Time Horizons Research Articles

Trade Liberalization and Mortality Rates: Evidence of Pro-Cyclical Mortality From Brazil.

We trace the evolution of all-cause mortality rates in Brazilian regions with varying exposure to trade-induced economic shocks before, during, and after liberalization reforms in the 1990s. We find consistent evidence of pro-cyclical mortality, with areas more exposed to tariff reductions experiencing larger declines in mortality across varying time horizons. The observed decline in mortality rates is evident across sex, age groups, and for both internal and external causes of mortality. We falsify the observed relationship between mortality and tariff reductions with analyses of causes of death that are plausibly unrelated to economic activity. Concerning proximate mechanisms involved in our finding of pro-cyclical mortality, we show that healthcare infrastructure expanded in local economies more affected by the trade-induced economic shock. This expansion was characterized by the increased capital-intensity of care, facilitated by the import of diagnostic technologies that reduce mortality from internal causes. We also find supporting evidence for the idea that pro-cyclical mortality is partially caused by a decrease in transport and non-transport-related accidents. Overall, our findings highlight an underappreciated dimension of trade policy effects, namely public health.

EXPRESS: Strategic Patent Disclosure: Unraveling the Influence of Temporal Preferences

We examine how the temporal preferences of a firm’s management and shareholders affect the textual properties of patent documents. We argue that low patent readability decreases imitation concerns due to lower information disclosure but increases concerns of patent invalidation or litigation. These concerns are likely to materialize at different points in time, implying that decision-makers with different time horizons prefer different levels of patent readability. Building a theory on the time horizon of institutional owners and CEOs, we hypothesize a positive relationship between institutional ownership and textual readability of patent documents. Our analysis of over 200,000 US patents provides empirical support for our theoretical contentions. Our findings suggest that the temporal preferences of firms’ decision-makers influence not only the extent of innovation activities but also the way in which firms craft and disseminate information about such activities.

Long-Run Behavior and Convergence of Dynamic Mean Field Equilibria

AbstractWe study the behavior of dynamic equilibria in mean field games with large time horizons in a dynamic consumer choice model. We show that if the stationary equilibrium in the associated infinite horizon game is unique, the dynamic equilibria of the finite horizon games converge to the stationary equilibrium of the infinite horizon game as the time horizon tends to infinity. If the stationary equilibrium is not unique, however, the situation becomes more involved. In this case, we show that in addition to convergence to the stationary equilibria, in the long run, the dynamic equilibria circle around randomized stationary equilibria for certain choices of boundary data.

Causality relationships between climate policy uncertainty, renewable energy stocks, and oil prices: a mixed-frequency causality analysis

This paper investigates the predictive relationships among climate policy uncertainty (CPU), oil prices, and renewable energy (RE) stock market returns, particularly highlighting the challenges posed by the varying data frequencies of these variables. The study utilizes a comprehensive dataset comprising monthly CPU, daily oil prices, and RE stock returns, sourced globally. By applying a mixed-frequency causality test (MFCT), the analysis reveals significant predictability across different time horizons, particularly highlighting the strong influence of oil prices on RE stock returns over short-term horizons, while CPU demonstrates a more pronounced effect over medium to long-term horizons. In contrast, the application of the classical Granger causality test on low-frequency (monthly) data indicates an insignificant relationship between CPU and RE stocks, suggesting that traditional models may overlook important predictive dynamics. The analysis was conducted using Matlab code, and the findings provide valuable insights for policymakers in designing effective climate policies and for investors in optimizing portfolio strategies and hedging against risks.

Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation

The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations and tail dependencies concerned pairings of three processes: Accumulation, Discharge and Run-off, which operate on major ice sheets in the West and East Antarctic and in Greenland. The elicitation enumerated dependencies between these processes under selected global temperature change scenarios over different future time horizons. These expert judgments allowed us to populate a Paired Copula Bayesian network model to obtain the estimated contributions of these ice sheets for future sea level rise. Including positive central tendency dependence and tail dependence increases the fatness of the upper tails of projected sea level rise distributions, an amplification important for designing and evaluating possible mitigation strategies. Detailing and jointly computing distributional dependencies and tail dependencies can be crucial components of good practice for assessing the influence of uncertainties on extreme values when modelling stochastic multifactorial processes.

Green bonds and clean energy stocks: Safe havens against global uncertainties? A wavelet quantile-based examination

The renewable energy sector has seen rapid investment growth over the last decade, with Green Bonds (GB) and Clean Energy Stocks (CLE) emerging as critical components of sustainable finance. Using daily data from 2013 to 2023, this study investigates the diverse roles of GB and CLE as potential safe havens under Geopolitical Risks (GEO) and Economic Policy Uncertainty (ECU). Applying an advanced Wavelet Quantile Regression (WQR) approach, we assess their effectiveness across different time horizons and risk conditions. The findings reveal that GB consistently offer stronger protection against economic risks, particularly in the short to mid-term, while CLE are more effective in shielding against geopolitical risks. Over longer periods, GB maintain their safe haven status, while CLE’s protective capabilities diminish. The results also highlight that GB and CLE behave differently across quantiles, with GB showing stability across extreme market conditions, making it a more reliable hedge overall. This study offers valuable policy insights for key stakeholders. Policymakers are encouraged to integrate GB and CLE into sustainable finance strategies, investors can mitigate risk by blending these assets, and portfolio managers should adjust allocations dynamically based on evolving market conditions. The results underscore the importance of green assets as hedges in times of global uncertainty, contributing to the growing literature on sustainable finance.

A Systematic Review of the Impact of Changes to Urban Green Spaces on Health and Education Outcomes, and a Critique of Their Applicability to Inform Economic Evaluation

Several reviews have consolidated the evidence on the impact of living near an urban green space on improving health and education outcomes and reducing mortality. However, there is limited evidence on the effectiveness or cost-effectiveness of specific improvements to these urban green spaces, which would help decision-makers make informed decisions on how to invest in urban green spaces. Therefore, this review synthesizes the impact of more specific changes to, or investments in, urban green spaces on health and education outcomes, synthesizes the cost-effectiveness of these interventions, and critiques the applicability of the evidence for an economic evaluation. We find that interventions targeted towards improving play areas or fitness equipment tended to have mostly positive impacts on physical activity, while interventions on improving walking path, or the overall greenery showed a more mixed impact on physical activity. There were only two studies on the impact of changes to urban green spaces on mental health, with only one finding a positive association of the intervention with depression, and there were no studies measuring the impact of changes to urban green spaces and educational outcomes. From a cost-effectiveness perspective, we find that typically very small improvements are required to make the interventions a cost-effective policy choice; however, we found several limitations with using the existing evidence to estimate the cost-effectiveness of the intervention. Overall, we found that most of the evidence does suggest that improvements to urban green spaces can lead to improvements in physical activity, but further research is needed on the impact on mental health and educational outcomes. Furthermore, additional evidence with longer time horizons, multi-sectoral benefits, distributional outcomes, and more consistent outcome measures would assist in informing cost-effectiveness and may ultimately lead to improved decision-making around investments for urban green spaces in specific contexts.

Investment Strategies for Young Professionals: Exploring Different Investment Options and Strategies Tailored for Young Professionals

As the variety of offers and the constant changes on the financial markets progress, the young professionals notice that the right time to start investing is now and the further down the line, the harder it will be. Yet, for individuals just starting their careers the challenges of financial strategies may be overwhelming. This paper aims to examine different investment options and plans available to young professional and aspiring investors looking at their unique needs, goals, and volatility/ time horizons. It covers familiar forms of investments including stocks, bonds and mutual funds, new forms of investment like ETFs, real estates, and cryptocurrencies. This paper provides a clear and detailed explanation of the main benefits and drawbacks of every kind of investment, which can be useful for building an effective investment portfolio. Moreover, the study reveals a rich literature on financial planning and education, efficiency of automated investment instruments, and risk mitigation as crucial factors for successful investment. Based on this reasoning, the young professionals are pointed out as being in a favourable position enjoy a long investment horizon and therefore are able to explore higher risk investment opportunity. But it states that diversification and prudent risks management are crucial in order to minimize incidences of a loss. The final steps in the report provides an easily understandable guide for young professionals that shows how they can build an investment portfolio that will provide good returns and be favourable for the future.

On the chemistry of the global warming potential of hydrogen

Hydrogen (H2) is considered a promising fuel to contribute to net-zero carbon emission goals. While hydrogen itself is not a greenhouse gas, leakage of hydrogen fuels causes indirect warming due to hydrogen’s influence on methane, tropospheric ozone, and stratospheric water vapor, with the methane term dominating the impact. Some studies consider a simple four-equation box model to explore the climate consequences of leakage from hydrogen fuel use relative to methane, while others have employed much more detailed global photochemical models. Here we use a comprehensive photochemical box model including 66 reactions to show and quantify how the analogous four-equation system is missing a critical OH feedback, leading it to overestimate the time-integrated methane response to a pulse of hydrogen by over 100%. We estimate a hydrogen global warming potential (GWP) relative to carbon dioxide of 28−11+18 on the 20-year time horizon and 10−4+7 on the 100-year time horizon based on the 66-reaction model and information from the literature. GWPs provide a measure of the relative global warming impact of emission of one gas compared to a selected reference gas per unit mass emitted. While CO2 is generally chosen for the reference, any gas can be used. We present the GWP of H2 using CH4 as the reference, as this choice cancels out some uncertainties that are common to both H2 and CH4. The GWP for H2 relative to CH4 from fossil fuel sources is 0.35−0.06+0.13 on time horizons beyond 15 years; put differently, we find that relative to an equivalent mass of emission of fossil CH4, hydrogen emission has a climate impact about three times smaller. These global warming potentials underscore that hydrogen leakage does contribute to climate change, emphasizing the importance of limiting both hydrogen and methane leakage if global net-zero greenhouse gas emissions are to be achieved by 2050.

A Global-in-Time Neural Network Approach to Dynamic Portfolio Optimization

ABSTRACT We discuss a neural network approach, which does not rely on dynamic programming techniques, to solve dynamic portfolio optimization problems subject to multiple investment constraints. The approach allows for objectives of a very general form encompassing both time-consistent and time-inconsistent objectives, as well as objectives requiring multi-level optimization. The number of parameters of the neural network remains independent of the number of portfolio rebalancing events. Compared to reinforcement learning, this technique avoids the computation of high-dimensional conditional expectations. The approach remains practical when considering large numbers of underlying assets, long investment time horizons or very frequent rebalancing events. We prove convergence of the numerical solution to the theoretical optimal solution of a large class of problems under fairly general conditions, and present ground truth analyses for a number of popular formulations, including mean-variance, mean-semi-variance, and mean-conditional value-at-risk problems. Numerical experiments show that if the investment objective functional is separable in the sense of dynamic programming, the correct time-consistent optimal investment strategy is recovered, otherwise we obtain the correct pre-commitment (time-inconsistent) investment strategy. This method is agnostic as to the underlying data generating assumptions, and results are illustrated using (i) parametric models for underlying asset returns, (ii) stationary block bootstrap resampling of empirical returns, and (iii) generative adversarial network (GAN)-generated synthetic asset returns.

Coal price, economic growth and electricity consumption in China under the background of energy transition

With the increasing share of renewable energy generation in China's electricity consumption and the dramatic fluctuation of coal prices in recent years, the discussion of the relationship between coal prices, economic growth and electricity consumption in China becomes more relevant. This paper collects monthly data from 2016 to 2023, uses industrial value added to represent economic growth, and explore the relationship between the three through a time-varying parameter vector autoregressive (TVP-VAR) model, which shows that: (1) The mutual impact effects among coal prices, economic growth, and electricity consumption vary across short-, medium- and long-term. This finding underscores the importance of considering different time horizons when evaluating the interactive effects of these variables. (2) Compared to 2017 and 2022, the lagged impact effects among the three have undergone certain changes, indicating that the understanding of their relationships should be contextualized within specific developmental phases. (3) The relationship between coal prices, economic growth, and different categories of electricity consumption exhibits heterogeneous characteristics, suggesting that recognition of these relationships should be based on specific types of power generation. This research enhances the understanding of the dynamic relationship between coal prices and the macroeconomy in China at the current stage.

Comparative Analysis of the Portfolio-balance Models of Exchange Rate Determination

The current study conducts a comparative analysis of Portfolio-Balance Models (PBM) developed by Branson, Kouri and Dornbusch to assess the role of expectations and time horizons in determining and forecasting the India–US exchange rate over the period 1996:Q2–2019:Q3. Notably, it improves the original models by integrating microstructure theory into their framework. The Autoregressive Distributed Lag Error-Correction Model (ARDL-ECM) is used to investigate both short run and long run behaviour of the models. Additionally, the study assesses out-of-sample forecasting accuracy of the modified models against the Random Walk Model (RWM) using the root mean square error metric. The estimation results reveal that models based on rational expectations are better than the static expectations model. Notably, the microeconomic determinant is counterintuitively significant only in the long run across all models. Furthermore, these modified models demonstrate superior out-of-sample forecasting abilities compared to RWM for alternative forecasting horizons. However, forecasting results over a 6-month period is better with short run models. Over 1-year and 2-year horizons, rational expectations models outperform the static expectations model. This study challenges the Meese–Rogoff puzzle, ensuring that PBM, when modified to incorporate microstructure theory, is valid and yields superior forecasting results compared to RWM. JEL Codes: F31, F32, C22

Predicting the long-term effects of electronic cigarette use on population health: a systematic review of modelling studies

ObjectiveTo systematically review and synthesise the findings of modelling studies on the population impacts of e-cigarette use and to identify potential gaps requiring future investigation.Data source and study selectionFour databases...

Can the sentiment of the official media predict the return volatility of the Chinese crude oil futures?

This study investigates whether the sentiment of Chinese official media towards crude oil influences price volatility of the Chinese crude oil futures (SC). By leveraging textual analysis through Bidirectional Encoder Representations from Transformers (BERT), we quantify the sentiment of oil-related articles published by the primary official media in China. Our main finding, building on both in-sample and out-of-sample analyses, robustly reveals that this sentiment significantly forecasts the one-day-ahead intraday return volatility of SC. Moreover, we extend the analysis to different time horizons (i.e., one-week and one-month-ahead) and find the prominent forecasting power of the official media sentiment as well. We also find that the official media sentiment fails to forecast the price volatility of WTI oil futures, implying that the official media sentiment contains some unique Chinese information. Overall, our study contributes to the existing literature on predicting the return volatility of the Chinese crude oil futures, and offers fresh insights into an essential yet underexplored sentiment, i.e., official media sentiment.

It is about time! Exploring the clashing timeframes of politics and public policy experiments

Abstract Although existing studies on experimental policymaking have acknowledged the importance of the political setting in which policy experiments take place, we lack systematic knowledge on how various political dimensions affect experimental policymaking. In this article, we address a specific gap in the existing understanding of the politics of experimentation: how political timeframes influence experimental policymaking. Drawing on theoretical discussions on experimental policymaking, public policy, electoral politics, and mediatization of politics, we outline expectations about how electoral and problem cycles may influence the timing, design, and learning from policy experiments. We argue electoral timeframes are likely to discourage politicians from undertaking large-scale policy experiments and if politicians decide to launch experiments, they prefer shorter designs. The electoral cycle may lead politicians to draw too hasty conclusions or ignore the experiment’s results altogether. We expect problem cycles to shorten politicians’ time horizons further as there is pressure to solve problems quickly. We probe the plausibility of our theoretical expectations using interview data from two different country contexts: Estonia and Finland.

Physical climate change exposure and firms' adaptation strategy

AbstractResearch SummaryThis article examines whether and how firms adapt to physical exposures to climate change. I build a novel dataset that compiles information on the adaptation strategies of publicly traded companies around the globe and merge it with climate science data. I find that firms are sensitive to the nature and level of forecasted climate change exposures, and that they adapt more often and more completely to those most salient to their business. Increased physical climate exposure heightens the perceived impact of climate change, leading to a higher degree of adaptation. Furthermore, the positive relationship between firms' climate change exposure and their adaptation is stronger for firms with greater environmental, social, and corporate governance capabilities and those with longer time horizons.Managerial SummaryCompanies are increasingly exposed to the physical impacts of climate change, yet little is known about how they adapt to these long‐term, systemic, and uncertain changes. This study investigates corporate adaptation strategies in response to climate change. By analyzing climate science data and climate change disclosure information from publicly traded companies worldwide, I find that most firms do not adapt to different physical climate change exposures. They adapt more often and more completely when facing higher forecasted climate exposures. Furthermore, firms' environmental, social, and corporate governance capabilities and their time horizons influence their adaptation to greater climate exposures. These findings suggest that targeted interventions may be necessary to improve corporate adaptation to climate change.

Quantifying the impact of prevalence-dependent adaptive behavior on COVID-19 transmission: A modeling case study in Maryland

The COVID-19 pandemic highlighted the need for robust epidemic forecasts, projecting health burden over short- and medium-term time horizons. Many COVID-19 forecasting models incorporate information on infection transmission, disease progression, and the effects of interventions, but few combine information on how individuals change their behavior based on altruism, fear, risk perception, or personal economic circumstances. Moreover, early models of COVID-19 produced under- and over-estimates, failing to consider the complexity of human responses to disease threat and prevention measures. In this study, we modeled adaptive behavior during the first year of the COVID-19 pandemic in Maryland, USA. The adapted compartmental model incorporates time-varying transmissibility informed on data of environmental factors (e.g., absolute humidity) and behavioral factors (aggregate mobility and perceived risk). We show that humidity and mobility alone did little to explain transmissibility after the first 100 days. Including adaptive behavior in the form of perceived risk as a function of hospitalizations more effectively explained inferred transmissibility and improved out-of-sample fit, demonstrating the model’s potential in real-time forecasting. These results demonstrate the importance of incorporating endogenous behavior in models, particularly during a pandemic, to produce more accurate projections, which could lead to more impactful and efficient decision making and resource allocation.

Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach

This study introduces a novel application of a temporal convolutional network (TCN) for projecting carbon tax prices, addressing the critical need for accurate forecasting in climate policy. Utilizing data from the World Carbon Pricing Database, we demonstrate that the TCN significantly outperformed traditional time series models in capturing the complex dynamics of carbon pricing. Our model achieved a 31.4% improvement in mean absolute error over ARIMA baselines, with an MAE of 2.43 compared to 3.54 for ARIMA. The TCN model also showed superior performance across different time horizons, demonstrating a 30.0% lower MAE for 1-year projections, and enhanced adaptability to policy changes, with only a 39.8% increase in prediction error after major shifts, compared to ARIMA’s 95.6%. These results underscore the potential of deep learning for enhancing the precision of carbon price projections, thereby supporting more informed and effective climate policy decisions. Our findings have significant implications for policymakers and stakeholders in the realm of carbon pricing and climate change mitigation strategies, offering a powerful tool for navigating the complex landscape of environmental economics.

EXTRACTING PRICE SIGNALS FROM THE TERM STRUCTURE OF COMMODITY AND CREDIT MARKETS USING AUSTRIAN BUSINESS CYCLE THEORY

Austrian School economists first explained the significance of interest rates for the structure of production in time. Furthermore, they showed how manipulating the level of interest and the credit supply causes the business cycle (v.Böhm-Bawerk, 1891; v.Hayek, 1938; v.Hayek, 1941 and v.Mises, 1949). More recently, Austrian School economists have elaborated the relationship between the term structure of interest rates and the business cycle (Barnett and Block, 2009; Bagus and Howden, 2010; Bagus et al., 2018; Potuzak and Nemec, 2020; Alonso-Neira and Sanchez-Bayon, 2023). At the same time, market practitioners understand the potency of yield curve inversions as a predictor of future recessions as a result of experience and observation unrelated to Austrian Business Cycle Theory (ABCT). Reference textbooks (Stigum and Crescensi, 2007) explain that capital allocators are pricing in future cuts to the central bank discount rate, thus inverting the yield curve. This ignores the role of free-market price signals in credit, which result from supply and demand fundamentals, and are the link connecting ABCT to term structures.The term structure of commodities markets is also affected by actual or anticipated shortfalls in production over different time horizons. This is widely understood, perhaps because settlement by physical delivery is commonplace and tangible (Hull, 2022). Even though credit and derivatives may at first glance seem more abstract, the same underlying market principles are at work there. The theoretical foundations of and recent work on ABCT provide the necessary tools to analyse both markets through the same lens.

Adapting Altman Z-score models for early warning signals: Evidence from delisted mining stocks on the Johannesburg Stock Exchange

ABSTRACT The aim of the study is to measure the predictive prowess of adapting the Altman’s Z-score models when used to predict financial health and subsequent delisting of mining companies listed on the Johannesburg Stock Exchange between 1994 and 2021. The study applied Altman’s Z-score models to the financials of listed mining companies to measure the adaptability of the model for predicting the financial health status of mining companies and subsequent delisting in 1-, 2- and 3-years’ time against the actual listing status of each company in the sample over corresponding time horizons. Key findings indicate that both models reliably predict delisting when the horizon is only a year in the future.