nep-cmp New Economics Papers
on Computational Economics
Issue of 2018‒05‒21
eighteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Determinants of foreign direct investment inflow in real estate sector By Rita Yi Man Li; Beiqi Tang
  2. Cluster detection and clustering with random start forward searches By Atkinson, Anthony C.; Riani, Marco; Cerioli, Andrea
  3. The Impact of ASEAN’S FTAs with China, Japan, Korea and Australia-New Zealand: An Analysis in GTAP Framework By Nugraheni, Reninta Dewi; Widodo, Tri
  4. An AB-SFC Model of Induced Technical Change along Classical and Keynesian Lines By Fanti, Lucrezia
  5. A mixed integer optimization approach for model selection in screening experiments By VÁZQUEZ-ALCOCER, Alan; SCHOEN, Eric D.; GOOS, Peter
  6. How Accurate is the Coordinate Price Pressure Index to Predict Mergers’ Coordinated Effects? By Ivaldi, Marc; Lagos, Vicente
  7. The Impact of the Tax Cuts and Jobs Act on Local Home Values By Martin, Hal
  8. Distributional regression forests for probabilistic precipitation forecasting in complex terrain By Lisa Schlosser; Torsten Hothorn; Reto Stauffer; Achim Zeileis
  9. Ensemble Learning for Cross-Selling Using Multitype Multiway Data By Zhongxia (Shelly) Ye
  10. Learning from man or machine: Spatial aggregation and house price prediction By Sommervoll, Dag Einar; Sommervoll, Åvald
  11. Time-varying fiscal multipliers in an agent based model with credit-rationing By Jean-Luc Gaffard; Mauro Napoletano; Andrea Roventini
  12. G-RDEM: A GTAP-based recursive dynamic CGE model for long-term baseline generation and analysis By Wolfgang Britz; Roberto Roson
  13. Disseminação de informações em sistemas socioecológicos : análise de um modelo híbrido de dinâmica de sistemas e modelagem baseada em agentes. By Rafael Faria de Abreu Campos; Dênis Antônio da Cunha; Newton Paulo Bueno
  14. Simulation and Evaluation of Zonal Electricity Market Designs By Hesamzadeh, Mohammad Reza; Holmberg, Pär; Sarfati, Mahir
  15. Investment subsidies and regional welfare: A dynamic framework By Korzhenevych, Artem; Bröcker, Johannes
  16. National policies for global emission reductions: Effectiveness of carbon emission reductions in international supply chains By Stefan Nabernegg; Birgit Bednar-Friedl; Pablo Munoz; Michaela Tietz; Johanna Vogel
  17. A Practical Guide to Parallelization in Economics By Jesús Fernández-Villaverde; David Zarruk Valencia
  18. Impacto económico del empredimiento en una economía regional: el caso de Andalucía By Joaquin Garcia-Tapial; M. Alejandro Cardenete

  1. By: Rita Yi Man Li; Beiqi Tang
    Abstract: Many of the previous research shows that ups and downs of real estate prices are affected by the foreigners' investments. Some countries such as Australia have implement relevant measures to lower the incentives of foreigners to invest in their housing sector. The objective of this paper is to examine the major determinants of FDI inflow in real estate sector in five countries, i.e. Korea, Japan, Australia, Canada and the UK via Artificial neural networks. It shall investigate the relationship between foreign direct investment inflow in real estate sector, residential property price index, gross domestic product per capita, global house price index growth rate, global housing price index, effective exchange rate, housing price to income index and natural disaster. The results show that there are different determinants in different countries. While global housing price index plays the most important role in foreign direct investment inflow in Korea, Japan and UK, Gross domestic product and house price to income ratio is the most important factor in Canada and Australia respectively.
    Keywords: Artificial Neural Network; Foreign Direct Investment Inflow
    JEL: R3
    Date: 2017–07–01
    URL: http://d.repec.org/n?u=RePEc:arz:wpaper:eres2017_349&r=cmp
  2. By: Atkinson, Anthony C.; Riani, Marco; Cerioli, Andrea
    Abstract: The forward search is a method of robust data analysis in which outlier free subsets of the data of increasing size are used in model fitting; the data are then ordered by closeness to the model. Here the forward search, with many random starts, is used to cluster multivariate data. These random starts lead to the diagnostic identification of tentative clusters. Application of the forward search to the proposed individual clusters leads to the establishment of cluster membership through the identification of non-cluster members as outlying. The method requires no prior information on the number of clusters and does not seek to classify all observations. These properties are illustrated by the analysis of 200 six-dimensional observations on Swiss banknotes. The importance of linked plots and brushing in elucidating data structures is illustrated. We also provide an automatic method for determining cluster centres and compare the behaviour of our method with model-based clustering. In a simulated example with 8 clusters our method provides more stable and accurate solutions than model-based clustering. We consider the computational requirements of both procedures.
    Keywords: brushing; data structure; forward search; graphical methods; linked plots; Mahalanobis distance; MM estimation; outliers; S estimation; Tukey’s biweight.
    JEL: C1
    Date: 2017–04–08
    URL: http://d.repec.org/n?u=RePEc:ehl:lserod:72291&r=cmp
  3. By: Nugraheni, Reninta Dewi; Widodo, Tri
    Abstract: ASEAN is one of dynamic and fast growing economic regionalism. ASEAN has shown rapid growth in trade liberalization with the free trade agreement (FTA), established with China Korea, Japan, Australia and New Zealand. The aim of this research is to investigate the effects of the free trade agreement between ASEAN-China (ACFTA), ASEAN-Korea (AKFTA), ASEAN-Japan (AJCEP), ASEAN-Australia-New Zealand (AANZFTA). The Computable General Equilibrium (CGE) model and the Global Trade Analysis Project (GTAP) database version 9 are applied with the partial and full liberalization scenarios. The GTAP simulations results shows that ACFTA provides a greater positive impact than the other FTAs for each region. In the long run, the welfare of each region has increased, the trade balance has decreased, the volume of exports and imports has increased.
    Keywords: ASEAN, Free Trade Agreement, Tariff Liberalisation, GTAP Simulations
    JEL: F14 F17
    Date: 2018–03–11
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:86693&r=cmp
  4. By: Fanti, Lucrezia
    Abstract: This paper introduces the classical idea about the so-called directed and induced technical change (ITC) within a Keynesian demand-side and evolutionary endogenous growth model in order to analyze the interplay among technical change, long-run economic growth and functional income distribution. An ITC process is analyzed within an Agent-Based Stock-Flow Consistent (AB-SFC) model, wherein credit-constrained heterogeneous firms choose both the intensity and the direction of the innovation towards a labor- or capital-saving choice of technique. In the long-run, the model reproduces the so-called Kaldor stylized facts (i.e. with a purely labor-saving technical change), however during the transitional phase the model shows a labor-saving/capital-using innovation pattern, as the aggregate output-capital ratio decreases until it stabilizes in the long-run, as well as declining labor share for long time periods and we can ascribe these evidences mainly to the directed technical change process. In order to stress the effective role of the innovation bias on the model dynamics, we compare the baseline scenario with a counterfactual scenario wherein a neutral technical progress is at work.
    Keywords: Agent-Based Macroeconomics; Stock-Flow Consistent Models; Induced Technical Change; Directed Innovation; Choice of Techniques; Labor Share; Growth and Distribution.
    JEL: E24 E25 O33 O41
    Date: 2018–03–10
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:86645&r=cmp
  5. By: VÁZQUEZ-ALCOCER, Alan; SCHOEN, Eric D.; GOOS, Peter
    Abstract: After completing the experimental runs of a screening design, the responses under study are analyzed by statistical methods to detect the active effects. To increase the chances of correctly identifying these effects, a good analysis method should: (1) provide alternative interpretations of the data, (2) reveal the aliasing present in the design, and (3) search only meaningful sets of effects as defined by user-specified restrictions such as effect heredity or constraints that include all the contrasts of a multi-level factor in the model. Methods like forward selection, the Dantzig selector or LASSO do not posses all these properties. Simulated annealing model search cannot handle other constraints than effect heredity. This paper presents a novel strategy to analyze data from screening designs that posses properties (1)-(3) in full. It uses modern mixed integer optimization methods that returns the results in a few minutes. We illustrate our method by analyzing data from real and synthetic experiments involving two-level and mixed-level screening designs. Using simulations, we show the capability of our method to automatically select the set of active effects and compare it to the benchmark methods.
    Keywords: Dantzig selector, Definitive screening design, LASSO, Sparsity, Two-factor interaction
    Date: 2018–05
    URL: http://d.repec.org/n?u=RePEc:ant:wpaper:2018007&r=cmp
  6. By: Ivaldi, Marc; Lagos, Vicente
    Abstract: The Coordinate Price Pressure Index (CPPI) measures the incentives of two competitors to engage in a particular type of Parallel Accommodating Conduct (PAC). Specifically, it measures the incentives of a leader firm to initiate a unilateral percentage price increase, with the expectation that a follower firm will match it. Using a large set of simulated markets, we measure the accuracy of the index in terms of predicting the impact of a merger on firms’ incentives to engage in PAC. Results suggest that the CPPI only displays a fair performance when predicting an increase in firm’s incentives to engage in PAC, and only in mergers in which the diversion ratio between the target and the acquiring firm is low. However, the index displays a poor performance when predicting mergers with a significant anticompetitive effect.
    Keywords: Coordinate Price Pressure Index ; Parallel Accommodating Conduct ; Merger Simulation
    JEL: K21 L41
    Date: 2018–04
    URL: http://d.repec.org/n?u=RePEc:tse:wpaper:32620&r=cmp
  7. By: Martin, Hal (Federal Reserve Bank of Cleveland)
    Abstract: This paper simulates changes to neighborhood home prices resulting from reforms to tax preferences in the recently passed Tax Cuts and Jobs Act (TCJA). The simulation uses federal tax data summarized at a fine geography to impute homeowner rents at the zip code level across six income classes. Employing a user cost framework, I model rents as a function of prices under the old tax law and under the TCJA. While the average price impact of the TCJA is found to be −5.7 percent, local effects range from 0 to −23 percent across zip codes. Variation across income class is also large. Simulations by income class suggest that the most severe declines in price occur for upper middle-income households ($100,000–$200,000). The paper also simulates partial versions of the TCJA that omit different features of the law that affect housing preference. I find that the higher standard deductions in the new law are the largest driver of price declines.
    Keywords: mortgage interest deduction; housing subsidy; income tax;
    JEL: H24 H31 R21
    Date: 2018–05–11
    URL: http://d.repec.org/n?u=RePEc:fip:fedcwp:1806&r=cmp
  8. By: Lisa Schlosser; Torsten Hothorn; Reto Stauffer; Achim Zeileis
    Abstract: To obtain a probabilistic model for a dependent variable based on some set of explanatory variables, a distributional approach is often adopted where the parameters of the distribution are linked to regressors. In many classical models this only captures the location of the distribution but over the last decade there has been increasing interest in distributional regression approaches modeling all parameters including location, scale, and shape. Notably, so-called non-homogenous Gaussian regression (NGR) models both mean and variance of a Gaussian response and is particularly popular in weather forecasting. More generally, the GAMLSS framework allows to establish generalized additive models for location, scale, and shape with smooth linear or nonlinear effects. However, when variable selection is required and/or there are non-smooth dependencies or interactions (especially unknown or of high-order), it is challenging to establish a good GAMLSS. A natural alternative in these situations would be the application of regression trees or random forests but, so far, no general distributional framework is available for these. Therefore, a framework for distributional regression trees and forests is proposed that blends regression trees and random forests with classical distributions from the GAMLSS framework as well as their censored or truncated counterparts. To illustrate these novel approaches in practice, they are employed to obtain probabilistic precipitation forecasts at numerous sites in a mountainous region (Tyrol, Austria) based on a large number of numerical weather prediction quantities. It is shown that the novel distributional regression forests automatically select variables and interactions, performing on par or often even better than GAMLSS specified either through prior meteorological knowledge or a computationally more demanding boosting approach.
    Keywords: parametric models, regression trees, random forests, recursive partitioning, probabilistic forecasting, GAMLSS
    Date: 2018–08
    URL: http://d.repec.org/n?u=RePEc:inn:wpaper:2018-08&r=cmp
  9. By: Zhongxia (Shelly) Ye (UTSA)
    Abstract: Cross-selling is an integral component of customer relationship management. Using relevant information to improve customer response rate is a challenging task in cross-selling. Incorporating multitype multiway customer behavioral, including related product, similar customer and historical promotion, data into cross-selling models is helpful in improving the classification performance. Customer behavioral data can be represented by multiple high-order tensors. Most existing supervised tensor learning methods cannot directly deal with heterogeneous and sparse multiway data in cross selling. In this study, two novel ensemble learning methods, multiple kernel support tensor machine (MK-STM) and multiple support vector machine ensemble (M-SVM-E), are proposed for crossselling using multitype multiway data. The MK-STM and the M-SVM-E can also perform feature selections from large sparse multitype multiway data. Based on these two methods, collaborative and non-collaborative ensemble learning frameworks are developed. In these frameworks, many existing classification and ensemble methods can be combined for classification using multitype multiway data. Computational experiments are conducted on two databases extracted from open access databases. The experimental results show that the MK-STM exhibits the best performance and has better performance than existing supervised tensor learning methods.
    Keywords: Audit committees, voluntary disclosures, director elections, auditor ratification
    JEL: M42
    Date: 2018–01–18
    URL: http://d.repec.org/n?u=RePEc:tsa:wpaper:0157acc&r=cmp
  10. By: Sommervoll, Dag Einar (Centre for Land Tenure Studies, Norwegian University of Life Sciences); Sommervoll, Åvald (Department of Informatics)
    Abstract: House prices vary with location. At the same time the border between two neighboring housing markets tends to be fuzzy. When we seek to explain or predict house prices we need to correct for spatial price variation. A much used way is to include neighborhood dummy variables. In general, it is not clear how to choose a spatial subdivision in the vast space of all possible spatial aggregations. We take a biologically inspired approach, where different spatial aggregations mutate and recombine according to their explanatory power in a standard hedonic housing market model. We find that the genetic algorithm consistently finds aggregations that outperform conventional aggregation both in and out of sample. A comparison of best aggregations of different runs of the genetic algorithm shows that even though they converge to a similar high explanatory power, they tend to be genetically and economically different. Differences tend to be largely confined to areas with few housing market transactions.
    Keywords: House price prediction; Machine learning; Genetic algorithm; Spatial aggregation
    JEL: C45 R21 R31
    Date: 2018–04–24
    URL: http://d.repec.org/n?u=RePEc:hhs:nlsclt:2018_004&r=cmp
  11. By: Jean-Luc Gaffard (Observatoire français des conjonctures économiques); Mauro Napoletano (Observatoire français des conjonctures économiques); Andrea Roventini (Laboratory of Economics and Management (LEM))
    Abstract: The authors build a simple agent-based model populated by households with heterogenous and time-varying financial conditions in order to study how fiscal multipliers can change over the business cycle and are affected by the state of credit markets. They find that deficit-spending fiscal policy dampens the effect of bankruptcy shocks and lowers their persistence. Moreover, the size and dynamics of government spending multipliers are related to the degree and persistence of credit rationing in the economy. On the contrary, in presence of balanced-budget rules, output permanently falls below pre-shock levels and the ensuing multipliers fall below one and are much lower than the ones emerging from the deficit-spending policy. Finally, the authors show that different conditions in the credit market significantly affect the size and the evolution of fiscal multipliers
    JEL: E63 E21 C63
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:spo:wpmain:info:hdl:2441/2d0r8783s48d1bllkeldav8hqp&r=cmp
  12. By: Wolfgang Britz (Institute for Food and Resource Economics, University of Bonn); Roberto Roson (Department of Economics, University Of Venice Cà Foscari; IEFE Bocconi University)
    Abstract: We motivate and detail the newly developed G-RDEM recursive-dynamic Computable General Equilibrium model as a tool for long-term counterfactual analysis and baseline generation from given GDP and population projections. It encompasses an AIDADS demand system with non-linear Engel curves, debt accumulation from foreign saving and introduces sector specific productivity changes, endogenous aggregate saving rates, as well as time-varying input-output coefficients. Parameters for these relationships are econometrically estimated or taken from published work. The core of the model is derived from the GTAP standard model and seamlessly incorporated into the modular and flexible CGEBox modelling platform. Accordingly, it can be applied with various other extensions such as GTAP-AEZ, GTAP-Water or a regional breakdown for Europe to 280 NUTS2 regions. G-RDEM maintains the flexible aggregation from the GTAP data base. It is open source, encoded in GAMS and can be steered by a Graphical User Interface, which also encompasses a tool to analyse results with tables, graphs and maps. Existing GDP and population projections for the Socio-Economic Pathways 1-5 can be directly incorporated for baseline construction. A comparison of the generated long-term structural composition of the economy against a simple recursive-dynamic variant, using the basic CDE demand system of the standard GTAP model, uniform productivity growth, fixed saving rates and technology parameters, and no debt accumulation shows that G-RDEM brings about much more plausible results, as well as a more realistic, internally consistent representation of the economic structure in a hypothetical future.
    Keywords: Computable General Equilibrium models; Long-run economic scenarios; Structural change
    JEL: C68 C82 C88 D58 E17 F43 O11 O40
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:ven:wpaper:2018:11&r=cmp
  13. By: Rafael Faria de Abreu Campos (Cedeplar-UFMG); Dênis Antônio da Cunha (UFV); Newton Paulo Bueno (FUCAPE Business School)
    Abstract: This study aimed to identify if it is possible, merely through opinion leaders, to disseminate information in irrigation systems. System Dynamics, Agent-Based Modeling, and Social Network Analysis approaches were used for the construction of a hybrid simulation model. An analysis of the characteristics and structural aspects of social networks of the Gorutuba Irrigation Perimeter, in the northern semiarid region of Minas Gerais, was performed. It was observed that the most central agents are in key positions for the flow of information or on compulsory routes for their spread, thus allowing them to filter, retain, or even distort the produced information. The results showed that policies to improve the flow of information to increase resilience of such systems should be based on targeting leading actors.
    Keywords: Dissemination of Information; Irrigation Systems; System Dynamics; Agent-Based Modeling; Social Network Analysis.
    JEL: Q15 Q54 Q58
    Date: 2018–04
    URL: http://d.repec.org/n?u=RePEc:cdp:texdis:td578&r=cmp
  14. By: Hesamzadeh, Mohammad Reza (Royal Institute of Technology (KTH)); Holmberg, Pär (Research Institute of Industrial Economics (IFN)); Sarfati, Mahir (Research Institute of Industrial Economics (IFN))
    Abstract: Zonal pricing with countertrading (a market-based redispatch) gives arbitrage opportunities to the power producers located in the export-constrained nodes. They can increase their profit by increasing the output in the dayahead market and decrease it in the real-time market (the inc-dec game). We show that this leads to large inefficiencies in a standard zonal market. We also show how the inefficiencies can be significantly mitigated by changing the design of the real-time market. We consider a two-stage game with oligopoly producers, wind-power shocks and real-time shocks. The game is formulated as a two-stage stochastic equilibrium problem with equilibrium constraints (EPEC), which we recast into a two-stage stochastic Mixed-Integer Bilinear Program (MIBLP). We present numerical results for a six-node and the IEEE 24-node system.
    Keywords: Two-stage game; Zonal pricing; Wholesale electricity market; Bilinear programming
    JEL: C61 C63 C72 D43 L13 L94
    Date: 2018–05–03
    URL: http://d.repec.org/n?u=RePEc:hhs:iuiwop:1211&r=cmp
  15. By: Korzhenevych, Artem; Bröcker, Johannes
    Abstract: Subsidising investment in lagging regions is an important regional policy instrument in many countries. Some argue that this instrument is not specific enough to concentrate the aid towards the regions that are lagging behind most, because investment subsidies benefit capital owners who might reside elsewhere, possibly in very rich places. Checking under which conditions this is true is thus highly policy relevant. The present paper studies regional investment subsidies in a multiregional neoclassical dynamic framework. We set up a model with trade in heterogeneous goods, with a perfectly integrated financial capital market and sluggish adjustment of regional capital stocks. Consumers and investors act under perfect foresight. We derive the equilibrium system, show how to solve it, and simulate actual European regional subsidies in computational applications. We find that the size of the welfare gains depends on the portfolio distribution held by the households. If households own diversified asset portfolios, we find that the supported regions gain roughly the amounts that are allocated to them in the form of investment subsidies. If they only own local capital stocks, a part of the money is lost through the drop in share prices. From the point of view of total welfare, the subsidy is not efficient. It can lead to a welfare loss for the EU as a whole and definitely leads to welfare losses in the rest of the world, from where investment ows to the supported EU regions.
    Keywords: Exporter wage premium,Heterogeneous firms,Ability differences of workers,Positive assortative matching,Trade and wage inequality
    JEL: C31 F12 F15 J31
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:zbw:tudcep:0218&r=cmp
  16. By: Stefan Nabernegg (University of Graz, Austria); Birgit Bednar-Friedl (University of Graz, Austria); Pablo Munoz (United Nations University, Bonn, Germany); Michaela Tietz (Environment Agency Austria, Vienna, Austria); Johanna Vogel (Environment Agency Austria, Vienna, Austria)
    Abstract: In a world with diverging emission reduction targets, national climate policies might be ineffective in reducing consumption-based CO2 emissions (carbon footprints), i.e. emissions of final demand that are embodied in domestic and international supply chains. We analyse a set of different policies in three areas with particularly high consumption-based emissions in Austria: building construction, public health, and transport. To capture both, substitution possibilities triggered by these policies and the induced emission reductions along the full global supply chain, our analysis combines a Computable General Equilibrium with a Multi-Regional Input-Output model. For building construction we find that a carbon added tax is highly effective in reducing consumption-based emissions whereas an information obligation on vacant dwellings combined with a penalty payment when vacant buildings are not made available is ineffective because of reallocated investment capital. Mandatory energy efficiency improvements in public health and mobility are found equally effective in reducing consumption- and production-based emissions while a decarbonization of domestic logistics stronger reduces production-based emissions. Overall, the effectiveness of policies, to mitigate consumption-based emissions, is therefore determined by the backward and forward linkages of the sector addressed by the policy as well as the substitution effects within final demand.
    Keywords: Carbon footprint; National climate policy; Emissions embodied in trade; Policy analysis; Computable general equilibrium analysis; Multi-regional input-output model
    JEL: C67 C68 F18 Q56
    Date: 2018–05
    URL: http://d.repec.org/n?u=RePEc:grz:wpaper:2018-10&r=cmp
  17. By: Jesús Fernández-Villaverde; David Zarruk Valencia
    Abstract: This guide provides a practical introduction to parallel computing in economics. After a brief introduction to the basic ideas of parallelization, we show how to parallelize a prototypical application in economics using, on CPUs, Julia, Matlab, R, Python, C++-OpenMP, Rcpp–OpenMP, and C++-MPI, and, on GPUs, CUDA and OpenACC. We provide code that the user can download and fork, present comparative results, and explain the strengths and weaknesses of each approach. We conclude with some additional remarks about alternative approaches.
    JEL: C63 C68 E37
    Date: 2018–04
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:24561&r=cmp
  18. By: Joaquin Garcia-Tapial (Universidad Loyola Andalucía); M. Alejandro Cardenete (Universidad Loyola Andalucía)
    Abstract: Although traditionally entrepreneurship has been considered as one of the engines of economic activity, it has not been until recent years that public authorities have made a planned and organized effort to support the entrepreneurial initiative. However, even though many millions of euros are invested annually in this support, the effectiveness of such investment is rarely measured in terms of the impact of entrepreneurial activity on the economy. For this reason, in this paper we analyze the effect of this activity (entrepreneurship) on a regional economy and its impact on it. To do so, we develop a Computable General Equilibrium (CGE) model for the Andalusian economy for 2015, within a top-down approach. The model is based in the Andalusian Social Accounting Matrix (SAM) updated for the year 2015. A SAM is a statistical-accounting instrument that collects all the information of an economic system and, in addition, closes the circular flow of incomes, considering direct, indirect and induced effects. This gives an overview of the implications of the economic flows on the different sectors of activity and at the same time details and completes them. The SAM for Andalusia 2015 has a disaggregation level of 35 economic activities (27 productive sectors plus 8 endogenous accounts that include items such as capital, consumption, labor, investment, taxes, public sector and sector Exterior). In order to obtain the impact vector for the entrepreneurial activity, necessary to make the estimates for each one of the activity sector, the statistical official information available about business creation in Andalusia has been used. The results will show the effects on Gross domestic product, Productive Output and employment creation and its distribution by sectors of Activity.
    Keywords: Social Accounting Matrix, Entrepreneurship, Andalusia, Regional Economy.
    JEL: C63 C68 D58 L26 R13
    Date: 2018–05
    URL: http://d.repec.org/n?u=RePEc:loy:wpaper:2018-001&r=cmp

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