Alternative Rules Research Articles

Monetary Policy and Net Exports Externalities in the Small Open Economy

AbstractWe extend a New Keynesian small open economy dynamic stochastic general equilibrium (DSGE) model with nontradable goods and intermediate inputs. We show that the optimal monetary policy in the small open economy is not necessarily isomorphic to the closed economy due to net exports externalities. The optimal policymaker is willing to take advantage of the externalities and to raise the real value of home production, along with stabilizing composite domestic inflation. Also, we rank alternative monetary policy rules, estimate the optimal monetary policy rule associated with welfare, and show that setting interest rates toward their target levels of composite domestic inflation and net exports is desirable.

Optimal adaptation to uncertain climate change

Enormous public investment will occur as communities adapt to climate change. Much of this investment will be irreversible and the future benefits are uncertain. The real options embedded in adaptation projects are therefore potentially important and their existence needs to be incorporated into investment decision-making. Standard real options analysis is inadequate for this purpose because the arrival of new climate information is unlikely to conform to the stochastic processes typically used in real options analysis. This paper presents a new framework that reflects current uncertainty about climate change and how that uncertainty might change over time. Optimal investment depends on current beliefs regarding the severity of future climate change, how quickly these beliefs will change in the future, and current economic conditions. Most of the net benefits of optimal investment can be captured if investment timing is decided using a simple alternative decision-making rule.

Preliminary Concept of Urban Air Mobility Traffic Rules

Driven by recent technological breakthroughs, the electric vertical take-off and landing (eVTOL) aircraft has gained considerable attention. The widespread demand for eVTOL aircraft can be attributed to their potential use in the commercialisation of urban air mobility (UAM) in low-altitude urban airspaces. However, the urban low-altitude airspace environment is complex. UAM has a high traffic density and the eVTOL aircraft specifications are not uniform. Particularly in commercial scenarios, controlling eVTOL aircraft and ensuring safety in UAMs are the two major problems that should be addressed in future studies. The design of reasonable traffic rules is a potential solution; hence, we organised a UAM traffic rule system and proposed several alternative UAM traffic rules from three perspectives: a single eVTOL aircraft, a certain route, and key control areas. In addition, we validated these traffic rules using multi-rotor and fixed-wing eVTOL aircraft. The results show that designing reasonable traffic rules can facilitate attaining the primary objectives of commercialisation of UAM.

Optimal asset allocation for commodity sovereign wealth funds

This paper studies the dynamic asset allocation problem faced by an infinitively lived commodity-based sovereign wealth fund under incomplete markets. Assuming that the fund receives a non-tradable stream of commodity revenues until a predetermined date, the optimal consumption and investment strategies are state and time-dependent. Using data from the Norwegian Petroleum Fund, we find that the optimal demand for equity should decrease gradually from 60% to 40% over the next 60 years. However, the solution is particularly sensitive to the correlation between oil and stock price changes. We also estimate wealth-equivalent welfare losses, relative to the optimal rule, when following alternative suboptimal investment rules.

What Can Time‐Series Regressions Tell Us About Policy Counterfactuals?

We show that, in a general family of linearized structural macroeconomic models, knowledge of the empirically estimable causal effects of contemporaneous and news shocks to the prevailing policy rule is sufficient to construct counterfactuals under alternative policy rules. If the researcher is willing to postulate a loss function, our results furthermore allow her to recover an optimal policy rule for that loss. Under our assumptions, the derived counterfactuals and optimal policies are robust to the Lucas critique. We then discuss strategies for applying these insights when only a limited amount of empirical causal evidence on policy shock transmission is available.

Analysis of the influence of heterogeneous expectations of economic agents on the stability of general equilibrium models with an open economy

The purpose of the publication is to study the influence of bounded rationality of agents on the ability of economic authorities to choose alternative policy rules that stabilize the dynamics of the relevant significant macroeconomic variables by simultaneously analyzing the entire range of model parameters. The scientific novelty lies in the fact that models with an open economy are analyzed, in which economic agents interact with the outside world. The article evaluates and compares behavioral neo-Keynesian models obtained with two alternative ways of introducing heterogeneous expectations. It is assumed that agents can be either short-sighted with a short-term forecast, or far-sighted forecasters. The difference does not matter when the agents have rational expectations, but it does matter when some of them form beliefs about the future according to some heuristics. Bayesian estimates based on the data of the Russian economy show that the behavioral model based on short-term forecasts is better in agreement with empirical data than the model based on long-term forecasts and even compared to the model with rational expectations of agents. Stability and stability analysis was carried out using a numerical procedure — Monte Carlo Filtration Mapping (MCF). This procedure generalizes and supplements the results obtained for a more limited set of parameters of low-dimensional models in which agents do not interact with the outside world. MCF-analysis shows that incorporating heterogeneous expectations reduces the stability and robustness of models. At the same time, a model based on predictors of long-term forecasting is less stable compared to models of short-term forecasting and with rational expectations of agents. An important result is a significant proportion of areas with unstable behavior of the studied models with heterogeneous expectations of agents, in which solutions are characterized by an explosive nature. With the help of Smirnov–Kolmogorov statistics, significant parameters were identified that determine the deterministic behavior of all analyzed models. An interesting result is: the response of the interest rate to changes in the output gap and changes in the real effective exchange rate does not affect the deterministic behavior of the models under study. All obtained results are confirmed by a posteriori Bayesian estimates for these parameters. The findings provide guidance to economists who study the processes of expectation formation with the help of microdata.

Entropy profiles of Schelling’s segregation model from the Wang–Landau algorithm

We evaluate Schelling's segregation outcomes from the square lattice, regular random networks, and clustered regular random networks by situating them in the probability distribution of the entire outcome space of satisfaction and segregation. To do so, we employ the Wang-Landau algorithm and calculate the entropy and the number of states as a function of satisfaction and segregation. According to the results, satisfaction tends to increase with segregation, irrespective of the network structure. Moreover, segregation occurs almost surely when satisfaction is maximized, which we also algebraically derive and confirm on infinite-size networks. The average ratios of the neighbors of the same tag are about 67% for the square lattice and regular random networks and about 73% for clustered regular random networks with a clustering coefficient of 0.37(1). Thus, clustering increases segregation on regular random networks. Further, we find that Schelling's path-dependent process generates sharper segregation than expected by random configurations, an outcome symptomatic of over-optimization from the social welfare perspective. Adopting an alternative rule restricting individuals' choice set may reduce segregation without compromising on satisfaction.

Alternative classification rules for two inverse gaussian populations with a common mean and order restricted scale-like parameters

The problem of classification into two inverse Gaussian populations with a common mean and ordered scale-like parameters is considered. Surprisingly, the maximum likelihood estimators (MLEs) of the associated model parameters have not been utilized for classification purposes. Note that the MLEs of the model parameters, including the MLE of the common mean, do not have closed-form expressions. In this paper, several classification rules are proposed that use the MLEs and some plug-in type estimators under order restricted scale-like parameters. In the sequel, the risk values of all the proposed estimators are compared numerically, which shows that the proposed plug-in type restricted MLE performs better than others, including the Graybill-Deal type estimator of the common mean. Further, the proposed classification rules are compared in terms of the expected probability of correct classification (EPC) numerically. It is seen that some of our proposed rules have better performance than the existing ones in most of the parameter space. Two real-life examples are considered for application purposes.

Alternative stopping rules to limit tree expansion for random forest models

Random forests are a popular type of machine learning model, which are relatively robust to overfitting, unlike some other machine learning models, and adequately capture non-linear relationships between an outcome of interest and multiple independent variables. There are relatively few adjustable hyperparameters in the standard random forest models, among them the minimum size of the terminal nodes on each tree. The usual stopping rule, as proposed by Breiman, stops tree expansion by limiting the size of the parent nodes, so that a node cannot be split if it has less than a specified number of observations. Recently an alternative stopping criterion has been proposed, stopping tree expansion so that all terminal nodes have at least a minimum number of observations. The present paper proposes three generalisations of this idea, limiting the growth in regression random forests, based on the variance, range, or inter-centile range. The new approaches are applied to diabetes data obtained from the National Health and Nutrition Examination Survey and four other datasets (Tasmanian Abalone data, Boston Housing crime rate data, Los Angeles ozone concentration data, MIT servo data). Empirical analysis presented herein demonstrate that the new stopping rules yield competitive mean square prediction error to standard random forest models. In general, use of the intercentile range statistic to control tree expansion yields much less variation in mean square prediction error, and mean square prediction error is also closer to the optimal. The Fortran code developed is provided in the Supplementary Material.

Retrospectives: On the Evolution of the Rules versus Discretion Debate in Monetary Policy

Episodes of macroeconomic upheaval associated with monetary policy failure have provided the stage for important debates on rules versus discretion. We discuss the main features, results, commonalities, and differences in the debates that emerged after three such episodes. The modern debate was born during the Great Inflation of the 1970s and focused on both rules versus discretion and the properties of alternative rules. The middle debate originated with Henry Simons and the Chicago School during the Great Depression in the 1930s and focuses on policy uncertainty. The earliest systematic debate involved the Currency and Banking Schools in Britain in the 1820s, but, in spite the views of many of its participants and doctrinal historians, it seems to have primarily been about the degree of activism under a single rule—that of the gold standard.

Alternative harvest control rules for achieving sustainable fisheries under the revised Fisheries Act in Japan: case studies in two Japanese sardine stocks

Alternative harvest control rules for achieving sustainable fisheries under the revised Fisheries Act in Japan: case studies in two Japanese sardine stocks

A comparison of allocation strategies for optimising clinical trial designs under variance heterogeneity

Balanced allocation of patients across treatments is a widely adopted and practical strategy in clinical trials. Under the assumption of variance homogeneity, a balanced allocation is known to possess desirable properties in terms of commonly considered operating characteristics such as power, type I error rate, and estimation accuracy. When this assumption is violated, the balanced allocation strategy can perform suboptimally in terms of these (and other) metrics of interest, compared to alternative allocation rules. Such allocation rules are examined under the assumption of response variance heterogeneity across treatments and within an adaptive framework. A blocked design is proposed in order to account for additional sources of variability which are incorporated into the proposed design through a mixed effects model. For this setting, two allocation strategies are derived: an efficiency-oriented and an outcome-oriented strategy. These target two different and potentially conflicting objectives (estimation accuracy and within-trial patient response, respectively) that may be optimised in the presence of variance heterogeneity. A comparison of the resulting allocation strategies is provided in the context of a clinical trial studying the effect of different treatment protocols on the level of inflammation caused by Rheumatoid Arthritis. The interrelation of common clinical trial objectives under the examined allocation strategies is explored, demonstrating the benefits but also the costs in terms of objectives that are not formally incorporated into the optimisation criterion in each case.

Computation of Convex Hull Prices in Electricity Markets With Non-Convexities Using Dantzig-Wolfe Decomposition

The presence of non-convexities in electricity markets has been an active research area for about two decades. The — inevitable under current marginal cost pricing — problem of guaranteeing that no market participant incurs losses in the day-ahead market is addressed in current practice through make-whole payments <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a.k.a.</i> uplift. Alternative pricing rules have been studied to deal with this problem. Among them, Convex Hull (CH) prices associated with minimum uplift have attracted significant attention. Several US Independent System Operators (ISOs) have considered CH prices but resorted to approximations, mainly because determining exact CH prices is computationally challenging, while providing little intuition about the price formation rationale. In this paper, we describe the CH price estimation problem by relying on Dantzig-Wolfe decomposition and Column Generation, as a tractable, highly paralellizable, and exact method — i.e., yielding exact, not approximate, CH prices — with guaranteed finite convergence. Moreover, the approach provides intuition on the underlying price formation rationale. A test bed of stylized examples provide an exposition of the intuition in the CH price formation. In addition, a realistic ISO dataset is used to support scalability and validate the proof-of-concept.

Adaptive Reservoir Management by Reforming the Zone-based Hedging Rules against Multi-year Droughts

In this study, the zone-based hedging rule, which is the main operating policy adopted from multipurpose reservoirs in Korea is adjusted to reflect the multi-year droughts caused by climate change. Annual synthetic inflow series with different magnitudes of long memory were generated using the autoregressive fractional integrated moving average (ARFIMA) model. The generated inflow series were then disaggregated into 10-day series and utilized as input variables to derive the alternative hedging rules. The alternative hedging rules from this study were used in adaptive reservoir management by newly updated information. Finally, the performance of the suggested policy is measured in terms of frequency and magnitude under the historical inflow series. As a result, adaptive reservoir management demonstrated improvements in the following terms of the frequency of critical failures (water deficit ratio greater than 30%): 6.14% of the simulation period in the status quo (SQ) policy, and 2.99% in the adaptive management. However, the overall reliability of the reservoir during the simulation horizon was better when operated with the SQ policy (41.19%) than the results from adaptive management (26.42%). Because this result is in a good agreement with the original objective of the hedging rules, the adaptive policy suggested in this study holds promise and may be utilized in further reservoir management with an increase of potential drought risk from climate change.

The promise of potential: A study on the effectiveness of jury selection to a prestigious visual arts program

AbstractWe analyze 11 years of admission decisions to a highly selective post‐graduate visual arts program. Our unique, longitudinal data includes detailed information about the selection procedures as well as the later artistic and economic achievements for both accepted and rejected applicants (n = 8,557). Regarding the predictive value of the selection procedures on later performance, our analyses show that the largest gain made is in the first, more cursory, pre‐selection round. We find that weeding out less promising applicants is easier than identifying later top‐performers, in terms of ex‐ante expectations. The subsequent or final selection round is resource intensive and consists of several interviews with multiple jury members. Here we uncover an implicit structure of criterion variables that helps explain the ex‐post admission decisions. Our simulations show that actual selection during the final round is slightly better than alternative decision rules (including a decision by lottery) in identifying applicants' potential. Nevertheless, measured by future performance, the gain from rigorous jury assessments relative to modest and cheaper selection methods is minimal. Our conclusions are maintained under several robustness checks.

Macroeconomic effects of fiscal rules for a commodity-exporting economy: avoiding procyclical bias in Kazakhstan

ABSTRACT This paper investigates the macroeconomic consequences of an alternative fiscal rule in the context of commodity price shocks for a commodity-exporting country. We build a DSGE model to explain the business cycle in an oil-exporting economy, estimated using macroeconomic data from Kazakhstan. Our results demonstrate that when fiscal policy is procyclical in response to a transitory negative oil price shock, and if a majority of households are non-Ricardian, then a one standard deviation drop in oil prices causes an output decline of about 0.19%. In contrast, if the fiscal policy is countercyclical and conducted according to the structural balance fiscal rule, output increases by about 0.13% in response to the same shock. We also report that countercyclical fiscal policy is robustly effective when the monetary policy is characterized by its active inflation stabilization framework.

Multimodal EEG and Eye Tracking Feature Fusion Approaches for Attention Classification in Hybrid BCIs

Often, various modalities capture distinct aspects of particular mental states or activities. While machine learning algorithms can reliably predict numerous aspects of human cognition and behavior using a single modality, they can benefit from the combination of multiple modalities. This is why hybrid BCIs are gaining popularity. However, it is not always straightforward to combine features from a multimodal dataset. Along with the method for generating the features, one must decide when the modalities should be combined during the classification process. We compare unimodal EEG and eye tracking classification of internally and externally directed attention to multimodal approaches for early, middle, and late fusion in this study. On a binary dataset with a chance level of 0.5, late fusion of the data achieves the highest classification accuracy of 0.609–0.675 (95%-confidence interval). In general, the results indicate that for these modalities, middle or late fusion approaches are better suited than early fusion approaches. Additional validation of the observed trend will require the use of additional datasets, alternative feature generation mechanisms, decision rules, and neural network designs. We conclude with a set of premises that need to be considered when deciding on a multimodal attentional state classification approach.

Rewriting the rules of land reform: counterinsurgency and the property rights gap in wartime Nicaragua

ABSTRACT The use of agrarian reform within civil war to diminish insurgent support and violence has been a key topic within conflict scholarship, particularly in rural societies. Yet, this research has largely overlooked the ways in which the dynamics of counterinsurgency itself shapes land reform institutions – the procedures governing redistribution and legalization. Focusing on Nicaragua’s Contra War (1980–1990) and its longer-term effects, this article illustrates how counterinsurgent warfare can prompt state elites to refashion the rules of agrarian reform and titling in ways that ultimately undermine the state’s ability to regulate land tenure. As the perceived threat posed by the Contra insurgency deepened and peasant producers defected to rebels’ side, the highly centralized revolutionary coalition in power implemented alternative rules structuring land provision to recover rural support and preserve incumbent political power. These new rules permitted the individual and provisional titling of unregistered parcels, widening the property rights gap. The case thus illustrates that the obstacles to wartime agrarian reform may not emerge from state weakness or incompetence, but from how strategic wartime imperatives perversely remake the rules of land redistribution and titling.

Optimal selection of touristic packages based on user preferences during sports mega-events

Sport mega-events, such as the Soccer World Cup or Olympic Games, attract many visitors from all over the world. Most of these visitors are also interested in, besides attending the sports events, visiting the host nation and the neighboring countries. In this paper, we focus on the upcoming FIFA World Cup Qatar 2022. As per the schedule of the tournament, a national team can play 7 matches at most. Therefore, a supporter will have six short breaks (of three to five days) between consecutive matches in addition to two longer ones, immediately before and after the tournament, during which they can plan some touristic trips. We study the problem faced by a touristic trip provider who wants to offer a set of touristic packages, chosen among a very large set of options, devoted to World-Cup related tourists. The number of packages offered must be limited due to organizational reasons and the necessity to guarantee a high participation in each trip. In this study, a set of user profiles is considered. It represents different categories of tourists, characterized by different preferences and budgets. Each user is supposed to pick the packages that maximize their satisfaction, considering their budget and time restraints. The goal of the company is to choose the set of packages to be offered that would maximize the average users satisfaction. To address this NP-Hard combinatorial optimization problem we provide a mathematical formulation and a matheuristic, named Consensus-Based Kernel Search (CKS), wherein an alternative rule is used to create the initial Kernel and partition variables in buckets. Computational results evidence the excellent performance of CKS and prove that the newly introduced algorithm systematically outperforms the classical Kernel Search.

Simulation Study of Scheduling Heuristics for Parallel Machines with Sequence-Independent Setups

In this paper we examine a dynamic job scheduling problem on identical, parallel machines. Jobs that belong to different families arrive to the system at random time intervals. Stochastic processing times of jobs are considered and each family has different due date allowances. The manufacturing system consists of a single input queue and several machines. When switching from one job type to another on some machine, a sequence-independent setup is incurred. The scheduling of jobs is performed by some priority rule and nine alternative rules are considered in this study. The discrete-event simulation model of the system is developed and the performance of the scheduling heuristics is assessed in terms of the metrics of mean cycle time, mean earliness and mean tardiness. A total of thirteen simulation cases is considered, where the differentiating factors of the cases are the average job interarrival times, the due date and the setup parameters of jobs.