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The event study model is a powerful econometric tool used for the purpose of estimating dynamic treatment effects. One of its most appealing features is that it provides a built-in graphical summary of results, which can reveal rich patterns of behavior. Another value of the picture is the estimated pre-event pseudo-\"effects\", which provide a type of placebo test. In this essay I aim to provide a framework for a shared understanding of these models. There are several (sometimes subtle) decisions and choices faced by users of these models, and I offer guidance for these decisions.

Here’s why. (a) The Hodrick-Prescott (HP) filter introduces spurious dynamic relations that have no basis in the underlying data-generating process. (b) Filtered values at the end of the sample are very different from those in the middle and are also characterized by spurious dynamics. (c) A statistical formalization of the problem typically produces values for the smoothing parameter vastly at odds with common practice. (d) There is a better alternative. A regression of the variable at date t on the four most recent values as of date t − h achieves all the objectives sought by users of the HP filter with none of its drawbacks.

Trade-offs exist between the point of early detection and the future cost of controlling any invasive species. Finding optimal levels of early detection, with post-border active surveillance, where time, space and randomness are explicitly considered, is computationally challenging. We use a stochastic programming model to find the optimal level of surveillance and predict damages, easing the computational challenge by combining a sample average approximation (SAA) approach and parallel processing techniques. The model is applied to the case of Asian Papaya Fruit Fly (PFF), a highly destructive pest, in Queensland, Australia. To capture the non-linearity in PFF spread, we use an agent-based model (ABM), which is calibrated to a highly detailed land-use raster map (50 m × 50 m) and weather-related data, validated against a historical outbreak. The combination of SAA and ABM sets our work apart from the existing literature. Indeed, despite its increasing popularity as a powerful analytical tool, given its granularity and capability to model the system of interest adequately, the complexity of ABM limits its application in optimizing frameworks due to considerable uncertainty about solution quality. In this light, the use of SAA ensures quality in the optimal solution (with a measured optimality gap) while still being able to handle large-scale decision-making problems. With this combination, our application suggests that the optimal (economic) trap grid size for PFF in Queensland is ~0.7 km, much smaller than the currently implemented level of 5 km. Although the current policy implies a much lower surveillance cost per year, compared with the $2.08 million under our optimal policy, the expected total cost of an outbreak is $23.92 million, much higher than the optimal policy of roughly $7.74 million.

Northern and southern fog events are identified over eastern China across 40 winters from 1981 to 2021. By performing composite analysis on these events, this study reveals that the formation of fog events is controlled by both dynamic and thermodynamic processes. The fog events were induced by Rossby wave trains over the Eurasian continent, leading to the development of surface wind and pressure anomalies, which favor the formation of fog events. The Rossby wave trains in northern and southern fog events are characterized by their occurrence in northern and southern locations, respectively, with different strengths. The water vapor fluxes that contribute to the enhancement of the northern fog events originate from the Yellow Sea and the East China Sea, whereas the southern fog events are characterized by water vapor from the East China Sea and the South China Sea. In both northern and southern fog events, dew point depression and positive A and K index anomalies are found in northern and southern regions of eastern China, which are indicative of supersaturated air and the unstable atmospheric saturation from the low to the middle troposphere, thus providing favorable conditions for the establishment of fog events in northern and southern regions of eastern China.

The law of entropy increase postulates the existence of irreversible processes in physics: the total entropy of an isolated system can increase, but cannot decrease. The annihilation of an electric current in normal metal with the generation of Joule heat because of a non-zero resistance is a well-known example of an irreversible process. The persistent current, an undamped electric current observed in a superconductor, annihilates after the transition into the normal state. Therefore, this transition was considered as an irreversible thermodynamic process before 1933. However, if this transition is irreversible, then the Meissner effect discovered in 1933 is experimental evidence of a process reverse to the irreversible process. Belief in the law of entropy increase forced physicists to change their understanding of the superconducting transition, which is considered a phase transition after 1933. This change has resulted to the internal inconsistency of the conventional theory of superconductivity, which is created within the framework of reversible thermodynamics, but predicts Joule heating. The persistent current annihilates after the transition into the normal state with the generation of Joule heat and reappears during the return to the superconducting state according to this theory and contrary to the law of entropy increase. The success of the conventional theory of superconductivity forces us to consider the validity of belief in the law of entropy increase.

Changes in monsoon precipitation have profound social and economic impacts as more than two-thirds of the world’s population lives in monsoon regions. Observations show a significant reduction in global land monsoon precipitation during the second half of the 20th century. Understanding the cause of this change, especially possible anthropogenic origins, is important. Here, we compare observed changes in global land monsoon precipitation during 1948–2005 with those simulated by 5 global climate models participating in the Coupled Model Inter-comparison Project-phase 5 (CMIP5) under different external forcings. We show that the observed drying trend is consistent with the model simulated response to anthropogenic forcing and to anthropogenic aerosol forcing in particular. We apply the optimal fingerprinting method to quantify anthropogenic influences on precipitation and find that anthropogenic aerosols may have contributed to 102% (62–144% for the 5–95% confidence interval) of the observed decrease in global land monsoon precipitation. A moisture budget analysis indicates that the reduction in precipitation results from reduced vertical moisture advection in response to aerosol forcing. Since much of the monsoon regions, such as India and China, have been experiencing rapid developments with increasing aerosol emissions in the past decedes, our results imply a further reduction in monsoon precipitation in these regions in the future if effective mitigations to reduce aerosol emissions are not deployed. The observed decline of aerosol emission in China since 2006 helps to alleviate the reducing trend of monsoon precipiptaion.

This book places thermodynamics on a system-theoretic foundation so as to harmonize it with classical mechanics. Using the highest standards of exposition and rigor, the authors develop a novel formulation of thermodynamics that can be viewed as a moderate-sized system theory as compared to statistical thermodynamics. This middle-ground theory involves deterministic large-scale dynamical system models that bridge the gap between classical and statistical thermodynamics. The authors' theory is motivated by the fact that a discipline as cardinal as thermodynamics--entrusted with some of the most perplexing secrets of our universe--demands far more than physical mathematics as its underpinning. Even though many great physicists, such as Archimedes, Newton, and Lagrange, have humbled us with their mathematically seamless eurekas over the centuries, this book suggests that a great many physicists and engineers who have developed the theory of thermodynamics seem to have forgotten that mathematics, when used rigorously, is the irrefutable pathway to truth. This book uses system theoretic ideas to bring coherence, clarity, and precision to an extremely important and poorly understood classical area of science.

Most state-of-the-art general circulation models cannot well simulate the diurnal cycle of precipitation, especially the nocturnal rainfall peak over land. This study assesses the diurnal cycle of precipitation simulated using the Global-to-Regional Integrated forecast SysTem (GRIST) in its numerical weather prediction (NWP) configuration at resolutions typical of current global climate models. In the refinement region, the variable-resolution model well distinguishes the distinct features of diurnal cycle. No apparent artificial features are observed in the transition zone of the variable-resolution mesh. The model also exhibits a similar diurnal cycle pattern to the observation in the coarse-resolution region. We further investigate the model behaviors of dynamics–physics interaction by analyzing hourly dynamical and thermodynamical diagnostics. Composite analysis based on rainfall peak time is applied to examine the model capability in distinguishing different precipitation processes of daytime and nighttime peaks. Over East Asia, the model well reproduces both the nocturnal-to-early-morning and the afternoon rainfall peaks. The model simulates the dominant contribution of large-scale upward moisture advection to the formation of stratiform-like rainfall peaks, and produces daytime surface-heating induced rainfall. Refinement of the resolution substantially increases the composited nighttime precipitation intensity but has little impact on the composite percentage. The model captures the realistic dynamical and thermodynamical conditions for the occurrence of nocturnal rainfall. These results demonstrate that the variable-resolution model is able to reproduce the diurnal cycle of climatological summer rainfall through realistic precipitation processes.

The classic papers by Newey and West (1987) and Andrews (1991) spurred a large body of work on how to improve heteroscedasticity- and autocorrelation-robust (HAR) inference in time series regression. This literature finds that using a larger-than-usual truncation parameter to estimate the long-run variance, combined with Kiefer-Vogelsang (2002, 2005) fixed-b critical values, can substantially reduce size distortions, at only a modest cost in (size-adjusted) power. Empirical practice, however, has not kept up. This article therefore draws on the post-Newey West/Andrews literature to make concrete recommendations for HAR inference. We derive truncation parameter rules that choose a point on the size-power tradeoff to minimize a loss function. If Newey-West tests are used, we recommend the truncation parameter rule S = 1.3T 1/2 and (nonstandard) fixed-b critical values. For tests of a single restriction, we find advantages to using the equal-weighted cosine (EWC) test, where the long run variance is estimated by projections onto Type II cosines, using ν = 0.4T 2/3 cosine terms; for this test, fixed-b critical values are, conveniently, t ν or F. We assess these rules using first an ARMA/GARCH Monte Carlo design, then a dynamic factor model design estimated using a 207 quarterly U.S. macroeconomic time series.

We propose a class of observation-driven time series models referred to as generalized autoregressive score (GAS) models. The mechanism to update the parameters over time is the scaled score of the likelihood function. This new approach provides a unified and consistent framework for introducing time-varying parameters in a wide class of nonlinear models. The GAS model encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity, autoregressive conditional duration, autoregressive conditional intensity, and Poisson count models with time-varying mean. In addition, our approach can lead to new formulations of observation-driven models. We illustrate our framework by introducing new model specifications for time-varying copula functions and for multi variate point processes with time-vary ing parameters. We study the models in detail and provide simulation and empirical evidence.