nep-cmp New Economics Papers
on Computational Economics
Issue of 2021‒02‒22
twenty-two papers chosen by
Stan Miles
Thompson Rivers University

  1. Least Squares Monte Carlo applied to Dynamic Monetary Utility Functions By Hampus Engsner
  2. On Technical Trading and Social Media Indicators in Cryptocurrencies' Price Classification Through Deep Learning By Marco Ortu; Nicola Uras; Claudio Conversano; Giuseppe Destefanis; Silvia Bartolucci
  3. Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module By Kumar Yashaswi
  4. Resource-robust valid inequalities for set covering and set partitioning models By Hoogendoorn, Y.N.; Dalmeijer, K.
  5. Aggregate Modeling and Equilibrium Analysis of the Crowdsourcing Market for Autonomous Vehicles By Xiaoyan Wang; Xi Lin; Meng Li
  6. Fighting the soaring prices of agricultural food products. VAT versus Trade tariffs exemptions in a context of imperfect competition in Niger : CGE and micro-simulation approach By Celine de Quatrebarbes; Bertrand Laporte; Stéphane Calipel
  7. Selecting Matchings via Multiwinner Voting: How Structure Defeats a Large Candidate Space By Niclas Boehmer; Markus Brill; Ulrike Schmidt-Kraepelin
  8. Integrating prediction in mean-variance portfolio optimization By Andrew Butler; Roy H. Kwon
  9. Multi-Horizon Equity Returns Predictability via Machine Learning By Lenka Nechvatalova
  10. KI in der Finanzbranche: Im Spannungsfeld zwischen technologischer Innovation und regulatorischer Anforderung By Bauer, Kevin; Hinz, Oliver; Weber, Patrick
  11. "Nowcasting and forecasting GDP growth with machine-learning sentiment indicators". By Oscar Claveria; Enric Monte; Salvador Torra
  12. A simulation framework to project pension spending: The Czech pension system By Falilou Fall; Paul Cahu
  13. Artificial Intelligence, Globalization, and Strategies for Economic Development By Anton Korinek; Joseph E. Stiglitz
  14. Deep Learning for Market by Order Data By Zihao Zhang; Bryan Lim; Stefan Zohren
  15. Deep Structural Estimation: With an Application to Option Pricing By Hui Chen; Antoine Didisheim; Simon Scheidegger
  16. Methods for Simulating Multi-dimensional Data for Financial Services Recommendation By Vasil Marchev; Angel Marchev Jr
  17. Surrogate Monte Carlo By A. Christian Silva; Fernando F. Ferreira
  18. Hedging of Financial Derivative Contracts via Monte Carlo Tree Search By Oleg Szehr
  19. Manifold Learning with Approximate Nearest Neighbors By Fan Cheng; Rob J Hyndman; Anastasios Panagiotelis
  20. Interview Hoarding By Vikram Manjunath; Thayer Morrill
  21. Simulation of the Costs and Benefits of Delayed Retirement: Evidence from Vietnam By Nguyen, Cuong
  22. Uncertainty and Forecastability of Regional Output Growth in the United Kingdom: Evidence from Machine Learning By Mehmet Balcilar; David Gabauer; Rangan Gupta; Christian Pierdzioch

  1. By: Hampus Engsner
    Abstract: In this paper we explore ways of numerically computing recursive dynamic monetary risk measures and utility functions. Computationally, this problem suffers from the curse of dimensionality and nested simulations are unfeasible if there are more than two time steps. The approach considered in this paper is to use a Least Squares Monte Carlo (LSM) algorithm to tackle this problem, a method which has been primarily considered for valuing American derivatives, or more general stopping time problems, as these also give rise to backward recursions with corresponding challenges in terms of numerical computation. We give some overarching consistency results for the LSM algorithm in a general setting as well as explore numerically its performance for recursive Cost-of-Capital valuation, a special case of a dynamic monetary utility function.
    Date: 2021–01
  2. By: Marco Ortu; Nicola Uras; Claudio Conversano; Giuseppe Destefanis; Silvia Bartolucci
    Abstract: This work aims to analyse the predictability of price movements of cryptocurrencies on both hourly and daily data observed from January 2017 to January 2021, using deep learning algorithms. For our experiments, we used three sets of features: technical, trading and social media indicators, considering a restricted model of only technical indicators and an unrestricted model with technical, trading and social media indicators. We verified whether the consideration of trading and social media indicators, along with the classic technical variables (such as price's returns), leads to a significative improvement in the prediction of cryptocurrencies price's changes. We conducted the study on the two highest cryptocurrencies in volume and value (at the time of the study): Bitcoin and Ethereum. We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) neural network and Attention Long Short Term Memory (ALSTM). We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance. Furthermore, the Grid Search technique was used to find the best hyperparameters values for each implemented algorithm. The study shows that, based on the hourly frequency results, the unrestricted model outperforms the restricted one. The addition of the trading indicators to the classic technical indicators improves the accuracy of Bitcoin and Ethereum price's changes prediction, with an increase of accuracy from a range of 51-55% for the restricted model, to 67-84% for the unrestricted model.
    Date: 2021–02
  3. By: Kumar Yashaswi
    Abstract: Dynamic Portfolio optimization is the process of distribution and rebalancing of a fund into different financial assets such as stocks, cryptocurrencies, etc, in consecutive trading periods to maximize accumulated profits or minimize risks over a time horizon. This field saw huge developments in recent years, because of the increased computational power and increased research in sequential decision making through control theory. Recently Reinforcement Learning(RL) has been an important tool in the development of sequential and dynamic portfolio optimization theory. In this paper, we design a Deep Reinforcement Learning(DRL) framework as an autonomous portfolio optimization agent consisting of a Latent Feature State Space(LFSS) Module for filtering and feature extraction of financial data which is used as a state space for deep RL model. We develop an extensive RL agent with high efficiency and performance advantages over several benchmarks and model-free RL agents used in prior work. The noisy and non-stationary behaviour of daily asset prices in the financial market is addressed through Kalman Filter. Autoencoders, ZoomSVD, and restricted Boltzmann machines were the models used and compared in the module to extract relevant time series features as state space. We simulate weekly data, with practical constraints and transaction costs, on a portfolio of S&P 500 stocks. We introduce a new benchmark based on technical indicator Kd-Index and Mean-Variance Model as compared to equal weighted portfolio used in most of the prior work. The study confirms that the proposed RL portfolio agent with state space function in the form of LFSS module gives robust results with an attractive performance profile over baseline RL agents and given benchmarks.
    Date: 2021–02
  4. By: Hoogendoorn, Y.N.; Dalmeijer, K.
    Abstract: For a variety of routing and scheduling problems in aviation, shipping, rail, and road transportation, the state-of-the-art solution approach is to model the prob- lem as a set covering type problem and to use a branch-price-and-cut algorithm to solve it. The pricing problem typically takes the form of a Shortest Path Problem with Resource Constraints (SPPRC). In this context, valid inequalities are known to be `robust' if adding them does not complicate the pricing problem, and `non- robust' otherwise. In this paper, we introduce `resource-robust' as a new category of valid inequalities between robust and non-robust that can still be incorporated without changing the structure of the pricing problem, but only if the SPPRC includes specic resources. Elementarity-robust and ng-robust are introduced as widely applicable special cases that rely on the resources that ensure elementary routes and ng-routes, respectively, and practical considerations are discussed. The use of resource-robust valid inequalities is demonstrated with an application to the Capacitated Vehicle Routing Problem. Computational experiments show that re- placing robust valid inequalities by ng-robust valid inequalities may result in better lower bounds, a reduction in the number of nodes in the search tree, and a decrease in solution time.
    Keywords: Resource-Robust, Valid Inequalities, Branch-Price-and-Cut.
    Date: 2021–01–12
  5. By: Xiaoyan Wang; Xi Lin; Meng Li
    Abstract: Autonomous vehicles (AVs) have the potential of reshaping the human mobility in a wide variety of aspects. This paper focuses on a new possibility that the AV owners have the option of "renting" their AVs to a company, which can use these collected AVs to provide on-demand ride services without any drivers. We call such a mobility market with AV renting options the "AV crowdsourcing market". This paper establishes an aggregate equilibrium model with multiple transport modes to analyze the AV crowdsourcing market. The modeling framework can capture the customers' mode choices and AV owners' rental decisions with the presence of traffic congestion. Then, we explore different scenarios that either maximize the crowdsourcing platform's profit or maximize social welfare. Gradient-based optimization algorithms are designed for solving the problems. The results obtained by numerical examples reveal the welfare enhancement and the strong profitability of the AV crowdsourcing service. However, when the crowdsourcing scale is small, the crowdsourcing platform might not be profitable. A second-best pricing scheme is able to avoid such undesirable cases. The insights generated from the analyses provide guidance for regulators, service providers and citizens to make future decisions regarding the utilization of the AV crowdsourcing markets for serving the good of the society.
    Date: 2021–02
  6. By: Celine de Quatrebarbes; Bertrand Laporte (CERDI - Centre d'Études et de Recherches sur le Développement International - Clermont Auvergne - UCA - Université Clermont Auvergne - CNRS - Centre National de la Recherche Scientifique); Stéphane Calipel (CERDI - Centre d'Études et de Recherches sur le Développement International - Clermont Auvergne - UCA - Université Clermont Auvergne - CNRS - Centre National de la Recherche Scientifique)
    Abstract: As happened in West Africa in 2008, in an imported inflation context, it is common for the governments to take short-term tax action to protect the poor: VAT or trade tariffs exemptions. As part of the tax-tariff transition, the comparison between Trade tariffs and VAT has already been the subject of much works. The introduction of VAT, as a tax on final consumption, is supposed to be optimal, due to its economically neutral aspect for production decisions. However, some authors show that in developing countries, a large informal sector affects this result. In this paper, we use a CGE model and a micro-simulation model to compare the effects of VAT and Trade tariffs exemptions to combat rising agricultural food prices. The approach is innovative because it integrates how VAT works in developing countries (VAT increases production costs for some producers), in a context of imperfect competition for the service of marketing products. Results show that VAT exemptions are more effective than Trade tariffs exemptions in limiting the effects of the rise in world prices on poverty in Niger. In the context of the current increase in food prices linked to the Covid-19 crisis (FAO, 2020), this issues may one again be in the limelight for the African governments.
    Keywords: Computable general equilibrium model,Imperfect competition,Indirect taxes,Poverty,Niger
    Date: 2021–02
  7. By: Niclas Boehmer; Markus Brill; Ulrike Schmidt-Kraepelin
    Abstract: Given a set of agents with approval preferences over each other, we study the task of finding $k$ matchings fairly representing everyone's preferences. We model the problem as an approval-based multiwinner election where the set of candidates consists of all possible matchings and agents' preferences over each other are lifted to preferences over matchings. Due to the exponential number of candidates in such elections, standard algorithms for classical sequential voting rules (such as those proposed by Thiele and Phragm\'en) are rendered inefficient. We show that the computational tractability of these rules can be regained by exploiting the structure of the approval preferences. Moreover, we establish algorithmic results and axiomatic guarantees that go beyond those obtainable in the general multiwinner setting. Assuming that approvals are symmetric, we show that proportional approval voting (PAV), a well-established but computationally intractable voting rule, becomes polynomial-time computable, and its sequential variant (seq-PAV), which does not provide any proportionality guarantees in general, fulfills a rather strong guarantee known as extended justified representation. Some of our positive computational results extend to other types of compactly representable elections with an exponential candidate space.
    Date: 2021–02
  8. By: Andrew Butler; Roy H. Kwon
    Abstract: Many problems in quantitative finance involve both predictive forecasting and decision-based optimization. Traditionally, predictive models are optimized with unique prediction-based objectives and constraints, and are therefore unaware of how those predictions will ultimately be used in the context of their final decision-based optimization. We present a stochastic optimization framework for integrating regression based predictive models in a mean-variance portfolio optimization setting. Closed-form analytical solutions are provided for the unconstrained and equality constrained case. For the general inequality constrained case, we make use of recent advances in neural-network architecture for efficient optimization of batch quadratic-programs. To our knowledge, this is the first rigorous study of integrating prediction in a mean-variance portfolio optimization setting. We present several historical simulations using global futures data and demonstrate the benefits of the integrated approach in comparison to the decoupled alternative.
    Date: 2021–02
  9. By: Lenka Nechvatalova (Institute of Economic Studies, Charles University and Institute of Information Theory and Automation, Czech Academy of Sciences Prague, Czech Republic)
    Abstract: We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer forecasting horizon. We also document the profitability of long-short portfolios, which were created using predictions of cumulative returns at various horizons, before and after accounting for transaction costs. There is a trade-off between higher transaction costs connected to frequent rebalancing and greater returns on shorter horizons. However, we show that increasing the forecasting horizon while matching the rebalancing period increases risk-adjusted returns after transaction cost for the U.S. We combine predictions of expected returns at multiple horizons using double-sorting and buy/hold spread, a turnover reducing strategy. Using double sorts significantly increases profitability on the U.S. sample. Buy/hold spread portfolios have better risk-adjusted profitability in the U.S.
    Keywords: Machine learning, asset pricing, horizon predictability, anomalies
    JEL: G11 G12 G15 C55
    Date: 2021–02
  10. By: Bauer, Kevin; Hinz, Oliver; Weber, Patrick
    Abstract: Die Künstliche Intelligenz (KI) gilt als Basistechnologie des 21. Jahrhunderts und führt, wenn auch in unterschiedlichen Geschwindigkeiten, zu drastischen Veränderungen in praktisch allen Industrien. Die Finanzbranche gehört dabei zu den Industrien, welche bereits heute mit am stärksten von diesen Umbrüchen betroffen sind. Unter anderem wird das klassische, relationale Bankgeschäft, aber auch das klassische Investmentgeschäft zunehmend durch KI-basierte Anwendungen verdrängt. Dabei befindet sich die Branche in einem komplexen Spannungsfeld zwischen den regulatorischen Anforderungen des Datenschutzes und dem Recht auf Information der Marktteilnehmer auf der einen (bspw. durch die DSGVO) und dem technologischen Innovationsdruck auf der anderen Seite. Dies führt dazu, dass eine Reihe von Besonderheiten bei der Konzeption, Entwicklung und Integration von KI-Anwendungen beachtet werden muss. Das vorliegende Whitepaper bietet eine Übersicht über den aktuellen Stand, Trends und die Potenziale von KI-Technologien in der Finanzbranche. Dabei wird ein besonderes Augenmerk auf mögliche Problemstellungen und Herausforderungen für Regulatorik gelegt, insbesondere die mit komplexen KI-Anwendungen verbundene Black-Box Problematik. Vor diesem Hintergrund wird die Notwendigkeit einer stärkeren Fokussierung auf eXplainable Artificial Intelligence (XAI) betont, die eine große Chance darstellt potentielle gravierende Probleme heutiger KI-Anwendungen zu beheben und gleichzeitig die Vorteile zu bewahren.
    Keywords: Künstliche Intelligenz,Regulatorik,Finanzbranche,eXplainable Artificial Intelligence,Machine Learning
    Date: 2021
  11. By: Oscar Claveria (AQR–IREA, Department of Econometrics, Statistics and Applied Economics, University of Barcelona, Diagonal 690, 08034 Barcelona, Spain.); Enric Monte (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC).); Salvador Torra (Riskcenter–IREA, Department of Econometrics, Statistics and Applied Economics, University of Barcelona (UB).)
    Abstract: We apply the two-step machine-learning method proposed by Claveria et al. (2021) to generate country-specific sentiment indicators that provide estimates of year-on-year GDP growth rates. In the first step, by means of genetic programming, business and consumer expectations are evolved to derive sentiment indicators for 19 European economies. In the second step, the sentiment indicators are iteratively re-computed and combined each period to forecast yearly growth rates. To assess the performance of the proposed approach, we have designed two out-of-sample experiments: a nowcasting exercise in which we recursively generate estimates of GDP at the end of each quarter using the latest survey data available, and an iterative forecasting exercise for different forecast horizons We found that forecasts generated with the sentiment indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool.
    Keywords: Forecasting, Economic growth, Business and consumer expectations, Symbolic regression, Evolutionary algorithms, Genetic programming. JEL classification: C51, C55, C63, C83, C93.
    Date: 2021–02
  12. By: Falilou Fall; Paul Cahu
    Abstract: This paper presents a simulation framework developed to assess the impact of ageing on the financial sustainability of the Czech pension system. It accompanies the publication OECD Reviews of Pension Systems: Czech Republic. The framework has two components: a macroeconomic model to project long-term GDP and a cohort model to simulate the evolution of pensions. The macroeconomic model takes into account the evolution of the labour force and productivity. The cohort model simulates the career of a representative sample of the working-age population and their path in retirement. It replicates and projects the main features of the labour market, in particular, participation, wage and unemployment. It captures non-linear features of the pension system and distributional effects. The model estimates and simulates the main demographic variables of the pension system, in particular, the number of old-age pensioners and disability pensioners. It allows to simulate different policy options to close the financing gap of the pension system. Pension spending is projected to increase to 11.9% of GDP in 2060 from 8.2% in 2018, leading to increasing deficits of the pension system. Among the different options to close the financing gap, further increasing the retirement age after 2030 in line with life expectancy gains appears to be the most efficient policy measure to boost growth and reduce the financing needs. However, additional measures would be needed to close the financing gap of the pension system.
    Keywords: Ageing, Czech Republic, financial sustainability of pension systems, Pay-as-you-go-system, pension reform, pension simulation frramework, pensions
    JEL: H55 J11 J26 J18
    Date: 2021–02–16
  13. By: Anton Korinek; Joseph E. Stiglitz
    Abstract: Progress in artificial intelligence and related forms of automation technologies threatens to reverse the gains that developing countries and emerging markets have experienced from integrating into the world economy over the past half century, aggravating poverty and inequality. The new technologies have the tendency to be labor-saving, resource-saving, and to give rise to winner-takes-all dynamics that advantage developed countries. We analyze the economic forces behind these developments and describe economic policies that would mitigate the adverse effects on developing and emerging economies while leveraging the potential gains from technological advances. We also describe reforms to our global system of economic governance that would share the benefits of AI more widely with developing countries.
    JEL: F6 F63 O3 O32
    Date: 2021–02
  14. By: Zihao Zhang; Bryan Lim; Stefan Zohren
    Abstract: Market by order (MBO) data - a detailed feed of individual trade instructions for a given stock on an exchange - is arguably one of the most granular sources of microstructure information. While limit order books (LOBs) are implicitly derived from it, MBO data is largely neglected by current academic literature which focuses primarily on LOB modelling. In this paper, we demonstrate the utility of MBO data for forecasting high-frequency price movements, providing an orthogonal source of information to LOB snapshots. We provide the first predictive analysis on MBO data by carefully introducing the data structure and presenting a specific normalisation scheme to consider level information in order books and to allow model training with multiple instruments. Through forecasting experiments using deep neural networks, we show that while MBO-driven and LOB-driven models individually provide similar performance, ensembles of the two can lead to improvements in forecasting accuracy -- indicating that MBO data is additive to LOB-based features.
    Date: 2021–02
  15. By: Hui Chen; Antoine Didisheim; Simon Scheidegger
    Abstract: We propose a novel structural estimation framework in which we train a surrogate of an economic model with deep neural networks. Our methodology alleviates the curse of dimensionality and speeds up the evaluation and parameter estimation by orders of magnitudes, which significantly enhances one's ability to conduct analyses that require frequent parameter re-estimation. As an empirical application, we compare two popular option pricing models (the Heston and the Bates model with double-exponential jumps) against a non-parametric random forest model. We document that: a) the Bates model produces better out-of-sample pricing on average, but both structural models fail to outperform random forest for large areas of the volatility surface; b) random forest is more competitive at short horizons (e.g., 1-day), for short-dated options (with less than 7 days to maturity), and on days with poor liquidity; c) both structural models outperform random forest in out-of-sample delta hedging; d) the Heston model's relative performance has deteriorated significantly after the 2008 financial crisis.
    Date: 2021–02
  16. By: Vasil Marchev (University of National and World Economy, Sofia, Bulgaria); Angel Marchev Jr (University of National and World Economy, Sofia, Bulgaria)
    Abstract: This study is part of bigger research about self-learning systems for management of individualized investment portfolios. In this research we present several approaches for generating multi-dimensional synthetic data in conformity with the business logic, correlations, previous datasets, concatenation, neural networks, etc. Each approach is described algorithmically, and a brief comparative analysis is conducted at the conclusion of the paper. All described approaches rely to a different extend on real data as input Ð whether aggregated distribution or partially available real data to be enriched horizontally or vertically.
    Keywords: self-learning systems, synthetic data, individualized investment portfolios.
    JEL: C63 C81 G29
    Date: 2021–02
  17. By: A. Christian Silva; Fernando F. Ferreira
    Abstract: This article proposes an artificial data generating algorithm that is simple and easy to customize. The fundamental concept is to perform random permutation of Monte Carlo generated random numbers which conform to the unconditional probability distribution of the original real time series. Similar to constraint surrogate methods, random permutations are only accepted if a given objective function is minimized. The objective function is selected in order to describe the most important features of the stochastic process. The algorithm is demonstrated by producing simulated log-returns of the S\&P 500 stock index.
    Date: 2021–02
  18. By: Oleg Szehr
    Abstract: The construction of approximate replication strategies for derivative contracts in incomplete markets is a key problem of financial engineering. Recently Reinforcement Learning algorithms for pricing and hedging under realistic market conditions have attracted significant interest. While financial research mostly focused on variations of $Q$-learning, in Artificial Intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,... This article introduces Monte Carlo Tree Search for the hedging of financial derivatives in realistic markets and shows that there are good reasons, both on the theoretical and practical side, to favor it over other Reinforcement Learning methods.
    Date: 2021–02
  19. By: Fan Cheng; Rob J Hyndman; Anastasios Panagiotelis
    Abstract: Manifold learning algorithms are valuable tools for the analysis of high-dimensional data, many of which include a step where nearest neighbors of all observations are found. This can present a computational bottleneck when the number of observations is large or when the observations lie in more general metric spaces, such as statistical manifolds, which require all pairwise distances between observations to be computed. We resolve this problem by using a broad range of approximate nearest neighbor algorithms within manifold learing algorithms and evaluating their impact on embedding accuracy. We use approximate nearest neighbors for statistical maifolds by exploiting the connection between Hellinger/Total variation distance for discrete distributions and the L2/L1 norm. Via a thorough empirical investigation based on the benchmark MNIST dataset, it is shown that approximate nearest neighbors lead to substantial improvements in computational time with little to no loss in the accuracy of the embedding produced by a manifold learning algorithm. This result is robust to the use of different manifold learning algorithms, to the use of different approximate nearest neighbor algorithms, and to the use of different measures of embedding accuracy. The proposed method is applied to learning statistical manifolds data on distributions of electricity usage. This application demonstrates how the proposed methods can be used to visualize and identify anomalies and uncover underlying structure within high-dimensional data in a way that is scalable to large datasets.
    Keywords: statistical manifold, dimension reduction, anomaly detection, k-d trees, Hellinger distance, smart meter data
    JEL: C55 C65 C80
    Date: 2021
  20. By: Vikram Manjunath; Thayer Morrill
    Abstract: Many centralized matching markets are preceded by interviews between the participants. We study the impact on the final match of an increase to the number of interviews one side of the market can participate in. Our motivation is the match between residents and hospitals where, due to the COVID-19 pandemic, interviews for the 2020-21 season of the NRMP match have switched to a virtual format. This has drastically reduced the cost to applicants of accepting interview offers. However, the reduction in cost is not symmetric since applicants, not programs, bore most of the costs of in-person interviews. We show that if doctors are willing to accept more interviews but the hospitals do not increase the number of interviews they offer, no doctor will be better off and potentially many doctors will be harmed. This adverse consequence results from a mechanism we describe as interview hoarding. We prove this analytically and characterize optimal mitigation strategies for special cases. We use simulations to extend the insights from our analytical results to more general settings.
    Date: 2021–02
  21. By: Nguyen, Cuong
    Abstract: Vietnam is experiencing one of the fastest rates of population ageing in the world, yet has a low retirement age at 55 for women and 60 for men. This paper identifies the impacts of raising the retirement age and assesses how these would translate into either net costs or net gains for the Vietnamese economy in the long term. First, the paper uses national and household-level data to assess how a change in the employment rate of older workers would impact the employment status and wages of younger workers and impact the school attendance of their grandchildren. Second, this paper conducts a cost-benefit analysis to assess the net annual benefit of raising the retirement age according to four policy scenarios. This calculation is used to project the net benefits of each scenario over 33 years. The paper finds that increasing the employment rate will not impact the employment rate of younger workers, and will only negatively impact their wages in households where an older woman would stop helping with housework in order to resume formal employment. Given these findings, this paper concludes that raising the retirement age will result in a net gain in all four policy scenarios and that gains will increase the higher the retirement age is raised. The gains from raising women’s retirement age will exceed those of raising men’s age in the long term as the female share of the formal workforce continues to grow.
    Keywords: Retirement age, delayed retirement, simulation, older people, Vietnam.
    JEL: J1
    Date: 2019–12–15
  22. By: Mehmet Balcilar (Eastern Mediterranean University, Famagusta, via Mersin 10, Northern Cyprus, Turkey); David Gabauer (Data Analysis Systems, Software Competence Center Hagenberg, Austria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: Utilizing a machine-learning technique known as random forests, we study whether regional output growth uncertainty helps to improve the accuracy of forecasts of regional output growth for twelve regions of the United Kingdom using monthly data for the period from 1970 to 2020. We use a stochastic-volatility model to measure regional output growth uncertainty. We document the importance of interregional stochastic volatility spillovers and the direction of the transmission mechanism. Given this, our empirical results shed light on the contribution to forecast performance of own uncertainty associated with a particular region, output growth uncertainty of other regions, and output growth uncertainty as measured for London as well. We find that output growth uncertainty significantly improves forecast performance in several cases, where we also document cross-regional heterogeneity in this regard.
    Keywords: Regional Output Growth, Uncertainty, United Kingdom, Forecasting, Machine Learning
    JEL: C22 C53 D8 E32 E37 R11
    Date: 2021–02

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