Explore the vast range of titles available.

We analyze a procedure common in empirical accounting and finance research where researchers use ordinary least squares to decompose a dependent variable into its predicted and residual components and use the residuals as the dependent variable in a second regression. This two-step procedure is used to examine determinants of constructs such as discretionary accruals, real activities management, discretionary book-tax differences, and abnormal investment. We show that the typical implementation of this procedure generates biased coefficients and standard errors that can lead to incorrect inferences, with both Type I and Type II errors. We further show that the magnitude of the bias in coefficients and standard errors is a function of the correlations between model regressors. We illustrate the potential magnitude of the bias in accounting research in four commonly used settings. Our results indicate significant bias in many of these settings. We offer three solutions to avoid the bias.

This paper makes several important contributions to the literature about non- parametric instrumental variables (NPIV ) estimation and inference on a structural function h0 and functionals of h0 .First, we derive sup-norm convergence rates for computationally simple sieve NPIV (series two-stage least squares) estimators of h0 and its derivatives. Second, we derive a lower bound that describes the best possible (minimax) sup-norm rates of estimating h0 and its derivatives, and show that the sieve NPIV estimator can attain the minimax rates when h0 is approximated via a spline or wavelet sieve. Our optimal sup-norm rates surprisingly coincide with the optimal root-mean-squared rates for severely ill-posed problems, and are only a logarithmic factor slower than the optimal root-mean- squared rates for mildly ill-posed problems. Third, we use our sup-norm rates to establish the uniform Gaussian process strong approximations and the score bootstrap uniform confidence bands (UCBs) for collections of nonlinear functionals of h0 under primitive conditions, allowing for mildly and severely ill-posed problems. Fourth, as applications, we obtain the first asymptotic pointwise and uniform inference results for plug-in sieve t -statistics of exact consumer surplus (CS) and deadweight loss (DL) welfare functionals under low-level conditions when demand is estimated via sieve NPIV. Our real data application of UCBs for exact CS and DL functionals of gasoline demand reveals interesting patterns and is applicable to other goods markets.

This paper proposes a fault detection framework for the condition monitoring of wind turbines. The framework models and analyzes the data in supervisory control and data acquisition systems. For log information, each event is mapped to an assembly based on the Reliawind taxonomy. For operation data, recurrent neural networks are applied to model normal behaviors, which can learn the long‐time temporal dependencies between various time series. Based on the estimation results, a two‐stage threshold method is proposed to determine the current operation status. The method evaluates the shift values deviating from the estimated behaviors and their duration time to attenuate the effect of minor fluctuations. The generated results from the framework can help to understand when the turbine deviates from normal operations. The framework is validated with the data from an onshore wind park. The numerical results show that the framework can detect operational risks and reduce false alarms.

Background:Instrumental variable (IV) methods are widely employed to estimate causal effects when concerns regarding unmeasured confounders. Although comparisons among several IV methods for binary outcomes exist, comprehensive evaluations are insufficient. Therefore, in this study, we aimed to conduct a simulation with some settings for a detailed comparison of these methods, focusing on scenarios where IVs are valid and under effect homogeneity with different instrument strengths.Methods:We compared six IV methods under 32 simulation scenarios: two-stage least squares (2SLS), two-stage predictor substitutions (2SPS), two-stage residual inclusions (2SRI), limited information maximum likelihood (LIML), inverse-variance weighted methods with a linear outcome model (IVWLI), and inverse-variance weighted methods with a non-linear model (IVWLL). By comparing these methods, we examined three key estimates: the parameter estimates of the exposure variable, the causal risk ratio, and the causal risk differences.Results:Based on the results, six IV methods could be classified into three groups: 2SLS and IVWLI, 2SRI and 2SPS, and LIML and IVWLL. The first pair showed a clear bias owing to outcome model misspecification. The second pair showed a relatively good performance when strong IVs are available; however, the estimates suffered from a significant bias when only weak IVs are used. The third pair produced relatively conservative results, although they were less affected by weak IV issues.Conclusions:The findings indicate that no panacea is available for the bias associated with IV methods. We suggest using multiple IV methods: one for primary analysis and another for sensitivity analysis.

We study estimation and inference in settings where the interest is in the effect of a potentially endogenous regressor on some outcome. To address the endogeneity, we exploit the presence of additional variables. Like conventional instrumental variables, these variables are correlated with the endogenous regressor. However, unlike conventional instrumental variables, they also have direct effects on the outcome, and thus are \"invalid\" instruments. Our novel identifying assumption is that the direct effects of these invalid instruments are uncorrelated with the effects of the instruments on the endogenous regressor. We show that in this case the limited-information-maximum-likelihood (liml) estimator is no longer consistent, but that a modification of the bias-corrected two-stage-least-square (tsls) estimator is consistent. We also show that conventional tests for over-identifying restrictions, adapted to the many instruments setting, can be used to test for the presence of these direct effects. We recommend that empirical researchers carry out such tests and compare estimates based on liml and the modified version of bias-corrected tsls. We illustrate in the context of two applications that such practice can be illuminating, and that our novel identifying assumption has substantive empirical content.

The trigeneration plants for combined cooling, heating, and electricity supply, or integrated energy systems (IES), are mostly based on gas reciprocating engines. The fuel efficiency of gas reciprocating engines depends essentially on air intake temperatures. The transformation of the heat removed from the combustion engines into refrigeration is generally conducted by absorption lithium-bromide chillers (ACh). The peculiarity of refrigeration generation in food technologies is the use of chilled water of about 12 °C instead of 7 °C as the most typical for ACh. This leads to a considerable cooling potential not realized by ACh that could be used for cooling the engine intake air. A refrigerant ejector chiller (ECh) is the simplest in design, cheap, and can be applied as the low-temperature stage of a two-stage absorption-ejector chiller (AECh) to provide engine intake air cooling and increase engine fuel efficiency as result. The monitoring data on gas engine fuel consumption and power were analyzed in order to evaluate the effect of gas engine cyclic air cooling.

The stochastic variational inequality (SVI) provides a unified form of optimality conditions of stochastic optimization and stochastic games which have wide applications in science, engineering, economics and finance. In the recent two decades, one-stage SVI has been studied extensively and widely used in modeling equilibrium problems under uncertainty. Moreover, the recently proposed two-stage SVI and multistage SVI can be applied to the case when the decision makers want to make decisions at different stages in a stochastic environment. The two-stage SVI is a foundation of multistage SVI, which is to find a pair of “here-and-now” solution and “wait-and-see” solution. This paper provides a survey of recent developments in analysis, algorithms and applications of the two-stage SVI.

Although both creativity and humor elicit experiences of surprise followed by appreciation, it remains unknown whether shared or distinct patterns of effective connectivity are involved in their processing. The present fMRI study used dynamic causal modeling and parametrical empirical Bayes analysis to examine the effective connectivity between the amygdala and frontoparietal network during two‐stage creativity and humor processing. We examined processing during the setup and punch line stages for creativity and humor, including typical forms (alternate uses for creativity and incongruity‐resolution humor), atypical forms (aesthetic uses for creativity and nonsense humor), and baseline forms. Our focus was on the mesolimbic pathway during the punch line stage. We found that the amygdala plays a key role in expectation violation and appreciation. Broadly, amygdala‐to‐IFG connectivity was important for evaluating typical and atypical forms of both creativity and humor, while amygdala‐to‐precuneus connectivity was involved in evaluating typical forms. Amygdala‐to‐IFG connectivity was involved in the expectation violation to resolution stage of processing for typical and atypical forms of creativity and humor. Amygdala‐to‐precuneus connectivity was involved in processing the novelty and usefulness of typical forms of creativity (alternate uses) and understanding others' intentions in typical forms of humor (incongruity‐resolution). Interestingly, VTA‐to‐amygdala connectivity was involved in processing the appreciation of both typical (incongruity‐resolution humor) and atypical (nonsense humor) forms of humor while amygdala‐to‐VTA connectivity was involved in processing the appreciation of atypical (aesthetic uses) forms of creativity. Altogether, these findings suggest that the amygdala and frontoparietal circuitry are critical for creativity and humor processing.

In this paper, we demonstrate that standard central limit theorem (CLT) results do not hold for means of non-parametric, conditional efficiency estimators, and we provide new CLTs that permit applied researchers to make valid inference about mean conditional efficiency or to compare mean efficiency across groups of producers. The new CLTs are used to develop a test of the restrictive 'separability' condition that is necessary for second-stage regressions of efficiency estimates on environmental variables. We show that if this condition is violated, not only are second-stage regressions difficult to interpret and perhaps meaningless, but also first-stage, unconditional efficiency estimates are misleading. As such, the test developed here is of fundamental importance to applied researchers using non-parametric methods for efficiency estimation. The test is shown to be consistent and its local power is examined. Our simulation results indicate that our tests perform well both in terms of size and power. We provide a real-world empirical example by re-examining the paper by Aly et al. (1990, Review of Economics and Statistics 72, 211-18) and rejecting the separability assumption implicitly assumed by Aly et al., calling into question results that appear in hundreds of papers that have been published in recent years.

Background: Instrumental variable (IV) methods are widely employed to estimate causal effects when concerns regarding unmeasured confounders. Although comparisons among several IV methods for binary outcomes exist, comprehensive evaluations are insufficient. Therefore, in this study, we aimed to conduct a simulation with some settings for a detailed comparison of these methods, focusing on scenarios where IVs are valid and under effect homogeneity with different instrument strengths.Methods: We compared six IV methods under 32 simulation scenarios: two-stage least squares (2SLS), two-stage predictor substitutions (2SPS), two-stage residual inclusions (2SRI), limited information maximum likelihood (LIML), inverse-variance weighted methods with a linear outcome model (IVWLI), and inverse-variance weighted methods with a non-linear model (IVWLL). By comparing these methods, we examined three key estimates: the parameter estimates of the exposure variable, the causal risk ratio, and the causal risk differences.Results: Based on the results, six IV methods could be classified into three groups: 2SLS and IVWLI, 2SRI and 2SPS, and LIML and IVWLL. The first pair showed a clear bias owing to outcome model misspecification. The second pair showed a relatively good performance when strong IVs are available; however, the estimates suffered from a significant bias when only weak IVs are used. The third pair produced relatively conservative results, although they were less affected by weak IV issues.Conclusion: The findings indicate that no panacea is available for the bias associated with IV methods. We suggest using multiple IV methods: one for primary analysis and another for sensitivity analysis.