Agricultural Precautionary Wealth

Using panel data, the relationship between income uncertainty and the stock of wealth through precautionary saving is examined. Evidence from Kansas data is consistent with the precautionary saving motive in that farm households facing greater uncertainty in income maintain larger stocks of wealth in order to smooth consumption. These results are found by regressing net worth against measures of permanent income (life-cycle income), measures of uncertainty, and demographic variables.precautionary saving, precautionary wealth, risk, Risk and Uncertainty,

Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs

Modern empirical work in Regression Discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This bandwidth yields an MSE-optimal RD treatment effect estimator, but is by construction invalid for inference. Robust bias corrected (RBC) inference methods are valid when using the MSE-optimal bandwidth, but we show they yield suboptimal confidence intervals in terms of coverage error. We establish valid coverage error expansions for RBC confidence interval estimators and use these results to propose new inference-optimal bandwidth choices for forming these intervals. We find that the standard MSE-optimal bandwidth for the RD point estimator is too large when the goal is to construct RBC confidence intervals with the smallest coverage error. We further optimize the constant terms behind the coverage error to derive new optimal choices for the auxiliary bandwidth required for RBC inference. Our expansions also establish that RBC inference yields higher-order refinements (relative to traditional undersmoothing) in the context of RD designs. Our main results cover sharp and sharp kink RD designs under conditional heteroskedasticity, and we discuss extensions to fuzzy and other RD designs, clustered sampling, and pre-intervention covariates adjustments. The theoretical findings are illustrated with a Monte Carlo experiment and an empirical application, and the main methodological results are available in \texttt{R} and \texttt{Stata} packages

On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference

Nonparametric methods play a central role in modern empirical work. While they provide inference procedures that are more robust to parametric misspecification bias, they may be quite sensitive to tuning parameter choices. We study the effects of bias correction on confidence interval coverage in the context of kernel density and local polynomial regression estimation, and prove that bias correction can be preferred to undersmoothing for minimizing coverage error and increasing robustness to tuning parameter choice. This is achieved using a novel, yet simple, Studentization, which leads to a new way of constructing kernel-based bias-corrected confidence intervals. In addition, for practical cases, we derive coverage error optimal bandwidths and discuss easy-to-implement bandwidth selectors. For interior points, we show that the MSE-optimal bandwidth for the original point estimator (before bias correction) delivers the fastest coverage error decay rate after bias correction when second-order (equivalent) kernels are employed, but is otherwise suboptimal because it is too "large". Finally, for odd-degree local polynomial regression, we show that, as with point estimation, coverage error adapts to boundary points automatically when appropriate Studentization is used; however, the MSE-optimal bandwidth for the original point estimator is suboptimal. All the results are established using valid Edgeworth expansions and illustrated with simulated data. Our findings have important consequences for empirical work as they indicate that bias-corrected confidence intervals, coupled with appropriate standard errors, have smaller coverage error and are less sensitive to tuning parameter choices in practically relevant cases where additional smoothness is available

Strapdown calibration and alignment study. Volume 2 - Procedural and parametric trade-off analyses document Final report

Parametric and procedural tradeoffs for alignment and calibration of inertial sensing uni

Life Satisfaction: Measurement Invariance and Correlations with Adolescent Adjustment

Background Low life satisfaction during adolescence has been associated with adjustment problems. There are few well-validated measures available to assess adolescents’ life-satisfaction. The purpose of this study was to investigate the structure of the Life Satisfaction Scale, evaluate its measurement invariance across sex and race/ethnicity, and investigate its associations with related constructs. Methods Participants were 3,340 adolescents from rural middle schools in Florida. Half the participants were female, 51% were White, 15% were Black, and 22% were Latinx. Adolescents completed the Life Satisfaction Scale, the Children’s Report of Exposure to Violence scale, and the Problem Behavior Frequency Scale. Results Confirmatory factor analysis found support for a single factor representing overall life satisfaction, and strong measurement invariance across race, but not across sex. There were significant differences in item thresholds such that girls at the same level of life satisfaction as boys, were more likely to endorse higher responses to items assessing satisfaction with school, with themselves, and with their friendships. Life satisfaction had significant negative correlations with violence exposure, problem behavior, and peer pressure for drug use. Conclusion Findings suggest that the Life Satisfaction Scale may be suitable for assessing life satisfaction across different groups of adolescents. Examining sex differences must be done cautiously as life satisfaction may have different meanings to boys and girls. The inverse correlations between life satisfaction, violence exposure and problem behavior across groups highlights the importance of developing sound measures to assess this important construct and determine how it relates to youth adjustment.https://scholarscompass.vcu.edu/gradposters/1076/thumbnail.jp

A synoptic description of coal basins via image processing

An existing image processing system is adapted to describe the geologic attributes of a regional coal basin. This scheme handles a map as if it were a matrix, in contrast to more conventional approaches which represent map information in terms of linked polygons. The utility of the image processing approach is demonstrated by a multiattribute analysis of the Herrin No. 6 coal seam in Illinois. Findings include the location of a resource and estimation of tonnage corresponding to constraints on seam thickness, overburden, and Btu value, which are illustrative of the need for new mining technology

Strapdown calibration and alignment study. Volume 2 - Procedural and parametric trade- off analyses document

Techniques for laboratory calibration and alignment of strapdown inertial sensing unit - procedural and parametric trade-off analyse

Modelling Contracts and Workflows for Verification and Enactment

The work presented in this thesis concerns some aspects related to the Modelling of Contracts and Workflows for Verification and Enactment. We have sought to gain some insight into the nature of contracts and workflows. in order that we may model them. primarily, for the purposes of verifying certain properties and for enacting them. Workflows help coordinate the enactment of business processes. A notable aspect of workflow technologies is the lack of formal semantics for workflow models. In this thesis, we consider the characterisation of workflow using a number of formal tools, viz. Milner's CCS, Cleaveland et ai's Prioritised CCS (which we abbreviate to PCCS) and the Situation Calculus (thanks mainly to Reiter), which is based on First-Order Logic. Using these, we provide formalisations of production workflows, which are somewhat rigid, inflexible structures, akin to production lines. We do so, in order that we may fiJo: their operational meaning for the purposes of verification and enactment. We define the Liesbet meta-model for production workflow to provide a reference ontology for the task of formalisation. We have also implemented a framework for the verification and enactment of Liesbet workflow models. Regarding verification, we are particularly interested in the key property of soundness, which is concerned with an absence of locking and redundant tasks in a workflow model. Our framework is capable of verifying this property of workflow models, as well as arbitrary temporally-extended constraints', which are constraints whose satisfaction is determined over successive states of enactment of a model. We also consider the definition of more flexible workflows, including collaborative workflows, using an approach that we have conceived called Institutional Workflow Modelling (IWM). The essence of IWM lies (in part) in the identification that the structure of a workflow model necessarily entails the existence of counts as relations. These relations prescribe how the occurrence of certain actions, in the context of a particular workflow model. count as the occurrence of other actions. We have also been interested in the modelling of contracts; and have found IWM to be useful as a foundational basis for contract modelling. ????????? Another fu.ndamental aspect of our IWM-based approach is a correspondence, which we have identified, between counts as relations and methods in Hierarchical Task Network (HTN)-based planning. Thus, we are able to advocate the use of an HTN-based planning framework for the verification of flexible workflows and contracts. We have implemented such a framework, whose planner is called Theodore. We define a sjmilar notion of soundness for flexible workflows and contracts, which the Theodore-based framework is able to verify, along with arbitrary temporallyextended constraints.Imperial Users onl

Regression Discontinuity Designs Using Covariates

We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions, and characterize the potential for estimation and inference improvements. We also present new covariate-adjusted mean squared error expansions and robust bias-corrected inference procedures, with heteroskedasticity-consistent and cluster-robust standard errors. An empirical illustration and an extensive simulation study is presented. All methods are implemented in \texttt{R} and \texttt{Stata} software packages