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Means, medians and SD for available socio‐economic status (SES) black‐white differences are here substituted for those of IQ in a between‐groups model published by the author over a decade ago. The goodness of fit of the SES variables used is compared with that for the earlier IQ data. Even when SES variables are relatively successful this can be viewed as additional evidence of the importance of IQ differences to black‐white differences in delinquency.

This chapter contributes to the growing global VAR (GVAR) literature by showing how global and national shocks can be identified within a GVAR framework. The usefulness of the proposed approach is illustrated in an application to the analysis of the interactions between public debt and real output growth in a multicountry setting, and the results are compared to those obtained from standard single country VAR analysis. We find that on average (across countries) global shocks explain about one-third of the long-horizon forecast error variance of output growth, and about one-fifth of the long-run variance of the rate of change of debt-to-GDP. Evidence on the degree of cross-sectional dependence in these variables and their innovations are exploited to identify the global shocks, and priors are used to identify the national shocks within a Bayesian framework. It is found that posterior median debt elasticity with respect to output is much larger when the rise in output is due to a fiscal policy shock, as compared to when the rise in output is due to a positive technology shock. The cross-country average of the median debt elasticity is 1.45 when the rise in output is due to a fiscal expansion as compared to 0.76 when the rise in output follows from a favorable output shock.

Bagging (bootstrap aggregating) is a smoothing method to improve predictive ability under the presence of parameter estimation uncertainty and model uncertainty. In Lee and Yang (2006), we examined how (equal-weighted and BMA-weighted) bagging works for one-step-ahead binary prediction with an asymmetric cost function for time series, where we considered simple cases with particular choices of a linlin tick loss function and an algorithm to estimate a linear quantile regression model. In the present chapter, we examine how bagging predictors work with different aggregating (averaging) schemes, for multi-step forecast horizons, with a general class of tick loss functions, with different estimation algorithms, for nonlinear quantile regression models, and for different data frequencies. Bagging quantile predictors are constructed via (weighted) averaging over predictors trained on bootstrapped training samples, and bagging binary predictors are conducted via (majority) voting on predictors trained on the bootstrapped training samples. We find that median bagging and trimmed-mean bagging can alleviate the problem of extreme predictors from bootstrap samples and have better performance than equally weighted bagging predictors; that bagging works better at longer forecast horizons; that bagging works well with highly nonlinear quantile regression models (e.g., artificial neural network), and with general tick loss functions. We also find that the performance of bagging may be affected by using different quantile estimation algorithms (in small samples, even if the estimation is consistent) and by using different frequencies of time series data.

The purpose of this paper is to derive the small-signal/canonical model derivation of the high-side active clamp forward converter (ACFC) with diode rectification for ideal and with resistive parasitics. It also covers the analysis of ACFC small-signal model with resistive parasitics using computer-aided modeling software Personal Computer Simulation Program with Integrated Circuit Emphasis (PSPICE) 16.6. The effects of variation of system parameters on the ACFC’s state transfer functions and operations have been highlighted in this paper.

The large-signal model and small-signal model of the ACFC with diode rectification has been derived using AC small-signal modeling approach.

The operating point of the converter changes with the consideration of resistive parasitics compared with the ideal case. The response obtained from the hardware matches with the time domain response of the averaged model and switch model developed in PSPICE.

This paper limits the study of ACFC small-signal behavior by using computer-aided design software PSPICE. The dead time of the converter is not considered because it is negligible when compared with the on and off time. The leakage inductance which plays a role in zero voltage switching of the ACFC switches is neglected in the analysis as it is very small compared to the magnetizing inductance. The switching losses are not considered in the modeling.

The mathematical computation of deriving the system transfer functions from canonical model is complex and time consuming.

The modeling with resistive parasitics improves the effectiveness of the equivalent model. Also, the analysis with computer-aided modeling software PSPICE gives reliable results in less time.

The Securities Exchange Commission mandated eXtensible Business Reporting Language (XBRL) filing data provide immediate availability and easy accessibility for both academics and practitioners. To be useful, this data should provide information for decisions, specifically, investment decisions. The purpose of this study is to examine whether the XBRL database can be used with models, developed in previous studies, predicting the directional movement of earnings. The study does not attempt to examine the validity of these models, but only the ability to use the data in the analysis of financial statements based on these models.

The study analyzes New York Stock Exchange companies’ XBRL data using a two-step logistic regression model. The model is then used to arrive at the directional movement of earnings between current and subsequent quarters. Additional models are created by dividing the sample into industry membership.

The results classified companies as realizing an increase or a decrease in earnings. The final model indicated a significant ability to predict earnings changes, on average about 65 per cent of the time, for the entire model, and 71 per cent, for the industry-based models (higher than those of previous studies based on COMPUSTAT). The investment strategy created average quarterly return between 2.8 and 10.7 per cent.

The originality of this study is in the way it examines the quality of XBRL data, by examining whether findings from prior research which relied on traditional databases (such as COMPUSTAT) still hold using XBRL data. The use of XBRL allows not only easier and less-costly access to the data but also the ability to adjust the models almost immediately as current information is posted, thus providing a much more relevant tool for investors, especially small investors.

This paper aims to better jointly estimate Value at Risk (VaR) and expected shortfall (ES) by using the joint regression combined forecasting (JRCF) model.

Combining different forecasting models in financial risk measurement can improve their prediction accuracy by integrating the individual models’ information. This paper applies the JRCF model to measure VaR and ES at 5%, 2.5% and 1% probability levels in the Chinese stock market. While ES is not elicitable on its own, the joint elicitability property of VaR and ES is established by the joint consistent scoring functions, which further refines the ES’s backtest. In addition, a variety of backtesting and evaluation methods are used to analyze and compare the alternative risk measurement models.

The empirical results show that the JRCF model outperforms the competing models. Based on the evaluation results of the joint scoring functions, the proposed model obtains the minimum scoring function value compared to the individual forecasting models and the average combined forecasting model overall. Moreover, Murphy diagrams’ results further reveal that this model has consistent comparative advantages among all considered models.

The JRCF model of risk measures is proposed, and the application of the joint scoring functions of VaR and ES is expanded. Additionally, this paper comprehensively backtests and evaluates the competing risk models and examines the characteristics of Chinese financial market risks.

Smog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction.

This paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness.

In order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies.

The proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective.

Based on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model.

The purpose of this paper is to develop a dynamic, stochastic, mechanistic simulation model of a dairy business to evaluate the cost and benefit streams coinciding with technology investments. The model was constructed to embody the biological and economical complexities of a dairy farm system within a partial budgeting framework. A primary objective was to establish a flexible, user‐friendly, farm‐specific, decision‐making tool for dairy producers or their advisers and technology manufacturers.

The basic deterministic model was created in Microsoft Excel (Microsoft, Seattle, Washington). The @Risk add‐in (Palisade Corporation, Ithaca, New York) for Excel was employed to account for the stochastic nature of key variables within a Monte Carlo simulation. Net present value was the primary metric used to assess the economic profitability of investments. The model was composed of a series of modules, which synergistically provide the necessary inputs for profitability analysis. Estimates of biological relationships within the model were obtained from the literature in an attempt to represent an average or typical US dairy. Technology benefits were appraised from the resulting impact on disease incidence, disease impact, and reproductive performance. In this paper, the model structure and methodology were described in detail.

Examples of the utility of examining the influence of stochastic input and output prices on the costs of culling, days open, and disease were examined. Each of these parameters was highly sensitive to stochastic prices and deterministic inputs.

Decision support tools, such as this one, that are designed to investigate dairy business decisions may benefit dairy producers.

An Activity-Based Costing (ABC) system generates a significant amount of detailed, complex data for management to evaluate and use, potentially reducing decision-making effectiveness. The purpose of this paper is to show how reducing the magnitude of detailed information that an ABC system provides can increase decision-making effectiveness.

This study develops a theoretical Weighted Average ABC model by taking ABC information and rearranging it to enhance decision-making effectiveness.

Weighted Average ABC provides cost assignments that are approximately the same to those of ABC in most situations. In Weighted Average ABC, the weighted average consumption ratios provide relevant decision-making information to determine which products are costlier. To reduce costs, management can focus on those costlier products or services and can request from the cost accountants additional detailed information concerning those costlier products or services.

This study adds to the ABC literature by developing Weighted Average ABC. However, the limitation of this study is that no actual data could be obtained from a company that uses ABC, and thus, this study develops an analytical model.

Weighted Average ABC may increase decision-making effectiveness in situations when managers need to make fast decisions.

This study develops a theoretical Weighted Average ABC model in which the weighted average activity consumption ratios of the product lines and the total overhead costs are the variables needed, thus skipping Stage 1 of ABC. This, in turn, reduces the amount of information provided to management. Accordingly, weighted Average ABC provides timelier and more manageable information for decision making.