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on Computational Economics |
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Issue of 2018‒05‒28
ten papers chosen by |
| By: | Widodo, Tri; Fitrady, Ardyanto; Alim Rosyadi, Saiful; Erdyas Bimanatya, Traheka |
| Abstract: | Domestic natural gas utilization in Indonesia suffers from lack of proper infrastructure and high transportation costs. The government might benefit from detailed estimation of demand to anticipate potentially fast-growing natural gas utilization in the future. Using Global Trade Analysis Project - Energy (GTAP-E) model simulation, this paper attempts to present a long-run estimation of natural gas demand in manufacturing sector for year 2025, 2030, and 2035. Chemical industry will remain the largest user of natural gas, followed by electricity, basic metal, and metal industry. To meet these demand, domestic production of natural gas should increase by 36.7 percent and 99.49 percent in 2025 and 2035, respectively. It brings us to the urge of massive investments in natural gas production and distribution. |
| Keywords: | natural gas, GTAP-E Model, energy demand |
| JEL: | Q41 Q47 |
| Date: | 2018–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:86887&r=cmp |
| By: | Daniel Guterding; Wolfram Boenkost |
| Abstract: | The Heston stochastic volatility model is a standard model for valuing financial derivatives, since it can be calibrated using semi-analytical formulas and captures the most basic structure of the market for financial derivatives with simple structure in time-direction. However, extending the model to the case of time-dependent parameters, which would allow for a parametrization of the market at multiple timepoints, proves more challenging. We present a simple and numerically efficient approach to the calibration of the Heston stochastic volatility model with piecewise constant parameters. We show that semi-analytical formulas can also be derived in this more complex case and combine them with recent advances in computational techniques for the Heston model. Our numerical scheme is based on the calculation of the characteristic function using Gauss-Kronrod quadrature with an additional control variate that stabilizes the numerical integrals. We use our method to calibrate the Heston model with piecewise constant parameters to the foreign exchange (FX) options market. Finally, we demonstrate improvements of the Heston model with piecewise constant parameters upon the standard Heston model in selected cases. |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1805.04704&r=cmp |
| By: | Erel, Isil (Ohio State University); Stern, Lea Henny (University of Washington); Tan, Chenhao (University of Colorado); Weisbach, Michael S. (Ohio State University) |
| Abstract: | Can an algorithm assist firms in their hiring decisions of corporate directors? This paper proposes a method of selecting boards of directors that relies on machine learning. We develop algorithms with the goal of selecting directors that would be preferred by the shareholders of a particular firm. Using shareholder support for individual directors in subsequent elections and firm profitability as performance measures, we construct algorithms to make out-of-sample predictions of these measures of director performance. We then run tests of the quality of these predictions and show that, when compared with a realistic pool of potential candidates, directors predicted to do poorly by our algorithms indeed rank much lower in performance than directors who were predicted to do well. Deviations from the benchmark provided by the algorithms suggest that firm-selected directors are more likely to be male, have previously held more directorships, have fewer qualifications and larger networks. Machine learning holds promise for understanding the process by which existing governance structures are chosen, and has potential to help real world firms improve their governance. |
| JEL: | G34 M12 M51 |
| Date: | 2018–03 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:ohidic:2018-05&r=cmp |
| By: | Jason Poulos |
| Abstract: | This paper proposes an alternative to the synthetic control method (SCM) for estimating the effect of a policy intervention on an outcome over time. Recurrent neural networks (RNNs) are used to predict counterfactual time-series of treated unit outcomes using only the outcomes of control units as inputs. Unlike SCM, the proposed method does not rely on pre-intervention covariates, allows for nonconvex combinations of control units, can handle multiple treated units, and can share model parameters across time-steps. RNNs outperform SCM in terms of recovering experimental estimates from a field experiment extended to a time-series observational setting. In placebo tests run on three different benchmark datasets, RNNs are more accurate than SCM in predicting the post-intervention time-series of control units, while yielding a comparable proportion of false positives. The proposed method contributes to a new literature that uses machine learning techniques for data-driven counterfactual prediction. |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1712.03553&r=cmp |
| By: | Katarzyna Growiec; Jakub Growiec; Bogumil Kaminski |
| Abstract: | Based on a calibrated computational multi-agent model with an overlapping generations structure, we investigate society-level consequences of creation and destruction of social ties at the individual level. The steady state of the simulated social network exhibits realistic small-world topology. We also find that societies whose social networks are relatively frequently reconfigured, display relatively higher social trust, willingness to cooperate, and economic performance - at the cost of lower social utility. Similar outcomes are found for societies where social tie dissolution is relatively weakly linked to family closeness. |
| Keywords: | social network structure, social network dynamics, social trust, willingness to cooperate, economic performance, computational multi-agent model. |
| JEL: | C63 D85 J31 L14 Z13 |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:sgh:kaewps:2018036&r=cmp |
| By: | Loza, Melissa (Universidad Privada Boliviana); Morales, Juan Antonio (IISEC, Universidad Católica Boliviana) |
| Abstract: | The supercycle of high commodity prices has had profound impacts on the Bolivian economy. We have investigated the relations of commodity prices with the main indicators of macroeconomic performance. We have found significant ties in our statistical analysis. An important question that we address concerns the consequences of the transitory export boom on the long term rate of growth of the economy. Also, given the importance of fiscal linkages with windfall income, we examined the procyclality of public expenditures based on an analysis of correlations of the cyclical components. We find positive but weak correlations. With export booms, consumption increases but also saving rates. Savings have been invested in foreign assets and, to a large extent, in construction. We find that commodity prices and per capita value added in the construction sector are co-integrated, suggesting a long term relationship. Also we looked into the real linkages of the hydrocarbons sector with other sectors of the economy through an Input-Output analysis. Finally, we have conducted a set of simulations, with a small Computable General Equilibrium Model adapted to the Bolivian conditions, to trace the domestic price and quantity effects of the commodity price increases. |
| Keywords: | price supercycle; transitory trade shocks; fiscal effects; procyclality; Bolivian economy; |
| JEL: | C32 C67 C68 E60 E62 |
| Date: | 2018–05–21 |
| URL: | http://d.repec.org/n?u=RePEc:ris:iisecd:2018_001&r=cmp |
| By: | Nomaler, Onder (ECIS, TU Eindhoven); Verspagen, Bart (UNU-MERIT, Maastricht University) |
| Abstract: | The current literature on the economic effects of machine learning, robotisation and artificial intelligence suggests that there may be an upcoming wave of substitution of human labour by machines (including software). We take this as a reason to rethink the traditional ways in which technological change has been represented in economic models. In doing so, we contribute to the recent literature on so-called perpetual growth, i.e., growth of per capita income without technological progress. When technology embodied in capital goods are sufficiently advanced, per capita growth becomes possible with a non-progressing state of technology. We present a simple Solow-like growth model that incorporates these ideas. The model predicts a rising wage rate but declining share of wage income in the steady state growth path. We present simulation experiments on several policy options to combat the inequality that results from this, including a universal basic income as well as an option in which workers become owners of "robots". |
| Keywords: | perpetual economic growth, economic effects of robots, income distribution |
| JEL: | O15 O41 O33 E25 P17 |
| Date: | 2018–05–23 |
| URL: | http://d.repec.org/n?u=RePEc:unm:unumer:2018023&r=cmp |
| By: | Hesamzadeh, M.; Holmberg, P.; Sarfati, M. |
| Abstract: | Zonal pricing with countertrading (a market-based redispatch) gives arbitrage opportunities to the power producers located in the export-constrained nodes. They can increase their profit by increasing the output in the day-ahead market and decrease it in the real-time market (the inc-dec game). We show that this leads to large inefficiencies in a standard zonal market. We also show how the inefficiencies can be significantly mitigated by changing the design of the real-time market. We consider a two-stage game with oligopoly producers, wind-power shocks and real-time shocks. The game is formulated as a two-stage stochastic equilibrium problem with equilibrium constraints (EPEC), which we recast into a two-stage stochastic Mixed-Integer Bilinear Program (MIBLP). We present numerical results for a six-node and the IEEE 24-node system. |
| Keywords: | Two-stage game, Zonal pricing, Wholesale electricity market, Bilinear programming |
| JEL: | C61 C63 C72 D43 L13 L94 |
| Date: | 2018–05–03 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:1829&r=cmp |
| By: | Harbrecht, Alexander; McKenna, Russell; Fischer, David; Fichtner, Wolf |
| Abstract: | This paper presents a stochastic bottom-up model to assess electric vehicles' (EV) impact on load profiles at different parking locations as well as their load management potential assuming different charging strategies. The central innovation lies in the consideration of socio-economic, technical and spatial factors, all of which influence charging behavior and location. Based on a detailed statistical analysis of a large dataset on German mobility, the most statistically significant influencing factors on residential charging behavior could be identified. Whilst household type and economic status are the most important factors for the number of cars per household, the driver's occupation has the strongest influence on the first departure time and parking time whilst at work. An inhomogeneous Markov-chain is used to sample a sequence of destinations of each car trip, depending (amongst other factors) on the occupation of the driver, the weekday and the time of the day. Probability distributions for the driven kilometres, driving durations and parking durations are used to derive times and electricity demand. The probability distributions are retrieved from a national mobility dataset of 70,000 car trips and filtered for a set of socio-economic and demographic factors. Individual charging behaviour is included in the model using a logistic function accounting for the sensitivity of the driver towards (low) battery SOC. The presented model is validated with this mobility dataset and shown to have a deviation in key household mobility characteristics of just a few percentage points. The model is then employed to analyse the impact of uncontrolled charging of BEV on the residential load profile. It is found that the absolute load peaks will increase by up to factor 8.5 depending on the loading infrastructure, the load in high load hours will increase by approx. a factor of 3 and annual electricity demand will approximately double. |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:kitiip:29&r=cmp |
| By: | Alogoskoufis, Spyros; Langfield, Sam |
| Abstract: | Euro area governments have committed to break the doom loop between bank risk and sovereign risk. But policymakers have not reached consensus on whether and how to reform the regulatory treatment of banks’ sovereign exposures. To inform policy discussions, this paper simulates portfolio reallocations by euro area banks under scenarios for regulatory reform. Simulations highlight a tension in regulatory design between concentration and credit risk. An area-wide low-risk asset—created by pooling and tranching cross-border portfolios of government debt securities— would resolve this tension by expanding the portfolio opportunity set. Banks could therefore reinvest into an asset that has both low concentration and low credit risk. JEL Classification: G01, G11, G21, G28 |
| Keywords: | bank regulation, sovereign risk, systemic risk |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:srk:srkwps:201874&r=cmp |