Rate Uncertainty Research Articles

Pension funds with longevity risk: An optimal portfolio insurance approach

We present a unified framework designed to provide an optimal investment strategy for members of a defined contribution pension plan. Our model guarantees a minimum retirement savings level, expressed as a target annuity, by assuming uncertainty in interest rates, labor income, and mortality during the accumulation phase. To protect the accumulated retirement capital against both investment and longevity risks, the present value of the guaranteed lifetime annuity is regarded as the baseline wealth to hold upon reaching the retirement date, while a purpose-oriented proportion portfolio strategy is employed to invest the residual wealth. By applying standard dynamic programming techniques, we determine a closed-form solution to the stochastic control problem with the objective of maximizing the expected utility of the final surplus, defined as the difference between the accumulated wealth and the target annuity value. The theoretical findings are bolstered by a comprehensive numerical analysis designed to assess the impact of longevity on investment policies, highlighting the suitability of our proposal for managing defined contribution schemes.

Choosing between additive and conventional manufacturing of spare parts: On the impact of failure rate uncertainties and the tools to reduce them

Recently, there has been great interest in using additive manufacturing (AM) to produce spare parts: allowing to produce spare parts with short lead times and close to the point of use, AM reduces the need for large inventories otherwise required by conventional manufacturing (CM) techniques to deal with intermittent spare parts demand. However, using AM to produce spare parts is limited by two main drawbacks: high production costs and uncertain failure rates arising from AM still being a relatively new production technique. While the former can be counterbalanced by inventory cost reductions, it is unclear how the latter impacts the sourcing option decision (i.e. AM or CM). We aim to fill this gap by studying a periodic review model in which spare parts demands follow a Poisson process. To make our analysis accurate and reliable, we leverage a material science approach to obtain realistic values for the failure rate uncertainties. From the results, it emerges that AM is heavily penalized by failure rate uncertainties much higher than those of CM. Consequently, we then focus on some recent tools developed to reduce AM failure rate uncertainties: porosity assessment and in-situ monitoring. We find that the failure rate uncertainty should be reduced by five and six percentage points to make it worthwhile to invest in porosity assessment and in-situ monitoring, respectively. Finally, we determine under which circumstances each tool is preferred over the other and found that porosity assessment is typically the most competitive uncertainty reduction tool.

A human reliability analysis method based on STPA-IDAC and BN-SLIM for driver take-over in Level 3 automated driving

Human factors play an important role in the take-over process of Level 3 (L3) automated driving. This paper combines Systems Theoretic Process Analysis (STPA) and Information, Decision and Action in Crew context (IDAC) for qualitative analysis and Bayesian Network (BN) and Success Likelihood Index Method (SLIM) for quantitative calculation to obtain the main performance shaping factors (PSFs) and evaluation indicators that cause human errors. Firstly, the STPA-IDAC method is used to analyze unsafe control actions (UCAs) for take-over process and form the mapping relationship of UCAs-IDA-PSFs. Secondly, the BN of human reliability analysis for take-over process is constructed based on the BN-SLIM method. Uncertainty in rates of PSFs and evaluation indicators is addressed in a probabilistic manner using expert opinions and empirical data. After diagnostic reasoning of BN, mean variation is used to identify the main PSFs and evaluation indicators. This method can effectively identify the main PSFs and evaluation indicators that cause human errors, facilitate risk assessment and management, and reduce the human error probability (HEP).

The Impact of ESG Rating Events on Corporate Green Technology Innovation under Sustainable Development: Perspectives Based on Informal Environmental Regulation of Social Systems

Corporate green technology innovation is an important driver to promote the green transformation of the manufacturing industry 4.0 and an important engine to achieve China’s carbon peak, carbon neutrality, and high-quality economic development. Based on the theory of informal environmental regulation of social systems, this study empirically analyzes the impact of ESG rating events on corporate green technology innovation by constructing a multi-period DID model using panel data of Chinese listed companies from 2010 to 2022 as the research sample. The findings suggest that ESG rating events and ESG scores can stimulate corporate green technology innovation. ESG rating uncertainty can inhibit corporate green technology innovation. The mediation effect analysis shows that ESG rating events can stimulate firms’ green technology innovation by reducing financing constraints, increasing the degree of corporate internal control, and increasing R&D investment. In addition, heterogeneity analyses indicate that ESG rating events have a better stimulating effect on the quantity of innovation of non-state-owned enterprises than state-owned enterprises, while the stimulating effect on the quality of innovation of non-state-owned enterprises is inferior to that of state-owned enterprises. Moreover, the innovation stimulation effect of ESG rating events shows a trend of east-high and west-low in geographical distribution. Therefore, the government should accelerate the construction of the ESG rating system, promote the degree of convergence with international standards, and improve the level of enterprises’ utilization of digital technology while paying attention to informal environmental regulation.

Estimation of the Uncertainty in Daytime Cirrus Cloud Radiative Forcing and Heating Rates Due to Ice Crystal Optics

Abstract The uncertainties in absolute daytime top-of-the-atmosphere (TOA) net cirrus cloud radiative forcing (CRF) and radiative heating rates are estimated at five Micro-Pulse Lidar Network (MPLNET) sites spanning the tropics to high-latitudes. One year of semi-transparent cirrus cloud (optical depth < 3.0 and cloud top temperature < −37 °C) measurements are subject to spectrally-consistent optical properties for nine different ice crystal habits, thus providing a range of possible forcing values. The annual average absolute daytime TOA net CRF is positive at Barbados, Kanpur, and, Singapore (0.59–0.67, 0.61–0.65, and 1.94–2.09 W∙m−2, respectively), negative at Fairbanks (−0.67 to −0.28 W∙m−2), and can regularly become positive or negative at Goddard Space Flight Center (GSFC) (−0.06 to 0.32 W∙m−2). The TOA CRF depends on ice crystal shape; in particular, plates lead to relatively large absolute values that decreases for bullet rosettes and columns. Uncertainties in daytime cirrus cloud radiative properties are estimated as the standard deviation of all possible outcomes when considering the different particle habits individually. Annually, the average uncertainty of the absolute daytime TOA net CRF ranges from 0.50–1.80 W∙m−2. In-cloud daytime net radiative heating rates are positive, on average, at all five sites (0.25–3.84 K/day) and have an estimated uncertainty of less than 0.30 K/day. The uncertainties in cirrus radiative forcing and heating that are characterized by assumptions regarding the ice crystal optical properties must be considered in downstream applications, including satellite retrievals and numerical weather prediction.

Food security in Pakistan: Investigating the spillover effect of Russia-Ukraine conflict

This is study investigates inter-connectedness of Russia-Ukraine conflict with wheat prices in Pakistan through oil prices, exchange rate, economic policy uncertainty. This objective is realized by employing cross-quantilogram, rolling window multiple correlation, and TVP-Var approaches on weekly data from 7 to 1–2021 to 9–3–2023. The estimated results of cross-quantilogram, rolling window multiple correlation reveal significant positive co-movement between Russian and Ukraine conflict and wheat prices. The major variables responsible for the spillover effect are oil prices, depreciation of exchange rate, and economic policy uncertainty. The finding urges to stabilize exchange rate and economic policy uncertainty along with finding alternative cheap energy sources.

Near Real-time Leak Location by Inverse Analysis Integrating Measurement Uncertainty

AbstractThis paper presents a novel model-based method for near real-time pipe burst location in water distribution networks by integrating measurement uncertainty into inverse analysis. The method accounts for expected errors between measured and computed values, providing a pipe burst location area whose size varies according to the expected error level and the burst size. The proposed method is demonstrated and compared with the traditional inverse approach using a real case study with artificial bursts of different sizes and with different pressure signal noise levels. The performance of both methods is also assessed and discussed considering the effect of seasonal water demands. The traditional inverse analysis fails to accurately locate the pipe burst events, and depending on the expected error level and pipe burst size, the obtained locations may be significantly further away from the real burst location. Conversely, the proposed method does not point to the exact burst location but provides an approximated area in which step-testing can be carried out to pinpoint the exact burst location; the size of this area can be larger or smaller depending on the burst flow rate and signal uncertainty.

Predicting Human Subcutaneous Bioavailability of Therapeutic Monoclonal Antibodies from Systemic Clearance and Volume of Distribution.

Subcutaneous delivery of monoclonal antibody therapeutics is often preferred to intravenous delivery due to better patient compliance and overall lower cost to the healthcare system. However, the systemic absorption of biologics dosed subcutaneously is often incomplete. The aim of this work was to describe a human bioavailability prediction method for monoclonal antibodies delivered subcutaneously that utilizes intravenous pharmacokinetic parameters as input. A two-compartment pharmacokinetic model featuring a parallel-competitive absorption pathway and a presystemic metabolism pathway was employed. A training data set comprised 19 monoclonal antibodies (geometric mean bioavailability of 68%), with previously reported human pharmacokinetic parameters, while a validation set included data compiled from 5 commercial drug products (geometric mean bioavailability of 69%). A single fitted absorption rate constant, paired with compound-specific estimates of presystemic metabolism rate proportional to compound-specific systemic clearance parameters, resulted in calculations of human subcutaneous bioavailability closely mimicking clinical data in the training data set with a root-mean-square error of 5.5%. Application of the same approach to the validation data set resulted in predictions characterized by 12.6% root-mean-square error. Factors that may have impacted the prediction accuracy include a limited number of validation data set compounds and an uncertainty in the absorption rate, which were subsequently discussed. The predictive method described herein provides an initial estimate of the subcutaneous bioavailability based exclusively on pharmacokinetic parameters available from intravenous dosing.

Nationwide prospective audit for the evaluation of appendicitis risk prediction models in adults: right iliac fossa treatment (RIFT)-Turkey.

Appendicitis is the most prevalent surgical emergency. The negative appendicectomy rate and diagnostic uncertainty are important concerns. This study aimed to assess the effectiveness of current appendicitis risk prediction models in patients with acute right iliac fossa pain. A nationwide prospective observational study was conducted, including all consecutive adult patients who presented with right iliac fossa pain. Diagnostic, clinical and negative appendicectomy rate data were recorded. The Alvarado score, Appendicitis Inflammatory Response (AIR), Raja Isteri Pengiran Anak Saleha Appendicitis (RIPASA) and Adult Appendicitis Score systems were calculated with collected data to classify patients into risk categories. Diagnostic value and categorization performance were evaluated, with use of risk category-based metrics including 'true positive rate' (percentage of appendicitis patients in the highest risk category), 'failure rate' (percentage of patients with appendicitis in the lowest risk category) and 'categorization resolution' (true positive rate/failure rate). A total of 3358 patients from 84 centres were included. Female patients were less likely to undergo surgery than men (71.5% versus 82.5% respectively; relative risk 0.866, 95% c.i. 0.834 to 0.901, P < 0.001); with a three-fold higher negative appendicectomy rate (11.3% versus 4.1% respectively; relative risk 2.744, 95% c.i. 2.047 to 3.677, P < 0.001). Ultrasonography was utilized in 56.8% and computed tomography in 75.2% of all patients. The Adult Appendicitis Score had the best diagnostic performance for the whole population; however, only RIPASA was significant in men. All scoring systems were successful in females patients, but Adult Appendicitis Score had the highest area under the receiver operating characteristic curve value. The RIPASA and the Adult Appendicitis Score had the best categorization resolution values, complemented by their exceedingly low failure rates in both male and female patients. Alvarado and AIR had extremely high failure rates in men. The negative appendicectomy rate was low overall, but women had an almost three-fold higher negative appendicectomy rate despite lower likelihood to undergo surgery. The overuse of imaging tests, best exemplified by the 75.2% frequency of patients undergoing computed tomography, may lead to increased costs. Risk-scoring systems such as RIPASA and Adult Appendicitis Score appear to be superior to Alvarado and AIR.

A closed-loop supply chain inventory model with stochastic demand, exchange rate, green investment, and carbon tax

In this paper, a mathematical inventory model for a closed-loop supply chain consisting of a single manufacturer and a single retailer is investigated under a stochastic environment and imperfect production. The manufacturer adopts a remanufacturing policy to recover returned products from the market. The model takes into account exchange rate disparities because the supply chain’s participants are in separate countries. The model also considers carbon emissions, which are expected to be produced from storage, transportation, and production. A carbon policy, namely carbon tax, is implemented to manage the overall emissions resulting from the supply chain. To cope with carbon restrictions, the manufacturer has an opportunity to invest in green technologies. The objective of the study is to find optimal shipment quantity, number of deliveries, safety factor, and green investment such that the joint total cost is minimized. An iterative procedure is suggested to solve the proposed problem, and a numerical example is presented for model validation. The findings imply that variations in the production rate and carbon tax have a significant influence on the model’s performance. Aside from that, the results reveal that uncertainty in the exchange rate and production flaws are important factors that must be considered by managers in making inventory decisions, especially those related to the number of deliveries, frequency of deliveries, and the amount of money invested in green technology. Finally, a sensitivity analysis is provided to explain the behavior of the model.

CENTRAL BANK INDEPENDENCE, INFLATION AND VOLATILITY NEXUS: DOES INDEPENDENCE CAN SOLVE INFLATION?

In this study, the relationship between central bank independence and both inflation rate and inflation uncertainty is examined with the help of panel data analysis for BRICS-T countries, using data for the period 1995-2017. The findings reveal several notable results. First, the study finds a statistically significant and negative relationship between the level of central bank independence and inflation in BRICS-T countries. Consequently, this means that a higher degree of independence is associated with lower inflation rates. Additionally, the study reveals a statistically significant and positive relationship between central bank independence and inflation uncertainty. This shows that inflation uncertainty tends to increase as the level of independence increases. These results shed light on the complex dynamics between central bank independence, inflation, and inflation uncertainty, providing valuable information for policymakers, central banks, and researchers interested in monetary policy effectiveness and macroeconomic stability. It is recommended that future studies investigate the underlying mechanisms driving these relationships and evaluate potential differences across countries and time periods

An output-feedback stabilisation scheme with exponential convergence for nonlinear systems with unknown polynomial-of-output growth rate

ABSTRACT This paper seeks adaptive output-feedback control which enables exponential stabilisation for nonlinear systems with unknown polynomial-of-output growth rate and dynamic uncertainties. The existence of uncertainties naturally requires the introduction of adaptive compensation mechanisms, while, convergence rate, as an important performance specification, is usually hard to attain in the context of adaptive control. For this, we are compelled to pursue a comprehensive and competent adaptive compensation mechanism which possesses dual capabilities of compensating uncertainties and affording convergence rate. Crucially, a subtle dynamic gain is designed with its dynamic updating law incorporating exponential-type time-varying information. Notably, the gain would not only deal with the unknown polynomial-of-output growth rate but particularly ensure exponential convergence with the convergence rate online adjusting in conformity to the dynamic uncertainties. Correspondingly, an adaptive output-feedback controller is constructed based on a dynamic-gain observer, which can render exponential convergence for system and observer states.

Comparison of Waymo rider-only crash data to human benchmarks at 7.1 million miles

Objectives This article examines the safety performance of the Waymo Driver, an SAE level 4 automated driving system (ADS) used in a rider-only (RO) ride-hailing application without a human driver, either in the vehicle or remotely. Methods ADS crash data were derived from NHTSA’s Standing General Order (SGO) reporting over 7.14 million RO miles through the end of October 2023 in Phoenix, Arizona, San Francisco, California, and Los Angeles, California, and compared to human benchmarks from the literature. Results When considering all locations together, the any injury reported crashed vehicle rate was 0.6 incidents per million miles (IPMM) for the ADS vs. 2.80 IPMM for the human benchmark, an 80% reduction or a human crash rate that is 5 times higher than the ADS rate. Police-reported crashed vehicle rates for all locations together were 2.1 IPMM for the ADS vs. 4.68 IPMM for the human benchmark, a 55% reduction or a human crash rate that was 2.2 times higher than the ADS rate. Police-reported crashed vehicle rate reductions for the ADS were statistically significant when compared in San Francisco and Phoenix, as well as combined across all locations and the any-injury-reported reductions were statistically significant in San Francisco and in all locations. The any property damage or injury comparison had statistically significant decreases in 3 comparisons but also nonsignificant results in 3 other benchmarks. When excluding ADS crashes with a delta-V less than 1 mph (a measure of sensitivity to lower reporting threshold), about half of the ADS collisions were excluded, resulting in comparisons that showed a large statistically significant reduction in all comparisons except for one comparison from San Francisco. Conclusions The statistically significant reductions in police-reported and any injury reported crash rates indicate a promising positive safety impact of ADS. The direction and significance of comparisons in the any property damage or injury outcome group are inconclusive due to difficulties in estimating a matching human benchmark. More research is needed on defining any property damage or injury benchmarks with clear lower reporting thresholds to reduce the systematic uncertainty in the benchmark rates. Together, these crash rate results contribute to the continuous growth in confidence, together with other methodologies, in a safety case approach.

Standard siren cosmology in the era of the 2.5-generation ground-based gravitational wave detectors: bright and dark sirens of LIGO Voyager and NEMO

The 2.5-generation (2.5G) ground-based gravitational wave (GW) detectors LIGO Voyager and NEMO are expected to be operational in the late 2020s and early 2030s. In this work, we explore the potential of GW standard sirens observed by the 2.5G GW detectors in measuring cosmological parameters, especially for the Hubble constant. Using GWs to measure cosmological parameters is inherently challenging, especially for 2.5G detectors, given their limited capability, which results in weaker constraints on cosmological parameters from the detected standard sirens. However, the measurement of the Hubble constant using standard siren observations from Voyager and NEMO is still promising. For example, using bright sirens from Voyager and NEMO can measure the Hubble constant with a precision of about 2% and 6% respectively, and using the Voyager-NEMO network can improve the precision to about 1.6%. Moreover, bright sirens can be used to break the degeneracy of cosmological parameters generated by CMB data, and to a certain extent, 2.5G detectors can also play a role in this aspect. Observations of dark sirens by 2.5G detectors can achieve relatively good results in measuring the Hubble constant, with a precision of within 2%, and if combining observations of bright and dark sirens, the precision of the Hubble constant measurement can reach about 1.4%. Finally, we also discussed the impact of the uncertainty in the binary neutron star merger rate on the estimation of cosmological parameters. We conclude that the magnificent prospect for solving the Hubble tension is worth expecting in the era of the 2.5G ground-based GW detectors.

Modeling residual-vehicle effects on uncertainty estimation of the connected vehicle penetration rate

In the transition to full deployment of connected vehicles (CVs), the CV penetration rate plays a key role in bridging the gap between partial and complete traffic information. Several innovative methods have been proposed to estimate the CV penetration rate using only CV data. However, these methods, as point estimators, may lead to biased estimations or suboptimal solutions when applied directly in modeling or system optimization. To avoid these problems, the uncertainty and variability in the CV penetration rate must be considered. Recently, a probabilistic penetration rate (PPR) model was developed for estimating such uncertainties. The key model input is a constrained queue length distribution composed exclusively of queues formed by red signals in undersaturation conditions with no residual vehicles. However, in real-world scenarios, due to random arrivals, residual vehicles are commonly carried over from one cycle to another in temporary overflow cycles in undersaturation conditions, which seriously restricts the applicability of the PPR model. To address this limitation, this paper proposes a Markov-constrained queue length (MCQL) model that can model the complex effects of residual vehicles on the CV penetration rate uncertainty. A constrained queue with residual vehicles is decomposed into four vehicle groups: observable constrained residual vehicles, unobservable constrained residual vehicles, unconstrained residual vehicles, and new arrivals. Although the first vehicle group is observable in the former cycle, the focus of this work is to model the residual vehicles from the second and third vehicle groups in combination with the new arrivals. The MCQL model includes four sub-models, namely, the residual-vehicle model, convolutional constrained queue model, constrained residual queue model, and observable residual queue model, to isolate and derive the distribution of the constrained vehicle set formed by the three latter vehicle groups. This distribution is then substituted into the PPR model to estimate the uncertainty. Comprehensive VISSIM simulations and applications to real-world datasets demonstrate that the proposed MCQL model can accurately model the residual-vehicle effect and estimate the uncertainty. Thus, the applicability of the PPR model is truly extended to real-world settings, regardless of the presence of residual vehicles. A simple stochastic CV-based adaptive signal control example illustrates the potential of the proposed model in real-world applications.

Global assessment of linear trend and seasonal variations of GNSS-IR sea level retrievals with nearby tide gauges

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) sea level retrievals and tide gauges at 40 globally distributed stations spanning from about 4.5 to 18 years are compared on a site-by-site basis, in terms of noise background, rate and seasonal variations by using the weighted least squares estimation (LSE) along with the Maximum likelihood estimation (MLE). The result shows that monthly GNSS-IR data agree well with tide gauges for most stations except the site Tuktoyaktuk, Canada. The mean correlation is 0.95 and the mean root mean square difference is 2.9 cm, respectively. The discrepancies of rate and seasonal amplitude estimates are within ± 1 cm for most stations. We confirm both the two sea level data exhibit temporal correlation, which has a great effect on the rate uncertainty estimates. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) favor Matérn and first-order autoregressive (AR1) the preferred stochastic model for the daily and monthly mean sea level time series, respectively. Owing to the data span dependence for the rate uncertainty estimates, to get an accuracy of sub-mm/yr in linear rate using the weighted LSE, at least 30 years of data (depends on data quality) is required. We recommend using long time series and a proper stochastic model for the rate estimation of sea level.

Energy-related uncertainty shocks and inflation dynamics in the U.S: A multivariate quantile-on-quantile regression approach

Existing literature suggests that uncertainty shocks can propagate like aggregate demand shocks or aggregate supply shocks. By way of extension, this study investigates the effect of energy-related uncertainty shocks on U.S. inflation while incorporating the effect of industrial production and interest rate uncertainty shocks. Using a multivariate quantile-on-quantile regression for the period 2000:M6 to 2019:M7, the findings reveal that energy-related uncertainty shocks amplify inflation by manifesting as cost-push shocks with a stronger connection emerging in quantiles slightly above the median quantile distribution of energy-related uncertainty. Although industrial production positively drives inflation, its effect is observed less around median quantiles of inflation than in the lower and upper quantiles. Furthermore, the effect of interest rate uncertainty is negative and stronger in quantiles around the median of inflation, suggesting that interest rate uncertainty behaves like aggregate demand shocks. Based on these findings, policy implications are offered.

Global supply chain network design for shelf-life products under uncertainty: a case study of pistachios

PurposeSupply chain management has become critical in today’s globalized environment, with growingly intense competition on the international level. The particular characteristics of modern trade have led companies to globalize and devise increasingly sophisticated supply chains to meet customer demand worldwide. Motivated by the need to address these challenges, we have developed a new model for a global supply chain that incorporates uncertainties in exchange rates, demand fluctuations, and the quantity of produce.Design/methodology/approachThe objective of the proposed model is to maximize supply chain profitability. Our model optimizes several critical decisions in the proposed global supply chain, including the location of domestic and foreign distribution centers, allocating the centers to customers, transportation mode selection, storage temperature, optimal farm purchase quantities, product flows across the network, and the shelf-life of products. Scenario-based stochastic programming approach is employed to account for the inherent uncertainties within the model. A pistachio supply chain is examined as a case study in this article, and the efficiency of the proposed model is demonstrated through computational results.FindingsThe model was solved using the CPLEX solver in GAMS and the results, the Sirjan DDC and Turkey FDC have been selected. In general, 40% of demand for customers from FDC (turkey) and 60% of demand from DDC (sirjan) is provided. Changes in the demand of foreign customers make the net profit more effective than changes in the demand for domestic customers. The decrease in exchange rate decreases the network profit with a higher slope and the increase in exchange rate will increase network profit with a relatively stable slope.Originality/valueWhile research on GSCs for perishable products has been ongoing for several years, the importance of the subject necessitates continued investigation in this area. This paper aimed to address this gap by presenting an optimization model for designing GSCs for perishable products under uncertainty and with various transportation modes. The proposed model was designed with the aim of improving supply chain performance and real-world applicability.

Implications of Activity Rate Characterization and Fault-Based Partitioning on Probabilistic Seismic Hazard Assessment: A Case Study of Extensional Tectonic Regime in Western Anatolia, Türkiye

ABSTRACT Complex fault geometries with multiple sets of inclined active faults pose a challenge to the accurate representation of fault-based seismic sources in probabilistic seismic hazard assessment (PSHA). In the absence of slip rates associated with fault segments and the presence of sparse seismic and geodetic data, the estimation of segment-specific activity rate includes significant uncertainty. This study proposes a comprehensive procedure for defining the segment-specific activity rates and associated uncertainties in fault-based PSHA for extensional tectonic regimes and applies the procedure in the northern margin of Western Anatolian Extensional Province. The seismic sources are modeled as rupture systems with individual fault segments using the connections between available active fault traces, first-order geological complexities, earthquake catalog, and focal mechanism solutions. Alternatives to estimate and partition segment-specific activity rates based on annual slip rate, seismicity rate, and moment rate are explored. Each alternative is implemented in PSHA, and the results are compared in terms of peak ground acceleration (PGA) maps for a 475-year return period. A comparison of the 475 yr PGA maps showed that the activity rates based on annual slip rates translate into higher hazard estimates and a more uniform distribution of PGA; whereas, the activity rates based on seismicity and moment rate result in a PGA distribution that is more sensitive to the occurrence and location of previous large-magnitude events. The approach utilized to partition activity rates among parallel segments has a noticeable effect in the areas where highly asymmetric fault activity is inferred from morphology. Hence, alternative approaches for estimation and partition of activity rates are combined to model the epistemic uncertainty in segment-specific activity rates, and a repeatable procedure is developed to build fault-based seismic source models in the presence of complex fault geometries.

The impact of real exchange rate uncertainty on exports by technology classification in emerging economies

The impact of real exchange rate uncertainty on exports by technology classification in emerging economies