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on Computational Economics |
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Issue of 2018‒09‒24
twelve papers chosen by |
| By: | Eitan Sapiro-Gheiler |
| Abstract: | The increasing digitization of political speech has opened the door to studying a new dimension of political behavior using text analysis. This work investigates the value of word-level statistical data from the US Congressional Record--which contains the full text of all speeches made in the US Congress--for studying the ideological positions and behavior of senators. Applying machine learning techniques, we use this data to automatically classify senators according to party, obtaining accuracy in the 70-95% range depending on the specific method used. We also show that using text to predict DW-NOMINATE scores, a common proxy for ideology, does not improve upon these already-successful results. This classification deteriorates when applied to text from sessions of Congress that are four or more years removed from the training set, pointing to a need on the part of voters to dynamically update the heuristics they use to evaluate party based on political speech. Text-based predictions are less accurate than those based on voting behavior, supporting the theory that roll-call votes represent greater commitment on the part of politicians and are thus a more accurate reflection of their ideological preferences. However, the overall success of the machine learning approaches studied here demonstrates that political speeches are highly predictive of partisan affiliation. In addition to these findings, this work also introduces the computational tools and methods relevant to the use of political speech data. |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1809.00741&r=cmp |
| By: | Nazareth, M.; Cunha, D.; Gurgel, A. |
| Abstract: | Agriculture is highly dependent on environment conditions, mainly temperature, precipitation and soil quality, thus it becomes the most vulnerable economic sector to the new climate conditions projected for the next decades. Therefore, knowing these impacts and the consequences for the rest of the economy is essential to map the effects and to elaborate, if necessary, mitigating environmental and economic policies. However, studies focusing on Brazil based on more regionalized data but linked to the rest of the world using dynamic computable general equilibrium (CGE) models are still very incipient. So this is precisely the gap that this article intends to fill, offering a modest contribution to the debate. Then, the objective of this paper is to determine the economic impact of the estimated changes in average agricultural productivity for the coming decades using a dynamic CGE model, the PAEGDyn linked to GTAP. Basically, the results found confirm trends in other works: the tropical regions in the world will be the most affected by the probable increase in the planet temperature, decreases in agricultural productivity and, thus, a reduction in economic performance. |
| Keywords: | Agricultural and Food Policy, Environmental Economics and Policy |
| Date: | 2018–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iaae18:275876&r=cmp |
| By: | Somsri Banditvilai (King Mongkut's Institute of Technology Ladkrabang); Siriluck Anansatitzin (King Mongkut's Institute of Technology Ladkrabang) |
| Abstract: | Accurate incidence forecasting of infectious disease such as dengue hemorrhagic fever is critical for early prevention and detection of outbreaks. This research presents a comparative study of three different forecasting methods based on the monthly incidence of dengue hemorrhagic fever. Holt and Winters method, Box-Jenkins method and Artificial Neural Networks were compared. The data were taken from the Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health starting from January, 2003 to December, 2016. The data were divided into 2 sets. The first set from January, 2003 to December, 2015 were used for constructing and selection the forecasting models. The second set from January, 2016 to December, 2016 were used for computing the accuracy of the forecasting model. The forecasting models were chosen by considering the smallest root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to measure the accuracy of the model. The results showed that Artificial Neural Networks obtained the smallest RMSE in the modeling process and the MAPE in the forecasting process was 14.05% |
| Keywords: | Dengue hemorrhagic fever, Time Series Forecasting, Holt-Winters method, Box-Jenkins method, Artificial Neural Networks |
| JEL: | C22 C45 |
| Date: | 2018–06 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iacpro:6409199&r=cmp |
| By: | Akina Ikudo; Julia Lane; Joseph Staudt; Bruce Weinberg |
| Abstract: | Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations. |
| JEL: | C8 J01 J24 |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24951&r=cmp |
| By: | Verteramo Chiu, Leslie J.; Tauer, Loren W.; Al-Mamun, Mohammad A.; Kaniyamattam, Karun; Smith, Rebecca L.; Grohn, Yrjo |
| Keywords: | Agricultural Finance, Agribusiness, Agricultural and Food Policy |
| Date: | 2017–06–30 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea17:258216&r=cmp |
| By: | ARNOLD, Florian; SÖRENSEN, Kenneth |
| Abstract: | The Location Routing Problem (LRP) unites two important challenges in the design of distribution systems. On the one hand, the delivery of goods to customers needs to be planned as e?ffectively as possible, and on the other hand, the location of depots from where these deliveries are executed has to be determined carefully. In the last years many heuristic approaches have been proposed to tackle LRPs. Usually, however, the computation of excellent solutions comes at the cost of an intricate algorithmic design. In this paper we introduce an effi cient heuristic for LRPs that is almost entirely based on a heuristic to solve routing problems. We estimate an upper bound for the number of open depots, and iteratively apply the routing heuristic on each remaining con?guration of open locations. Despite its simple design, the heuristic competes with the best results in literature, and can also be readily adapted to solve problems of very large scale. |
| Keywords: | Vehicle routing problem, Heuristics, Location routing |
| Date: | 2018–06 |
| URL: | http://d.repec.org/n?u=RePEc:ant:wpaper:2018010&r=cmp |
| By: | Ikudo, Akina (University of California, Los Angeles); Lane, Julia (New York University); Staudt, Joseph (U.S. Census Bureau); Weinberg, Bruce A. (Ohio State University) |
| Abstract: | Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations. |
| Keywords: | UMETRICS, occupational classifications, machine learning, administrative data, transaction data |
| JEL: | J0 J21 J24 |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp11738&r=cmp |
| By: | ITF |
| Abstract: | This report examines how the optimised use of new shared transport modes can change the future of mobility in the Auckland area in New Zealand. Based on computer simulations of different shared mobility scenarios, the study shows that introducing ride sharing and Taxi-Bus services can significantly reduce C02 emissions and improve accessibility while lowering mobility costs and improving service quality for users. Most scenarios also reduce congestion and release public parking space for other uses. The simulations show that new shared modes work particularly effectively in tandem with public transport supply such as rail and bus rapid transit (BRT), for which they can act as feeders. A survey and focus groups for the study explored how willing citizens in the Auckland area are to using shared mobility solutions. Together, the findings provide an evidence base for decision makers to weigh opportunities and challenges created by new forms of shared transport services. The work forms part of a series of studies on shared mobility in different urban and metropolitan contexts.This report is part of the International Transport Forum’s Case-Specific Policy Analysis series. These are topical studies on specific issues carried out by the ITF in agreement with local institutions. |
| Date: | 2017–11–27 |
| URL: | http://d.repec.org/n?u=RePEc:oec:itfaac:41-en&r=cmp |
| By: | Akina Ikudo; Julia Lane; Joseph Staudt; Bruce Weinberg |
| Abstract: | Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations. |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:18-37&r=cmp |
| By: | Farah Zawaideh (Irbid National University); Raed Sahawneh (Irbid National University) |
| Abstract: | Automatic text categorization (TC) has become one of the most interesting fields for researchers in data mining, information retrieval, web text mining, as well as natural language processing paradigms due to the vast number of new documents being retrieved for various information retrieval systems. This paper proposes a new TC technique, which classifies Arabic language text documents using the naïve Bayesian classifier attached to a genetic algorithm, model; this algorithm classifies documents by generating a random sample of chromosomes that represent documents in the corpus. The developed model aims to enhance the work of naïve Bayesian classifier through applying the genetic algorithm model. Experiment results show that the precision and recall are increased when testing higher number of documents; the precision was ranged from 0.8 to 0.97 for different testing environment; the number of genes that is placed in every chromosome is also tested and experiments show that the best value for the number of genes is 50 genes |
| Keywords: | Data mining, Text classification, Genetic algorithm, Naïve Bayesian Classifier, N-gram processing |
| JEL: | C80 |
| Date: | 2018–06 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iacpro:6409186&r=cmp |
| By: | Victor Chernozhukov; Whitney K Newey; Rahul Singh |
| Abstract: | Many objects of interest can be expressed as an L2 continuous functional of a regression, including average treatment effects, economic average consumer surplus, expected conditional covariances, and discrete choice parameters that depend on expectations. Debiased machine learning (DML) of these objects requires a learning a Riesz representer (RR). We provide here Lasso and Dantzig learners of the RR and corresponding learners of affine and other nonlinear functionals. We give an asymptotic variance estimator for DML. We allow for a wide variety of regression learners that can converge at relatively slow rates. We give conditions for root-n consistency and asymptotic normality of the functional learner. We give results for non affine functionals in addition to affine functionals. |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1809.05224&r=cmp |
| By: | Mallory, Mindy; Kuethe, Todd; Hubbs, Todd |
| Keywords: | Agricultural Finance, Risk and Uncertainty |
| Date: | 2018–04–06 |
| URL: | http://d.repec.org/n?u=RePEc:ags:scc018:276141&r=cmp |