|
on Computational Economics |
|
Issue of 2018‒03‒26
eleven papers chosen by |
| By: | Stephan Eckstein; Michael Kupper |
| Abstract: | This paper presents a widely applicable approach to solving (multi-marginal, martingale) optimal transport and related problems via neural networks. The core idea is to penalize the optimization problem in its dual formulation and reduce it to a finite dimensional one which corresponds to optimizing a neural network with smooth objective function. We present numerical examples from optimal transport, martingale optimal transport, portfolio optimization under uncertainty and generative adversarial networks that showcase the generality and effectiveness of the approach. |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1802.08539&r=cmp |
| By: | María T. Alvarez-Martínez; Salvador Barrios; Diego d'Andria; Maria Gesualdo; Gaëtan Nicodème; Jonathan Pycroft |
| Abstract: | This paper estimates the size and macroeconomic effects of base erosion and profit shifting (BEPS) using a computable general equilibrium model designed for corporate taxation and multinationals. Our central estimate of the impact of BEPS on corporate tax losses for the EU amounts to €36 billion annually or 7.7% of total corporate tax revenues. The USA and Japan also appear to loose tax revenues respectively of €101 and €24 billion per year or 10.7% of corporate tax revenues in both cases. These estimates are consistent with gaps in bilateral multinationals´ activities reported by creditor and debtor countries using official statistics for the EU. Our results suggest that by increasing the cost of capital, eliminating profit shifting would slightly reduce investment and GDP. It would however raise corporate tax revenues thanks to enhanced domestic production. This in turn could reduce other taxes and increase welfare. |
| Keywords: | BEPS, corporate taxation, profit shifting, tax avoidance, CGE model |
| JEL: | C68 E62 H25 H26 H87 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_6870&r=cmp |
| By: | Mathias Kirchner (WIFO); Mark Sommer (WIFO); Claudia Kettner-Marx (WIFO); Daniela Kletzan-Slamanig (WIFO); Katharina Köberl (WIFO); Kurt Kratena (WIFO) |
| Abstract: | We assess distributive, macroeconomic, and CO2 emission impacts of CO2 tax schemes in Austria by applying the macroeconomic input-output model DYNK[AUT]. The tax schemes analysed focus primarily on CO2 emissions not covered by the European Emission Trading System (ETS), applying different CO2 tax rates as well as tax compensation schemes. We perform comparative scenario analysis for our model's base year (i.e., short-term impacts). Our model simulations indicate that – without tax compensation – impacts on households can be regressive if measured as tax burden relative to income, and are found to be rather proportional if measured as tax burden relative to expenditure or as changes in total expenditure and income. Lower income households benefit more from tax compensations (lump sum payments), i.e., CO2 taxes with compensation measures for households lead to progressive tax burden impacts. Energy-related CO2 emissions decrease quite substantially in non-ETS sectors, although households react inelastic. Value added in most non-ETS industry and service sectors declines only slightly without tax compensation and commodity import shares are hardly affected. Decreasing employers' social contribution (i.e., lowering labour costs) mitigates negative impacts in most non-ETS industry and service sectors. GDP decreases very moderately without tax recycling, depending on the tax rate. Employment effects are similar but smaller. Tax recycling leads to negligible GDP impacts and increases employment. Our simulations thus suggest that CO2 taxes could be a crucial and socially acceptable element within a comprehensive set of policy instruments in order to contribute to achieving greenhouse-gas emission targets for non-ETS sectors in Austria. |
| Keywords: | climate change, CO2 taxes, distributive impacts, macroeconomic modelling |
| Date: | 2018–02–23 |
| URL: | http://d.repec.org/n?u=RePEc:wfo:wpaper:y:2018:i:558&r=cmp |
| By: | Glenn P. Jenkins (Department of Economics, Queen's University, Kingston, Canada and Eastern Mediterranean University, North Cyprus); Chun-Yan Kuo (Senior Fellow, John Deutsch International, Department of Economics, Queen’s University, Canada,) |
| Abstract: | In this paper, we develop a model to simulate policies and revenues for a value-added taxes (VAT) system in countries that have an indirect tax system containing sales, excise taxes, and tariffs. An application of the model is carried out for Nepal, which has recently introduced the VAT to replace its sales tax system and rationalize its excise and tariff systems. The study shows that, in a developing country, tax policies that might seem very realistic and politically noncontroversial are likely to yield a very narrow tax base. If a government of a developing country wants to rely more on the VAT over time, it must move aggressively to broaden the base and enhance compliance. |
| Keywords: | :VAT, reform, revenue, model, Asia, Nepal |
| URL: | http://d.repec.org/n?u=RePEc:qed:dpaper:5512&r=cmp |
| By: | Yang Wang; Dong Wang; Yaodong Wang; You Zhang |
| Abstract: | Portfolio selection is the central task for assets management, but it turns out to be very challenging. Methods based on pattern matching, particularly the CORN-K algorithm, have achieved promising performance on several stock markets. A key shortage of the existing pattern matching methods, however, is that the risk is largely ignored when optimizing portfolios, which may lead to unreliable profits, particularly in volatile markets. We present a risk-aversion CORN-K algorithm, RACORN-K, that penalizes risk when searching for optimal portfolios. Experiments on four datasets (DJIA, MSCI, SP500(N), HSI) demonstrate that the new algorithm can deliver notable and reliable improvements in terms of return, Sharp ratio and maximum drawdown, especially on volatile markets. |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1802.10244&r=cmp |
| By: | Frank Z. Xing; Erik Cambria; Lorenzo Malandri; Carlo Vercellis |
| Abstract: | Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction. However, in what manner the market participants are affected by public mood has been rarely discussed. As a result, there has been little progress in leveraging public mood for the asset allocation problem, as the application is preferred in a trusted and interpretable way. In order to address the issue of incorporating public mood analyzed from social media, we propose to formalize it into market views that can be integrated into the modern portfolio theory. In this framework, the optimal market views will maximize returns in each period with a Bayesian asset allocation model. We train two neural models to generate the market views, and benchmark the performance of our model using market views on other popular asset allocation strategies. Our experimental results suggest that the formalization of market views significantly increases the profitability (5% to 10%) of the simulated portfolio at a given risk level. |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1802.09911&r=cmp |
| By: | Daniel Kinn |
| Abstract: | Many macroeconomic policy questions may be assessed in a case study framework, where the time series of a treated unit is compared to a counterfactual constructed from a large pool of control units. I provide a general framework for this setting, tailored to predict the counterfactual by minimizing a tradeoff between underfitting (bias) and overfitting (variance). The framework nests recently proposed structural and reduced form machine learning approaches as special cases. Furthermore, difference-in-differences with matching and the original synthetic control are restrictive cases of the framework, in general not minimizing the bias-variance objective. Using simulation studies I find that machine learning methods outperform traditional methods when the number of potential controls is large or the treated unit is substantially different from the controls. Equipped with a toolbox of approaches, I revisit a study on the effect of economic liberalisation on economic growth. I find effects for several countries where no effect was found in the original study. Furthermore, I inspect how a systematically important bank respond to increasing capital requirements by using a large pool of banks to estimate the counterfactual. Finally, I assess the effect of a changing product price on product sales using a novel scanner dataset. |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1803.00096&r=cmp |
| By: | Victor Chernozhukov; Whitney Newey; James Robins |
| Abstract: | We provide adaptive inference methods for linear functionals of sparse linear approximations to the conditional expectation function. Examples of such functionals include average derivatives, policy effects, average treatment effects, and many others. The construction relies on building Neyman-orthogonal equations that are approximately invariant to perturbations of the nuisance parameters, including the Riesz representer for the linear functionals. We use L1-regularized methods to learn approximations to the regression function and the Riesz representer, and construct the estimator for the linear functionals as the solution to the orthogonal estimating equations. We establish that under weak assumptions the estimator concentrates in a 1/root n neighborhood of the target with deviations controlled by the normal laws, and the estimator attains the semi-parametric efficiency bound in many cases. In particular, either the approximation to the regression function or the approximation to the Rietz representer can be "dense" as long as one of them is sufficiently "sparse". Our main results are non-asymptotic and imply asymptotic uniform validity over large classes of models. |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1802.08667&r=cmp |
| By: | Hassan Dadashi |
| Abstract: | We study the optimal investment-consumption problem for a member of defined contribution plan during the decumulation phase. For a fixed annuitization time, to achieve higher final annuity, we consider a variable consumption rate. Moreover, to eliminate the ruin possibilities and having a minimum guarantee for the final annuity, we consider a safety level for the wealth process which consequently yields a Hamilton-Jacobi-Bellman (HJB) equation on a bounded domain. We apply the policy iteration method to find approximations of solution of the HJB equation. Finally, we give the simulation results for the optimal investment-consumption strategies, optimal wealth process and the final annuity for different ranges of admissible consumptions. Furthermore, by calculating the present market value of the future cash flows before and after the annuitization, we compare the results for different consumption policies. |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1803.00611&r=cmp |
| By: | J. Christopher Westland; Tuan Q. Phan; Tianhui Tan |
| Abstract: | This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a fashion that can be used to predict loan profitability. The projection of these private networks onto networks of mobile phone communication and geographical locations from mobile phone GPS potentially give loan providers access to private information through graph and location metrics which we used to predict loan profitability. Graph topology was found to be an important predictor of loan profitability, explaining over 5.5% of variability. Networks of borrower location information explain an additional 19% of the profitability. Machine learning algorithms were applied to the data set previously analyzed to develop the predictive model and resulted in a 4% reduction in mean squared error. |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1802.10000&r=cmp |
| By: | Tierney, Heather L.R.; Kim, Jiyoon (June); Nazarov, Zafar |
| Abstract: | Using structured machine learning, this paper examines the effect that temporal aggregation has on big data from Google Analytics and Google Trends. Specifically, daily and weekly data from the Charleston Area Convention and Visitors Bureau (CACVB) website from January 2008 to March 2009 via Google Analytics and weekly, monthly, and quarterly data from Google Trends for seven economic variables from 2004 to 2011 are examined. Taking into account the different levels of aggregation, the CDFs and the estimated regression results are examined. The Kolmogorov-Smirnov test rejects the null of equivalent data distributions in the vast majority of cases for the CACVB data, but this is not the case for the economic variable. Through data mining, this paper also finds that aggregation has the potential of affecting the level of integration and the regression results for both the CACVB data and the seven economic variables. |
| Keywords: | Big Data, Machine Learning, Data Mining, Aggregation, Unit roots, Scaling Effects, Normalization Effects |
| JEL: | C19 C43 |
| Date: | 2018–01–30 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:84474&r=cmp |