VR技术在城轨车辆检修实训课程教学中的应用研究

随着VR技术的发展与在各个领域的应用,VR技术在高等职业教育实训教学中也发挥着越来越重要的作用。论文重点研究了城轨车辆检修专业实训课程在教学中存在的诸多问题,探讨了VR技术在实训课程教学中的具体应用。论文提出将VR技术引入实训教学中,从而提高学生的学习效率,培养学生的综合职业岗位能力。     

关于VR技术在会计实践教学中的应用

随着我国改革开放政策的实行,微电子技术的应用也越来越广泛,进而推动了社会的发展和前进。VR技术将现实的情景以高度逼真的模拟形势展现在课堂上,用低成本、高效益、操作简单等优势打破了传统的理论结合实践的教学方式。论文从VR技术与会计实践教学的关系,以及结合目前会计实践教学存在的问题,分析了VR技术在教学中的优点,并对目前教学应用提出几点建议,为模拟实践教学提供一些参考思路。     

虚拟现实在军地物流一体化中的应用研究

简要介绍了虚拟现实技术,分析了军地一体化物流对虚拟现实技术应用的迫切需求以及虚拟现实技术的优势．介绍了虚拟仿真技术在物流方面的应用并提出了具体的仿真步骤。     

“互联网+”背景下VR电商的风险预测分析

众所周知,VR可谓是智能时代里遥望的下一个文明时代的台阶,电商行业携手虚拟现实技术开创未来的同时也将面临种种困难。论文首先论述了VR技术及其现状,其次叙述了VR电商的发展前景和重要性,然后从资金投入、人才、技术、市场等方面预测VR+电商发展中可能存在的风险,利用较新数据和举例分析等方法详细分析几大风险因素,最后简要地阐明了对VR电商的展望。     

论VR技术在建筑室内设计中的应用

在建筑室内设计中,使用VR设计能够提高工作效率和质量,实现建筑行业的变革。论文主要围绕VR技术在建筑室内设计中的应用优势、具体应用以及应用路径三个方面进行分析,以供参考。     

基于VR技术的抽油机设计与制造的应用研究

随着VR(虚拟现实)技术的发展,其广泛应用在各个领域如教育、军事、娱乐、工业等。虚拟现实是指利用电脑模拟产生一个三维空间的虚拟世界,提供使用者关于视觉、听觉、触觉等感官的模拟,让使用者能够及时、没有限制地观察三度空间内的事物。本文应用VR技术完成了抽油机整机的虚拟装配、实时碰撞检测、装配工艺规划,解决了在动态仿真过程中齿轮运动模拟、驴头、游梁总成、吊臂等运动模拟、抽油绳的运动模拟,最终完成了抽油机整机的三维视景仿真过程。     

基于Internet和VR技术的会计虚拟教学模式研究

本文提出利用虚拟现实(VR)技术,构建一个基于Internet和VR技术的“虚拟会计教学模式”,解决会计学教学上理论和实践脱节的问题,并进而创建一个网络化(实时、便利)、个性化(实现因材施教)和交互式的会计教育平台。本文较详细地介绍了在互联网上建立基于虚拟现实技术的“虚拟会计教学模式”的设计方法和实现方法,并对“虚拟会计教学模式”的交互做了探索。     

5G时代背景下新型C2C旅游平台的探究

当前中国5G技术的应用与发展为各行业找到发展的新增长点,也即将成为助力中国经济发展的新动能。近年来,国内外旅游产业蓬勃发展,旅游经济井喷式增长,居家旅游、“宅”旅游也变得十分新潮,成为时下年轻人旅游消费的新途径。在5G时代背景下,通过VR技术实现以沉浸式虚拟旅游为基础的爱好偏向型C2C旅游平台的搭建与运营,必将为旅游产业迎来新一轮经济增长点。     

Improved likelihood ratio tests for cointegration rank in the VAR model

We suggest improved tests for cointegration rank in the vector autoregressive (VAR) model and develop asymptotic distribution theory and local power results. The tests are (quasi-)likelihood ratio tests based on a Gaussian likelihood, but as usual the asymptotic results do not require normally distributed innovations. Our tests differ from existing tests in two respects. First, instead of basing our tests on the conditional (with respect to the initial observations) likelihood, we follow the recent unit root literature and base our tests on the full likelihood as in, e.g., Elliott et al. (1996). Second, our tests incorporate a “sign” restriction which generalizes the one-sided unit root test. We show that the asymptotic local power of the proposed tests dominates that of existing cointegration rank tests.     

Small‐sample testing inference in symmetric and log‐symmetric linear regression models

This paper deals with the issue of testing hypotheses in symmetric and log‐symmetric linear regression models in small and moderate‐sized samples. We focus on four tests, namely, the Wald, likelihood ratio, score, and gradient tests. These tests rely on asymptotic results and are unreliable when the sample size is not large enough to guarantee a good agreement between the exact distribution of the test statistic and the corresponding chi‐squared asymptotic distribution. Bartlett and Bartlett‐type corrections typically attenuate the size distortion of the tests. These corrections are available in the literature for the likelihood ratio and score tests in symmetric linear regression models. Here, we derive a Bartlett‐type correction for the gradient test. We show that the corrections are also valid for the log‐symmetric linear regression models. We numerically compare the various tests and bootstrapped tests, through simulations. Our results suggest that the corrected and bootstrapped tests exhibit type I probability error closer to the chosen nominal level with virtually no power loss. The analytically corrected tests as well as the bootstrapped tests, including the Bartlett‐corrected gradient test derived in this paper, perform with the advantage of not requiring computationally intensive calculations. We present a real data application to illustrate the usefulness of the modified tests.     

Testing panel data regression models with spatial error correlation

This paper derives several lagrange multiplier (LM) tests for the panel data regression model with spatial error correlation. These tests draw upon two strands of earlier work. The first is the LM tests for the spatial error correlation model discussed in Anselin (Spatial Econometrics: Methods and Models, Kluwer Academic Publishers, Dordrecht; Rao's score test in spatial econometrics, J. Statist. Plann. Inference 97 (2001) 113) and Anselin et al. (Regional Sci. Urban Econom. 26 (1996) 77), and the second is the LM tests for the error component panel data model discussed in Breusch and Pagan (Rev. Econom. Stud. 47(1980) 239) and Baltagi et al. (J. Econometrics 54 (1992) 95). The idea is to allow for both spatial error correlation as well as random region effects in the panel data regression model and to test for their joint significance. Additionally, this paper derives conditional LM tests, which test for random regional effects given the presence of spatial error correlation. Also, spatial error correlation given the presence of random regional effects. These conditional LM tests are an alternative to the one-directional LM tests that test for random regional effects ignoring the presence of spatial error correlation or the one-directional LM tests for spatial error correlation ignoring the presence of random regional effects. We argue that these joint and conditional LM tests guard against possible misspecification. Extensive Monte Carlo experiments are conducted to study the performance of these LM tests as well as the corresponding likelihood ratio tests.     

Specification Testing of Markov Switching Models*

This paper proposes a set of formal tests to address the goodness‐of‐fit of Markov switching models. These formal tests are constructed as tests of model consistency and of both parametric and non‐parametric encompassing. The formal tests are then combined with informal tests using simulation in combination with non‐parametric density and conditional mean estimation. The informal tests are shown to be useful in shedding light on the failure (or success) of the encompassing tests. Several examples are provided.     

Frequency Domain Tests for Residual Serial Correlation in Cointegration Regressions

This paper introduces tests for residual serial correlation in cointegrating regressions. The tests are devised in the frequency domain by using the spectral measure estimates. The asymptotic distributions of the tests are derived and test consistency is established. The asymptotic distributions are obtained by using the assumptions and methods that are different from those used in Grenander and Rosenblatt (1957) and Durlauf (1991). Small-scale simulation results are reported to illustrate the finite sample performance of the tests under various distributional assumptions on the data generating process. The distributions considered are normal and t-distributions. The tests are shown to have stable size at sample sizes as large as 50 or 100. Additionally, it is shown that the tests are reasonably powerful against the ARMA residuals. An empirical application of the tests to investigate the ‘weak-form’ efficiency in the foreign exchange market is also reported.     

Generalized runs tests for the IID hypothesis

We provide a family of tests for the IID hypothesis based on generalized runs, powerful against unspecified alternatives, providing a useful complement to tests designed for specific alternatives, such as serial correlation, GARCH, or structural breaks. Our tests have appealing computational simplicity in that they do not require kernel density estimation, with the associated challenge of bandwidth selection. Simulations show levels close to nominal asymptotic levels. Our tests have power against both dependent and heterogeneous alternatives, as both theory and simulations demonstrate.     

Some rank tests for one–sided many–one comparisons

We compare some nonparametric tests for the (/+ 1)–sample problem with additive effects under the constraint that in every sample the treatment effect is not less than that in the first sample, i.e. of some control. The behavior of the Pitman efficiency of the respective tests (essentially tests of a Kruskal–Wallis–, Wilcoxon–, Fligner–Wolfe–, Steel–, and Nemenyi–type) is discussed which turns out to depend on the level and power of the tests as well as on the directions, from which the alternative tends to the hypothesis. It will be shown that none of the tests under consideration is uniformly superior to the others.     

Testing Integration of Macroeconomic Time Series in Transitional Socialist Economies. A Modification of Perron Test

The presence of structural breaks reduces the power of integration tests. A number of methods were suggested to improve the statistical properties of integration tests in the presence of structural breaks. The most known are Perron tests, which allow to test for the level of integration of time series with one structural break. Perron tests allow for two types of structural breaks: additive outlier an innovative outlier. These tests are, however, not very useful in testing the level of integration of macroeconomic time series in countries in transition from centrally-planned to market economy. In such case one should expect two structural breaks to affect the time series: one at the beginning and one at the end of the transformation process. Test that allows for two additive outlier type structural breaks in time series is developed in this paper. This test has superior power as compared to standard Dickey-Fuller and Perron tests. This paper provides asymptotic distribution as well as finite sample properties of proposed test. Therefore practitioners receive a reliable tool for analyzing macroeconomic processes in transitional economies. This revised version was published online in July 2006 with corrections to the Cover Date.     

Forecast Rationality Tests in the Presence of Instabilities,with Applications to Federal Reserve and Survey Forecasts

This paper proposes a framework to implement regression‐based tests of predictive ability in unstable environments, including, in particular, forecast unbiasedness and efficiency tests, commonly referred to as tests of forecast rationality. Our framework is general: it can be applied to model‐based forecasts obtained either with recursive or rolling window estimation schemes, as well as to forecasts that are model free. The proposed tests provide more evidence against forecast rationality than previously found in the Federal Reserve's Greenbook forecasts as well as survey‐based private forecasts. It confirms, however, that the Federal Reserve has additional information about current and future states of the economy relative to market participants. Copyright © 2015 John Wiley & Sons, Ltd.     

KMT-type inequalities and goodness-of-fit tests

The implications of the probability inequality of Komløs, Major and Tusnády (1975) for the theory of goodness-of-fit tests, especially tests based on stochastic integrals with respect to the basic martingale in the random censoring model, are discussed. Choices of the integrand of the stochastic integral which yield highly efficient generalized rank and supremum type tests are given for the simple as well as the composite null hypothesis.     

Dynamic specification tests for dynamic factor models

We derive computationally simple expressions for score tests of misspecification in parametric dynamic factor models using frequency domain techniques. We interpret those diagnostics as time domain moment tests which assess whether certain autocovariances of the smoothed latent variables match their theoretical values under the null of correct model specification. We also reinterpret reduced‐form residual tests as checking specific restrictions on structural parameters. Our Gaussian tests are robust to nonnormal, independent innovations. Monte Carlo exercises confirm the finite‐sample reliability and power of our proposals. Finally, we illustrate their empirical usefulness in an application that constructs a US coincident indicator.     

Measuring the economic significance of mean-variance spanning

This paper investigates the economic significance of mean-variance spanning tests using three classical statistical tests in a unified framework. I show how to compute confidence intervals about the Sharpe ratios of tangent portfolios, the variance of return of minimum variance portfolios, as well as the certainty equivalent utility gains. I apply this statistical framework to the question of whether US investors should diversify internationally. The analysis suggests that a strong statistical rejection of the hypothesis that there is no improvement in the minimum variance portfolio’s standard deviation of return does not imply that there are no significant economic benefits to be made in terms of a substantial risk reduction. These results have important implications for empirical tests of mean-variance spanning as well as empirical assets pricing tests and minimum variance bounds on stochastic discount factors.