Browsing by Author "Ensor, Katherine"
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Item Approximate dynamic factor models for mixed frequency data(2015-10-15) Zhao, Xin; Ensor, Katherine; Kimmel, Marek; Sizova, NataliaTime series observed at different temporal scales cannot be simultaneously analyzed by traditional multivariate time series methods. Adjustments must be made to address issues of asynchronous observations. For example, many macroeconomic time series are published quarterly and other price series are published monthly or daily. Common solutions to the analysis of asynchronous time series include data aggregation, mixed frequency vector autoregressive models, and factor models. In this research, I set up a systematic approach to the analysis of asynchronous multivariate time series based on an approximate dynamic factor model. The methodology treats observations of various temporal frequencies as contemporaneous series. A two-step model estimation and identification scheme is proposed. This method allows explicit structural restrictions that account for appropriate temporal ordering of the mixed frequency data. The methodology consistently estimates the dynamic factors, however, no prior knowledge on the factors is required. To ensure a computationally efficient robust algorithm and model specification, I make use of modern penalized likelihood methodologies. The fitted model captures the effects of temporal relationships across the asynchronous time series in an interpretable manner. The methodology is studied through simulation and applied to several examples. The simulations and examples demonstrate good performance in model specification, estimation and out-of-sample forecasting.Item Covariance Estimation in Dynamic Portfolio Optimization: A Realized Single Factor Model*(SSRN, 2009) Kyj, Lada; Ostdiek, Barbara; Ensor, KatherineRealized covariance estimation for large dimension problems is little explored and poses challenges in terms of computational burden and estimation error. In a global minimum volatility setting, we investigate the performance of covariance conditioning techniques applied to the realized covariance matrices of the 30 DJIA stocks. We find that not only is matrix conditioning necessary to deliver the benefits of high frequency data, but a single factor model, with a smoothed covariance estimate, outperforms the fully estimated realized covariance in one-step ahead forecasts. Furthermore, a mixed-frequency single-factor model - with factor coefficients estimated using low-frequency data and variances estimated using high-frequency data performs better than the realized single-factor estimator. The mixed-frequency model is not only parsimonious but it also avoids estimation of high-frequency covariances, an attractive feature for less frequently traded assets. Volatility dimension curves reveal that it is difficult to distinguish among estimators at low portfolio dimensions, but for well-conditioned estimators the performance gain relative to the benchmark 1/N portfolio increases with N.Item Impact of News on Crude Oil Futures(2017-04-21) Han, Yu; Ensor, Katherine; Ostdiek, Barbara; Turnbull, StuartCrude oil futures are worlds the most actively traded commodity futures, with more than 3 billion barrels per year in open interest. In this thesis we use related news to model the price dynamics of oil futures. We examine the empirical patterns of oil market news data processed by Thompson Reuters News Analytics, plus the intraday trading data of the WTI futures price traded on NYMEX. Then we build a three factor stochastic model for futures prices on the whole curve, using interest rate, convenience yield and spot price. The Kalman filter was used to obtain quasi-maximum likelihood estimators. We found that news can significantly explain the price movements and volatility clustering, as well as its skewness and kurtosis. We also found that negative news has an higher explanatory power of price dynamics than positive news, indicating an asymmetrical behavior of information with different tones.Item Modeling SPX Volatility to Improve Options Pricing(Rice University, 2021) Aiman, Jared; Iglesias, Vicente; Sarkar, Sumit; Ensor, Katherine; Dobelman, John A.In this project, we develop a model to predict future stock market volatility and facilitate more accurate options pricing. The Black Scholes model gives an expected premium for an options contract; however, it uses an unknown fixed parameter referred to as volatility. We advance this by using a modified Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH) model that uses previous returns, as well as the market’s expectation of future volatility, to better predict future volatility. Additionally, we apply an Autoregressive Moving Average (ARMA) model to predict the value of future stock prices. We find that our model is able to model volatility better than using either the market volatility or a traditional GJR-GARCH model alone. This is particularly true due to our model’s ability to capture the dependence between the S&P 500 returns and the changes in the market’s expectation of volatility.Item Predicting Student Loan Debt: A Hierarchical Time Series Analysis(Rice University, 2021) Elsesser, George; Ensor, KatherineIn recent years, and especially in response to the Covid-19 pandemic, much attention has been brought to the issue of rapidly increasing student debt. Yet in the field of time series analysis, there is a dearth of studies examining trends in student loan debt. This is likely due to the impression of simple, yet steep, linear increase in student loan debt over the last decade. However, trends in this type of debt are much more complicated when a complete picture of the hierarchical nature of this data is considered. One objective of this project is to generate accurate forecasts with the impact of Covid-19 in mind not only for outstanding student loan debt, but also for sub-categories of this value based on loan status: such as loans in default or loans in repayment. To facilitate this, traditional hierarchical forecasting methods were compared to newer methods, namely MinT and its recent adaptations. Our findings indicate that MinT forecast reconciliation with the use of structural scaling results in the mostaccurate forecasts across the aggregation structure. Although not the main focus of this study, the second-level forecasts indicate a forecasted 3 1% increase in default rates between the first quarter of 2020 the second quarter of 2022 and a 12% decrease in dollars outstanding for enrolled students during the same time period.Item Racial residential segregation shapes the relationship between early childhood lead exposure and fourth-grade standardized test scores(National Academy of Sciences, 2022) Bravo, Mercedes A.; Zephyr, Dominique; Kowal, Daniel; Ensor, Katherine; Miranda, Marie LynnRacial/ethnic disparities in academic performance may result from a confluence of adverse exposures that arise from structural racism and accrue to specific subpopulations. This study investigates childhood lead exposure, racial residential segregation, and early educational outcomes. Geocoded North Carolina birth data is linked to blood lead surveillance data and fourth-grade standardized test scores (n = 25,699). We constructed a census tract-level measure of racial isolation (RI) of the non-Hispanic Black (NHB) population. We fit generalized additive models of reading and mathematics test scores regressed on individual-level blood lead level (BLL) and neighborhood RI of NHB (RINHB). Models included an interaction term between BLL and RINHB. BLL and RINHB were associated with lower reading scores; among NHB children, an interaction was observed between BLL and RINHB. Reading scores for NHB children with BLLs of 1 to 3 µg/dL were similar across the range of RINHB values. For NHB children with BLLs of 4 µg/dL, reading scores were similar to those of NHB children with BLLs of 1 to 3 µg/dL at lower RINHB values (less racial isolation/segregation). At higher RINHB levels (greater racial isolation/segregation), children with BLLs of 4 µg/dL had lower reading scores than children with BLLs of 1 to 3 µg/dL. This pattern becomes more marked at higher BLLs. Higher BLL was associated with lower mathematics test scores among NHB and non-Hispanic White (NHW) children, but there was no evidence of an interaction. In conclusion, NHB children with high BLLs residing in high RINHB neighborhoods had worse reading scores.Item SIBaR: a new method for background quantification and removal from mobile air pollution measurements(European Geosciences Union, 2021) Actkinson, Blake; Ensor, Katherine; Griffin, Robert J.Mobile monitoring is becoming increasingly popular for characterizing air pollution on fine spatial scales. In identifying local source contributions to measured pollutant concentrations, the detection and quantification of background are key steps in many mobile monitoring studies, but the methodology to do so requires further development to improve replicability. Here we discuss a new method for quantifying and removing background in mobile monitoring studies, State-Informed Background Removal (SIBaR). The method employs hidden Markov models (HMMs), a popular modeling technique that detects regime changes in time series. We discuss the development of SIBaR and assess its performance on an external dataset. We find 83 % agreement between the predictions made by SIBaR and the predetermined allocation of background and non-background data points. We then assess its application to a dataset collected in Houston by mapping the fraction of points designated as background and comparing source contributions to those derived using other published background detection and removal techniques. The presented results suggest that the SIBaR-modeled source contributions contain source influences left undetected by other techniques, but that they are prone to unrealistic source contribution estimates when they extrapolate. Results suggest that SIBaR could serve as a framework for improved background quantification and removal in future mobile monitoring studies while ensuring that cases of extrapolation are appropriately addressed.Item Spatial Variability in Relationships between Early Childhood Lead Exposure and Standardized Test Scores in Fourth Grade North Carolina Public School Students (2013–2016)(National Institute of Environmental Health Sciences, National Institutes of Health, 2024) Bravo, Mercedes A.; Kowal, Daniel R.; Zephyr, Dominique; Feldman, Joseph; Ensor, Katherine; Miranda, Marie LynnBackground:Exposure to lead during childhood is detrimental to children’s health. The extent to which the association between lead exposure and elementary school academic outcomes varies across geography is not known.Objective:Estimate associations between blood lead levels (BLLs) and fourth grade standardized test scores in reading and mathematics in North Carolina using models that allow associations between BLL and test scores to vary spatially across communities.Methods:We link geocoded, individual-level, standardized test score data for North Carolina public school students in fourth grade (2013–2016) with detailed birth records and blood lead testing data retrieved from the North Carolina childhood blood lead state registry on samples typically collected at 1–6 y of age. BLLs were categorized as: 1μg/dL (reference), 2μg/dL, 3–4μg/dL and ≥5μg/dL. We then fit spatially varying coefficient models that incorporate information sharing (smoothness), across neighboring communities via a Gaussian Markov random field to provide a global estimate of the association between BLL and test scores, as well as census tract–specific estimates (i.e., spatial coefficients). Models adjusted for maternal- and child-level covariates and were fit separately for reading and math.Results:The average BLL across the 91,706 individuals in the analysis dataset was 2.84μg/dL. Individuals were distributed across 2,002 (out of 2,195) census tracts in North Carolina. In models adjusting for child sex, birth weight percentile for gestational age, and Medicaid participation as well as maternal race/ethnicity, educational attainment, marital status, and tobacco use, BLLs of 2μg/dL, 3–4μg/dL and ≥5μg/dL were associated with overall lower reading test scores of −0.28 [95% confidence interval (CI): −0.43, −0.12], −0.53 (−0.69, −0.38), and −0.79 (−0.99, −0.604), respectively. For BLLs of 1μg/dL, 2μg/dL, 3–4μg/dL and ≥5μg/dL, spatial coefficients—that is, tract-specific adjustments in reading test score relative to the “global” coefficient—ranged from −9.70 to 2.52, −3.19 to 3.90, −11.14 to 7.85, and −4.73 to 4.33, respectively. Results for mathematics were similar to those for reading.Conclusion:The association between lead exposure and reading and mathematics test scores exhibits considerable heterogeneity across North Carolina communities. These results emphasize the need for prevention and mitigation efforts with respect to lead exposures everywhere, with special attention to locations where the cognitive impact is elevated. https://doi.org/10.1289/EHP13898Item Topological Data Analysis and theoretical statistical inference for time series dependent data and error in parametric choices(2022-07-14) Aguilar, Alex; Ensor, KatherineTopological data analysis extracts topological features by examining the shape of the data through persistent homology to produce topological summaries, such as the persistence landscape. While the persistence landscape makes it easier to conduct statistical analysis, the Strong Law of Large Numbers and a Central Limit Theorem for the persistence landscape applies to independent and identically distributed copies of a random variable. Therefore, we developed a Strong Law of Large Numbers and a Central Limit Theorem for the persistence landscape when the stochastic component of our series is driven by an autoregressive process of order one. Theoretical results for the persistence landscape are demonstrated computationally and applied to financial time series.Item Understanding Houston’s Growth: Real Estate New Development Data Analysis(Rice University, 2024-04-20) Ruan, Jian; Chi, Eric; Ensor, Katherine; McGuffey, ElizabethMarket research serves as the foundation for successful real estate investment and development, guiding strategic site selection and opportunity identification. This study focuses on analyzing the evolving landscape of real estate development in Houston through a multifaceted approach encompassing geographic information systems (GIS) visualization to explore the spatial dynamics and diversity of new developments, time series analysis to examine temporal trends of new development applications, and machine learning techniques to predict future urban growth trajectories. K-means clustering is employed to segment the market and reveal unique characteristics based on development distribution, while the impact of new developments on nearby home values is quantified through linear panel regression and tree-based models, offering insights into the complex relationships between development and property valuation. By integrating these complementary analyses, this research aims to provide a comprehensive understanding of Houston’s real estate development landscape, informing strategic decision-making processes and contributing to the interdisciplinary discourse on urban growth dynamics, market trends, and the interplay between development and valuation.Item Wastewater surveillance of SARS-CoV-2 and influenza in preK-12 schools shows school, community, and citywide infections(Elsevier, 2023) Wolken, Madeline; Sun, Thomas; McCall, Camille; Schneider, Rebecca; Caton, Kelsey; Hundley, Courtney; Hopkins, Loren; Ensor, Katherine; Domakonda, Kaavya; Prashant, Kalvapalle; Persse, David; Williams, Stephen; Stadler, Lauren B.Wastewater surveillance is a passive and efficient way to monitor the spread of infectious diseases in large populations and high transmission areas such as preK-12 schools. Infections caused by respiratory viruses in school-aged children are likely underreported, particularly because many children may be asymptomatic or mildly symptomatic. Wastewater monitoring of SARS-CoV-2 has been studied extensively and primarily by sampling at centralized wastewater treatment plants, and there are limited studies on SARS-CoV-2 in preK-12 school wastewater. Similarly, wastewater detections of influenza have only been reported in wastewater treatment plant and university manhole samples. Here, we present the results of a 17-month wastewater monitoring program for SARS-CoV-2 (n = 2176 samples) and influenza A and B (n = 1217 samples) in 51 preK-12 schools. We show that school wastewater concentrations of SARS-CoV-2 RNA were strongly associated with COVID-19 cases in schools and community positivity rates, and that influenza detections in school wastewater were significantly associated with citywide influenza diagnosis rates. Results were communicated back to schools and local communities to enable mitigation strategies to stop the spread, and direct resources such as testing and vaccination clinics. This study demonstrates that school wastewater surveillance is reflective of local infections at several population levels and plays a crucial role in the detection and mitigation of outbreaks.