otherwise, there is a risk of slip in the development schedule and/or software quality. A time series model is used to predict the number of bugs created during development. The model depends on the previous numbers of bugs created. The model also depends, in an exogenous manner, on the previous numbers of new features resolved and improvements resolved. This model structure would allow hypothetical release plans to be compared by assessing their predicted impact on testing and defect- fixing time. The VARX time series …

Estimation And Identification Of Change Points In Panel Models With Nonstationary Or Stationary Regressors And Error Term, Badi H. Baltagi, Chihwa Kao, Long Liu

Center for Policy Research

This paper studies the estimation of change point in panel models. We extend Bai (2010) and Feng, Kao and Lazarová (2009) to the case of stationary or nonstationary regressors and error term, and whether the change point is present or not. We prove consistency and derive the asymptotic distributions of the Ordinary Least Squares (OLS) and First Difference (FD) estimators. We find that the FD estimator is robust for all cases considered.

Investigating Use Of Beta Coefficients For Stock Predictions, Jeffrey Swensen

Williams Honors College, Honors Research Projects

By using previous stock market data, investors can get a good sense of how to invest for the future. A common way to determine what stocks are riskier than others is by using the beta coefficient. This paper investigates the relationship between the overall S&P 500 market and certain individual stocks to see if we can use past stock return data to predict the future riskiness of certain stocks. Correlation between the individual stocks and the S&P 500 will allow us to determine the relationship between the two. Finding the beta coefficients for the individual stock market will allow investors …

Ranking Interesting Changes In Correlation Coefficient Matrix Results From Varying Data Partitions In Causal Graphic Modeling, Yesica Daniela Bravo Gonzalez

Master's Theses

Problem

In life we need to compare situations in order to select the best solution. The study in this paper is about analyzing data (variables), which is also called data mining. There are situations where it is not enough to compare variables among themselves at one specific moment. Sometimes it is necessary to compare the behavior of variables at different periods of time and know how they behave at different times in order to select the best arrangements for any situation.

Method

To find correlation among variables, traffic intersections were simulated so they could be compared, since the correlation coefficient …

Online Detection Of Outliers And Structural Breaks Using Sequential Monte Carlo Methods, Richard Wanjohi

Graduate Theses and Dissertations

Outliers and structural breaks occur quite frequently in time series data. Whereas outliers often contain valuable information

about the process under study, they are known to have serious negative impact on statistical data analysis. Most obvious effect is model misspecification and biased parameter estimation which results in wrong conclusions and inaccurate predictions. Structural time series consist of underlying features such as level, slope, cycles or seasonal components. Structural breaks are permanent disruptions of one or more of these components and might be a signal of serious changes in the observed process.

Detecting outliers and estimating the location of structural breaks …

Spatiotemporal Crime Analysis, James Q. Tay, Abish Malik, Sherry Towers, David Ebert

The Summer Undergraduate Research Fellowship (SURF) Symposium

There has been a rise in the use of visual analytic techniques to create interactive predictive environments in a range of different applications. These tools help the user sift through massive amounts of data, presenting most useful results in a visual context and enabling the person to rapidly form proactive strategies. In this paper, we present one such visual analytic environment that uses historical crime data to predict future occurrences of crimes, both geographically and temporally. Due to the complexity of this analysis, it is necessary to find an appropriate statistical method for correlative analysis of spatiotemporal data, as well …

Genetic Predictors Of Metabolic Side Effects Of Diuretic Therapy, Jorge L. Del Aguila

Dissertations and Theses (Open Access)

Thiazide diuretics are a recommended first-line monotherapy for hypertension (i.e.SBP>140 mmHg or DBP>90 mmHg). Even so, diuretics are associated with adverse metabolic side effects, such as hyperlipidemia, hyperglycemia and hypokalemia which increase the risk of developing type II diabetes. This thesis used three analytical strategies to identify and quantify genetic factors that contribute to the development of adverse metabolic effects due to thiazide diuretic treatment. I performed a genome-wide association study (GWAS) and meta-analysis of the change in fasting plasma glucose and triglycerides in response to HCTZ from two different clinical trials: the Pharmacogenomic Evaluation of Antihypertensive Responses …

High Frequency Data: Modeling Durations Via The Acd And Log Acd Models, Lilian Cheung

Honors Scholar Theses

This thesis proposes a method of finding initial parameter estimates in the Log ACD₁ model for use in recursive estimation. The recursive estimating equations method is applied to the Log ACD₁ model to find recursive estimates for the unknown parameters in the model. A literature review is provided on the ACD and Log ACD models, and on the theory of estimating equations. Monte Carlo simulations indicate that the proposed method of finding initial parameter estimates is viable. The parameter estimation process is demonstrated by fitting an ACD model and a Log ACD model to a set of IBM …

Time Series Decomposition Using Singular Spectrum Analysis, Cheng Deng

Electronic Theses and Dissertations

Singular Spectrum Analysis (SSA) is a method for decomposing and forecasting time series that recently has had major developments but it is not yet routinely included in introductory time series courses. An international conference on the topic was held in Beijing in 2012. The basic SSA method decomposes a time series into trend, seasonal component and noise. However there are other more advanced extensions and applications of the method such as change-point detection or the treatment of multivariate time series. The purpose of this work is to understand the basic SSA method through its application to the monthly average sea …

A Stochastic Parameter Regression Model For Long Memory Time Series, Rose Marie Ocker

Boise State University Theses and Dissertations

In a complex and dynamic world, the assumption that relationships in a system remain constant is not necessarily a well-founded one. Allowing for time-varying parameters in a regression model has become a popular technique, but the best way to estimate the parameters of the time-varying model is still in discussion. These parameters can be autocorrelated with their past for a long time (long memory), but most of the existing models for parameters are of the short memory type, leaving the error process to account for any long memory behavior in the response variable. As an alternative, we propose a long …

Mediation Analysis With Time-Varying Exposures And Mediators, Tyler J. Vanderweele, Eric Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

In this paper we consider mediation analysis when exposures and mediators vary over time. We give non-parametric identification results, discuss parametric implementation, and also provide a weighting approach to direct and indirect effects based on combining the results of two marginal structural models. We also discuss how our results give rise to a causal interpretation of the effect estimates produced from longitudinal structural equation models. When there are no time-varying confounders affected by prior exposure and mediator values, identification of direct and indirect effects is achieved by a longitudinal version of Pearl's mediation formula. When there are time-varying confounders affected …

Set-Based Tests For Genetic Association In Longitudinal Studies, Zihuai He, Min Zhang, Seunggeun Lee, Jennifer A. Smith, Xiuqing Guo, Walter Palmas, Sharon L.R. Kardia, Ana V. Diez Roux, Bhramar Mukherjee

The University of Michigan Department of Biostatistics Working Paper Series

Genetic association studies with longitudinal markers of chronic diseases (e.g., blood pressure, body mass index) provide a valuable opportunity to explore how genetic variants affect traits over time by utilizing the full trajectory of longitudinal outcomes. Since these traits are likely influenced by the joint effect of multiple variants in a gene, a joint analysis of these variants considering linkage disequilibrium (LD) may help to explain additional phenotypic variation. In this article, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set …

Natural Phenomena As Potential Influence On Social And Political Behavior: The Earth’S Magnetic Field, Jackie R. East

Theses and Dissertations--Political Science

Researchers use natural phenomena in a number of disciplines to help explain human behavioral outcomes. Research regarding the potential effects of magnetic fields on animal and human behavior indicates that fields could influence outcomes of interest to social scientists. Tests so far have been limited in scope. This work is a preliminary evaluation of whether the earth’s magnetic field influences human behavior it examines the baseline relationship exhibited between geomagnetic readings and a host of social and political outcomes. The emphasis on breadth of topical coverage in these statistical trials, rather than on depth of development for any one model, …

Modelling And Analysis On Noisy Financial Time Series, Jinsong Leng

Research outputs 2014 to 2021

Building the prediction model(s) from the historical time series has attracted many researchers in last few decades. For example, the traders of hedge funds and experts in agriculture are demanding the precise models to make the prediction of the possible trends and cycles. Even though many statistical or machine learning (ML) models have been proposed, however, there are no universal solutions available to resolve such particular prob-lem. In this paper, the powerful forward-backward non-linear filter and wavelet-based denoising method are introduced to remove the high level of noise embedded in financial time series. With the filtered time series, the statistical …