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on Computational Economics |
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Issue of 2021‒10‒25
eighteen papers chosen by |
| By: | Zhu, Di; Liu, Yu; Yao, Xin; Fischer, Manfred M. |
| Abstract: | Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non- euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution - commonly known as filters or kernels - in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI. |
| Date: | 2021–10–19 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wus046:8360&r= |
| By: | Erik Heilmann (University of Kassel); Janosch Henze (University of Kassel); Heike Wetzel (University of Kassel) |
| Abstract: | Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system, forecasts have to meet more complex requirements, such as increasing temporal and spatial resolution of data. The concept of machine learning can meet these requirements by providing different model approaches and a standardized process of model selection. This paper provides a concise and comprehensible introduction to the topic by discussing the concept of machine learning in the context of energy economics and presenting an exemplary application to electricity load data. For this, we introduce and demonstrate the structured machine learning process containing the preparation, model selection and test of forecast models. This process is intended to serve as a general guideline for energy economists and practitioners who need to apply sophisticated forecast models. |
| Keywords: | machine learning, electricity consumption forecast, artificial neural network, time series forecast |
| JEL: | C45 C53 Q47 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:mar:magkse:202135&r= |
| By: | Yektansani, Kiana; Azizi, SeyedSoroosh |
| Keywords: | Environmental Economics and Policy, Research Methods/Statistical Methods, Resource/Energy Economics and Policy |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea21:313902&r= |
| By: | Curtis Nybo |
| Abstract: | Recently artificial neural networks (ANNs) have seen success in volatility prediction, but the literature is divided on where an ANN should be used rather than the common GARCH model. The purpose of this study is to compare the volatility prediction performance of ANN and GARCH models when applied to stocks with low, medium, and high volatility profiles. This approach intends to identify which model should be used for each case. The volatility profiles comprise of five sectors that cover all stocks in the U.S stock market from 2005 to 2020. Three GARCH specifications and three ANN architectures are examined for each sector, where the most adequate model is chosen to move on to forecasting. The results indicate that the ANN model should be used for predicting volatility of assets with low volatility profiles, and GARCH models should be used when predicting volatility of medium and high volatility assets. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.09489&r= |
| By: | Atallah, Shadi S. |
| Keywords: | Environmental Economics and Policy, Research Methods/Statistical Methods, Community/Rural/Urban Development |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea21:314090&r= |
| By: | Sallin, Aurelién |
| Abstract: | While the number of students with identified special needs is increasing in developed countries, there is little evidence on academic outcomes and labor market integration returns to special education. I present results from the first ever study to examine short- and longterm returns to special education programs using recent methods in causal machine learning and computational text analysis. I find that special education programs in inclusive settings have positive returns on academic performance in math and language as well as on employment and wages. Moreover, I uncover a positive effect of inclusive special education programs in comparison to segregated programs. However, I find that segregation has benefits for some students: students with emotional or behavioral problems, and nonnative students. Finally, using shallow decision trees, I deliver optimal placement rules that increase overall returns for students with special needs and lower special education costs. These placement rules would reallocate most students with special needs from segregation to inclusion, which reinforces the conclusion that inclusion is beneficial to students with special needs. |
| Keywords: | returns to education, special education, inclusion, segregation, causal machine learning, computational text analysis |
| JEL: | H52 I21 I26 J14 C31 Z13 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:usg:econwp:2021:09&r= |
| By: | Matteo Coronese; Martina OCcelli; Francesco Lamperti; Andrea Roventini |
| Abstract: | This paper presents a novel agent-based model of land use and technological change in the agricultural sector under environmental boundaries, finite available resources and changing land productivity. In particular, we model a spatially explicit economy populated by boundedly-rational farmers competing and innovating to fulfill an exogenous demand for food, while coping with a changing environment shaped by their production choices. Given the strong technological and environmental uncertainty, farmers learn and adaptively employ heuristics which guide their decisions on engaging in innovation and imitation activities, hiring workers, acquiring new farms, deforesting virgin areas and abandoning unproductive lands. Such activities in turn impact on land productivity, food production, food prices and land use. We firstly show that the model can replicate key stylized facts of the agricultural sector. We then extensively explore its properties across several scenarios featuring different institutional and behavioral settings. Finally, we showcase the properties of model in different applications considering deforestation and land abandonment; soil degradation; and climate impacts. |
| Keywords: | Land use; Agent-based model; Technological change; Environmental boundaries; Sustainability. |
| Date: | 2021–10–17 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2021/35&r= |
| By: | Anese, Gianluca; Corazza, Marco; Costola, Michele; Pelizzon, Loriana |
| Abstract: | Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the implemented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns. |
| Keywords: | Public financial news,Stock market,NLP,Dictionary,LSTM neural networks,Investor sentiment,S&P 500 |
| JEL: | G14 G17 C45 C63 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewp:322&r= |
| By: | Aur\'elien Sallin |
| Abstract: | While the number of students with identified special needs is increasing in developed countries, there is little evidence on academic outcomes and labor market integration returns to special education. I present results from the first ever study to examine short- and long-term returns to special education programs using recent methods in causal machine learning and computational text analysis. I find that special education programs in inclusive settings have positive returns on academic performance in math and language as well as on employment and wages. Moreover, I uncover a positive effect of inclusive special education programs in comparison to segregated programs. However, I find that segregation has benefits for some students: students with emotional or behavioral problems, and nonnative students. Finally, using shallow decision trees, I deliver optimal placement rules that increase overall returns for students with special needs and lower special education costs. These placement rules would reallocate most students with special needs from segregation to inclusion, which reinforces the conclusion that inclusion is beneficial to students with special needs. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.08807&r= |
| By: | Lin Li |
| Abstract: | We propose a novel portfolio trading system, which contains a feature preprocessing module and a trading module. The feature preprocessing module consists of various data processing operations, while in the trading part, we integrate the portfolio weight rebalance function with the trading algorithm and make the trading system fully automated and suitable for individual investors, holding a handful of stocks. The data preprocessing procedures are applied to remove the white noise in the raw data set and uncover the general pattern underlying the data set before the processed feature set is inputted into the trading algorithm. Our empirical results reveal that the proposed portfolio trading system can efficiently earn high profit and maintain a relatively low drawdown, which clearly outperforms other portfolio trading strategies. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.05299&r= |
| By: | Bazzana, Davide; Colturato, Michele; Savona, Roberto |
| Abstract: | We model the learning process of market traders during the unprecedented COVID-19 event. We introduce a behavioral heterogeneous agents’ model with bounded rationality by including a correction mechanism through representativeness (Gennaioli et al., 2015). To inspect the market crash induced by the pandemic, we calibrate the STOXX Europe 600 Index, when stock markets suffered from the greatest single-day percentage drop ever. Once the extreme event materializes, agents tend to be more sensitive to all positive and negative news, subsequently moving on to close-to-rational. We find that the deflation mechanism of less representative news seems to disappear after the extreme event. |
| Keywords: | Farm Management, Risk and Uncertainty |
| Date: | 2021–10–20 |
| URL: | http://d.repec.org/n?u=RePEc:ags:feemwp:314928&r= |
| By: | Fritz Breuss |
| Abstract: | Many studies with different methods (CGE models, DSGE models, structural gravity equations) have recently evaluated EU's Single Market. The problem with all these studies is that they use complex models with data sets which are not replicable. The aim of this paper is to develop a simple EU model which uses readily accessible data, and which is replicable in EViews. First the 10 equations macro model is used to evaluate Austria's EU membership since 1995. Then the same prototype model is applied to make a comparison of the integration effects of a selected number of EU Member States. Our simple EU model covers the essential economic effects of EU integration of EU's Single Market, the introduction of the Euro, and the following EU enlargements: increase in intra-EU trade, price reduction because of more competition, the impact of the net budget position vis à vis the EU budget, and lastly that on growth. |
| Keywords: | European Integration, Model Simulations, Country Studies |
| Date: | 2021–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wfo:wpaper:y:2021:i:638&r= |
| By: | Emanuele Citera (Department of Economics, New School for Social Research); Shyam Gouri Suresh (Department of Economics, Davidson College and Department of Economics, FLAME University); Mark Setterfield (Department of Economics, New School for Social Research) |
| Abstract: | We construct a model of cyclical growth with agent-based features designed to study the network origins of aggregate fluctuations from a demand-side perspective. In our model, aggregate fluctuations result from variations in investment behavior at firm level motivated by endogenously-generated changes in `animal spirits' or the state of long run expectations (SOLE). In addition to being influenced by their own economic conditions, firms pay attention to the performance of first-degree network neighbours, weighted (to differing degrees) by the centrality of these neighbours in the network, when revising their SOLE. This allows us to analyze the effects of the centrality of linked network neighbours on the amplitude of aggregate fluctuations. We show that the amplitude of fluctuations is significantly affected by the eigenvector centrality, and the weight attached to the eigenvector centrality, of linked network neighbours. The dispersion of this effect about its mean is shown to be similarly important, resulting in the possibility that network properties can result in `great moderations' giving way to sudden increases in the volatility of aggregate economic performance. |
| Keywords: | Aggregate fluctuations, cyclical growth, animal spirits, state of long run expectations, agent-based model, random network, preferential attachment, small world |
| JEL: | C63 E12 E32 E37 O41 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:new:wpaper:2118&r= |
| By: | Majid Hashemi |
| Abstract: | This study analyzes the extent to which electricity consumers of different income levels would increase electricity consumption and change their coping behavior to deal with power outages in response to electricity reliability improvements. The empirical analysis is conducted in two steps: (1) using an unsupervised machine learning technique, a nationally representative sample of Nepalese households is segmented into similar clusters based on the reliability constraints they face; and, (2) using regression models, the impact of reliability improvements on consumption and coping decisions is estimated. The findings point out that improved reliability is positively correlated with the probability of electric appliance ownership. The interaction of income and reliability-constraint indicators suggests that the unreliable electricity supply constrains households equally at all income levels. Moreover, the results from an ordered probit model with three off-grid backup decision alternatives indicate no association between coping decisions and income in the first two income quintiles. In contrast, higher-income quintiles are associated with significant changes in coping behavior when reliable electricity is available from the grid. Putting this paper’s findings into an energy-policy perspective, a connection to the grid by itself does not necessarily translate to realized benefits from electricity consumption. The reliability of the service plays a critical role for households at all income levels. |
| Keywords: | Electricity demand, Electricity reliability, Coping behavior, K-means clustering analysis, Low-income countries |
| JEL: | Q40 Q41 L94 O13 C38 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:qed:wpaper:1469&r= |
| By: | Jouchi Nakajima (Bank of Japan); Hiroaki Yamagata (Bank of Japan); Tatsushi Okuda (Bank of Japan); Shinnosuke Katsuki (Bank of Japan); Takeshi Shinohara (Bank of Japan) |
| Abstract: | This paper discusses the Price Sentiment Index (PSI), a quantitative indicator of firms' outlook for general prices proposed by Otaka and Kan (2018). The PSI is developed from the textual data of the Economy Watchers Survey conducted by the Cabinet Office; it is computed by extracting firms' views from survey comments, using text analysis. In this paper, we revisit the PSI and quantitatively analyze the determinants of changes in the PSI and the relationship between the PSI and macroeconomic variables. We also address a shortcoming in the text analysis used for computing the PSI that we discover when examining the performance of the PSI since the COVID-19 outbreak. The results of our analyses show that the PSI tends to precede consumer prices by several months and that it reflects various factors affecting price developments, including demand factors associated with the business cycle and cost factors such as changes in raw materials prices and exchange rates. Our analysis suggests that the PSI is a useful monthly indicator of inflation expectations, in that it captures the price-setting stance of firms responding to the Economy Watchers Survey. While the PSI is subject to large short-term fluctuations, it can be used to complement other indicators used for the analysis of price developments such as the output gap, existing indicators of inflation expectations, and anecdotal information from various sources. |
| Keywords: | Inflation Expectations; Machine Learning; Text Analysis; Big Data |
| JEL: | C53 C55 E31 E37 |
| Date: | 2021–10–15 |
| URL: | http://d.repec.org/n?u=RePEc:boj:bojwps:wp21e12&r= |
| By: | Johansson, Anders C. (Stockholm China Economic Research Institute); Zhu, Zhen (Kent Business School, University of Kent) |
| Abstract: | We explore the linkages between social media marketing activeness and reputational assets on digital platforms with a unique sample of over 8,000 customer-to-customer (C2C) sellers registered on both Taobao, China’s largest C2C online shopping platform, and Sina Weibo, China’s largest microblogging platform. A unique collaborative effort between the two platforms enables us to examine whether C2C sellers are motivated to engage in marketing activities on a separate social media platform. Applying machine learning and natural language processing methods, we first identify whether C2C sellers conduct social media marketing on their microblogs. We then differentiate between earned and owned reputation factors accumulated on both platforms and test their relationships to social media marketing activeness. We find that earned reputation factors on both platforms are significantly associated with social media marketing activeness. However, we identify a conflict of owned reputation factors between the two platforms, which provides a potential explanation for the limited success of the cross-platform collaboration. |
| Keywords: | social media marketing; reputational assets; electronic commerce; China |
| JEL: | L81 M15 M30 M31 |
| Date: | 2021–10–15 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:hascer:2021-053&r= |
| By: | Marcel Wieting (KU Leuven, Department of Management, Strategy and Innovation (MSI), Naamsestraat 69, 3000 Leuven, Belgium); Geza Sapi (Düsseldorf Institute for Competition Economics, Heinrich Heine University of Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Deutschland) |
| Abstract: | We analyze algorithmic pricing on Bol.com, the largest online marketplace in the Netherlands and Belgium. Based on more than two months of pricing data for around 2,800 popular products, we find that algorithmic sellers can both increase and reduce the price of the Buy Box (the most prominently displayed offer for a product). Consistently with collusion, algorithms benefit from each other's presence: Prices are particularly high if two algorithms bid against each other and there is a medium number of sellers in the market. We identify several algorithmic pricing patterns that are often associated with collusion. Algorithmic sellers are more likely to win the Buy Box, implying that consumers may face inflated prices more often. We also document efficiencies due to algorithmic pricing. With a sufficient number of competitors, algorithmic sellers reduce the Buy Box price and compete particularly fiercely. Algorithms furthermore reduce prices in monopoly markets. We explain this by the inability of traditional product managers to manually adjust prices product-by-product for a large number of items, which automated agents may correct. Overall, our findings call for careful policy with respect to pricing algorithms, that considers both the risk of collusion and the need to preserve potential efficiencies. |
| Keywords: | Algorithmic pricing; Artificial intelligence; Collusion; Forensic economics |
| JEL: | D42 D82 L42 |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:net:wpaper:2106&r= |
| By: | Hamza Bouzekri (G-SCOP_GCSP - Gestion et Conduite des Systèmes de Production - G-SCOP - Laboratoire des sciences pour la conception, l'optimisation et la production - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes); Gülgün Alpan (G-SCOP_GCSP - Gestion et Conduite des Systèmes de Production - G-SCOP - Laboratoire des sciences pour la conception, l'optimisation et la production - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes); Vincent Giard (LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Efficient management of port resources plays a crucial role in reducing vessel stay times and avoiding the payment of demurrage charges. In this paper, we focus on the integrated Laycan and Berth Allocation and Quay Crane Assignment Problem (LBACAP), which considers three problems in an integrated way: the Laycan Allocation Problem, the dynamic continuous Berth Allocation Problem and the time-invariant Quay Crane Assignment Problem. Since these problems have different decision levels, a change of decision time scale is made inside the planning horizon. To ensure that this integrated problem is as close as possible to reality, we consider non-working periods and tidal ports with multiple quays that have different water depths. The integer programming model proposed for the LBACAP aims to find an efficient schedule for berthing chartered vessels with an efficient quay crane assignment, and to propose laycans (laydays and canceling) to new vessels to charter. In a second part, we focus on the integrated Laycan and Berth Allocation and Specific Quay Crane Assignment Problem (LBACASP), which extends the LBACAP model to include the assignment of a set of specific quay cranes to each vessel, considering the productivity of quay cranes (homogeneous or heterogeneous) and their maximum outreach. Moreover, we use predicates in the formulation of both models, which ensure maximum flexibility in their implementation, thereby improving significantly their computational performance. Finally, the computational study on several classes of generated test instances shows that problems with up to 100 vessels can be solved to optimality. |
| Keywords: | Scheduling,Laycan allocation,Berth and quay crane assignment,Predicates,Integer programming |
| Date: | 2021–01–04 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-02480102&r= |