The Finite-Sample E ects of VAR Dimensions on OLS Bias, OLS Variance, and Minimum MSE Estimators

Vector autoregressions (VARs) are important tools in time series analysis. However, relatively little is known about the nite-sample behaviour of parameter estimators. We address this issue, by investigating ordinary least squares (OLS) estimators given a data generating process that is a purely nonstationary rst-order VAR. Speci cally, we use Monte Carlo simulation and numerical optimization to derive response surfaces for OLS bias and variance, in terms of VAR dimensions, given correct speci cation and several types of over-parameterization of the model: we include a constant, and a constant and trend, and introduce excess lags. We then examine the correction factors that are required for the least squares estimator to attain minimum mean squared error (MSE). Our results improve and extend one of the main nite-sample multivariate analytical bias results of Abadir, Hadri and Tzavalis (Econometrica 67 (1999) 163), generalize the univariate variance and MSE ndings of Abadir (Economics Letters 47 (1995) 263) to the multivariate setting, and complement various asymptotic studies.Finite-sample bias, Monte Carlo simulation, nonstationary time series, response surfaces, vector autoregression.

A Review of the Effects on IRT Item Parameter Estimates with a Focus on Misbehaving Common Items in Test Equating

Many studies have investigated the topic of change or drift in item parameter estimates in the context of item response theory (IRT). Content effects, such as instructional variation and curricular emphasis, as well as context effects, such as the wording, position, or exposure of an item have been found to impact item parameter estimates. The issue becomes more critical when items with estimates exhibiting differential behavior across test administrations are used as common for deriving equating transformations. This paper reviews the types of effects on IRT item parameter estimates and focuses on the impact of misbehaving or aberrant common items on equating transformations. Implications relating to test validity and the judgmental nature of the decision to keep or discard aberrant common items are discussed, with recommendations for future research into more informed and formal ways of dealing with misbehaving common items

An Illustration of a Mantel-Haenszel Procedure to Flag Misbehaving Common Items in Test Equating

In this study the Mantel-Haenszel procedure, widely used in studies for identifying differential item functioning, is proposed as an alternative to the delta-plot method and applied in a test-equating context for flagging common items that behave differentially across cohorts of examinees. The Mantel-Haenszel procedure has the advantage of conditioning on ability when making comparisons of performance of two examinee groups on an item. There are schemes for interpreting the effect size of differential performance, which can inform the decision as to whether to retain those items in the common-item pool, or to discard them. Data from a statewide assessment are analyzed to illustrate the use of this procedure. Advantages of this methodology are discussed and limitations regarding test design that may make its application difficult are described. Accessed 17,544 times on https://pareonline.net from September 11, 2008 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Moral Judgments in Social Dilemmas: How Bad is Free Riding?

In the last thirty years, economists and other social scientists have investigated people’s normative views on distributive justice. Here we study people’s normative views in social dilemmas, which underlie many situations of economic and social significance. Using insights from moral philosophy and psychology we provide an analysis of the morality of free riding. We use experimental survey methods to investigate people’s moral judgments empirically. We vary others’ contributions, the framing (“give-some” vs. “take-some”) and whether contributions are simultaneous or sequential. We find that moral judgments of a free rider depend strongly on others’ behaviour; and that failing to give is condemned more strongly than withdrawing all support.moral judgments, moral psychology, framing effects, public goods experiments, free riding

Moral Judgments in Social Dilemmas: How Bad is Free Riding?

In the last thirty years, economists and other social scientists have investigated people’s normative views on distributive justice. Here we study people’s normative views in social dilemmas, which underlie many situations of economic and social significance. Using insights from moral philosophy and psychology we provide an analysis of the morality of free riding. We use experimental survey methods to investigate people’s moral judgments empirically. We vary others’ contributions, the framing (“give-some” vs. “take-some”) and whether contributions are simultaneous or sequential. We find that moral judgments of a free rider depend strongly on others’ behaviour; and that failing to give is condemned more strongly than withdrawing all support.moral judgments, moral psychology, framing effects, public goods experiments, free riding

The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators

Vector autoregressions (VARs) are important tools in time series analysis. However, relatively little is known about the finite-sample behaviour of parameter estimators. We address this issue, by investigating ordinary least squares (OLS) estimators given a data generating process that is a purely nonstationary first-order VAR. Specifically, we use Monte Carlo simulation and numerical optimisation to derive response surfaces for OLS bias and variance, in terms of VAR dimensions, given correct specification and several types of over-parameterisation of the model: we include a constant, and a constant and trend, and introduce excess lags. We then examine the correction factors that are required for the least squares estimator to attain the minimum mean squared error (MSE). Our results improve and extend one of the main finite-sample multivariate analytical bias results of Abadir, Hadri and Tzavalis [Abadir, K.M., Hadri, K., Tzavalis, E., 1999. The influence of VAR dimensions on estimator biases. Econometrica 67, 163–181], generalise the univariate variance and MSE findings of Abadir [Abadir, K.M., 1995. Unbiased estimation as a solution to testing for random walks. Economics Letters 47, 263–268] to the multivariate setting, and complement various asymptotic studies

A generic rigorous model for a long track stereo satellite sensors.

The aim of this thesis is to develop a generic rigorous sensor model for high resolution optical satellite sensors, with along track stereoscopic capabilities, in order to orientate directly and simultaneously all the along track stereo images. In other words, the idea is to determine the orbit of the satellite platform covering the time acquisition of all images, using satellite photogrammetry in combination with astrodynamics, thus finding common exterior orientation parameters for all images directly or indirectly. As a result, the number of unknown parameters is reduced and also the correlation between them, thus giving a more stable solution. Moreover, the simultaneous solution extends the narrow field of view of each satellite image because all along track images are treated as one iconic image, with the field of view equal to the angle between the first and the last image. Great effort is made in order to define the essential forces which are involved in the acquisition of the pushbroom images, according to the needed accuracy and the data provided. The fundamental assumptions is that Kepler motion is maintained along the acquisition time of all the along track images. Various versions of the model are developed, based on different orbit determination-propagation methods. The first one, based on the Kepler problem (orbit propagation), can be used for more than two along track images. The second one is based on Gauss-Lambert method which can be used only for two along track images like SPOT-HRS and TERRA-ASTER. The final one is based on Herrick-Gibbs method which is combined with the Gauss-Lambert method in order to be used in the case of more than two along track images. An accuracy assessment is made of the above different orbit determination-propagation methods. It is possible to extract the exterior orientation of all images together directly, without Ground Control Points using the metadata information, with accepted accuracy. The model is evaluated using TERRA-ASTER and SPOT5-HRS imagery with precision close to pixel size. Finally the accuracy of the along track model is compared with the accuracy of single image sensor model and of a commercial sensor model (Leica Photogrammetry Suite)

The basic chemistry of exercise-induced DNA oxidation:oxidative damage, redox signalling and their interplay

Acute exercise increases reactive oxygen and nitrogen species generation. This phenomenon is associated with two major outcomes: (1) redox signalling and (2) macromolecule damage. Mechanistic knowledge of how exercise-induced redox signalling and macromolecule damage are interlinked is limited. This review focuses on the interplay between exercise-induced redox signalling and DNA damage, using hydroxyl radical (·OH) and hydrogen peroxide (H2O2) as exemplars. It is postulated that the biological fate of H2O2 links the two processes and thus represents a bifurcation point between redox signalling and damage. Indeed, H2O2 can participate in two electron signalling reactions but its diffusion and chemical properties permit DNA oxidation following reaction with transition metals and ·OH generation. It is also considered that the sensing of DNA oxidation by repair proteins constitutes a non-canonical redox signalling mechanism. Further layers of interaction are provided by the redox regulation of DNA repair proteins and their capacity to modulate intracellular H2O2 levels. Overall, exercise-induced redox signalling and DNA damage may be interlinked to a greater extent than was previously thought but this requires further investigation

Message Passing Attention Networks for Document Understanding

Graph neural networks have recently emerged as a very effective framework for processing graph-structured data. These models have achieved state-of-the-art performance in many tasks. Most graph neural networks can be described in terms of message passing, vertex update, and readout functions. In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD). We also propose several hierarchical variants of MPAD. Experiments conducted on 10 standard text classification datasets show that our architectures are competitive with the state-of-the-art. Ablation studies reveal further insights about the impact of the different components on performance. Code is publicly available at: https://github.com/giannisnik/mpad .Comment: Accepted at AAAI'2