Search

We develop an econometric modelling framework to forecast commodity prices taking into account potentially different dynamics and linkages existing at different states of the world and using different performance measures to validate the predictions. We assess the extent to which the quality of the forecasts can be improved by entertaining different regime‐dependent threshold models considering different threshold variables. We evaluate prediction quality using both loss minimization and profit maximization measures based on directional accuracy, directional value, the ability to predict turning points, and the returns implied by a simple trading strategy. Our analysis provides overwhelming evidence that allowing for regime‐dependent dynamics leads to improvements in predictive ability for the Goldman Sachs Commodity Index, as well as for its five sub‐indices (energy, industrial metals, precious metals, agriculture, and livestock). Our results suggest the existence of a trade‐off between predictive ability based on loss and profit measures, which implies that the particular aim of the prediction exercise carried out plays a very important role in terms of defining which set of models is the best to use. [ABSTRACT FROM AUTHOR]

This article focuses on three main limitations of the literature regarding the impact of corruption on democracy. These are the distinctions between causality and correlation, channels underlying the impact and the shape of the relationship. The study uses recent developments in panel data causality and non-linear econometrics, breaks democracy down into different components and analyses the form of the relationship. We find that the relationship between the variables is often non-linear. Control of Corruption leads to a higher democracy; the components of democracy are also affected by corruption. These findings add to the literature in three respects: (i) corruption has a causal effect on democracy and, hence, single equation estimation poses problems, (ii) assuming linearity of the relationships between control of corruption and democracy is also possibly problematic and (iii) the channels of transmission of the effects of corruption on democracy are diverse. [ABSTRACT FROM AUTHOR]

In the pursuit of achieving the strategic objectives of "dual carbon," the green economy has emerged as an inevitable trend in China's national economic development. In an effort to effectively promote the establishment of a sustainable civilization and establish a modern green economic system, the implementation of a green financial system is regarded as a significant measure. GC (GC represents Green credit. The following statements are redundant)as a derivative form of green finance, serves as the primary channel for financing the green economy, and It is also the core force driving HQD(HQD represents high-quality development of green economy). Despite its significance, the academic literature does not provide a thorough examination of the specific effects of GC on the HQD. Building upon this foundation, the article elaborates on the internal mechanism of GC for HQD on the basis of mathematical model construction and scientifically evaluates the comprehensive index of HQD in Chinese provinces using entropy method. Introducing the SYS-GMM model, the Quantile regression model, and panel data to concentrate on the path of the role of GC in promoting the HQD, as well as regional differences. The panel threshold model and Bootstrap test method are embedded in the analysis to clarify potential threshold effects and identify corresponding transmission mechanisms. The research provides evidence that: (1) GC has a significant "pulling effect" on the HQD, and this conclusion remains valid even when considering endogeneity issues; (2) Quantile regression analysis reveals that the "pull effect" of GC is proportional to the index of HQD at the provincial level; (3) Analysis of the transmission mechanism reveals that the relationship between GC and the HQD is influenced by green innovation, which serves as a mediating factor; (4)Analysis of the threshold effect reveals that moderate environmental regulation enhances the "pull effect" of GC on the HQD. These research conclusions provide valuable guidance for developing a scientifically reasonable approach to promoting HQD at the provincial level in China. [ABSTRACT FROM AUTHOR]

AbstractThe paper examines the relationship between health and economic growth using a dynamic panel with threshold effect and endogeneity. The study uses a sample of 136 countries over the period of 1965–2015 and the results indicate that the relationship between health and economic growth is nonlinear. The findings suggest that the impact of health on economic growth is only positive in countries that have attained minimum benchmarks of health improvement. These findings carry significant policy implications. Low-income countries need a comprehensive approach, encompassing multiple factors, such as education enhancement, productivity boost, and entrepreneurship promotion, that go beyond health promotion to foster substantial economic growth. In countries already attaining satisfactory health outcomes, policies prioritizing improved health outcomes, through directing investments towards Research and Development can serve as an effective means to promote sustainable economic growth.

This paper uses inter-provincial panel data from 2011 to 2017, a linear regression model, and a threshold model to conduct empirical analyses of the impact of the digital economy on China’s overall economic growth and the three main sectors of industry. The paper then investigates the impact and effects the digital economy has had on the economic growth of the three main sectors of industry in China’s eastern, central, and western regions. Finally, the paper investigates the most significant differences among the various regions and the threshold effects of urbanization levels on the relationship between the digital economy and economic growth. The findings indicate a significantly positive correlation between the digital economy and regional economic growth. Moreover, geographical factors notably influence this correlation. The digital economy exerts a positive effect on all sectors of industry. It may not substantially impact industrial development in regions with highly developed infrastructure. Regarding the other regions, the digital economy exhibits varying degrees of impact due to the differences in the specific indicators. The conclusion drawn by the threshold model is that the magnitude of the threshold effect correlates with geographic factors. No threshold effect was observed in the eastern region, while the threshold effect occurred in the central region when the urbanization levels for the provinces were below 0.6645. Similarly, the threshold effect was noted in the western region when the urbanization level was below 0.3931. Considering all of this, the study also offers policy recommendations that will help balance the regional development of digital economies, accelerate the digital transformation of traditional industries, enhance digital infrastructure construction, refine the formulation and implementation of data policy, and establish relevant incentive mechanisms. [ABSTRACT FROM AUTHOR]

We propose a reinforcement learning (RL) algorithm for generating a trading strategy in a realistic setting, that includes transaction costs and factors driving the asset dynamics. We benchmark our algorithm against the analytical optimal solution, available when factors are linear and transaction costs are quadratic, showing that RL is able to mimic the optimal strategy. Then we consider a more realistic setting, including non‐linear dynamics, that better describes the WTI spot prices time series. For these more general dynamics, an optimal strategy is not known and RL becomes a viable alternative. We show that on synthetic data generated from WTI spot prices, the RL agent outperforms a trader that linearizes the model to apply the theoretical optimal strategy. [ABSTRACT FROM AUTHOR]

The paper examines the relationship between health and economic growth using a dynamic panel with threshold effect and endogeneity. The study uses a sample of 136 countries over the period of 1965–2015 and the results indicate that the relationship between health and economic growth is nonlinear. The findings suggest that the impact of health on economic growth is only positive in countries that have attained minimum benchmarks of health improvement. These findings carry significant policy implications. Low-income countries need a comprehensive approach, encompassing multiple factors, such as education enhancement, productivity boost, and entrepreneurship promotion, that go beyond health promotion to foster substantial economic growth. In countries already attaining satisfactory health outcomes, policies prioritizing improved health outcomes, through directing investments towards Research and Development can serve as an effective means to promote sustainable economic growth. IMPACT STATEMENT: The paper provides an in-depth analysis of the relationship between health and economic growth. By explicitly examining potential non-linearities and thresholds, the study offers insights into how variations in health status may influence economic growth differently across different thresholds or stages of development. By identifying the key health thresholds, the research provides policymakers with actionable insights to allocate resources effectively. This guidance is critical for crafting targeted policies that maximize the impact of health investments on economic development. [ABSTRACT FROM AUTHOR]

How does network structure impact the speed and reach of social contagions? The current view holds that random links facilitate "simple" contagion, but when agents require multiple reinforcements for "complex" adoption, clustered networks are better conduits of social influence. We show that in complex contagion, even low probabilities of adoption upon a single contact would activate an exponential contagion process that tilts the balance in favor of random networks. On the other hand, underappreciated but critical to the race between random and clustered networks is how long agents engage with contagion. Switching back to prior practice and the inactivation of senders and especially receivers shorten the window of engagement for convincing distant contacts and weaken the reach of diffusion on random networks. We propose a simplified framework where clustering primarily enables contagion when repetition matters and receivers lose interest quickly; otherwise, diffusion, simple or complex, is faster on random networks than clustered ones. These mechanisms can inform designing social networks, structuring groups, and seeding of ideas and innovations at a time when the increasing inflow of content from various media limits actors' engagement with each item, whereas expanding network size and connections speeds up diffusion through distant contacts. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2023.1658. [ABSTRACT FROM AUTHOR]

Abstract Key message We have examined thermal control for embryo growth and seed germination in European ash. Using historical data (span of 36 years) of embryo index in matured seeds, we showed that maturation is not influenced by the annual average temperature. Secondary dormancy in ash’s seeds is easily induced, and the thermal window for both embryo growth and seed germination was between 10 and 20 °C. Context Seed germination plays a crucial role in establishing plant communities. Ash seeds are characterized by deep morphophysiological dormancy, usually overcome by 32 weeks of stratification. Observed climate changes might disrupt ash’s thermal window for embryo growth and seed germination. Thus, proper examination of seed adaptability can be important for forest management. Aims We investigated the effect of temperature on the embryo index (ratio between embryo and seed length) as well as embryo growth and germination of European ash (Fraxinus excelsior L.) seeds under different thermal conditions. Methods We used seeds collected at the same site in Poland between 1985 and 2021 to first analyze the influence of the growing season on the initial embryo index in mature seeds. Subsequently, we examined embryo growth and germination of seeds at constant temperatures of 3°, 10°, 15°, 20°, 25°, and 30 °C from seeds collected in 2021. Using the modeled rate of embryo growth and germination, we predicted the cardinal temperatures for those processes in ash seeds. Results The embryo index of ash seeds varied; however, there was no relationship between the index and year of seed collection. The base temperature for embryo growth was 1.4 ± 1.1 °C, with a ceiling temperature of 29.9 ± 2.0 °C and an optimal temperature of 15.7 ± 0.3 °C. The base temperature for germination was at − 1.8 ± 0.3 °C. Due to the induction of secondary dormancy, the ceiling temperature of germination was impossible to establish. Conclusions The initial embryo index of mature ash seeds seems to be an individual trait and was not affected by local average annual temperatures. The most effective temperature ranges for embryo growth and germination were similar, however narrow (between 10 and 15 °C). Temperatures above 20 °C inducted secondary dormancy, hampering embryo growth as well as germination. Such a narrow thermal window for embryo growth can potentially act as a limiting factor for the natural regeneration of ash seeds.

Key message: We have examined thermal control for embryo growth and seed germination in European ash. Using historical data (span of 36 years) of embryo index in matured seeds, we showed that maturation is not influenced by the annual average temperature. Secondary dormancy in ash's seeds is easily induced, and the thermal window for both embryo growth and seed germination was between 10 and 20 °C. Context: Seed germination plays a crucial role in establishing plant communities. Ash seeds are characterized by deep morphophysiological dormancy, usually overcome by 32 weeks of stratification. Observed climate changes might disrupt ash's thermal window for embryo growth and seed germination. Thus, proper examination of seed adaptability can be important for forest management. Aims: We investigated the effect of temperature on the embryo index (ratio between embryo and seed length) as well as embryo growth and germination of European ash (Fraxinus excelsior L.) seeds under different thermal conditions. Methods: We used seeds collected at the same site in Poland between 1985 and 2021 to first analyze the influence of the growing season on the initial embryo index in mature seeds. Subsequently, we examined embryo growth and germination of seeds at constant temperatures of 3°, 10°, 15°, 20°, 25°, and 30 °C from seeds collected in 2021. Using the modeled rate of embryo growth and germination, we predicted the cardinal temperatures for those processes in ash seeds. Results: The embryo index of ash seeds varied; however, there was no relationship between the index and year of seed collection. The base temperature for embryo growth was 1.4 ± 1.1 °C, with a ceiling temperature of 29.9 ± 2.0 °C and an optimal temperature of 15.7 ± 0.3 °C. The base temperature for germination was at − 1.8 ± 0.3 °C. Due to the induction of secondary dormancy, the ceiling temperature of germination was impossible to establish. Conclusions: The initial embryo index of mature ash seeds seems to be an individual trait and was not affected by local average annual temperatures. The most effective temperature ranges for embryo growth and germination were similar, however narrow (between 10 and 15 °C). Temperatures above 20 °C inducted secondary dormancy, hampering embryo growth as well as germination. Such a narrow thermal window for embryo growth can potentially act as a limiting factor for the natural regeneration of ash seeds. [ABSTRACT FROM AUTHOR]

This paper reconsiders the flattening of the Phillips curve that has taken place over the last twenty-five years, together with the recent claim that a wage-price spiral could ignite an inflationary process. The two aspects are interrelated. The time-variation nature of the parameters plays a fundamental role in feeding a process of instability that may affect the reappearance of inflationary episodes. This paper first examines the extent of the U.S. Phillips curve flattening and nonlinearity by comparing the results of different econometric specifications and models. Based on these findings, the use of a regime-switching device, particularly studied by Richard Day, is proposed and applied to a Harrod–Minsky macro model. Three results are worth mentioning. The detection of nonlinearities in the U.S. Phillips curve leading to breaks in the curve; the ability of the model to generate robust bounded fluctuations, in which both Phillips curve loops and distributive loops can arise; the presence of both wage-led and profit-led distributive loops in the same model with different combinations of real and monetary parameters that can generate complex dynamics. [ABSTRACT FROM AUTHOR]

In this paper, the relationship between energy consumption and total factor productivity (TFP) in the industrial sector in Iran has been investigated using threshold-type nonlinear relationships. The results of estimating a two-threshold model in a sample including data related to 110 industrial branches during the years 2002-2019 show that the effect of energy consumption on the TFP in the industry sector is state-dependent on the energy consumption state or regime and the energy consumption variable coefficients in all regimes has a negative and significant effect on the TFP. If the energy consumption in Iran's industrial sector exceeds a certain threshold, the negative effect of energy consumption on the TFP will intensify. Also, the effect of energy consumption on the TFP in the industry sector is a state dependent on the TFP state or regime, so in low TFP regimes or states, the effect of energy consumption on the TFP of industries is considerable, but in high TFP regimes, industries with higher TFP have a much lower negative impact of energy consumption on their TFP.

The aim of this study is to test the possible non-linear effect of environmental policy stringency on carbon emissions and thus make policy recommendations for emission reduction. For this purpose data for the period 1995–2015 for selected emerging countries were used. According to the findings obtained from fixed-effects panel threshold regressions environmental policy stringency has no significant effect on the relationship between gross domestic product per capita and carbon dioxide emissions. However, it has statistically significant effect if the share of the service sector and the foreign direct investment are taken as regime-dependent variables. Accordingly, in the high policy stringency regime an increase in the share of the service sector and the foreign direct investment reduce emission levels. In the case of using market-based environmental regulations the threshold effect faced by foreign direct investment is much more pronounced. In order to reduce carbon emissions it is recommended to increase environmental policy stringency, especially in market-based tools.

This paper investigates the impact of corruption on sustainable development using the Seo and Shin (2016) threshold model for a panel data set of 96 to 103 developed and developing countries from 1996 to 2019. The paper contributes to the ongoing debate about whether corruption greases or sands the wheels of sustainable development. All results support the hypothesis of the existence of thresholds. Examining the full sample, the impact of corruption on sustainable development is regime-specific and dependent on the quality of governance. In developed countries, corruption always hinders sustainable development, while in developing countries the impact of corruption on sustainable development is regime-specific and dependent on the quality of governance. When the quality of governance is low, highly corrupt countries achieve higher levels of sustainable development, which is consistent with the "grease the wheels" hypothesis. [ABSTRACT FROM AUTHOR]

Background: Litter size at birth (LSB) is one of the most important economic traits in sheep and could be used in genetic improvement schemes for meat production. LSB is inherently a categorical trait and should be analysed with threshold models. Objective: Bayesian threshold models were used to analyze sheep LSB to estimate genetic parameters. Methods: Data was based on 7,901 LSB records from 14,968 dams and 682 sires collected from 1986 to 2012 at Makouie Sheep Breeding Station in Iran. Means of posterior distributions (MPDs) of LSB's genetic parameters were estimated, and the best-fitted models were selected using the deviance information criterion. Results: In the repeated measurement analysis, the estimated direct and maternal heritabilities, and permanent environmental effect (±SE), according to the best-fitted model (model 5), were 0.01 (0.010), 0.02 (0.014), and 0.01 (0.011), respectively. In the univariate analysis, the best estimates of direct and maternal heritabilities were 0.12 (0.064) and 0.08 (0.045), respectively. An increasing trend for direct and maternal heritabilities was observed in parity 2 (0.15 (0.082) and 0.25 (0.083), respectively). In the bivariate analysis, the best estimates of direct and maternal heritabilities for LSB were 0.03 (0.027) and 0.22 (0.041), respectively. The direct and maternal genetic correlations among parities were 0.25 (0.054) and 0.12 (0.021), respectively. Conclusions: The results showed a considerable influence of environmental factors on LSB in each parity of sheep; also, statistically different genetic parameters (p

Abstract According to innovation diffusion theories, the adoption of a new product is the result of a dynamic process whereby individuals become likelier to adopt as others do. Agent-based modelling has emerged as a useful technique to model and study processes of innovation diffusion within artificial societies, as it allows to easily programme and simulate the interaction of multiple agents among them and with their environment. Despite a large body of literature dealing with innovation of diffusions, including the use of agent-based modelling, there has been little to no consideration of two elements that are important features of consumption: the presence of multiple characteristics of goods, and that of price-premiums on the presence of added characteristics. We propose an agent-based model of the diffusion of such goods, and study its emerging properties when compared to standard ones. Our goal is to try and understand how social interaction affects the consumption of goods that are complex rather than uni-dimensional, and whose prices depend on the number of dimensions (characteristics) that are present. Testing the model for different parameters shows that as goods become more complex, social interaction becomes an increasingly important explanatory variable for purchases. This opens up interesting avenues of discussion for those seeking to bring together innovation diffusion theories and goods’ complexity, and can be linked with a number of issues in the social and sustainability sciences.

In this paper multivariate State Space (SS) models are used to evaluate and forecast household loans in Brazil, taking into account two Google search terms in order to identify credit demand: financiamento (type of loan used to finance goods) and empréstimo (a more general type of loan). Our framework is coupled with nonlinear features, such as Markov-switching and threshold point. We explore these nonlinearities to build identification strategies to disentangle the supply and demand forces which drive the credit market to equilibrium over time. We also show that the underlying nonlinearities significantly improves the performance of SS models on forecasting the household loans in Brazil, particularly in short-term horizons. [ABSTRACT FROM AUTHOR]

Background: Litter size at birth (LSB) is one of the most important economic traits in sheep and could be used in genetic improvement schemes for meat production. LSB is inherently a categorical trait and should be analysed with threshold models. Objective: Bayesian threshold models were used to analyze sheep LSB to estimate genetic parameters. Methods: Data was based on 7,901 LSB records from 14,968 dams and 682 sires collected from 1986 to 2012 at Makouie Sheep Breeding Station in Iran. Means of posterior distributions (MPDs) of LSB's genetic parameters were estimated, and the best-fitted models were selected using the deviance information criterion. Results: In the repeated measurement analysis, the estimated direct and maternal heritabilities, and permanent environmental effect (±SE), according to the best-fitted model (model 5), were 0.01 (0.010), 0.02 (0.014), and 0.01 (0.011), respectively. In the univariate analysis, the best estimates of direct and maternal heritabilities were 0.12 (0.064) and 0.08 (0.045), respectively. An increasing trend for direct and maternal heritabilities was observed in parity 2 (0.15 (0.082) and 0.25 (0.083), respectively). In the bivariate analysis, the best estimates of direct and maternal heritabilities for LSB were 0.03 (0.027) and 0.22 (0.041), respectively. The direct and maternal genetic correlations among parities were 0.25 (0.054) and 0.12 (0.021), respectively. Conclusions: The results showed a considerable influence of environmental factors on LSB in each parity of sheep; also, statistically different genetic parameters (p<0.05) were obtained from one parity to another, indicating the different and large influences of genetic and environmental factors for each parity. [ABSTRACT FROM AUTHOR]

It is now widely accepted that, to model the dynamics of daily financial returns, volatility models have to incorporate the so-called leverage effect. We derive the asymptotic behaviour of the squared residuals autocovariances for the class of asymmetric power GARCH model when the power is unknown and is jointly estimated with the model's parameters. We then deduce a portmanteau adequacy test based on the autocovariances of the squared residuals. These asymptotic results are illustrated by Monte Carlo experiments. An application to real financial data is also proposed. [ABSTRACT FROM AUTHOR]

Objective The objective of the study was to investigate the possibility of utilizing an early litter size trait as an indirect selection trait for longevity and to estimate genetic parameters between sow stayability and litter size at different parities using a linear-threshold model for longevity in Thai Large White (LW) and Landrace (LR) populations. Methods The data included litter size at first, second, and third parities (NBA1, NBA2, and NBA3) and sow stayability from first to fourth farrowings (STAY14). The data was obtained from 10,794 LR and 9,475 LW sows. Genetic parameters were estimated using the multiple-trait animal model. A linear-threshold model was used in which NBA1, NBA2, and NBA3 were continuous traits, while STAY14 was considered a binary trait. Results Heritabilities for litter size were low and ranged from 0.01 to 0.06 for both LR and LW breeds. Similarly, heritabilities for stayability were low for both breeds. Genetic associations between litter size and stayability ranged from 0.43 to 0.65 for LR populations and 0.12 to 0.55 for LW populations. The genetic correlation between NBA1 and STAY14 was moderate and in a favorable direction for both LR and LW breeds (0.65 and 0.55, respectively). Conclusion A linear-threshold model could be utilized to analyze litter size and sow stayability traits. Furthermore, selection for litter size at first parity, which was the genetic trait correlated with longevity, is possible when one attempts to improve lifetime productivity in Thai swine populations.

Copyright of Modern Economic Science is the property of Modern Economic Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Loss and degradation of grasslands in the Great Plains region have resulted in major declines in abundance of grassland bird species. To ensure future viability of grassland bird populations, it is crucial to evaluate specific effects of environmental factors among species to determine drivers of population decline and develop effective conservation strategies. We used threshold models to quantify the effects of land cover and weather changes in "lesser prairie‐chicken" and "greater prairie‐chicken" (Tympanuchus pallidicinctus and T. cupido, respectively), northern bobwhites (Colinus virginianus), and ring‐necked pheasants (Phasianus colchicus). We demonstrated a novel approach for estimating landscape conditions needed to optimize abundance across multiple species at a variety of spatial scales. Abundance of all four species was highest following wet summers and dry winters. Prairie chicken and ring‐necked pheasant abundance was highest following cool winters, while northern bobwhite abundance was highest following warm winters. Greater prairie chicken and northern bobwhite abundance was also highest following cooler summers. Optimal abundance of each species occurred in landscapes that represented a grassland and cropland mosaic, though prairie chicken abundance was optimized in landscapes with more grassland and less edge habitat than northern bobwhites and ring‐necked pheasants. Because these effects differed among species, managing for an optimal landscape for multiple species may not be the optimal scenario for any one species.

In optimal stopping problems, people are asked to choose themaximum out of a sequence of values, under the constraintthat a number can only be chosen when it is presented. Wepresent a series of threshold models of human decision makingon optimal stopping problems, including a new hierarchicalmodel that assumes individual differences in threshold settingare controlled by deviations or biases from optimality associatedwith risk propensity, and is applicable to optimal stoppingproblems of any length. Using Bayesian graphical modelingmethods, we apply the models to previous data involving 101participants with large individual differences who completedsets of length 5 and length 10 problems. Our results demonstratethe effectiveness of the bias-from-optimal hierarchicalmodel, find individual differences in thresholds that peopleuse, but also find that these individual differences are stableacross the two optimal stopping tasks.

The recent flattening of the Phillips curve has stimulated new empirical research and theoretical discussions regarding the nonlinear nature of the changes in the parameters. The objective of the present paper is twofold: to detect the relevant type of the implied nonlinearity and look for some general model capable of generating a Phillips curve mimicking the empirical one. We find evidence of a convex US price Phillips curve, from 1961 q1 to 2019 q4, assessed both by piecewise and threshold models. The result presents some degree of novelty regarding the role of supply shocks and model-specific convexities; in addition, it supports the use of a regime-switching macro system. The latter accomplishes three tasks. It can generate a Phillips curve resembling its empirical counterparts; it creates a medium-run endogenous cycle where unemployment is not a NAIRU; finally, it opens new perspectives on economic policy issues. [ABSTRACT FROM AUTHOR]

The ability to predict when societies will replace one social norm for another can have significant implications for welfare, especially when norms are detrimental. A popular theory poses that the pressure to conform to social norms creates tipping thresholds which, once passed, propel societies toward an alternative state. Predicting when societies will reach a tipping threshold, however, has been a major challenge because of the lack of experimental data for evaluating competing models. We present evidence from a large-scale laboratory experiment designed to test the theoretical predictions of a threshold model for social tipping and norm change. In our setting, societal preferences change gradually, forcing individuals to weigh the benefit from deviating from the norm against the cost from not conforming to the behavior of others. We show that the model correctly predicts in 96% of instances when a society will succeed or fail to abandon a detrimental norm. Strikingly, we observewidespread persistence of detrimental norms even when individuals determine the cost for nonconformity themselves as they set the latter too high. Interventions that facilitate a common understanding of the benefits from change help most societies abandon detrimental norms. We also show that instigators of change tend to be more risk tolerant and to dislike conformity more. Our findings demonstrate the value of threshold models for understanding social tipping in a broad range of social settings and for designing policies to promote welfare. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the feasibility of a genomic evaluation program for Acute hepatopancreatic necrosis disease (AHPND) resistance in a commercial population of Penaeus vannamei. AHPND is caused by a strain of Vibrio parahaemolyticus (VpAHPND) producing a binary toxin. It is a World Organization for Animal Health (WOAH, Paris, France)-listed bacterial disease that has caused severe economic losses to the shrimp industry worldwide in recent years. The working hypothesis of this study is that the genetic variation in a commercial P. vannamei population allows an efficient genetic and genomic selection program. To investigate the genetic parameters for AHPND resistance, a controlled challenge experiment was performed at the Shrimp Improvement System biosafety laboratory in Miami, Florida, following an immersion challenge protocol using 4.95 × 107 CFU/mL bacteria and Specific Pathogen Free (SPF) P. vannamei post larvae (n = 2,400) representing 40 selected families. The bioassay was terminated at 3 days post-challenge. Each family consisted of 60 animals, with one-half of the families (n = 20) representing a fast-growth (FG) line and the remaining one-half (n = 20) representing a slow-growth (SG) line. Survival was recorded as a binary of dead or alive at the end of the three days challenge experiment. Variance components were estimated under an animal threshold binary model using a Gibbs sampler. Estimation of genetic parameters was done within lines and by combining all data records. The average hazard risk ratio (HRR) of survival was 80% greater for the FG line (average mortality 40%) than the SG line (average mortality 20%, P < 0.05) based on survival analysis with a 31% mortality rate combining all data records. The posterior mean estimates of heritabilities were 0.14 (± 0.06) for FG, 0.16 (± 0.07) for SG, and 0.38 (± 0.08), combining all records. Estimated breeding values (EBVs) based on the animal threshold binary model ranged from -0.76 to 1.01 on the liability scale. Spearmans' rank correlation coefficient between the average EBV of parents and the average mortality rates of corresponding families was 0.98. The results suggest that a breeding program for AHPND resistance is feasible. Moreover, the heritabilities suggest that both lines will respond to mass selection due to the presence of additive genetic variation for APHND-survivability. The next step of this project is to genotype animals from every family and implement a genomic selection program. Additionally, high throughput laboratory results and histopathology scores will be introduced in the genetic evaluation as correlated traits. [ABSTRACT FROM AUTHOR]

Blockchain is distributed database provides encrypted transaction tracking. Similarly, Crypto money or cryptocurrency can be defined as digital currency or asset designed as alternative exchange tool, which uses cryptography to secure transactions based on blockchain database systematic is traded like cash. Moreover, without having central control system and authority, Bitcoin attracts investors more and more with this feature. In this context, the importance of this study may well explain factors affecting cryptocurrency prices in Bitcoin specific. It is used while choosing variables that determine Bitcoin prices, not only financial and economic factors, but also Bitcoin mining and Bitcoin Google Trend Index. While electricity unit costs from variables are included in model design; It is thought that electrical energy costs consumed by CPUs and GPUs of computers used by Bitcoin miners may have impact on Bitcoin prices In this study, bitcoin prices were modeled primarily with Box-Jenkins method with using between 2015-2020 daily data. In this study, it has been conducted with chosen control variables which are Gold unit prices, oil prices, Euro / Dollar parity, S&P 500 Index, LIBOR, Bitcoin Google Trend Index and electricity unit costs were determined as control variables and their effect on bitcoin prices was analyzed based on the Box-Jenkins model. Box-Jenkins modelling is chosen because this model has more power on forecasting future values with using AR and MA process together. [ABSTRACT FROM AUTHOR]

Objective: The objective of the study was to investigate the possibility of utilizing an early litter size trait as an indirect selection trait for longevity and to estimate genetic parameters between sow stayability and litter size at different parities using a linear-threshold model for longevity in Thai Large White (LW) and Landrace (LR) populations. Methods: The data included litter size at first, second, and third parities (NBA1, NBA2, and NBA3) and sow stayability from first to fourth farrowings (STAY14). The data was obtained from 10,794 LR and 9,475 LW sows. Genetic parameters were estimated using the multipletrait animal model. A linear-threshold model was used in which NBA1, NBA2, and NBA3 were continuous traits, while STAY14 was considered a binary trait. Results: Heritabilities for litter size were low and ranged from 0.01 to 0.06 for both LR and LW breeds. Similarly, heritabilities for stayability were low for both breeds. Genetic associations between litter size and stayability ranged from 0.43 to 0.65 for LR populations and 0.12 to 0.55 for LW populations. The genetic correlation between NBA1 and STAY14 was moderate and in a favorable direction for both LR and LW breeds (0.65 and 0.55, respectively). Conclusion: A linear-threshold model could be utilized to analyze litter size and sow stayability traits. Furthermore, selection for litter size at first parity, which was the genetic trait correlated with longevity, is possible when one attempts to improve lifetime productivity in Thai swine populations. [ABSTRACT FROM AUTHOR]

Financial performance has become a trending concept in corporate finance and strategic management in recent times especially in the aftermath of the Global Financial Crisis (GFC) of 2007-2009. As an antidote to charter value erosion caused by stiff competition from incumbents in traditional banking activities, banks venture into non-core banking activities as a diversification and survival strategy. The purpose of the study is to determine the optimal threshold levels of non-interest income that stimulate financial performance of ten Zimbabwean commercial banks using non-interest income disaggregates over 2009-2020. Unfortunately, studies that examine the non-interest-income-financial performance nexus of banks involved in intermediation are scant and inconclusive. Furthermore, the use of threshold models to crack this puzzle are conspicuous. The study employed Fully Modified Ordinary Least Squares and Threshold difference Generalized Methods of Moment nonlinear threshold approaches. The aggregated and disentangled non-interest income dynamic optimal thresholds found are 26 %, 17 % and 10 % whereas the average static ones are 35 %, 28 % and 17 %. Compared to aggregated non-interest income, the study reveals that the disaggregates pose a greater positive impact on financial performance of banks in the upper regime than their counterparts in the lower regime. In addition, more banks were found to be operating below the required minimum thresholds. To avert episodes of bank failures and hedging against banking sector fragility, commercial bank managers should come up with well-diversified portfolios of income-generating ventures. Also, central bank regulations must promote non-interest activities, competition, growth and reduce leverage of commercial banks., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Loss and degradation of grasslands in the Great Plains region have resulted in major declines in abundance of grassland bird species. To ensure future viability of grassland bird populations, it is crucial to evaluate specific effects of environmental factors among species to determine drivers of population decline and develop effective conservation strategies. We used threshold models to quantify the effects of land cover and weather changes in "lesser prairie‐chicken" and "greater prairie‐chicken" (Tympanuchus pallidicinctus and T. cupido, respectively), northern bobwhites (Colinus virginianus), and ring‐necked pheasants (Phasianus colchicus). We demonstrated a novel approach for estimating landscape conditions needed to optimize abundance across multiple species at a variety of spatial scales. Abundance of all four species was highest following wet summers and dry winters. Prairie chicken and ring‐necked pheasant abundance was highest following cool winters, while northern bobwhite abundance was highest following warm winters. Greater prairie chicken and northern bobwhite abundance was also highest following cooler summers. Optimal abundance of each species occurred in landscapes that represented a grassland and cropland mosaic, though prairie chicken abundance was optimized in landscapes with more grassland and less edge habitat than northern bobwhites and ring‐necked pheasants. Because these effects differed among species, managing for an optimal landscape for multiple species may not be the optimal scenario for any one species. [ABSTRACT FROM AUTHOR]

Howell Tong (simplified Chinese: 汤家豪) is a pioneer in the field of nonlinear time series analysis, linking it with deterministic chaos. He is the father of the threshold time series models, which have extensive applications in ecology, economics, epidemiology and finance. Since October 1, 2009, he has been an Emeritus Professor at the London School of Economics and was twice (2009, 2010) holder of the Saw Swee Hock Professorship of Statistics at the National University of Singapore. He was a Distinguished Visiting Professor of Statistics at the University of Hong Kong from 2005 to 2013. He got a Master in Science degree in 1969 and a Doctor of Philosophy degree in 1972 from the University of Manchester Institute of Science and Technology (UMIST), where he studied under Maurice Priestley. From 1999 to September 2009, Tong was Chair of Statistics at LSE and founded the Centre for the Analysis of Time Series. Between 1997 and 2004, Tong was also Chair Professor of Statistics, Founding Dean of the Graduate School and later Pro-Vice Chancellor, University of Hong Kong. He was elected a Fellow of the Institute of Mathematical Statistics in 1993, an Honorary Fellow of the Institute of Actuaries, England in 1999, and a Foreign Member of the Norwegian Academy of Science and Letters in 2000. In 2000, he became the first statistician to win the (class II) the State Prize in Natural Sciences in China. In 2002, the University of Hong Kong gave him their then-highest award, the Distinguished Research Achievement Award, carrying with it a research grant of HK$1,000,000 per annum for three years. The Royal Statistical Society, UK, awarded him their Guy Medal in Silver in 2007 in recognition of his "...many important contributions to time series analysis over a distinguished career and in particular for his fundamental and highly influential paper 'Threshold autoregression, limit cycles and cyclical data,' read to the Society in 1980, which paved the way for a major body of work in non-linear time series modelling." In 2012, the International Chinese Statistical Association awarded him the Distinguished Achievement Award. Tong is also a Distinguished Professor-at-Large at the University of Electronic Science and Technology of China in Chengdu, China, and a Distinguished Visiting Professor at Tsinghua University in Beijing, China. [ABSTRACT FROM AUTHOR]

This study examines whether threshold models allow to better understand the dynamic relationship between spot and futures prices for crude oil and natural gas. Our findings are threefold. First, we show that the futures curve delivers relatively accurate forecasts for energy commodity prices. Second, we provide evidence that the relationship between spot and futures prices is regime dependent but accounting for this property does not improve the quality of out-of-sample forecasts. Third, we demonstrate that using information on the dynamics of financial variables (exchange rates, stock and uncertainty indices, interest rates or industrial and precious metal prices) does not contribute to the quality of futures-based forecasts. This suggests that the predictive content of these variables is already contained in futures prices. [ABSTRACT FROM AUTHOR]

Geographical distribution of color phenotypes and associations with ecological predictors remains poorly understood. An important geographic pattern concerning this topic is Gloger's rule, which predicts the increase of pigmentation in endothermic animals from cold and dry to warm and wet environments. Didelphid marsupials exhibit a variety of color patterns, ranging from light and dark uniform to more complex colorations. However, surprisingly little is known about the adaptive significance of dark coloration in this singular group of mammals. Using a phylogenetic comparative approach, we investigated whether coloration in different body regions of didelphids (i.e., dorsum and face) is associated with variables representing heat and humidity of the environment, as predicted by Gloger's rule. We demonstrated that Gloger's rule explains the interspecific color variation in American marsupials, especially when considering the facial region. Thus, dark coloration was more frequent among didelphid species occupying warm and wet environments than cold and dry environments. We also discuss the selective forces that can potentially explain coat color variation in didelphid marsupials, including camouflage, pathogen resistance, and pleiotropy hypotheses. [ABSTRACT FROM AUTHOR]

Patterns of discrimination are often complex (i.e., multiplicative), with different identities combining to yield especially potent discrimination. For example, Black men are disproportionately stopped by police to a degree that cannot be explained by the simple (i.e., additive) effects of being Black and being male. Researchers often posit corresponding mental representations (e.g., intersectional stereotypes for Black men) to account for these complex outcomes. We suggest that complex discrimination can be explained by simple stereotypes combined with threshold models of behavior—for example, "if someone's threat level seems higher than X, stop that person." Simulations provide proof of this concept. We show how gender-by-race discrimination in both promotions and police stops can be explained by simple stereotypes. We also explore race-by-age discrimination in police stops, in which racial disparities are greater for young adolescents. This work suggests that complex behaviors can sometimes arise from relatively simple cognitions. [ABSTRACT FROM AUTHOR]

The role of the exchange rates in emerging market economies that have adopted inflation targeting (IT) is a critical and contentious issue in the relevant literature. This article investigates whether an exchange rate-augmented Taylor rule describes the Central Bank of Republic of Turkey's (CBRT) monetary policy. Covering the period from 2002:1 to 2017:8 it also explores possible nonlinearities in the reaction function of the CBRT by employing a Threshold Vector Autoregressive (TVAR) model. The linear estimation of the model highlighted the importance of the exchange rate in monetary policy under IT. The results of the nonlinear model indicate that the stance of monetary policy was asymmetric with respect to exchange rate movements during the conventional IT period. However, the asymmetric monetary policy stance disappeared in the aftermath of the Global Financial Crisis. Increasing considerations of financial stability undermined the asymmetric policy stance of the CBRT in the post-crisis period. [ABSTRACT FROM AUTHOR]

Threshold estimation methods are developed for dynamic panels with individual specific fixed effects covering short time periods. Maximum likelihood estimation of the threshold and slope parameters is proposed using first difference transformations. Threshold estimate is shown to be consistent and its asymptotic distribution is nonstandard when the number of individuals grows to infinity for a fixed time period; the slope estimates are consistent and asymptotically normally distributed. The method is applied to a sample of 74 countries and 11 periods of 5-year averages to determine the effect of inflation rate on long-run economic growth. [ABSTRACT FROM AUTHOR]

We propose to apply a time series-based nonlinear mechanism in the threshold autoregression form in order to examine the possible relationship between economic growth rate and its potential determinants included debt-to-GDP indicator. Our approach employs threshold variables instead of exogenous variables and time series data instead of panel data to reveal the economic instruments that have determined the business cycle in European countries for the last 2 decades—starting from 1995. The purpose of the study is to reveal the mechanism of growth (measured in terms of GDP growth rate and industrial production growth rate) given different macroeconomic indicators, such as public debt, rate of inflation, interest rate, and rate of unemployment with the level of growth itself serving as the threshold variables. We identify that the monetary mechanism played an important role in diagnosing the phases of business cycle in most European economies which is in line with liberal economic policy dominating in the observed period. The initial level of debt-to-GDP ratio as its increase within the recession period was of no value for the economic growth pattern. [ABSTRACT FROM AUTHOR]

According to the recognition heuristic (RH), for decision domains where recognition is a valid predictor of a choice criterion, recognition alone is used to make inferences whenever one object is recognized and the other is not, irrespective of further knowledge. Erdfelder, Küpper-Tetzel, and Mattern (2011) questioned whether the recognition judgment itself affects decisions or rather the memory strength underlying it. Specifically, they proposed to extend the RH to the memory state heuristic (MSH), which assumes a third memory state of uncertainty in addition to recognition certainty and rejection certainty. While the MSH already gathered significant support, one of its basic and more counterintuitive predictions has not been tested so far: In guessing pairs (none of the objects recognized), the object more slowly judged as unrecognized should be preferred, since it is more likely to be in a higher memory state. In this paper, we test this prediction along with other recognition latency predictions of the MSH, thereby adding to the body of research supporting the MSH.

It has been well stated that high values of blood pressure constitute a risk factor for cardiovascular diseases [20], with the latter being the number one death cause in Portugal. The main interest of the present study is to model the high values of systolic blood pressure in the individuals of the population who are most at risk, i.e., the elderly. This group frequently suffers from a specific type of hypertension pathology, known as isolated systolic hypertension. With that purpose the Peaks Over Threshold methodology was applied, which consists in fitting a generalized Pareto distribution to the excesses above a sufficiently high threshold. The model will be able to estimate extreme quantiles and tail probabilities.

Threshold models are often used in genetic analysis of categorical data, such as calving ease. Solutions in the liability scale are easily transformed into probabilities; therefore, estimated breeding values are published as the probability of expressing the category of main interest and are the industry's gold standard because they are easy to interpret and use for selection. However, because threshold models involve nonlinear equations and probability functions, implementing such a method is complex. Challenges include long computing time and convergence issues, intensified by including genomic data. Linear models are an alternative to overcome those challenges. Estimated breeding values computed using linear models are highly correlated (≥0.96) with those from threshold models; however, the lack of a transformation from the observed to the probability scale limits the use of linear models. The objective of this study was to propose transformations from observed to probability scale analogous to the transformation from liability to probability scale. We assessed computing time, peak memory use, correlations between estimated breeding values, and estimated genetic trends from linear and threshold models. With 11M animals in the pedigree and almost 965k genotyped animals, linear models were 5× faster to converge than threshold models (32 vs. 145 h), and peak memory use was the same (189 GB). The transformations proposed provided highly correlated probabilities from linear and threshold models. Correlations between direct (maternal) estimated breeding values from linear and threshold models and transformed to probabilities were ≥0.99 (0.97) for all animals in the pedigree, sires with/without progeny records, or animals with phenotypic records; therefore, estimated genetic trends were analogous, suggesting no loss of genetic progress in breeding programs that would adopt linear instead of threshold models. Furthermore, linear models reduced computing time by 5-fold compared to the threshold models; this enables weekly genetic evaluations and opens the possibility of using multi-trait models for categorical traits to improve selection effectiveness., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)