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on Computational Economics |
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Issue of 2019‒06‒10
seventeen papers chosen by |
| By: | Abedinnia, Hamid |
| Abstract: | The current cumulative PhD thesis consists of six papers published in/submitted to scientific journals. The focus of the thesis is to develop new solution approaches for scheduling problems encountering in manufacturing as well as in logistics. The thesis is divided into two parts: “ma-chine scheduling in production” and “scheduling problems in logistics” each of them consisting three papers. To have most comprehensive overview of the topic of machine scheduling, the first part of the thesis starts with two systematic review papers, which were conducted on tertiary level (i.e., re-viewing literature reviews). Both of these papers analyze a sample of around 130 literature re-views on machine scheduling problems. The first paper use a subjective quantitative approach to evaluate the sample, while the second papers uses content analysis which is an objective quanti-tative approach to extract meaningful information from massive data. Based on the analysis, main attributes of scheduling problems in production are identified and are classified into sever-al categories. Although the focus of both these papers are set to review scheduling problems in manufacturing, the results are not restricted to machine scheduling problem and the results can be extended to the second part of the thesis. General drawbacks of literature reviews are identi-fied and several suggestions for future researches are also provided in both papers. The third paper in the first part of the thesis presents the results of 105 new heuristic algorithms developed to minimize total flow time of a set of jobs in a flowshop manufacturing environ-ment. The computational experiments confirm that the best heuristic proposed in this paper im-proves the average error of best existing algorithm by around 25 percent. The first paper in second part is focused on minimizing number of electric tow-trains responsi-ble to deliver spare parts from warehouse to the production lines. Together with minimizing number of these electric vehicles the paper is also focused to maximize the work load balance among the drivers of the vehicles. For this problem, after analyzing the complexity of the prob-lem, an opening heuristic, a mixed integer linear programing (MILP) model and a taboo-search neighborhood search approach are proposed. Several managerial insights, such as the effect of battery capacity on the number of required vehicles, are also discussed. The second paper of the second part addresses the problem of preparing unit loaded devices (ULDs) at air cargos to be loaded latter on in planes. The objective of this problem is to mini-mize number of workers required in a way that all existing flight departure times are met and number of available places for building ULDs is not violated. For this problem, first, a MILP model is proposed and then it is boosted with a couple of heuristics which enabled the model to find near optimum solutions in a matter of 10 seconds. The paper also investigates the inherent tradeoff between labor and space utilization as well as the uncertainty about the volume of cargo to be processed. The last paper of the second part proposes an integrated model to improve both ergonomic and economic performance of manual order picking process by rotating pallets in the warehouse. For the problem under consideration in this paper, we first present and MILP model and then pro-pose a neighborhood search based on simulated annealing. The results of numerical experiment indicate that selectively rotating pallets may reduce both order picking time as well as the load on order picker, which leads to a quicker and less risky order picking process. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:113650&r=all |
| By: | Ishita Chakraborty (School of Management, Yale University); Minkyung Kim (School of Management, Yale University); K. Sudhir (Cowles Foundation & School of Management, Yale University; School of Management, Yale University) |
| Abstract: | The authors address two novel and signi?cant challenges in using online text reviews to obtain attribute level ratings. First, they introduce the problem of inferring attribute level sentiment from text data to the marketing literature and develop a deep learning model to address it. While extant bag of words based topic models are fairly good at attribute discovery based on frequency of word or phrase occurrences, associating sentiments to attributes requires exploiting the spatial and sequential structure of language. Second, they illustrate how to correct for attribute self-selection—reviewers choose the subset of attributes to write about—in metrics of attribute level restaurant performance. Using Yelp.com reviews for empirical illustration, they ?nd that a hybrid deep learning (CNN-LSTM) model, where CNN and LSTM exploit the spatial and sequential structure of language respectively provide the best performance in accuracy, training speed and training data size requirements. The model does particularly well on the “hard” sentiment classi?cation problems. Further, accounting for attribute self-selection signi?cantly impacts sentiment scores, especially on attributes that are frequently missing. |
| Keywords: | Text mining, Natural language processing (NLP), Convolutional neural networks (CNN), Long-short term memory (LSTM) Networks, Deep learning, Lexicons, Endogeneity, Self-selection, Online reviews, Online ratings, Customer satisfaction |
| JEL: | M1 M3 C8 C5 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:cwl:cwldpp:2176&r=all |
| By: | Gentry Johnson; Brian Quistorff; Matt Goldman |
| Abstract: | When pre-processing observational data via matching, we seek to approximate each unit with maximally similar peers that had an alternative treatment status--essentially replicating a randomized block design. However, as one considers a growing number of continuous features, a curse of dimensionality applies making asymptotically valid inference impossible (Abadie and Imbens, 2006). The alternative of ignoring plausibly relevant features is certainly no better, and the resulting trade-off substantially limits the application of matching methods to "wide" datasets. Instead, Li and Fu (2017) recasts the problem of matching in a metric learning framework that maps features to a low-dimensional space that facilitates "closer matches" while still capturing important aspects of unit-level heterogeneity. However, that method lacks key theoretical guarantees and can produce inconsistent estimates in cases of heterogeneous treatment effects. Motivated by straightforward extension of existing results in the matching literature, we present alternative techniques that learn latent matching features through either MLPs or through siamese neural networks trained on a carefully selected loss function. We benchmark the resulting alternative methods in simulations as well as against two experimental data sets--including the canonical NSW worker training program data set--and find superior performance of the neural-net-based methods. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.12020&r=all |
| By: | Vasilis Syrgkanis; Victor Lei; Miruna Oprescu; Maggie Hei; Keith Battocchi; Greg Lewis |
| Abstract: | We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where the experimenter randomizes over which user will receive a recommendation to take an action, and we are interested in the effect of the downstream action. We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxiliary models (each corresponding to a separate prediction task). The reduction enables the use of all recent algorithmic advances (e.g. neural nets, forests). We show that the estimated effect model is robust to estimation errors in the auxiliary models, by showing that the loss satisfies a Neyman orthogonality criterion. Our approach can be used to estimate projections of the true effect model on simpler hypothesis spaces. When these spaces are parametric, then the parameter estimates are asymptotically normal, which enables construction of confidence sets. We applied our method to estimate the effect of membership on downstream webpage engagement on TripAdvisor, using as an instrument an intent-to-treat A/B test among 4 million TripAdvisor users, where some users received an easier membership sign-up process. We also validate our method on synthetic data and on public datasets for the effects of schooling on income. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.10176&r=all |
| By: | Salecker, Jan; Dislich, Claudia; Wiegand, Kerstin; Meyer, Katrin M.; Pe'er, Guy |
| Abstract: | Spatially-explicit simulation models are commonly used to study complex ecological and socio-economic research questions. Often these models depend on detailed input data, such as initial land-cover maps to set up model simulations. Here we present the landscape generator EFFortS-LGraf that provides artificially-generated land-use maps of agricultural landscapes shaped by small-scale farms. EFForTS-LGraf is a process-based landscape generator that explicitly incorporates the human dimension of land-use change. The model generates roads and villages that consist of smallholder farming households. These smallholders use different establishment strategies to create fields in their close vicinity. Crop types are distributed to these fields based on crop fractions and specialization levels. EFForTS-LGraf model parameters such as household area or field size frequency distributions can be derived from household surveys or geospatial data. This can be an advantage over the abstract parameters of neutral landscape generators. We tested the model using oil palm and rubber farming in Indonesia as a case study and validated the artificially-generated maps against classified satellite images. Our results show that EFForTS-LGraf is able to generate realistic land-cover maps with properties that lie within the boundaries of landscapes from classified satellite images. An applied simulation experiment on landscape-level effects of increasing household area and crop specialization revealed that larger households with higher specialization levels led to spatially more homogeneous and less scattered crop type distributions and reduced edge area proportion. Thus, EFForTS-LGraf can be applied both to generate maps as inputs for simulation modelling and as a stand-alone tool for specific landscape-scale analyses in the context of ecological-economic studies of smallholder farming systems. |
| Keywords: | landscape generator,agent-based model,ABM,NetLogo,process-based,Indonesia |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:crc990:29&r=all |
| By: | Jean-Marc Montaud (CATT - Centre d'Analyse Théorique et de Traitement des données économiques - UPPA - Université de Pau et des Pays de l'Adour) |
| Abstract: | In Mali's current context where the crops sector is particularly exposed and vulnerable to agricultural drought, this study assesses the economy-wide impacts of such events and the potential effectiveness of some adaptation strategies. Using a dynamic computable general equilibrium model, we conduct counterfactual simulations of various scenarios accounting for different levels of intensity and frequency of droughts over a 15-year period. We first show how mild, moderate, and intense droughts currently experienced by the country affect its economic performances and considerably degrade its households' welfare. We also show how these negative impacts could be aggravated in the future by the likely increased number of intense droughts threatened by global climate change. However, we finally show that there appears to be some room for Mali to manoeuvre in terms of drought-risk management policies, such as fostering the use of drought-tolerant crop varieties, improving drought early warning systems or extending irrigation capacities. |
| Keywords: | Climate variability,General Equilibrium,Agriculture,Food Security,Mali |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02141050&r=all |
| By: | Jaelle Scheuerman; Jason L. Harman; Nicholas Mattei; K. Brent Venable |
| Abstract: | In many real world situations, collective decisions are made using voting. Moreover, scenarios such as committee or board elections require voting rules that return multiple winners. In multi-winner approval voting (AV), an agent may vote for as many candidates as they wish. Winners are chosen by tallying up the votes and choosing the top-$k$ candidates receiving the most votes. An agent may manipulate the vote to achieve a better outcome by voting in a way that does not reflect their true preferences. In complex and uncertain situations, agents may use heuristics to strategize, instead of incurring the additional effort required to compute the manipulation which most favors them.In this paper, we examine voting behavior in multi-winner approval voting scenarios with complete information. We show that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation. Instead, voters tend to prioritize the candidates with the highest utilities. Using simulations, we demonstrate the effectiveness of these heuristics in situations where agents only have access to partial information. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.12104&r=all |
| By: | Benjamín García; Sebastián Guarda; Markus Kirchner; Rodrigo Tranamil |
| Abstract: | The Extended Model for Analysis and Simulations (XMAS) is the Central Bank of Chile's newest dynamic stochastic general equilibrium (DSGE) model for macroeconomic projections and monetary policy analysis. Building on Medina and Soto (2007), the model includes several new features, in line with recent developments in the modeling of small open economies, particularly commodityexporting emerging economies such as Chile. The extensions over the base model include the modeling of non-core inflation dynamics, a commodity sector with endogenous production and investment, a labor market with search and matching frictions that allows for labor variation on both the intensive and extensive margins, an augmented fiscal block, as well as additional shocks and other real and nominal frictions. These features allow for a more granular analysis and more comprehensive forecasts of the Chilean economy, improving the fit of the model to macroeconomic data in several dimensions. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:chb:bcchwp:833&r=all |
| By: | Ranjiva Munasinghe; Leslie Kanthan; Pathum Kossinna |
| Abstract: | We propose a simpler derivation of the probability density function of Feller Diffusion using the Fourier Transform and solving the resulting equation via the Method of Characteristics. We also discuss simulation algorithms and confirm key properties related to hitting time probabilities via the simulation. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.10737&r=all |
| By: | Vegard H. Larsen; Leif Anders Thorsrud; Julia Zhulanova |
| Abstract: | We investigate the role played by the media in the expectations formation process of households. Using a news-topic-based approach we show that news types the media choose to report on, e.g., (Internet) technology, health, and politics, are good predictors of households' stated in ation expectations. In turn, in a noisy information model setting, augmented with a simple media channel, we document that the underlying time series properties of relevant news topics explain the timevarying information rigidity among households. As such, we not only provide a novel estimate showing the degree to which information rigidities among households vary across time, but also provide, using a large news corpus and machine learning algorithms, robust and new evidence highlighting the role of the media for understanding infl ation expectations and information rigidities. |
| Keywords: | Expectations, Media, Machine Learning, Inflation |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:bny:wpaper:0075&r=all |
| By: | Felbermayr, Gabriel; Gröschl, Jasmin Katrin; Heiland, Inga; Stehn, Jürgen |
| Abstract: | Die Autoren skizzieren die ökonomischen Kosten für die EU-Mitgliedstaaten bei einer Umkehr des Integrationsprozesses durch eine Abwicklung der Europäischen Zollunion, des Europäischen Binnenmarktes, der Europäischen Währungsunion, der Schengen-Vereinbarung und der Freihandelsabkommen der EU mit Drittländern. Sie kommen zu dem Ergebnis, dass eine Abwicklung des EU-Binnenmarktes die stärksten negativen Effekte auf Produktion, Handel und Einkommen der Mitgliedsländer hätte. Besonders stark betroffen wären die Länder in Mittel-und Osteuropa, während etablierte EU-Mitglieder wie Deutschland, Frankreich oder Italien eher unterdurchschnittlich hohe Kosten zu tragen hätten. Die anderen, ebenfalls untersuchten Desintegrationsschritte hätten geringere Kosten zur Folge, diese wären aber durchaus spürbar, da sie Jahr für Jahr nach dem Desintegrationsschock anfallen würden. |
| Keywords: | Costs of a Non-EU,Trade Costs,Simulation,Kosten einer Nicht-EU,Handelskosten |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifwkpb:125&r=all |
| By: | Enoch Cheng; Clemens C. Struck |
| Abstract: | This paper develops a Monte-Carlo backtesting procedure for risk premia strategies and employs it to study Time-Series Momentum (TSM). Relying on time-series models, empirical residual distributions and copulas we overcome two key drawbacks of conventional backtesting procedures. We create 10,000 paths of different TSM strategies based on the S&P 500 and a cross-asset class futures portfolio. The simulations reveal a probability distribution which shows that strategies that outperform Buy-and-Hold in-sample using historical backtests may out-of-sample i) exhibit sizeable tail risks ii) underperform or outperform. Our results are robust to using different time-series models, time periods, asset classes, and risk measures. |
| Keywords: | Monte-Carlo; Extreme Value Theory; Backtesting; Risk premia; Time-Series Momentum |
| JEL: | C12 C52 G12 F37 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:ucn:wpaper:201906&r=all |
| By: | Chakrabarti, Anindya S.; Kumar, Sudarshan |
| Abstract: | Central banks of different countries are some of the largest economic players at the global scale and they are not static in their monetary policy stances. They change their policies substantially over time in response to idiosyncratic or global factors affecting the economies. A very prominent and empirically documented feature arising out of central banks’ actions, is that the relative importance assigned to inflation vis-a-vis output fluctuations evolve substantially over time. We analyze the leading and lagging behavior of central banks of various countries in terms of adopting low inflationary environment vis-a-vis high weight assigned to counteract output fluctuations, in a completely data-driven way. To this end, we propose a new methodology by combining complex Hilbert principle component analysis with state-space models in the form of Kalman filter. The CHPCA mechanism is non-parametric and provides a clean identification of leading and lagging behavior in terms of phase differences of time series in the complex plane. We show that the methodology is useful to characterize the extent of coordination (or lack thereof), of monetary policy stances taken by central banks in a cross-section of developed and developing countries. In particular, the analysis suggests that US Fed led other countries central banks in the pre-crisis period in terms of pursuing low-inflationary regimes. |
| Date: | 2019–06–03 |
| URL: | http://d.repec.org/n?u=RePEc:iim:iimawp:14608&r=all |
| By: | Min Huang; Guo Luo |
| Abstract: | We present a simple, fast, and accurate method for pricing a variety of discretely monitored options in the Black-Scholes framework, including autocallable structured products, single and double barrier options, and Bermudan options. The method is based on a quadrature technique, and it employs only elementary calculations and a fixed one-dimensional uniform grid. The convergence rate is $O(1/N^4)$ and the complexity is $O(MN\log N)$, where $N$ is the number of grid points and $M$ is the number of observation dates. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.13407&r=all |
| By: | Shiori Inoue (Institute for Monetary and Economic Studies, Bank of Japan (E-mail: shiori.inoue@boj.or.jp)); Masashi Une (Director, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: masashi.une@boj.or.jp)) |
| Abstract: | The use of artificial intelligence, particularly machine learning (ML), is being extensively discussed in the financial sector. ML systems, however, tend to have specific vulnerabilities as well as those common to all information technology systems. To effectively deploy secure ML systems, it is critical to consider in advance how to address potential attacks targeting the vulnerabilities. In this paper, we classify ML systems into 12 types on the basis of the relationships among entities involved in the system and discuss the vulnerabilities and threats, as well as the corresponding countermeasures for each type. We then focus on typical use cases of ML systems in the financial sector, and discuss possible attacks and security measures. |
| Keywords: | Artificial Intelligence, Machine Learning System, Security, Threat, Vulnerability |
| JEL: | L86 L96 Z00 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:ime:imedps:19-e-05&r=all |
| By: | Sinha, Ankur; Kedas, Satishwar; Kumar, Rishu; Malo, Pekka |
| Abstract: | Financial sector is expected to be at the forefront of the adoption of machine learning methods, driven by the superior performance of the data-driven approaches over traditional modelling approaches. There has been a widespread interest in automatically extracting information from financial news flow as the signals might be useful for investment decisions. While quantitative finance focuses on analysis of structured financial data for investment decisions, the potential of utilizing unstructured news flow in decision making is not fully tapped. Research in financial news analytics tries to address this gap by detecting events and aspects that provide buy, sell or hold information in news, commonly interpreted as financial sentiments. In this paper, we develop a framework utilizing information theoretic concepts and machine learning methods that understands the context and is capable of extracting buy, sell or hold information contained within news headlines. The proposed framework is also capable of detecting conflicting sentiments on multiple companies within the same news headline, which to our best knowledge has not been studied earlier. Further, we develop an information system which analyzes the news flow in real-time, allowing users to track financial sentiments by company, sector and index via a dashboard. Through this study we make three dataset related contributions - firstly, a training dataset consisting of more than 12,000 news headlines annotated for entities and their relevant financial sentiments by multiple annotators, secondly, an entity database of over 1,000 financial and economic entities relevant to Indian economy and their forms of appearance in news media amounting to over 5,000 phrases and thirdly, make improvements in existing financial dictionaries. Using the proposed system, we study the effect of the information derived from daily news flow in the years 2012 to 2017, over the Indian broad market equity index NSE 500, and show that the information has predictive value. |
| Date: | 2019–04–30 |
| URL: | http://d.repec.org/n?u=RePEc:iim:iimawp:14607&r=all |
| By: | Tarek A. Hassan (Boston University, NBER, and CEPR); Stephan Hollander (Tilburg University); Laurence van Lent (Frankfurt School of Finance and Management); Ahmed Tahoun (London Business School) |
| Abstract: | We adapt simple tools from computational linguistics to construct a new measure of political risk faced by individual US firms: the share of their quarterly earnings conference calls that they devote to political risks. We validate our measure by showing it correctly identifies calls containing extensive conversations on risks that are political in nature, that it varies intuitively over time and across sectors, and that it correlates with the firm’s actions and stock market volatility in a manner that is highly indicative of political risk. Firms exposed to political risk retrench hiring and investment and actively lobby and donate to politicians. These results continue to hold after controlling for news about the mean (as opposed to the variance) of political shocks. Interestingly, the vast majority of the variation in our measure is at the firm level rather than at the aggregate or sector level, in the sense that it is neither captured by the interaction of sector and time fixed effects, nor by heterogeneous exposure of individual firms to aggregate political risk. The dispersion of this firm-level political risk increases significantly at times with high aggregate political risk. Decomposing our measure of political risk by topic, we find that firms that devote more time to discussing risks associated with a given political topic tend to increase lobbying on that topic, but not on other topics, in the following quarter. |
| Keywords: | Political uncertainty, quantification, firm-level, lobbying |
| JEL: | D8 E22 E24 E32 E6 G18 G32 G38 H32 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:bos:iedwpr:dp-325&r=all |