nep-cmp New Economics Papers
on Computational Economics
Issue of 2021‒07‒19
nineteen papers chosen by
Stan Miles
Thompson Rivers University

  1. A new firm-level model of corporate sector interactions and fragility: The Corporate Agent-Based (CAB) model By Robert Hillman; Sebastian Barnes; George Wharf; Duncan MacDonald
  2. Neural network regression for Bermudan option pricing By Bernard Lapeyre; Jérôme Lelong
  3. A Neural Frequency-Severity Model and Its Application to Insurance Claims By Dong-Young Lim
  4. Credit scoring using neural networks and SURE posterior probability calibration By Matthieu Garcin; Samuel St\'ephan
  5. End-to-End Risk Budgeting Portfolio Optimization with Neural Networks By Ayse Sinem Uysal; Xiaoyue Li; John M. Mulvey
  6. Predicting Exporters with Machine Learning By Francesca Micocci; Armando Rungi
  7. Estimation and Machine Learning Prediction of Imports of Goods in European Countries in the Period 2010-2019 By Costantiello, Alberto; Laureti, Lucio; Leogrande, Angelo
  8. The impact of COVID-19 on corporate fragility in the United Kingdom: Insights from a new calibrated firm-level Corporate Sector Agent-Based (CAB) Model By Sebastian Barnes; Robert Hillman; George Wharf; Duncan MacDonald
  9. Role of the Media in the Inflation Expectation Formation Process By Tetiana Yukhymenko
  10. Exploiting Symmetry in High-Dimensional Dynamic Programming By Mahdi Ebrahimi Kahou; Jesús Fernández-Villaverde; Jesse Perla; Arnav Sood
  11. Big Data is Decision Science: the Case of Covid-19 Vaccination By Jacques Bughin; Michele Cincera; Dorota Reykowska; Rafal Ohme
  12. The Effectiveness of Strategies to Contain Sars-Cov-2: Testing, Vaccinations, and NPIs By Gabler, Janos; Raabe, Tobias; Röhrl, Klara; Gaudecker, Hans-Martin von
  13. Short-term electricity price forecastingmodels comparative analysis : Machine Learning vs. Econometrics By Antoine FerrÉ; Guillaume de Certaines; Jérôme Cazelles; Tancrède Cohet; Arash Farnoosh; Frédéric Lantz
  14. Image Content, Complexity, and the Market Value of Art By Stephen Sheppard
  15. Who should get vaccinated? Individualized allocation of vaccines over SIR network By Toru Kitagawa; Guanyi Wang
  16. Clustering and attention model based for Intelligent Trading By Mimansa Rana; Nanxiang Mao; Ming Ao; Xiaohui Wu; Poning Liang; Matloob Khushi
  17. Financial Innovation in the 21st Century: Evidence from U.S. Patents By Josh Lerner; Amit Seru; Nick Short; Yuan Sun
  18. A Dynamic Stock-Flow Model for the Argentine Economy By Gabriel Michelena
  19. A Multicountry Macroeconometric Model for EAEU By Aizhan Bolatbayeva

  1. By: Robert Hillman; Sebastian Barnes; George Wharf; Duncan MacDonald
    Abstract: This paper develops a new large-scale firm-level simulation model, the Corporate Sector Agent-Based (CAB) Model, which is applied to analyse the COVID-19 shock and policy options in Barnes, Hillman, MacDonald and Wharf (2021). Agent-based models (ABMs) simulate the interaction of autonomous agents to generate emergent aggregate behaviours. The CAB model takes into account: heterogeneity across firms; a realistic customer-supplier network; interactions between firms; rule-of-thumb behaviour by firms and bankruptcy constraints.
    Keywords: agent-based modelling, bankruptcy, Covid-19, credit guarantees, financial stability, firm dynamics, firm-level data, input-output analysis, network analysis, short-time working schemes
    JEL: D21 D22 D57 D85 E27 G33
    Date: 2021–07–13
    URL: http://d.repec.org/n?u=RePEc:oec:ecoaaa:1675-en&r=
  2. By: Bernard Lapeyre (CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique - ENPC - École des Ponts ParisTech, MATHRISK - Mathematical Risk Handling - UPEM - Université Paris-Est Marne-la-Vallée - ENPC - École des Ponts ParisTech - Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique); Jérôme Lelong (DAO - Données, Apprentissage et Optimisation - LJK - Laboratoire Jean Kuntzmann - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)
    Abstract: The pricing of Bermudan options amounts to solving a dynamic programming principle, in which the main difficulty, especially in high dimension, comes from the conditional expectation involved in the computation of the continuation value. These conditional expectations are classically computed by regression techniques on a finite dimensional vector space. In this work, we study neural networks approximations of conditional expectations. We prove the convergence of the well-known Longstaff and Schwartz algorithm when the standard least-square regression is replaced by a neural network approximation. We illustrate the numerical efficiency of neural networks as an alternative to standard regression methods for approximating conditional expectations on several numerical examples.
    Keywords: Deep learning,Bermudan options,Regression methods,Optimal stopping,Neural networks,optimal stopping,regression methods,deep learning,neural networks
    Date: 2021
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-02183587&r=
  3. By: Dong-Young Lim
    Abstract: This paper proposes a flexible and analytically tractable class of frequency-severity models based on neural networks to parsimoniously capture important empirical observations. In the proposed two-part model, mean functions of frequency and severity distributions are characterized by neural networks to incorporate the non-linearity of input variables. Furthermore, it is assumed that the mean function of the severity distribution is an affine function of the frequency variable to account for a potential linkage between frequency and severity. We provide explicit closed-form formulas for the mean and variance of the aggregate loss within our modelling framework. Components of the proposed model including parameters of neural networks and distribution parameters can be estimated by minimizing the associated negative log-likelihood functionals with neural network architectures. Furthermore, we leverage the Shapely value and recent developments in machine learning to interpret the outputs of the model. Applications to a synthetic dataset and insurance claims data illustrate that our method outperforms the existing methods in terms of interpretability and predictive accuracy.
    Date: 2021–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2106.10770&r=
  4. By: Matthieu Garcin; Samuel St\'ephan
    Abstract: In this article we compare the performances of a logistic regression and a feed forward neural network for credit scoring purposes. Our results show that the logistic regression gives quite good results on the dataset and the neural network can improve a little the performance. We also consider different sets of features in order to assess their importance in terms of prediction accuracy. We found that temporal features (i.e. repeated measures over time) can be an important source of information resulting in an increase in the overall model accuracy. Finally, we introduce a new technique for the calibration of predicted probabilities based on Stein's unbiased risk estimate (SURE). This calibration technique can be applied to very general calibration functions. In particular, we detail this method for the sigmoid function as well as for the Kumaraswamy function, which includes the identity as a particular case. We show that stacking the SURE calibration technique with the classical Platt method can improve the calibration of predicted probabilities.
    Date: 2021–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2107.07206&r=
  5. By: Ayse Sinem Uysal; Xiaoyue Li; John M. Mulvey
    Abstract: Portfolio optimization has been a central problem in finance, often approached with two steps: calibrating the parameters and then solving an optimization problem. Yet, the two-step procedure sometimes encounter the "error maximization" problem where inaccuracy in parameter estimation translates to unwise allocation decisions. In this paper, we combine the prediction and optimization tasks in a single feed-forward neural network and implement an end-to-end approach, where we learn the portfolio allocation directly from the input features. Two end-to-end portfolio constructions are included: a model-free network and a model-based network. The model-free approach is seen as a black-box, whereas in the model-based approach, we learn the optimal risk contribution on the assets and solve the allocation with an implicit optimization layer embedded in the neural network. The model-based end-to-end framework provides robust performance in the out-of-sample (2017-2021) tests when maximizing Sharpe ratio is used as the training objective function, achieving a Sharpe ratio of 1.16 when nominal risk parity yields 0.79 and equal-weight fix-mix yields 0.83. Noticing that risk-based portfolios can be sensitive to the underlying asset universe, we develop an asset selection mechanism embedded in the neural network with stochastic gates, in order to prevent the portfolio being hurt by the low-volatility assets with low returns. The gated end-to-end with filter outperforms the nominal risk-parity benchmarks with naive filtering mechanism, boosting the Sharpe ratio of the out-of-sample period (2017-2021) to 1.24 in the market data.
    Date: 2021–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2107.04636&r=
  6. By: Francesca Micocci; Armando Rungi
    Abstract: In this contribution, we exploit machine learning techniques to predict out-of-sample firms' ability to export based on the financial accounts of both exporters and non-exporters. Therefore, we show how forecasts can be used as exporting scores, i.e., to measure the distance of non-exporters from export status. For our purpose, we train and test various algorithms on the financial reports of 57,021 manufacturing firms in France in 2010-2018. We find that a Bayesian Additive Regression Tree with Missingness In Attributes (BART-MIA) performs better than other techniques with a prediction accuracy of up to $0.90$. Predictions are robust to changes in definitions of exporters and in the presence of discontinuous exporters. Eventually, we argue that exporting scores can be helpful for trade promotion, trade credit, and to assess firms' competitiveness. For example, back-of-the-envelope estimates show that a representative firm with just below-average exporting scores needs up to $44\%$ more cash resources and up to $2.5$ times more capital expenses to reach full export status.
    Date: 2021–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2107.02512&r=
  7. By: Costantiello, Alberto; Laureti, Lucio; Leogrande, Angelo
    Abstract: In this article we estimate the imports of goods in European countries in the period 2010-2019 for 28 countries. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS. Our results show that “Imports of Goods” is negatively associated with “Private Consumption Expenditure at Current Prices”, “Consumption of Fixed Capital”, and “Gross Domestic Product” and positively associated with “Harmonised consumer price index” and “Gross Operating Surplus: Total Economy”. Finally, we compare a set of predictive models based on different machine learning techniques using RapidMiner, and we find that “Gradient Boosted Trees”, “Random Forest”, and “Decision Tree” are more efficient then “Deep Learning”, “Generalized Linear Model” and “Support Vector Machine”, in the sense of error minimization, to forecast the degree of “Imports of Goods”.
    Keywords: General Trade, Global Outlook, International Economic Order and Integration, Empirical Studies of Trade, Trade Forecasting and Simulation.
    JEL: F00 F01 F02 F14 F17
    Date: 2021–07–05
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:108663&r=
  8. By: Sebastian Barnes; Robert Hillman; George Wharf; Duncan MacDonald
    Abstract: Covid-19 and the associated restrictions on interaction have led to an unprecedented shock to activity and firms’ balance sheets. To assess the impact, this paper applies a new large-scale firm-level simulation model calibrated to the United Kingdom (UK). The paper specifically examines the Coronavirus Job Retention Scheme (CJRS) furlough program and a credit guarantee.The Corporate Sector Agent-Based (CAB) Model (Hillman, Barnes, Wharf and MacDonald, 2021) takes into account: heterogeneity across firms; interactions between firms across a realistic customer-supplier network; and rule-of-thumb behaviour by firms and bankruptcy constraints. The model amplifies the effect of shocks and generates substantial persistence and overshooting, as well as displaying a number of non-linearities. The CAB uses a data-rich approach based on ORBIS firm-level data and the OECD Input-Output tables. Simulations in this paper are calibrated to the observed path of UK output in 2020.
    Keywords: agent-based modelling, bankruptcy, Covid-19, credit guarantees, financial stability, firm dynamics, firm-level data, input-output analysis, network analysis, short-time working schemes
    JEL: D21 D22 D57 D85 E27 G33
    Date: 2021–07–13
    URL: http://d.repec.org/n?u=RePEc:oec:ecoaaa:1674-en&r=
  9. By: Tetiana Yukhymenko (National Bank of Ukraine)
    Abstract: This research highlights the role played by the media in the inflation expectations formation process of different types of respondents in Ukraine. Using a large news corpus and machine learning techniques I constructed news-based measures transforming text into quantitative indicators, which reflect news topics relevant to inflation expectations. As such, I found evidence that the different news topics have an impact on inflation expectations and can explain part of their variance. Thus, my results can help understand inflation expectations, especially as anchoring inflation expectations remains a key challenge for central banks.
    Keywords: Inflation expectations; natural language processing; textual data; machine learning
    JEL: C55 C82 D84 E31 E58
    Date: 2021–06–30
    URL: http://d.repec.org/n?u=RePEc:gii:giihei:heidwp13-2021&r=
  10. By: Mahdi Ebrahimi Kahou; Jesús Fernández-Villaverde; Jesse Perla; Arnav Sood
    Abstract: We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The „curse of dimensionality“ is avoided due to four complementary techniques: (1) exploiting symmetry in the approximate law of motion and the value function; (2) constructing a concentration of measure to calculate high-dimensional expectations using a single Monte Carlo draw from the distribution of idiosyncratic shocks; (3) sampling methods to ensure the model fits along manifolds of interest; and (4) selecting the most generalizable over-parameterized deep learning approximation without calculating the stationary distribution or applying a transversality condition. As an application, we solve a global solution of a multi-firm version of the classic Lucas and Prescott (1971) model of „investment under uncertainty.“ First, we compare the solution against a linear-quadratic Gaussian version for validation and benchmarking. Next, we solve nonlinear versions with aggregate shocks. Finally, we describe how our approach applies to a large class of models in economics.
    Keywords: dynamic programming, deep learning, breaking the curse of dimensionality
    JEL: C45 C60 C63
    Date: 2021
    URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_9161&r=
  11. By: Jacques Bughin; Michele Cincera; Dorota Reykowska; Rafal Ohme
    Abstract: Data science has been proven to be an important asset to support better decision-making in a variety of settings, whether it is for a scientist to better predict climate change, for a company to better predict sales, or for a government to anticipate voting preferences. In this research, we leverage Random Forest (RF) as one of the most effective machine learning techniques using big data to predict vaccine intent in five European countries. The findings support the idea that outside of vaccine features, building adequate perception of the risk of contamination, as well securing institutional and peer trust are key nudges to convert skeptics to get vaccinated against the covid-19. What machine learning techniques further add beyond traditional regression techniques, is some extra granularity in factors affecting vaccine preferences (twice more factors than logistic regression). Other factors that emerge as predictors of vaccine intent are compliance appetite with non-pharmaceutical protective measures, as well as perception of the crisis duration.
    Keywords: Attitudes, Big data, Covid-19, iCode™, Machine learning techniques, Random Forest, Response time, Vaccination
    Date: 2021–07
    URL: http://d.repec.org/n?u=RePEc:ict:wpaper:2013/327150&r=
  12. By: Gabler, Janos (IZA); Raabe, Tobias (quantilope); Röhrl, Klara (University of Bonn); Gaudecker, Hans-Martin von (University of Bonn)
    Abstract: In order to slow the spread of the CoViD-19 pandemic, governments around the world have enacted a wide set of policies limiting the transmission of the disease. Initially, these focused on non-pharmaceutical interventions; more recently, vaccinations and large-scale rapid testing have started to play a major role. The objective of this study is to explain the quantitative effects of these policies on determining the course of the pandemic, allowing for factors like seasonality or virus strains with different transmission profiles. To do so, the study develops an agent-based simulation model, which is estimated using data for the second and the third wave of the CoViD-19 pandemic in Germany. The paper finds that during a period where vaccination rates rose from 5% to 40%, rapid testing had the largest effect on reducing infection numbers. Frequent large-scale rapid testing should remain part of strategies to contain CoViD-19; it can substitute for many non-pharmaceutical interventions that come at a much larger cost to individuals, society, and the economy.
    Keywords: COVID-19, agent based simulation model, rapid testing, non-pharmaceutical interventions
    JEL: C63 I18
    Date: 2021–06
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp14510&r=
  13. By: Antoine FerrÉ (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Guillaume de Certaines (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Jérôme Cazelles (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Tancrède Cohet (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Arash Farnoosh (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Frédéric Lantz (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School)
    Abstract: This paper gives an overview of several models applied to forecast the day-ahead prices of the German electricity market between 2014 and 2015 using hourly wind and solar productions as well as load. Four econometric models were built: SARIMA, SARIMAX, Holt-Winters and Monte Carlo Markov Chain Switching Regimes. Two machine learning approaches were also studied: a Gaussian mixture classification coupled with a random forest and finally, an LSTM algorithm. The best performances were obtained using the SARIMAX and LSTM models. The SARIMAX model makes good predictions and has the advantage through its explanatory variables to better capture the price volatility. The addition of other explanatory variables could improve the prediction of the models presented. The RF exhibits good results and allows to build a confidence interval. The LSTM model provides excellent results, but the precise understanding of the functioning of this model is much more complex.
    Keywords: Energy Markets,Renewable Energy,Econometric modelling,Bootstrap Method,Merit-Order effect
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03262208&r=
  14. By: Stephen Sheppard (Williams College)
    Abstract: This paper presents an approach to measuring the complexity and content of art images that is based on information theory and can be replicated using widely-available analytic tools. The approach is combined with other machine learning algorithms to produce image content measurements for a sample of over 313,000 works offered for sale at auction over the past four decades. The work was produced by 1090 artists employing a variety of styles and using a variety of media and support. Drawing on approaches from economics, mathematics, computer science and psychology, models are estimated to measure the association of image complexity and other image characteristics with the auction price for which the painting was sold. The results support the hypothesis that art buyers have a preference for image complexity and are willing to pay for it. A one standard error increase in the entropy of the image is estimated to be associated with an increased market value of 138%, other factors held equal. We also examine and estimate the impact of faces, likelihood of the image containing racy or adult content, and other content measures. While these don't have as large an estimated impact as image complexity, many of them have large impacts that suggest such measures should be more widely applied in understanding the determinants of the market values of art.
    Keywords: Art Market, Image Processing, Information, Complexity
    JEL: Z11 C81
    Date: 2021–07–01
    URL: http://d.repec.org/n?u=RePEc:wil:wileco:2021-08&r=
  15. By: Toru Kitagawa (Institute for Fiscal Studies and cemmap and University College London); Guanyi Wang (Institute for Fiscal Studies)
    Abstract: How to allocate vaccines over heterogeneous individuals is one of the important policy decisions in pandemic times. This paper develops a procedure to estimate an individualized vaccine allocation policy under limited supply, exploiting social network data containing individual demographic characteristics and health status. We model the spillover effects of vaccination based on a Heterogeneous-Interacted-SIR network model and estimate an individualized vaccine allocation policy by maximizing an estimated social welfare (public health) criterion incorporating these spillovers. While this optimization problem is generally an NP-hard integer optimization problem, we show that the SIR structure leads to a submodular objective function, and provide a computationally attractive greedy algorithm for approximating a solution that has a theoretical performance guarantee. Moreover, we characterise a finite sample welfare regret bound and examine how its uniform convergence rate depends on the complexity and riskiness of the social network. In the simulation, we illustrate the importance of considering spillovers by comparing our method with targeting without network information.
    Date: 2020–12–14
    URL: http://d.repec.org/n?u=RePEc:ifs:cemmap:59/20&r=
  16. By: Mimansa Rana; Nanxiang Mao; Ming Ao; Xiaohui Wu; Poning Liang; Matloob Khushi
    Abstract: The foreign exchange market has taken an important role in the global financial market. While foreign exchange trading brings high-yield opportunities to investors, it also brings certain risks. Since the establishment of the foreign exchange market in the 20th century, foreign exchange rate forecasting has become a hot issue studied by scholars from all over the world. Due to the complexity and number of factors affecting the foreign exchange market, technical analysis cannot respond to administrative intervention or unexpected events. Our team chose several pairs of foreign currency historical data and derived technical indicators from 2005 to 2021 as the dataset and established different machine learning models for event-driven price prediction for oversold scenario.
    Date: 2021–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2107.06782&r=
  17. By: Josh Lerner; Amit Seru; Nick Short; Yuan Sun
    Abstract: We develop a unique dataset of 24 thousand U.S. finance patents granted over last two decades to explore the evolution and production of financial innovation. We use machine learning to identify the financial patents and extensively audit the results to ensure their reasonableness. We find that patented financial innovation is substantial and economically important, with the number of annual grants expanding from a few dozen in the 1990s to over 2000 in the 2010s. The subject matter of financial patents has changed, consistent with the industry’s shift in revenue and value-added towards household investors and borrowers. The surge in financial patenting was driven by information technology firms and others outside of financial sector, which collectively accounted for 69% of the awards. The location of innovation has shifted, with banks moving this activity from regions with tight financial regulation to more permissive ones. High-tech regions have attracted financial innovation by payments, IT, and other non-financial firms. Turning to the source of these ideas, while academic knowledge remained associated with more valuable patents, citations in finance patents to academic papers, especially in those by banks, fell sharply.
    JEL: G20 O31
    Date: 2021–07
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:28980&r=
  18. By: Gabriel Michelena (Central Bank of Argentina)
    Abstract: This document develops a Consistent Stock-Flow (SFC) model for the analysis of macroeconomic variables in Argentina. The main utility of SFC models is associated with the possibility of performing counterfactual exercises to evaluate different modifications of fiscal, tax, monetary and commercial policy. These models are characterized by the use of social accounting matrices (SAM), which allows a breakdown of the capital account and financial instruments of each institutional sector. This improves accounting consistency, since the SAM contains the main transactions of the real sector, as well as the monetary flows between the different institutions: households, companies, banks, government, central bank and the rest of the world. This model was developed with the objective of making medium-term projections on the main flows and stocks of the Argentine economy, complementing the results of other existing models in the literature.
    Keywords: monetary policy, simulations, stock-flow model
    JEL: C54 E16 E58
    Date: 2021–02
    URL: http://d.repec.org/n?u=RePEc:bcr:wpaper:202189&r=
  19. By: Aizhan Bolatbayeva (NAC Analytica, Nazarbayev University)
    Abstract: This paper introduces a macroeconometric multicountry model for the Eurasian Economic Union (EAEU). The model consists of five single-country models of the union member states: Armenia, Belarus, Kazakhstan, Kyrgyzstan and Russia. The purpose of the research is to explain the structural relationship between the economies, evaluate the impact of internal and external shocks, and analyze the transmission mechanism of shocks across countries. The single-country models are linked to each other by the equations of bilateral trade and bilateral exchange rate. We find that the model fits actual data on main macroeconomic indicators of the countries in a dynamic ex-post simulation over 2004-2018. We also evaluate the effect of world trade and monetary policy shocks on the economies of the member states of the EAEU.
    Keywords: EAEU; Cowles Commission approach; Structural macroeconomic model; Simulation
    JEL: B22 E17 E27
    Date: 2020–12
    URL: http://d.repec.org/n?u=RePEc:ajx:wpaper:11&r=

This nep-cmp issue is ©2021 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at http://nep.repec.org. For comments please write to the director of NEP, Marco Novarese at <director@nep.repec.org>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.