Econometric Inference, Cyclical Fluctuations, and Superior Information

This paper presents and assesses a procedure to estimate conventional parameters characterizing fluctuations at the business cycle frequency, when the economic agents' information set is superior to the econometrician's one. Specifically, we first generalize the conditions under which the econometrician can estimate these "cyclical fluctuation" parameters from augmented laws of motion for forcing variables that fully recover the agents' superior information. Second, we document the econometric properties of the estimates when the augmented laws of motion are possibly misspecified. Third, we assess the ability of certain information criteria to detect the presence of superior information.Block bootstrap, Hidden variables, laws of motion for forcing variables, Monte Carlo simulations

Equity Premia and State-Dependent Risks

This paper analyzes the empirical relations between equity premia and state-dependent consumption and market risks. These relations are derived from a flexible specification of the CCAPM with mixture distribution, which admits the existence of two regimes. Focusing on the market return, we find that the consumption and market risks are priced in each state, and the responses of expected equity premia to these risks are state dependent. Extending to various portfolio returns, we show that the responses to downside consumption risks are the most important, they are almost always statistically larger than the responses to upside consumption risks, and they are much larger for firms having smaller sizes and facing more financial distresses.Mixture of truncated normals, downside and upside consumption and market risks

Macroeconomic Effects of Terrorist Shocks in Israel

This paper estimates a structural vector autoregression model to assess the dynamic effects of terrorism on output and prices in Israel over the post-1985 period. Long-run restrictions are used to obtain an interpretation of the effects of terrorism in terms of aggregate demand and supply curves. The empirical responses of output and prices suggest that the immediate effects of terrorism are similar to those associated with a negative demand shock. Such leftward shift of the aggregate demand curve is consistent with the adverse effects of terrorism on most components of aggregate expenditure, which have been documented in previous studies. In contrast, the long-term consequences of terrorism are similar to those related to a negative supply shock. Such leftward shift of the long-run aggregate supply curve suggests the potential existence of adverse effects of terrorism on the determinants of potential output, which have not been considered so far.Goods market, output, price, and terrorist indices, structural vector autoregressions, long-run identifying restrictions, dynamic responses and variance decompositions

Prediction intervals for travel time on transportation networks

Estimating travel-time is essential for making travel decisions in transportation networks. Empirically, single road-segment travel-time is well studied, but how to aggregate such information over many edges to arrive at the distribution of travel time over a route is still theoretically challenging. Understanding travel-time distribution can help resolve many fundamental problems in transportation, quantifying travel uncertainty as an example. We develop a novel statistical perspective to specific types of dynamical processes that mimic the behavior of travel time on real-world networks. We show that, under general conditions, travel-time normalized by distance, follows a Gaussian distribution with route-invariant (universal) location and scale parameters. We develop efficient inference methods for such parameters, with which we propose asymptotic universal confidence and prediction intervals of travel time. We further develop our theory to include road-segment level information to construct route-specific location and scale parameter sequences that produce tighter route-specific Gaussian-based prediction intervals. We illustrate our methods with a real-world case study using precollected mobile GPS data, where we show that the route-specific and route-invariant intervals both achieve the 95\% theoretical coverage levels, where the former result in tighter bounds that also outperform competing models.Comment: 24 main pages, 4 figures and 4 tables. This version includes many changes to the previous on

Improving the generalizability and robustness of large-scale traffic signal control

A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. In this work, we study the robustness of such methods along two axes. First, sensor failures and GPS occlusions create missing-data challenges and we show that recent methods remain brittle in the face of these missing data. Second, we provide a more systematic study of the generalization ability of RL methods to new networks with different traffic regimes. Again, we identify the limitations of recent approaches. We then propose using a combination of distributional and vanilla reinforcement learning through a policy ensemble. Building upon the state-of-the-art previous model which uses a decentralized approach for large-scale traffic signal control with graph convolutional networks (GCNs), we first learn models using a distributional reinforcement learning (DisRL) approach. In particular, we use implicit quantile networks (IQN) to model the state-action return distribution with quantile regression. For traffic signal control problems, an ensemble of standard RL and DisRL yields superior performance across different scenarios, including different levels of missing sensor data and traffic flow patterns. Furthermore, the learning scheme of the resulting model can improve zero-shot transferability to different road network structures, including both synthetic networks and real-world networks (e.g., Luxembourg, Manhattan). We conduct extensive experiments to compare our approach to multi-agent reinforcement learning and traditional transportation approaches. Results show that the proposed method improves robustness and generalizability in the face of missing data, varying road networks, and traffic flows

Ensemble Methods for Survival Data with Time-Varying Covariates

Survival data with time-varying covariates are common in practice. If relevant, such covariates can improve on the estimation of a survival function. However, the traditional survival forests - conditional inference forest, relative risk forest and random survival forest - have accommodated only time-invariant covariates. We generalize the conditional inference and relative risk forests to allow time-varying covariates. We compare their performance with that of the extended Cox model, a commonly used method, and the transformation forest method, designed to detect non-proportional hazards deviations and adapted here to accommodate time-varying covariates, through a comprehensive simulation study in which the Kaplan-Meier estimate serves as a benchmark and the integrated L2 difference between the true and estimated survival functions is used for evaluation. In general, the performance of the two proposed forests substantially improves over the Kaplan-Meier estimate. Under the proportional-hazard setting, the best method is always one of the two proposed forests, while under the non-proportional hazards setting, it is the adapted transformation forest. We use K-fold cross-validation to choose between the methods, which is shown to be an effective tool to provide guidance in practice. The performance of the proposed forest methods for time-invariant covariate data is broadly similar to that found for time-varying covariate data

La relation entre le contexte de l'évaluation du rendement et l'indulgence de l'évaluateur

La présente étude s'insère dans les multiples efforts de recherche déployés pour mieux circonscrire les paramètres de l'évaluation du rendement. À partir du modèle de Murphy et Cleveland (1995), les auteurs développent une méthodologie originale qui permet de tester empiriquement auprès de 106 fonctionnaires de la fonction publique québécoise la motivation de l'évaluateur à produire des évaluations indulgentes de leurs subordonnés. Les résultats révèlent que l'indulgence s'avère une réponse à un contexte défavorable d'évaluation : les variables contextuelles influencent significativement les appréciations faites par l'évaluateur.There has been a substantial amount of research on performance appraisal. Practically all of this work has focused on understanding and improving a rater's ability to provide accurate ratings. Thus a plethora of research exists regarding such issues as the effect of rating formats and training on the ability to perform accurate evaluations of subordinates. More recently, several researchers have suggested that increasing the quality of performance ratings can only occur through a better understanding of the cognitive processes that underlie performance judgments. This perspective argues that rating errors and inaccurate appraisals are a function of the rater's information processing capabilities. Recent models of performance appraisal have focused on motivational factors rather then cognitive deficits as explanations for apparent rater errors. Despite recent calls for more work in this area, very few studies have investigated the motivation to inflate ratings in the performance appraisal context. The purpose of this paper is to examine the relationship between the performance appraisal context and rater motivation to inflate ratings. Hypotheses were developed from the Murphy and Cleveland (1995) model of rater leniency. The assumption underlying the study is that rating inaccuracy is predominantly intentional.The participants in the study were 106 managers in the Quebec public sector. Rating inflation was defined as the discrepancy between public and private performance appraisal ratings for a target ratee. A standardized interview and two questionnaires were used to collect the data. To gather public rating for the target ratee, each participant was asked to get an anonymous copy of the target ratee's last performance appraisal from the human resource department. Each participant's private ratings of the target ratee were collected. Private performance ratings consisted of the rater's personal judgment of the employee's performance during the most recent performance appraisal period. These ratings were made during the interview on a copy of the appraisal form normally used by the ratee. After finishing the interview, the researcher gave participants a first questionnaire and one month later sent the second questionnaire. The questionnaires included measures of context variables.As expected, raters' perceptions of the performance appraisal context are related to rating behaviour. Specifically, the results show that the quality of the interpersonal relationship between supervisor and subordinate influence rating inflation. The ratings of an employee in low-quality relationships are inflated. In contrast, supervisory ratings are more accurate for employees in high-quality relationships. A supervisons perceptions of subordinates' self rating of their performance is related to rating inflation. This accountability pressure might arise because supervisors wish to avoid conflict. The level of rating inflation varies across raters and in relation to the type of standard used to judge performance. The lack of clear performance standards is related to rating inflation. Discomfort in giving feedback was not significantly related to rater motivation to inflate ratings. The results also indicate that the purpose of performance ratings effects rating inflation. Rating inflation will be more likely to occur when performance appraisal is not linked to human resource management decisions. Rater trust in the appraisal System is likely to affect rater motivation. Low trust in the System is related to rating inflation. As predicted, a rater's impression of management activities is related to rating inflation. Raters are likely to inflate ratings to maintain a positive image of the organization and to maintain an appropriate image vis-a-vis his or her subordinates. Consistent with the hypothesis, managers may be more likely to inflate ratings when there are political influences within the performance appraisal process. Overall, the findings suggest that the performance appraisal context does affect rater behaviour. This research helps to bridge the gap between practice and research in the area of performance appraisal.El présente estudio se intégra dentro de los multiples esfuerzos de investigaciòn desarrollados para mejor comprender los paràmetros de evaluaciòn del rendimiento. A partir del modelo de Murphy y Cleveland (1995), los autores desarrollaron una metodologia original que permite probar empìricamente de un grupo de 106 funcionarios de la funciòn publica de Québec, la motivaciòn del evaluador de producir evaluaciones indulgentes de sus subordinados. Los resultados revelaron que la indulgencia se muestra como una respuesta a un contexto desfavorable de evaluaciòn: las variables conceptuales influencian significativamente las apreciaciones hechas por el evaluador

Enhancing student learning and engagement of scientific concepts through case studies in integrated science

Context-based learning activities, such as case studies, that bring to light the relevance of science increase student engagement, improve student performance, and attract students to study science at the university level. Meanwhile, integrated science education is based on an approach that emphasizes the interconnectedness of scientific fields, such as astronomy, chemistry, physics, biology, Earth sciences, and computer science. By incorporating case studies in integrated science courses, students are provided with real-world scenarios that enable them to explore the interdisciplinary nature of science while acquiring a deeper understanding of foundational scientific concepts and their application to real-world situations. These case studies foster active and collaborative learning, helping students develop their problem-solving and critical thinking skills by analyzing and interpreting data from multiple scientific perspectives. Furthermore, this approach can stimulate students to formulate innovative solutions to problems, enhancing their creativity and scientific curiosity. Overall, an integrated science approach that centers on case studies creates a more engaging and effective learning environment that can lead to improved outcomes in science education. This presentation discusses the implementation of this approach at the university level and provides practical ideas on its implementation