Computational Economics
http://lists.repec.orgmailman/listinfo/nep-cmp
Computational Economics
2017-11-12
Would a Euro's Depreciation Improve the French Economy?
http://d.repec.org/n?u=RePEc:iza:izadps:dp11094&r=cmp
In this paper, we use a Micro-Macro model to evaluate the effects of a euro's depreciation on the French economy, both at the macro and micro level. Our Micro-Macro model consists of a Microsimulation model that includes an arithmetical model for the French fiscal system and two behavioral models used to simulate the effects on consumption behavior and labor supply, and a multisectoral CGE model which simulates the macroeconomic effects of a reform or a shock. The integration of the two models is made using an iterative (or sequential) approach. We find that a 10% euro's depreciation stimulates the aggregate demand by increasing exports and reducing imports which increases production and reduces the unemployment rate in the economy. At the individual level, we find that the macroeconomic shock reduces poverty and, to a lesser extent, income inequality. In particular, the decrease in the equilibrium wage, determined in the macro model, slightly reduces the available income for people who have already a job, while the reduction in the level of unemployment permits to some individuals to find a job, substantially increasing their income and, in many cases, bringing them out of poverty.
Magnani, Riccardo
Piccoli, Luca
CarrĂ©, Martine
Spadaro, Amedeo
exchange rates, microsimulation, CGE models
2017-10
Global Optimization issues in Supervised Learning. An overview
http://d.repec.org/n?u=RePEc:aeg:report:2017-11&r=cmp
The paper presents an overview of global issues in optimization methods for Supervised Learning (SL). We focus on Feedforward Neural Networks with the aim of reviewing global methods specifically devised for the class of continuous unconstrained optimization problems arising both in Multi Layer Perceptron/Deep Networks and in Radial Basis Networks. We first recall the learning optimization paradigm for FNN and we briefly discuss global scheme for the joined choice of the network topologies and of the network parameters. The main part of the paper focus on the core subproblem which is the unconstrained regularized weight optimization problem. We review some recent results on the existence of local-non global solutions of the unconstrained nonlinear problem and the role of determining a global solution in a Machine Learning paradigm. Local algorithms that are widespread used to solve the continuous unconstrained problems are addressed with focus on possible improvements to exploit the global properties. Hybrid global methods specifically devised for SL optimization problems which embed local algorithms are discussed at the end.
Laura Palagi
Supervised Learning ; Feedforward Neural Networks ; Global Optimization ; Weights Optimization ; Hybrid algorithms
2017
IMPACT AND DISTRIBUTION OF CLIMATIC DAMAGES: A METHODOLOGICAL PROPOSAL WITH A DYNAMIC CGE MODEL APPLIED TO GLOBAL CLIMATE NEGOTIATIONS
http://d.repec.org/n?u=RePEc:rtr:wpaper:0226&r=cmp
The UNFCCC Parties Paris Agreement entered into force on 4 November 2016 represents a step forward in involving all countries in mitigation actions, even though still based on a voluntary approach and lacking the involvement of some major polluting countries. The underinvestment in mitigation actions depends on market and policy failures and the absence of market signals internalizing the economic losses due to climatic damage contributes to underestimating potential benefits from global action. We highlight how crucial is the vulnerability of a country to climate change in defining the threat and action strategies. A dynamic climate-economy CGE model is developed by including a monetary evaluation of regional damages associated with climate change. By considering alternative damage estimations, results show that internalizing climatic costs changes the bargaining position of countries in climate negotiations. Consequently, damage costs should be given greater importance when defining the implementation of a global climate agreement.
Valeria Costantini
Giorgia Sforna
Anil Markandya
Elena Paglialunga
Climate change damage costs; Climate negotiations; Burden sharing; Mitigation costs; GTAP; CGE.
2017-10
Foreign Direct Investment in the Ready-Made Garments Sector of Bangladesh : Macro and Distributional Implications
http://d.repec.org/n?u=RePEc:ngi:dpaper:17-10&r=cmp
Bangladesh, being a labor-abundant country, benefits from foreign direct investment (FDI) as it is considered as a supplement to domestic investment for this capital-scarce economy. We examine how the benefits of increased FDI in the ready-made garments (RMG) sector are transmitted and shared among households with different characteristics, and the appropriate government policies to mitigate adverse distributional problems, if any, created from the increased FDI. To address these issues, we develop a computable general equilibrium model for Bangladesh that describes competition between local firms and multinational enterprises (MNEs) in the RMG sector and the distributional impacts of FDI among households. Our simulation results demonstrate that an increase in FDI promotes both output and exports in the RMG sector. However, because of the competition between MNEs and domestic firms, the output of domestic firms would fall slightly. Scrutinizing the welfare effects among household groups, we find that the benefits of FDI-induced growth would affect all household groups unevenly. We also demonstrate that the benefits could be shared equitably among household groups with skill development programs targeted at the adversely affected household groups.
Sharif M. Hossain
Nobuhiro Hosoe
2017-10
Evaluating South African Fiscal and Monetary Policy Using a Wavelet-Based Model
http://d.repec.org/n?u=RePEc:ctn:dpaper:2017-08&r=cmp
This paper models South African fiscal and monetary policy in an open economy context, using a wavelet-based optimal control model. We then use the model to simulate fiscal and monetary strategies under different levels of policy restrictions. This research applies the Maximal Overlap Discrete Wavelet Transform (MODWT) to post-apartheid South African quarterly GDP data and other pertinent macro data, and then uses these decomposed variables to build a large state-space linear-quadratic tracking model. Using a political targeting design for the frequency range weights, we then simulate jointly optimal fiscal and monetary policy where: (1) both fiscal and monetary policy are dually emphasized, (2) fiscal policy is unrestricted while monetary policy largely restricted, and (3) only monetary policy is relatively active, while fiscal spending is heavily restricted. This paper adds to recent research by incorporating an external sector by using the South African real effective exchange rate as a driver of output.
Patrick Matthew Crowley
David Hudgins
2017
Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests
http://d.repec.org/n?u=RePEc:ecl:stabus:3576&r=cmp
Many scientific and engineering challenges--ranging from personalized medicine to customized marketing recommendations--require an understanding of treatment effect heterogeneity. In this paper, we develop a non-parametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely used random forest algorithm. In the potential outcomes framework with unconfoundedness, we show that causal forests are pointwise consistent for the true treatment effect, and have an asymptotically Gaussian and centered sampling distribution. We also discuss a practical method for constructing asymptotic confidence intervals for the true treatment effect that are centered at the causal forest estimates. Our theoretical results rely on a generic Gaussian theory for a large family of random forest algorithms. To our knowledge, this is the first set of results that allows any type of random forest, including classification and regression forests, to be used for provably valid statistical inference. In experiments, we find causal forests to be substantially more powerful than classical methods based on nearest-neighbor matching, especially in the presence of irrelevant covariates.
Wager, Stefan
Athey, Susan
2017-07
Generalized Random Forests
http://d.repec.org/n?u=RePEc:ecl:stabus:3575&r=cmp
We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations. Following the literature on local maximum likelihood estimation, our method operates at a particular point in covariate space by considering a weighted set of nearby training examples; however, instead of using classical kernel weighting functions that are prone to a strong curse of dimensionality, we use an adaptive weighting function derived from a forest designed to express heterogeneity in the specified quantity of interest. We propose a flexible, computationally efficient algorithm for growing generalized random forests, develop a large sample theory for our method showing that our estimates are consistent and asymptotically Gaussian, and provide an estimator for their asymptotic variance that enables valid confidence intervals. We use our approach to develop new methods for three statistical tasks: non-parametric quantile regression, conditional average partial effect estimation, and heterogeneous treatment effect estimation via instrumental variables. A software implementation, grf for R and C++, is available from CRAN.
Athey, Susan
Tibshirani, Julie
Wager, Stefan
2017-07