524 research outputs found

    Slip ramp spacing design for truck only lanes using microscopic simulation

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    This research work proposes recommendations for slip ramp spacing between the TOLs and the general GPLs along Missouri rural interstate highways using a microscopic simulation model, VISSIM. Simulation of peak period rural traffic conditions indicated that heavy vehicle speeds were directly proportional to the lengths of the merge, diverge, and link sections. The proposed design recommendations for slip ramp spacing are based on the results of these section lengths. Design of experiments was carried out using a central composite design. As the slip ramp spacing depended heavily on the lane change behavior of drivers, a sensitivity analysis was performed of the main lane change parameter in VISSIM that analyzed its effect on the speed flow characteristics of heavy vehicles. This work provides practitioners and state Departments of Transportation (DOTs) with design recommendations for slip ramp spacing and lengths of merge, link, and diverge for the corridors of the future project --Abstract, page iv

    Optimal dynamic taxation with respect to firms

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    Investment;Corporate Tax;investeringen

    Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.

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    This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture. The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks. In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd

    Essays on Testing Hypotheses When Non-stationarity Exists in Panel Data Models

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    This dissertation consists of two essays on testing hypotheses in panel data models when non-stationarity exists in the model. This is done under the high-dimensional framework where both n (cross-section dimension) and T (time series dimension) are large. In the first essay, I discuss the limiting distribution of the t-statistic; using different panel data estimators and propose using the t-statistic based on Feasible GLS estimator. In the second essay, I develop the bootstrap F-statistic for cross-sectional independence in a panel data model with factor structure. The first essay considers the problem of hypotheses testing in a simple panel data regression model with random individual effects and serially correlated disturbances. Following Baltagi, Kao and Liu (2008), I allow for the possibility of non-stationarity in the regressor and/or the disturbance term. While Baltagi et al. (2008) focus on the asymptotic properties and distributions of the standard panel data estimators, this essay focuses on test of hypotheses in this setting. One important finding, is that unlike the time series case, one does not necessarily need to rely on the super-efficient type AR estimator by Perron and Yabu (2009) to make inference in panel data. In fact, I show that the simple t-ratio always converges to the standard normal distribution regardless of whether the disturbances and/or the regressor are stationary. One caveat is that this may not be robust to heteroskedasticity of the error terms, but it is robust to heterogenous AR parameters across individuals. The Monte Carlo simulations in support of all the results are also provided in this essay. The second essay discusses testing hypotheses of cross-sectional dependence in a panel data model with an introduction of factor structure. Following Bai (2003, 2004, 2009) and Bai, Kao and Ng (2009), I again allow for the possibility of non-stationarity in the regressor and the factor. I give attention to test of hypotheses using F-tests in this setting. The limiting distribution of F-statistics under the high-dimensional framework has not been derived yet in the literature perhaps because of its theoretical complexity. To circumvent this difficulty, this essay suggests the use of wild bootstrap F-tests based on simulation results under various cases where both regressors and factors can be stationary or non-stationary. The Monte Carlo results show that the bootstrap F-tests perform well in testing cross-sectional independence and are recommended in practice. They have the advantage of being feasible even when we do not observe the factors and do not require for formal theoretical approximations. It is also shown that the bootstrap F-tests are robust to heteroskedasticity but sensitive to serial correlation

    Spartan Daily, June 1, 1951

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    Volume 39, Issue 153https://scholarworks.sjsu.edu/spartandaily/11571/thumbnail.jp

    Collinearity and consequences for estimation: a study and simulation

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    Uniform Inference after Pretesting for Exogeneity

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    Pretesting for exogeneity has become a routine in many empirical applications involving instrumental variables (IVs) to decide whether the ordinary least squares (OLS) or the two-stage least squares (2SLS) method is appropriate. Guggenberger (2010) shows that the second-stage t-test– based on the outcome of a Durbin- Wu-Hausman type pretest for exogeneity in the first-stage– has extreme size distortion with asymptotic size equal to 1 when the standard asymptotic critical values are used. In this paper, we first show that the standard residual bootstrap procedures (with either independent or dependent draws of disturbances) are not viable solutions to such extreme size-distortion problem. Then, we propose a novel hybrid bootstrap approach, which combines the residual-based bootstrap along with an adjusted Bonferroni size-correction method. We establish uniform validity of this hybrid bootstrap in the sense that it yields a two-stage test with correct asymptotic size. Monte Carlo simulations confirm our theoretical findings. In particular, our proposed hybrid method achieves remarkable power gains over the 2SLS-based t-test, especially when IVs are not very strong

    Uniform Inference after Pretesting for Exogeneity

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    Pretesting for exogeneity has become a routine in many empirical applications involving instrumental variables (IVs) to decide whether the ordinary least squares (OLS) or the two-stage least squares (2SLS) method is appropriate. Guggenberger (2010) shows that the second-stage t-test– based on the outcome of a Durbin- Wu-Hausman type pretest for exogeneity in the first-stage– has extreme size distortion with asymptotic size equal to 1 when the standard asymptotic critical values are used. In this paper, we first show that the standard residual bootstrap procedures (with either independent or dependent draws of disturbances) are not viable solutions to such extreme size-distortion problem. Then, we propose a novel hybrid bootstrap approach, which combines the residual-based bootstrap along with an adjusted Bonferroni size-correction method. We establish uniform validity of this hybrid bootstrap in the sense that it yields a two-stage test with correct asymptotic size. Monte Carlo simulations confirm our theoretical findings. In particular, our proposed hybrid method achieves remarkable power gains over the 2SLS-based t-test, especially when IVs are not very strong
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