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on Computational Economics |
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Issue of 2021‒09‒06
ten papers chosen by |
| By: | Andres, Antonio Rodriguez; Otero, Abraham; Amavilah, Voxi Heinrich |
| Abstract: | Applications of machine learning techniques to economic problems are increasing. These are powerful techniques with great potential to extract insights from economic data. However, care must be taken to apply them correctly, or the wrong conclusions may be drawn. In the technology clubs literature, after applying a clustering algorithm, some authors train a supervised machine learning technique, such as a decision tree or a neural network, to predict the label of the clusters. Then, they use some performance metric (typically, accuracy) of that prediction as a measure of the quality of the clustering configuration they have found. This is an error with potential negative implications for policy, because obtaining a high accuracy in such a prediction does not mean that the clustering configuration found is correct. This paper explains in detail why this modus operandi is not sound from theoretical point of view and uses computer simulations to demonstrate it. We caution policy and indicate the direction for future investigations. |
| Keywords: | Machine learning; clustering, technological change; technology clubs; knowledge economy; cross-country |
| JEL: | C45 C53 O38 O57 P41 |
| Date: | 2021–05–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:109138&r= |
| By: | Dat Thanh Tran; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis |
| Abstract: | Data normalization is one of the most important preprocessing steps when building a machine learning model, especially when the model of interest is a deep neural network. This is because deep neural network optimized with stochastic gradient descent is sensitive to the input variable range and prone to numerical issues. Different than other types of signals, financial time-series often exhibit unique characteristics such as high volatility, non-stationarity and multi-modality that make them challenging to work with, often requiring expert domain knowledge for devising a suitable processing pipeline. In this paper, we propose a novel data-driven normalization method for deep neural networks that handle high-frequency financial time-series. The proposed normalization scheme, which takes into account the bimodal characteristic of financial multivariate time-series, requires no expert knowledge to preprocess a financial time-series since this step is formulated as part of the end-to-end optimization process. Our experiments, conducted with state-of-the-arts neural networks and high-frequency data from two large-scale limit order books coming from the Nordic and US markets, show significant improvements over other normalization techniques in forecasting future stock price dynamics. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.00983&r= |
| By: | Lisa Crosato; Caterina Liberati; Marco Repetto |
| Abstract: | Academic research and the financial industry have recently paid great attention to Machine Learning algorithms due to their power to solve complex learning tasks. In the field of firms' default prediction, however, the lack of interpretability has prevented the extensive adoption of the black-box type of models. To overcome this drawback and maintain the high performances of black-boxes, this paper relies on a model-agnostic approach. Accumulated Local Effects and Shapley values are used to shape the predictors' impact on the likelihood of default and rank them according to their contribution to the model outcome. Prediction is achieved by two Machine Learning algorithms (eXtreme Gradient Boosting and FeedForward Neural Network) compared with three standard discriminant models. Results show that our analysis of the Italian Small and Medium Enterprises manufacturing industry benefits from the overall highest classification power by the eXtreme Gradient Boosting algorithm without giving up a rich interpretation framework. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2108.13914&r= |
| By: | Philipp Maximilian Mueller; Björn-Martin Kurzrock |
| Abstract: | Due to numerous documents and the lack of widely acknowledged standards, the capture and provision of information in transaction processes frequently remains challenging. Since construction and maintenance come with substantial costs, the evaluation of the structural condition and maintenance requirements as well as the assessment of contracts and legal structures are important in real estate transactions. The quality and completeness of digital building documentation is increasingly becoming a factor as deal maker and deal breaker. Artificial intelligence can well assist in the classification of documents and extraction of information This research provides fundamentals for generating a (semi-)automated standardized technical and legal assessment of buildings. Based on a large building documentation set from (institutional) investors, the potential for digital processing, automated classification and information extraction through machine learning algorithms is demonstrated. For this purpose, more than 400 document classes are derived, reviewed, prioritized and principally checked for machine readability. In addition, key information is structured and prioritized for technical and legal due diligence. The paper highlights recommendations for improving the machine readability of documents and indicates the potential for partially automating technical and legal due diligence processes. The practical recommendations are relevant for investors, owners, users and service providers who depend on specific real estate information as well as for companies that develop or use software tools. For policymaking, the research offers some guidance for standardizing documents to support digital information processing in real estate. The recommendations are helpful for improving information processing and in general, promoting the use of automated information extraction based on machine learning in real estate. |
| Keywords: | digital building documentation; Due diligence; Machine Learning; property research trust |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_65&r= |
| By: | Marcelo Cajias; Willwersch Jonas; Lorenz Felix; Franz Fuerst |
| Abstract: | Machine Learning (ML) can detect complex relationships to solve problems in various research areas. To estimate real estate prices and rents, ML represents a promising extension to the hedonic literature since it is able to increase predictive accuracy and is more flexible than the standard regression-based hedonic approach in handling a variety of quantitative and qualitative inputs. Nevertheless, its inferential capacity is limited due to its complex non-parametric structure and the ‘black box’ nature of its operations. In recent years, research on Interpretable Machine Learning (IML) has emerged that improves the interpretability of ML applications. This paper aims to elucidate the analytical behaviour of ML methods and their predictions of residential rents applying a set of model-agnostic methods. Using a dataset of 58k apartment listings in Frankfurt am Main (Germany), we estimate rent levels with the eXtreme Gradient Boosting Algorithm (XGB). We then apply Permutation Feature Importance (PFI), Partial Dependence Plots (PDP), Individual Conditional Expectation Curve (ICE) and Accumulated Local Effects (ALE). Our results suggest that IML methods can provide valuable insights and yield higher interpretability of ‘black box’ models. According to the results of PFI, most relevant locational variables for apartments are the proximity to bars, convenience stores and bus station hubs. Feature effects show that ML identifies non-linear relationships between rent and proximity variables. Rental prices increase up to a distance of approx. 3 kilometer to a central bus hub, followed by steep decline. We therefore assume tenants to face a trade-off between good infrastructural accessibility and locational separation from the disamenities associated with traffic hubs such as noise and air pollution. The same holds true for proximity to bar with rents peaking at 1 km distance. While tenants appear to appreciate nearby nightlife facilities, immediate proximity is subject to rental discounts. In summary, IML methods can increase transparency of ML models and therefore identify important patterns in rental markets. This may lead to a better understanding of residential real estate and offer new insights for researchers as well as practitioners. |
| Keywords: | Explainable Artifical Intelligence; housing; Machine Learning; Non parametric hedonic models |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_104&r= |
| By: | Benedict von Ahlefeldt-Dehn; Marcelo Cajias; Wolfgang Schäfers |
| Abstract: | The commercial real estate market is opaque and build upon complex relationships of countless property market and macroeconomic factors. Yet, office markets are due to its sheer volume and importance for numerous market players such as investors, developers, mortgage underwriters and valuation firms broadly researched. Especially, the prediction of property market indicators found strong interest among researchers and practitioners in the field of commercial real estate. Thus, the literature proposes three main frameworks for predicting office rents, among other. The estimation via multiple equation models such as error correction mechanism models (e.g. Hendershott et al., 2002; Ke and White, 2009; McCartnery, 2012) or interlinked demand and supply models (e.g. Rosen, 1984; Hendershott et al., 1999; Kim, 2012), reduced form single equation models (e.g. Matysiak and Tsolacos, 2003; Voigtländer, 2010; Kiehelä and Falkenbach, 2014) or autoregressive models (e.g. McGough and Tsolacos, 1995; Brooks and Tsolacos, 2000; Stevenson and McGarth, 2003). However, the limitations of the applied methods lay within the econometric methods itself. “Traditional” statistical modeling as an approximation of causality will only understand trends and relationships in the underlying market to the degree the employed econometric methods themselves can mirror. In contrast, more recent methodological attempts such as machine learning can be seen as a process of selecting the relevant features leading to a trade-off between precision and stability of a predictive model (Conway, 2018). This however, creates opportunities to expand and enhance existing efforts – in a way that complex and non-linear relationships within the data are captured. Many studies (e.g Dabrowski and Adamczyk, 2010; Rafatirad, 2017, Cajias and Ertl, 2018; Mayer et al., 2019) apply advanced machine learning methods to residential markets and demonstrate that “traditional” linear hedonic models can be outperformed. While linear models are found to produce less volatile predictions advanced machine learning methods yield more accurate results. Promising results can also be shown in commercial real estate markets. In particular, the aim of research is the performance assessment of the forecasting of office rents in European markets with advanced machine learning methods. A dataset of European markets with office prime rents and market as well as macroeconomic indicators is analysed and advanced machine learning models are estimated. A “traditional” linear regression model (ordinary least squares) functions as a benchmark for the evaluation of the employed methods: random forest and extreme gradient boosting. In particular, the prediction power and forecasting ability is assessed in- and out-of-sample, respectively. The tree-based advanced machine learning methods yield promising estimations in the observed markets. It becomes clear that in commercial real estate markets complex and non-linear relationships are present and can effectively be estimated by non-parametric econometric models. By the application of these methods the estimation error (out-of-sample) can be reduced by up to 60 percent. To the best of the authors knowledge such applications of machine learning methods in commercial real estate markets has not been considered in prior research. However, in the area of textual analysis results show that commercial real estate markets can be forecasted on the basis of market sentiment (e.g. Beracha et al., 2019). The capability of improving the forecasting power with advanced machine learning methods creates value and transparency for numerous market players and authorities. |
| Keywords: | commercial real estate; Forecast; Machine Learning; Office Rent |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_49&r= |
| By: | Matthias Soot; Sabine Horvath; Hans-Berndt Neuner; Alexandra Weitkamp |
| Abstract: | In this work, we compare the results of multiple linear regression analysis (MLR) with spatial analysis method (geographically weighted regressions (GWR)) and an artificial neural network (ANN) approach deriving a state-wide model for property yields. The database consists of approx. 3000 purchase prices in the market of multi-family houses collected in the purchase price database of Lower Saxony (Germany). The purchases occurred between 2014 and 2018. The locational quality as well as the theoretical age (deprecation) of the real estates are the influencing variables in the analysis. In the GWR, different fixed and variable kernels are used. The approaches are evaluated using cross-validation procedure with quality parameters like the root mean square error (RMSE), the mean absolute error (MAE) and the error below 5% (eb5). The first analysis shows that GWR leads to better results in comparison to classical approaches (MLR) because local phenomena can be modelled. Also, the approach of ANN is superior in comparison to the classical regression analysis because of its ability of nonlinear modelling. In this dataset, the ANN cannot reach the accuracy of GWR which leads to the conclusion, that the spatial inhomogeneity has a bigger influence than a data non-linearity. Further investigation shows that the complexity of the data and the amount of available data plays a key role in the performance of ANN. |
| Keywords: | ANN; GWR; Multi family houses; property yields |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_44&r= |
| By: | Christl, Michael; De Poli, Silvia; Kucsera, Dénes; Lorenz, Hanno |
| Abstract: | This paper analyzes the impact of the COVID-19 crisis on household income in Austria, using detailed administrative labor market data, in combination with micro-simulation techniques, that enable specific labor market transitions to be modeled. We find that discretionary fiscal policy measures in Austria are key to counteracting the inequality- and poverty-enhancing effect of COVID-19. Additionally, we find that females tend to experience a greater loss in terms of market income. The Austrian tax-benefit system, however, reduces this gender differences. Disposable income has dropped by around 1% for both males and females. By comparison, males profit mainly from short-time work scheme, while females profit especially from other discretionary policy measures, such as the one-off payment for children. |
| Keywords: | COVID-19,EUROMOD,micro-simulation,STW,automatic stabilizers |
| JEL: | D31 E24 H24 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:917&r= |
| By: | Liang Lu; Ruby Nguyen; Md Mamunur Rahman; Jason Winfree |
| Abstract: | The food supply chain has experienced major disruptions from both demand and supply sides during the Covid-19 pandemic. While some consequences such as food waste are directly caused by the disruption due to supply chain inefficiency, others are indirectly caused by a change in consumer’s preferences. As a result, evaluating food supply chain resilience is a difficult task. With an attempt to understand impacts of demand on the food supply chain, we developed an agent-based model based on the case of Idaho’s potato supply chain. Results showed that not only the magnitude but also the timing of the demand shock will have different impacts on various stakeholders of the supply chain. Our contribution to the literature is two-fold. First, the model helps explain why food waste and shortages may occur with dramatic shifts in consumer demand. Second, this paper provides a new angle on evaluating the various mitigation strategies and policy responses to disruptions beyond Covid-19. |
| JEL: | L1 Q11 |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29166&r= |
| By: | Laura Gabrielli; Aurora Ruggeri; Massimiliano Scarpa |
| Abstract: | In order to plan and manage low-carbon investments in wide real estate assets, thereby meeting the European energy efficiency requirements recently presented in EU Directive 2018/844, a methodological change in both research and practice is now necessary. Since a sharp increase in retrofit rates of large building stocks is going to be promoted, new strategic approaches should take into consideration building portfolios as a whole, overcoming the single-building perspective, so that to identify the level of energy retrofit leading to the overall maximum benefit. In this contribution, a decision support system is developed for the automatic assessment of both the monetary and non-monetary benefits produced by a retrofit investment, and determine the optimal efficiency program over a large building stock. The core idea is to consider the energy enhancement as an optimization issue and identify the configuration of retrofit design that brings to the greatest possible benefit, by balancing conflicting objectives, and within several constraints. As far as the monetary benefit is concerned, we estimate the savings produced by the investment over a life-cycle perspective. Among the non-monetary values, we first consider the environmental benefit in terms of avoided CO2 emissions. We also assess the value of the improved indoor comfort and the value of the safeguard of the building, when the energy efficiency is also intended as a measure to protect the heritage. To this end, a set of different and interdisciplinary techniques has been employed, such as parametric energy modelling, neural network analysis, economic and financial feasibility assessment, calculation of thermal comfort indexes (Fanger), multi-criteria approaches (Analytic Hierarchy Process), and multi-objective constrained optimization analysis. Among the results of this research, the extreme flexibility in comparing countless design scenarios and the simplicity of application of the model developed are the most important contributions obtained. The effectiveness of the decision-making tool was then verified through the implementation on a case study of an interesting and heterogeneous portfolio of buildings located in Northern Italy. |
| Keywords: | automatic assessment; building stock; low carbon investment; Neural network analysis |
| JEL: | R3 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2021_126&r= |