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NEW ANALYTICAL TOOLS AND TECHNIQUES FOR ECONOMIC POLICYMAKING
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NEW APPROACHES TO ECONOMIC CHALLENGES NAEC, NEW ANALYTICAL TOOLS AND TECHNIQUES FOR ECONOMIC POLICYMAKING. Understanding of economic issues such as growth financial crises systemic risk innovation and sustainability can. benefit from the revolution taking place across a range of scientific disciplines and in the social sciences This. revolution is being driven by the interaction between technological progress in computing and communications and. the new sources and greater quantities of data this makes available. This NAEC conference offers a timely opportunity for policy makers academics and researchers in economics to. discuss the state of the art policy applications emerging from the new analytical tools and techniques It will look at. how methodological innovations and inter disciplinary approaches such as agent based modelling nowcasting. machine learning and network analysis could contribute to better understanding of the complexity and interaction. of our economic financial social and environmental systems. Monday 15 April,9 30 10 00 Opening remarks,Angel Gurria OECD Secretary General video. Gabriela Ramos OECD Chief of Staff and Sherpa,Laurence Boone OECD Chief Economist. Martine Durand OECD Chief Statistician, 10 00 11 00 Session 1 Why Do We Need New Analytical Tools and Techniques. Moderator Gabriela Ramos OECD Chief of Staff and Sherpa. J Doyne Farmer Director of Complexity Economics Institute for New Economic. Thinking and Santa Fe Institute and Rebuilding Macroeconomics. J Doyne Farmer is Director of the Complexity Economics programme at the Institute for New Economic. Thinking at the Oxford Martin School and the Baillie Gifford Professor at Mathematical Institute at the. University of Oxford as well as an External Professor at the Santa Fe Institute. His current research is in economics including agent based modeling financial instability and. technological progress He was a founder of Prediction Company a quantitative automated trading firm. that was sold to the United Bank of Switzerland in 2006 His past research includes complex systems. dynamical systems theory time series analysis and theoretical biology. During the eighties he was an Oppenheimer Fellow and the founder of the Complex Systems Group at. Los Alamos National Laboratory While a graduate student in the 70 s he build the first wearable digital. computer which was successfully used to predict the game of roulette. Robert Axtell Chair of the Department of Computational Social Science at. George Mason University and Santa Fe Institute, Robert Axtell is an Associate Professor of the Santa Fe Institute who works at the intersection of.
economics behavioral game theory and multi agent systems computer science His most recent. research attempts to emerge a macroeconomy from tens of millions of interacting agents He is. Department Chair of the new Department of Computational Social Science at George Mason University. Fairfax Virginia USA He teaches courses on agent based modeling mathematical modeling and. game theory His research has been published in Science Proceedings of the National Academy of. Sciences USA and leading field journals Popular accounts have appeared in newspapers magazines. books online on the radio and in museums His is the developer of Sugarscape an early attempt to do. social science with multi agent systems andco author of Growing Artificial Societies Social Science. from the Bottom Up MIT Press 1996 Previously he was a Senior Fellow at the Brookings Institution. Washington D C USA and a founding member of the Center on Social and Economic Dynamics there. He holds an interdisciplinary Ph D from Carnegie Mellon University Pittsburgh USA. NEW APPROACHES TO ECONOMIC CHALLENGES NAEC, Jean Philippe Bouchaud Chairman Capital Fund Management CFM and. Rebuilding Macroeconomics, Jean Philippe Bouchaud is Chairman and Chief Scientist He supervises the research and maintains. strong links between the research team and the academic world He is also a professor at Ecole. Polytechnique where he teaches Statistical Mechanics and a course on Complex Systems He joined. CFM in 1994, William H Janeway Faculty of Economics Cambridge University and Advisor. Warburg Pincus, William H Janeway has lived a double life of theorist practitioner according to the legendary economist. Hyman Minsky who first applied that term to him twenty five years ago In his role as practitioner Bill. Janeway has been an active growth equity investor for more than 40 years He is a senior advisor at. Warburg Pincus where he has been responsible for building the information technology investment. practice as well as a director of Magnet Systems and O Reilly Media As a theorist he is an affiliated. member of the Faculty of Economics of Cambridge University a member of the board of directors of the. Social Science Research Council and the Fields Institute for Research in the Mathematical Sciences. and of the Advisory Board of the Princeton Bendheim Center for Finance He is a co founder and member. of the Governing Board of the Institute for New Economic Thinking INET and a member of the Board. of Managers of the Cambridge Endowment for Research in Finance CERF Following publication in. November 2012 his book Doing Capitalism in the Innovation Economy Markets Speculation and the. State Cambridge University Press became a classic The fully revised and updated second. edition Doing Capitalism in the Innovation Economy Reconfiguring the Three Player Game between. Markets Speculators and the State was published in May 2018. Michael Jacobs Professorial Research Fellow Sheffield Political Economy. Research Institute SPERI, Michael Jacobs is a Professorial Fellow and Head of Engagement and Impact He is an economist and.
political theorist specialising in post neoliberal political economy climate change and environmental. policy and green and social democratic thought He is responsible for oversight and leadership with. respect to SPERI s engagement and impact work, Michael leads SPERI s Corporate Power the Global Economy research theme with Merve Sancak. Prior to joining SPERI Michael was Director of the IPPR Commission on Economic Justice based at the. UK think tank the Institute for Public Policy Research He was principal author and editor of the. Commission s final report Prosperity and Justice A Plan for the New Economy 2018. Originally a community worker and adult educator Michael later became a director and then managing. director of CAG Consultants where he worked in local economic development and sustainable. development He was subsequently an ESRC research fellow at Lancaster University and the LSE He. was General Secretary of the think tank and political association the Fabian Society from 1997 2003. From 2004 2007 Michael was a member of the Council of Economic Advisers at the UK Treasury and. from 2007 2010 he was a Special Adviser to Prime Minister Gordon Brown with responsibility for energy. environment and climate policy, After leaving government in 2010 Michael advised governments and others on international climate. change policy in the run up to the UN Climate Conference in Paris in December 2015 He was a founder. and senior adviser to the Global Commission on the Economy and Climate. NEW APPROACHES TO ECONOMIC CHALLENGES NAEC,11 15 12 30 Session 2 Nowcasting. Moderator Lucrezia Reichlin Professor of Economics London Business School. Laurent Ferrara Head of International Macro Division Banque de France. When are Google data useful to nowcast GDP An approach via pre selection and. Nowcasting GDP growth is extremely useful for policy makers to assess macroeconomic conditions in. real time In this paper we aim at nowcasting euro area GDP with a large database of Google search. data Our objective is to check whether this specific type of information can be useful to increase GDP. nowcasting accuracy and when once we control for official variables In this respect we estimate shrunk. bridge regressions that integrate Google data optimally screened through a targeting method and we. empirically show that this approach provides some gain in pseudo real time nowcasting of euro area. GDP quarterly growth Especially we get that Google data bring useful information for GDP nowcasting. for the four first weeks of the quarter when macroeconomic information is lacking However as soon as. official data become available their relative nowcasting power vanishes In addition a true real time. analysis confirms that Google data constitute a reliable alternative when official data are lacking. When are Google data useful to nowcast GDP An approach via pre selection and shrinkage. Laurent Ferrara is Head of the International Macroeconomics Division at the Banque de France in Paris. in charge of the outlook and macroeconomic forecasting for advanced economies as well as global. policy issues such as exchange rates commodities or global imbalances Main tasks of this division of. around 20 people are policy briefing preparation of international meetings ECB IMF OECD G20 and. economic research He is also involved in academics and has been appointed Adjunct Professor of. Economics at the University of Paris Nanterre in September 2011. Laurent Ferrara is Director of the International Institute of Forecasters an international association. aiming at bridging the gap between theory and applications in forecasting through the organisation of. workshops and conferences and the publication of an academic journal the International Journal of. Forecasting He is also an associate editor of this journal. Dr Ferrara holds a PhD in Applied Mathematics from the University of Paris North 2001 and a Research. Habilitation in Economics from the University of Paris 1 Panth on Sorbonne 2007 His academic. research mainly focuses on macroeconomic forecasting international economics econometric methods. non linear modelling and business cycle analysis He published more than 50 papers in international and. national academic journals chapters in books as well as book on time series analysis and forecasting. Elias Albagli Chief Economist Central Bank of Chile. Real time VAT data in Chile Applications for Monetary and Financial Policy. The Central Bank of Chile has been developing new administrative data sources for advancing its. analytical and forecasting tools Since 2016 the tax administration authority SII mandates transaction. between firms to be electronically transmitted in real time for VAT accounting purposes This wealth of. data allows several important advances both for projection and macroeconomic analysis purposes as. well as for structural economic research We highlight three applications First real time transactions. allow computing value added proxies for several sectors enhancing nowcasting capacity and eventually. diminishing the lag between economic activity data and Monetary Policy decisions by about a month. Second the complete network structure also allows to better interpret linkages between supply and. demand side of national accounts in real time For instance an expansion of wholesale machine and. equipment intermediation sector can be linked precisely with the end user sectors which are increasing. fixed investment This knowledge is of particular interest for a commodity exporting country where mining. investment has different lags and spillovers on overall economic activity as other sectors Third merging. this data with credit information also available for the universe of Chilean firms is of potential use in. detecting macroeconomic and financial stability risks Indeed the network structure in real time can be. used to detect disruptions i e imminent firm closure assess their spillovers to interconnected firms. customer and supplier and predict the overall macroeconomic impact across different sectors as well. as their implications for debt service capacity of affected firms. NEW APPROACHES TO ECONOMIC CHALLENGES NAEC, El as Albagli is the Director of Central Bank of Chile s Monetary Policy Division since August. 2018 Previously he was Director of Central Bank of Chile s Research Division since June 2018. Previously he was Manager of Modeling and Economic Analysis of the Bank December 2014 through. June 2018 He holds a Bachelor s degree in Business and a Master s in Financial Economics from the. Catholic University of Chile 2002 where he received the best graduating student award He received. his Ph D in Economics from Harvard University in 2010 Previously he worked in the Central Bank s. Economic Research Division most recently as a senior economist June 2013 to November 2014 and. also as an economic analyst 2002 2005 He was an assistant professor of Economics and Finance at. the University of Southern California from 2010 to 2013 Mr Albagli has taught courses on Financial. Markets and Macroeconomics at different institutions including the Economics Department at the. Catholic University of Chile and the Economics and Business Management Department at the University. of Chile He has published numerous journal articles book chapters and working papers on issues. related to macroeconomics and financial markets,14 00 16 00 Session 3 Agent Based Modelling.
Moderator J Doyne Farmer Director of Complexity Economics Institute for New. Economic Thinking University of Oxford and Rebuilding Macroeconomics. Alissa Kleinnijenhuis Researcher Mathematical Institute Oxford and Institute. for New Economic Thinking University of Oxford and Thom Wetzer DPhil. candidate in Law and Finance University of Oxford,Foundations of System Wide Stress Testing. Microprudential stress tests have been credited for restoring confidence in the banking sys tem and. allowing for a successful recapitalisation of banks Bernanke 2013 They have gained enormous. importance in the post crisis regulatory toolkit Their core goal is to assess systemic risk Despite their. victories microprudential stress tests lack interconnections and thereby their ability to consider. endogenously amplified systemic risk This fundamental deficiency impairs their ability to assess. resilience which has led various academics and regulators to call for system wide stress tests e g. Brazier 2017 Yet no generic method exists yet Anderson et al 2018 Challenges include. Anderson et al 2018 to capture systemic risk amplification mechanisms comprehensively and. consistently to encapsulate the behavioural responses to shocks to encompass interactions between. constraints and behaviour to con sistently incorporate non banks to reect the heterogeneity of. objectives resources and constraints Danielsson and Shin 2003 to exibly adjust to a changing. financial system and to deal with a lack of suffciently granular and well covered data. In this paper we propose a novel method for system wide stress testing that is to our knowledge the. first to jointly tackle these challenges It consists of five building blocks institutions contracts constraints. markets and behaviour Together these allow to track contagious dynamics in a multiplex network and. assess fragility under various policy set ups We illustrate the power of this method by providing an. implementation of the building blocks We show that systemic risk may be significantly underestimated. if microprudential stress tests are not supplemented with a macroprudential overlay Based on the tool s. foundations credible stress system wide stress tests can be built to crown the macroprudential toolkit. Alissa Kleinnijenhuis is a D Phil Candidate in Financial and Computational Mathematics at the. University of Oxford and the Institute for New Economic Thinking at the Oxford Martin School She is. also affiliated with the Oxford Man Institute of Quantitative Finance She works under supervision of. Professor J Doyne Farmer Her research focuses on system wide stress testing and systemic risk. Alissa acts as a visiting academic to the Bank of England London and has also conducted research. on stress testing at the European Central Bank Frankfurt In addition her professional experience. includes work for Morgan Stanley London and Rogge Global Partners London Alissa holds a B A. Hons in Mathematics and Economics from University College Utrecht partially completed at UC Santa. Barbara and an M Sc in Mathematics and Finance from Imperial College London. Thom Wetzer is a DPhil Candidate in Law and Finance at the Oxford Faculty of Law the Oxford Man. Institute for Quantitative Finance and the Institute for New Economic Thinking at the Oxford Martin. School His research examines incentive misalignments and the role of law in mitigating them with a. particular focus on systematic misalignments that generate systemic risk in financial systems He also. works on climate risk in the context of the post carbon transition. NEW APPROACHES TO ECONOMIC CHALLENGES NAEC, Thom has been a visiting scholar at Columbia Law School the Berkeley School of Law and Yale. University and is currently a visiting academic at the Bank of England and an academic consultant at. the European Central Bank He has worked at the European Commission Goldman Sachs and De. Brauw Blackstone Westbroek and is a Global Shaper at the World Economic Forum. Stanislao Gualdi Research Fellow Capital Fund Management. Optimal Inflation Target Insights from an Agent Based Model. Which level of ination should Central Banks be targeting We investigate this issue in the context of a. simplified Agent Based Model of the economy Depending on the value of the parameters that describe. the behaviour of agents in particular ination anticipations we find a rich variety of behaviour at the. macro level Without any active monetary policy our ABM economy can be in a high ination high output. state or in a low ination low output state Hyper ination deation and business cycles between. coexisting states are also found We then introduce a Central Bank with a Taylor rule based ination. target and study the resulting aggregate variables Our main result is that too low ination targets are in. general detrimental to a CB monitored economy One symptom is a persistent under realisation of. ination perhaps similar to the current macroeconomic situation Higher ination targets are found to. improve both unemployment and negative interest rate episodes Our results are compared with the. predictions of the standard DSGE model, Stanislao Gualdi is a Research Fellow at Capital Fund Management He has a PhD in Theoretical. Physics from the University of Fribourg and a Master s degree in Theoretical Physics from Sapienza. Universit di Roma He was a Postdoctoral Researcher at Ecole Centrale Paris. Torsten Heinrich Researcher Institute for New Economic Thinking University of. An ABM of the insurance reinsurance sector Conclusions for systemic risk market. structure and the insurance cycle, Risk models are employed in the insurance and reinsurance industry to assess the probability and size. of risk events Under the new Solvency II regulations the choice of models that can be used in the. insurance sector has become severely limited This creates a danger in the sense that all insurance. companies may rise and fall in tandem making the sector brittle and creating a public welfare problem. We present here a novel agent based model of the catastrophe insurance and reinsurance sectors to. study this constraint More than other branches of the insurance industry catastrophe insurance is. subject to heavy tailed distributions which occur for damage size peril frequency claims losses and. bankruptcy events As a consequence agent heterogeneity interaction and stochastic influences are of. crucial importance Characterising the system requires studying ensembles of counterfactual cases and. considering not only the mean but also the dispersion of realisations Agent based modeling is well. suited to fulfill these requirements We discuss the properties of the model We substantiate the validation. of modeling decisions with economy level firm level and contract level data We explain micro and. macro calibration of the model And we show some selected results 1 The reproduction of the insurance. cycle 2 The frequency and distribution of bankruptcies across different settings with different levels of. risk model homogeneity This aspect is directly connected to systemic risk in insurance 3 Other. systematic effects of risk model homogeneity 3 The sensitivity of the model with respect to parameters. such as the rate of market entry the interest rate and the capital retention requirements 4 The resulting. market structures in terms of firm sizes and relative shares of insurance and reinsurance business 5 A. market with several operational risk models is a lot more profitable more competitive and has a higher. capacity than a market with only one risk model, Torsten Heinrich is a researcher at the Institute for New Economic Thinking INET at the Oxford Martin.
School of the University of Oxford His work is concentrated methodologically in the fields of agent based. modelling game theory complexity economics and evolutionary economics but also empirical work on. industrial organisation and technological change among other areas with an interest in new. technologies in the potential they create and in economic social and political consequences their. implementation could entail He studied economics at the Dresden University of Technology Dresden. Germany and the Universidad Aut noma de Madrid Madrid Spain graduating in 2007 He received. his PhD from the University of Bremen Germany in 2011 with a thesis on technological change and. growth patterns in the presence of network effects Working on complexity systems agent based. modeling simulation and strategic games in economics he has edited special issues in scientific journals. and authored both journal articles and monographs He holds a post doc position at the Institute for New. Economic Thinking INET at the University of Oxford UK and teaches at the University of Bremen. NEW APPROACHES TO ECONOMIC CHALLENGES NAEC, Francois Lafond Senior Research Officer Institute for New Economic Thinking. University of Oxford, Automation and bottlenecks in occupational mobility a data driven network model. Many existing jobs are prone to automation raising important concerns about the future of employment. However history suggests that alongside automation different jobs are created so that it is crucial to. understand job transitions To do so we impose an automation shock on a network based labour market. model We construct an occupational mobility network where nodes are occupations and edges link. occupations that are similar enough for a worker to transition from one to the other We then model the. dynamics of employment unemployment and vacancies at the occupation level based on exogenous. automation related reallocation of labour demand separations and vacancies opening rates job. search and matching After discussing the model s calibration and its ability to reproduce the Beveridge. curve we study occupation specific unemployment and long term unemployment 27 or more weeks. As expected in highly automated occupations workers are more likely to be unemployed or to stay. unemployed for a long period However the network structure also plays an important role workers in. occupations with a similar degree of automation can have fairly different outcomes depending on the. position of the occupation in the mobility network Automation may cause bottlenecks in the mobility. network with workers unable to find jobs for long periods Our work highlights that retraining schemes. must not necessarily be directed towards workers in occupations with high risk of automation but towards. workers with limited transition possibilities, Fran ois Lafond is a senior research officer at the Institute for New Economic Thinking at the Oxford. Martin School Programme on Technological and Economic Change at the Smith School for Enterprise. and the Environment and an associate member of Nuffield college He received his PhD from UNU. MERIT Maastricht University His main areas of research are in the economics of innovation. environmental economics networks and complex systems applied econometrics and forecasting. Christoph Siebenbrunner DPhil student Mathematical Institute University of. Money creation and liquid funding needs, Starting from a conceptual discussion about money creation as opposed to loanable funds and money. multiplier theories we categorise different forms of lending broadly speaking bank lending versus non. bank lending whereby we equate the former with money creation and the latter with loanable funds. theories At this point we provide a defnition of shadow banking which we link to all lending that is not. bank lending and hence while increasing leverage for individual borrowers does not increase system. wide money stocks The purpose is then to put forward two concluding thoughts First the notion of. money creation as a result of banks loan creation for the private sector is compatible with the notion of. liquid funding needs in a multi bank system in which liquid fund transfers across banks happen naturally. Second conventional interest rate based monetary policy has a bearing on macroeconomic dynamics. precisely due to that multi bank structure It would lose its impact in the hypothetical case that only one. singular commercial bank would exist To illustrate the latter two points we develop a simple agent. based model with a focus on the bank loan creation and destruction due to repayment process as. opposed to pure intermediary lending through capital markets banks proprietary trading and all other. non bank financial and non financial institution types channeling of funds within the system The model. comprises bank agents money creators non bank intermediaries pure channelers a central bank. liquid base money provider and private sector agents that borrow from banks or non banks to finance. their consumption, Christoph Siebenbrunner is a doctoral student in Mathematics and a member of the Complexity.
Economics research group under the supervision of Prof Doyne Farmer at Oxford University His. research focusses on modelling systemic risk and financial systems in general using a wide array of. methodologies including statistical modelling network analysis and agent based modelling He has. seven years of professional experience working as a stress testing expert and quantitative modeller for. central banks including the ECB and the Austrian National Bank. Discussant, Robert Axtell Chair of the Department of Computational Social Science at George. Mason University and Santa Fe Institute,NEW APPROACHES TO ECONOMIC CHALLENGES NAEC. Robert Axtell is an Associate Professor of the Santa Fe Institute who works at the intersection of. economics behavioral game theory and multi agent systems computer science His most recent. research attempts to emerge a macroeconomy from tens of millions of interacting agents He is. Department Chair of the new Department of Computational Social Science at George Mason University. Fairfax Virginia USA He teaches courses on agent based modeling mathematical modeling and. game theory His research has been published in Science Proceedings of the National Academy of. Sciences USA and leading field journals Popular accounts have appeared in newspapers magazines. books online on the radio and in museums His is the developer of Sugarscape an early attempt to do. social science with multi agent systems andco author of Growing Artificial Societies Social Science. from the Bottom Up MIT Press 1996 Previously he was a Senior Fellow at the Brookings Institution. Washington D C USA and a founding member of the Center on Social and Economic Dynamics there. He holds an interdisciplinary Ph D from Carnegie Mellon University Pittsburgh USA. 16 30 18 00 Session 4 Network Analysis, Moderator David Chavalarias Director of the Complex System Institute of Paris idF. Rajan Patel Technical Specialist Bank of England,Textual complexity in bank regulation. Reforms following the financial crisis of 2007 08 have increased the volume of bank regulation and led. to concerns about increased complexity But there are few empirical measures of regulatory complexity. beyond simple page counts To measure precisely the change in regulatory complexity we define it as. a property of how standards are articulated in regulatory texts and calculate linguistic and structural. indicators that reflect the cognitive costs required to process texts We extract these measures from a. dataset that covers the near universe of prudential rules for banks in the United Kingdom including EU. directives and supervisory guidance in 2007 and 2017 To understand the drivers of complexity we. compare different regulatory tools capital liquidity remuneration and the relative contribution of. international versus national rules We also compare the complexity of rules that apply to small versus. large institutions and benchmark UK with US regulation Network maps that visualise textual cross. references show that there are many peripheral rules and a few very highly connected ones and that. the latter are not necessarily the rules that were at the centre of the post crisis debate Similarly the. distributions of measures of lexical diversity conditionality length and readability are skewed Finally. we combine structural and linguistic complexity to create a map that highlights opportunities for rule. simplification, Rajan Patel is a Technical Specialist at the Bank of England where his work involves understanding how.
banks and insurers are responding to prudential regulation He collaborates with people from across the. Bank of England to identify and prioritise examples of regulatory arbitrage and unintended consequences. from regulation He helps policy experts and supervisors mitigate associated risks to the Bank s. regulatory objectives Rajan holds a masters in Economics from the London School of Economics and. Political Science LSE, Gert Buiten Senior Researcher and Sjoerd Hooijmaaijers Researcher. Statistics Netherlands, A methodology for estimating the Dutch interfirm trade network. Currently the National Accounts aggregate Input Output Tables are the main statistical instrument for. analysing the complex circular effects of the economic cycle A network approach on a micro level would. boost the analytic capabilities This paper describes the methodology developed by Statistics. Netherlands for estimating the Dutch interfirm trade network using a combination of input data on trade. transactions and auxiliary information from existing official statistics The estimates result in a directed. and weighted network dataset There are various possible sources for collecting Input data on. relationships such as bank transaction data overviews of debtors and creditors from company. administrations and survey data Each of these sources has some drawbacks and must be. complemented by an imputation method for completing the picture The past decade a growing body of. methods has been developed for estimating interfirm networks Most of these methods combine macro. or meso economic marginals such as aggregate turnover by industry group with one or more. assumptions on the distribution of variables by company as well as on interfirm connections by company. The paper describes how this can first be expanded upon by using aggregate public data from Supply. Use Tables and Input Output Tables as auxiliary data allowing for a matching procedure between. NEW APPROACHES TO ECONOMIC CHALLENGES NAEC, suppliers and users by commodity group Secondly it describes a further improvement based on using. non public micro data on enterprises The paper concludes with some examples of applications and. analyses that can be developed using the estimated dataset. Guert Buiten studied Economic and Social History and works at Statistics Netherlands since 1986 He. is one of the inventors of the Business Cylce Clock that is now widely used internationally also by the. OECD In recent years he has been involved in various innovative research activities including setting. up cooperations with commercial banks for the use of transaction data applying Complexity and Network. Theory in official statistics and the research project for developing a methodology for deriving the interfirm. trade network, Sjoerd Hooijmaaijers holds a Masters degree in both Economics and Data Science He has been. working at Statistics Netherlands since 2017 in the research project for developing a methodology for. deriving the interfirm trade network, Thomas Hurd Professor of Mathematics McMaster University Toronto.
Systemic Cascades in Financial Networks, Systemic risk SR the study of financial crises has long been understood see e g Kaufman 1994 to. involve cascades of contagious shocks of different types notably funding liquidity shocks such as bank. panics and runs and solvency shocks caused by failed banks Compared to systems arising in other. areas of applied science the real world financial system is extraordinarily complex in a diversity of. aspects A popular approach to SR see Nier et al 2007 Hurd 2016 has been to construct. deliberately simplifed random financial networks RFNs and explore how different bank behaviour. characteristics lead to cascades and amplification of shocks This paper advances this network science. approach by introducing a rich new class of inhomogeneous financial networks IFN each of which. consists of a random connectivity skeleton graph of N banks on which is defined a random collection. of balance sheets and interbank exposures Viewed as a multidimensional random variable such an IFN. has an amenable dependence structure known as locally tree like independence The banks are. assumed to follow crisis management strategies that boil down to sets of deterministic rules called. cascade mechanisms Then in scenarios of a large random shock that weakens the system su ciently. a cascade sequence of secondary shocks will follow converging to a cascade equilibrium representing. the final outcome of the crisis While detailed simulations are always possible for moderate values of N. instead we will focus on the kind of stylised facts that might be deduced from analytic cascade dynamics. in the N 1 limit Topics of interest include spillover effects of international linkages between two. countries comparing partial recovery at default to hard zero recovery firesales in bipartite IFNs. After an extensive research career in mathematical physics Tom Hurd turned to the mathematical. study of financial markets in the late 1990s Since then he has built an international research reputation. with many publications in areas such as portfolio theory interest rate modeling and credit risk His work. is currently focussed on modeling systemic risk that is the stability of financial networks and he has. recently published a book on the subject Over the years he has supervised numerous M Sc and Ph D. research students in financial mathematics many of whom have moved on to careers in banking He is. currently Director of the Master in Financial Mathematics program MPhimac at McMaster. Igor Linkov Risk and Decision Science Team Lead US Army Engineer. Research and Development Center, Resilience and Efficiency in Interconnected Networks. Dr Igor Linkov leads the Risk and Decision Science Team and Focus Area at the US Army Engineer. Research and Development Center He is currently leading several projects implementing resilience. management for cyber systems critical infrastructure energy and environment He has published widely. on environmental policy environmental modeling and risk analysis including thirteen books and over. 200 peer reviewed papers and book chapters Dr Linkov has organised more than twenty national and. international conferences and continuing education workshops including 2014 workshop on Risk and. Resilience in Berlin He is recipient of two Army medals for outstanding civilian service and Society for. Risk Analysis Chauncey Starr Award for exceptional contribution to Risk Analysis and Fellow Award. NEW APPROACHES TO ECONOMIC CHALLENGES NAEC,Tuesday 16 April. 9 30 11 00 Session 5 Machine Learning and Big Data. Moderator Martine Durand OECD Chief Statistician, Claudio Cozza Assistant Professor of Economics Department of Economic and. Legal studies University of Naples Parthenope, Can we predict firms innovativeness The identification of innovation performers in an.
Italian region through a supervised learning approach. The study shows the feasibility of predicting firms expenditures in innovation as reported in the. Community Innovation Survey applying a supervised machine learning approach on a sample of Italian. firms Using an integrated dataset of administrative records and balance sheet data designed to include. all informative variables related to innovation but also easily accessible for most of the cohort random. forest algorithm is implemented to obtain a classification model aimed to identify firms that are potential. innovation performers The performance of the classifier estimated in terms of AUC is 0 794 Although. innovation investments do not always result in patenting the model is able to identify 71 92 of firms with. patents More encouraging results emerge from the analysis of the inner working of the model predictors. identified as most important such as firm size sector belonging and investment in intangible assets. confirm previous findings of literature but in a completely different framework The outcomes of this study. are considered relevant for both economic analysts because it demonstrates the potential of data driven. models for understanding the nature of innovation behaviour and practitioners such as policymakers or. venture capitalists who can benefit by evidence based tools in the decision making process. Claudio Cozza is assistant professor in Economic Policy at the Department of Economic and Legal. studies of the University of Naples Parthenope Italy He holds a Degree University of Rome. Sapienza and a Ph D University of Ferrara in industrial economics His research interests include the. Economics of Science Technology and Innovation R D internationalisation innovation systems and. policies Regional Development and Internationalisation of both Advanced and Emerging Multinational. Corporations He has been researcher at the European Commission JRC at ISTAT the Italian National. Statistical Office and has conducted research and teaching activity in several Italian Universities and. research organisations, Janna Axenbeck Researcher Digital Economy ZEW Leibniz Centre for. European Economic Research, What Do Websites Say About Firm Level Innovation A Machine Learning Approach. This paper explores which website characteristics can identify whether a firm is innovative or not Firm. websites entail information about new products key personnel decisions strategies and relationships. with other firms These aspects are all related to a firm s innovative activity Extracting this information by. means of website characteristics would allow to construct efficient innovation indicators that enable an. automatised timely and comprehensive analysis of firm level innovation activities I use German firms. from the Mannheim Innovation Panel MIP classified as either innovative or not and extract their. websites content by applying the ARGUS web scraper which allows me to collect texts as well as. hyperlinks I apply several methods like keyword search topic modelling and other natural language. processing tools to gather website characteristics Then I analyse which characteristics correlate most. with a firm s innovation status reported in the MIP Additionally by using polynomial and interaction. features as well as logistic regression and lasso regularisation I identify which combination of website. characteristics best predicts a firm s innovation status My preliminary results show that innovation related. keywords like innovat or change the sum of hyperlinks and the amount of English language on a. website significantly correlate with whether a firm is innovative or not Moreover website information. significantly improves predictions about a firm innovation status and website characteristics correlate. stronger with product than with process innovators. Janna Axenbeck is a Researcher in Digital Economy ZEW Leibniz Centre for European Economic. Research since October 2017 She completed her bachelor s degree in socioeconomics at Hamburg. University and her master s degree in public economics at the Free University of Berlin including. exchange semesters at Sciences Po Lille and National Taiwan University During her master s. programme she focused on innovation economics and policy Her major research interest is in the impact. of digitisation on sustainability,NEW APPROACHES TO ECONOMIC CHALLENGES NAEC. Michael Obersteiner Programme Director Ecosystems Services and. Management International Institute for Applied Systems Analysis IIASA. Algorithmic policy making through Hypernudging, Michael Obersteiner is Programme Director of the Ecosystems Services and Management Programme. ESM at the International Institute for Applied Systems Analysis IIASA in Laxenburg Austria His. current research team counts 100 staff members constituting the largest global land use and rural. development model cluster in the world They generate high quality data products on the biophysical and. socio economic dimensions of land resources management using novel methods of citizen science. approaches The ESM modelling teams use massive databases for impact assessments of integrated. policies in various geographies For example the ESM model cluster is used for background calculations. by Parties to the UNFCCC such as EU USA Brazil Indonesia and Congo basin countries feeding into. the INtended National Contributions INDC for COP21 in Paris Other ongoing research integrates high. frequency household data logs from specialized mobile apps from remote farm households subject to. climate risks to design innovative solutions for international donor and national planning agencies. Michael has been the principle investigator and manager of more than 30 international projects covering. diverse policy and science fields mostly focus on developing sustainable development pathways subject. to climate risks These projects have built research communities to produce key data sets derived from. Earth observation assets mainly focusing on land use attributes These project have also contributed to. numerous model intercomparision exercises such as AGMIP Agriculture model intercomparision. project ISIMIP The Inter Sectoral Impact Model Intercomparison Project and EMF Energy Modelling. Forum Dr Obersteiner also served as a seconded Staff Expert for the Group on Earth Observations. GEO at the World Meteorological Organization WMO in Geneva leading a cross cut task on socio. economic benefit assessments of Earth observing systems In addition he has been a consultant to a. number of national and international organizations including inter alia the European Commission WWF. OECD Worldbank and other national and international institutions He is author of over 250 scientific. papers covering many disciplinary science fields, lvaro G mez Losada Scientific officer Directorate General Joint Research.
Centre European Commission Seville Spain,From Big Data to Smart Data. Machine Learning applications require in principle massive amounts of data in order to enhance the. knowledge discovery process In addition to availability and volume real world datasets suffer from. several imperfections As a consequence noise in data could lead to poor performance in statistical. models However useful and valuable knowledge extracted from data is directly related to the quality of. the data used and not necessarily to its quantity Datasets from companies and governments can be. considered as smart data focusing on veracity and value but not on volume in the sense that they can. be effectively used for planning monitoring and intelligent decision policy making. In this study three use cases using data science methodologies and medium sized smart datasets are. described The first is an e commerce application consisting of a price recommender system using an. item item collaborative filtering approach Using as the only input time series TS price data the user. item rating triple was emulated using a bivariate frequency distribution of values in the TS The second. is an environment application where the background concentration of several air pollutants from 2001 to. 2017 in Madrid Spain is studied using Hidden Markov models Finally a demographic application. consists of different machine learning algorithms to forecast Spanish population one year in advance. using Eurostat data The experimental evaluation of these data science techniques and datasets shows. that the framework proposed can successfully deal with medium sized smart datasets in terms of. performance resulting in low computing time and good accuracy From these results some economic. policymaking issues are discussed, lvaro G mez Losada is currently providing direct support to the big data lead scientist of the Joint. Research Centre of the European Commission He is specialised in the design implementation and. evaluation of machine learning algorithms and recommendation systems He obtained its PhD and BSc. in Statistics at University of Seville Spain in 2016 and 2012 respectively He also holds a MSc in Applied. Statistics at University of Granada Spain Previous experience as Data scientist in the Innovation. Department of Banco Santander Madrid Since 2017 he teaches Artificial intelligence Statistics and. Mathematics at Spanish National University of Distance Education UNED He also holds a BSc in. Biology from the University of C rdoba Spain,NEW APPROACHES TO ECONOMIC CHALLENGES NAEC. 11 15 12 45 Session 6 Complexity and Social Science. Moderator Angus Armstrong Director of Rebuilding Macroeconomics Network. Chris Kempes Professor Santa Fe Institute, Scaling theory and the structure of diverse systems. Scaling theory which describes the changes of a system s features according to its size has recently. had many successes in characterising the structure of diverse systems ranging from single organisms to. entire cities In the biological sciences where the theory is better developed simple mechanisms and. physical constraints have been connected with a wide variety of physiological and ecological features. This approach provides a quantitative theory for predicting key ecological tradeoffs with implications for. environmental policy Recently scaling relationships have been observed for a variety of features in. human systems such as the patent production rate and violent crime rate as a function of city size While. these relationships are promising for developing simple explanations of human organisations there are. still many questions about the fundamental mechanisms underlying these systems and what they imply. for policy and planning decisions In this talk I will review the biological scaling literature as an example of. the type of theory that may be applicable to human systems I will then discuss the current theory of urban. and institutional scaling along with implications for understanding inequality within cities and the. effectiveness of the US higher education system, Chris Kempes is a Resident Faculty at the Santa Fe Institute SFI He did a Ph D programme in physical.
biology at MIT and was an Omidyar Fellow at SFI following a postdoctoral fellowship with the NASA Ames. Research Center and Caltech, Using mathematical and computational techniques he studies how simple theoretical principles inform a. variety of phenomena ranging from major evolutionary life history transitions to the biogeography of plant. traits to the organisation of bacterial communities He is particularly interested in biological architecture. as a mediator between physiology and the local environment. Penny Mealy Postdoctoral Research Officer Institute for New Economic. Thinking University of Oxford,Interpreting economic complexity. Economic complexity approaches which apply network analysis to detailed export and employment data. have shed new light on industrial specialisation patterns and economic development Two network. measures in particular the country based Economic Complexity Index ECI and product based Product. Complexity Index PCI have been particularly successful at explaining cross country variation in per. capita GDP and economic growth In this paper we show that the measures are not what they were. thought to be While previous studies had conceptually framed the ECI with reference to notions of the. diversity or number of products a country can export we show that the ECI instead reflects of the type. of products countries are able to export Specifically we demonstrate that the ECI is mathematically. equivalent to a widely used spectral clustering algorithm which optimally separates a similarity graph. into two balanced components We also demonstrate that the measure can be seen as a dimensionality. reduction algorithm which collapses the high dimensional space of country export data onto a single one. dimensional ordering that places countries with similar exports close together in the ordering and those. with dissimilar exports far apart Our results casts a number of existing results in the economic complexity. literature in a new light and also highlight some important ramifications for applications of the economic. complexity measures to policy making, Penny Mealy is a post doctoral researcher at the Institute of New Economic Thinking and the School of. Geography and the Environment and is currently working on the Oxford Martin School Programme on the. Post Carbon Transition She is also leading a project on Practical Wisdom in a Complex World at the. Bennett Institute of Public Policy at the University of Cambridge. Penny s PhD at Oxford was on quantitative approaches for analysing productive capabilities She applied. these methods to provide insights into long run development the division of labour and the transition to. the green economy Her broader research interests include economic complexity technological evolution. transformational change network science and agent based modelling Previously Penny worked as an. economist in Australia where she predominantly focused on issues relating to energy resources and. climate change,NEW APPROACHES TO ECONOMIC CHALLENGES NAEC. Ross A Hammond Director of the Centre on Social Dynamics and Policy. Brookings Institute and Santa Fe Institute, Using ABM and new data streams to study public health.
Overview of multiple published and soon to be published studies. Ross A Hammond is the Betty Bofinger Brown Distinguished Associate Professor of Public Health and. Social Policy at Washington University in St Louis an External Professor at the Santa Fe Institute and. Director of the Center on Social Dynamics Policy and Senior Fellow in Economics at the Brookings. Institution His research applies complex systems science modeling methodologies such as agent based. modeling to problems in social science and health Current research topics include obesity etiology and. prevention food systems tobacco control health disparities and early childhood development. Hammond has published extensively in general science and disciplinary journals across social science. biology medicine and public health including Lancet JAMA Pediatrics PNAS American Journal of. Public Health Evolution and Journal of Conflict Resolution and his work has been featured in The. Atlantic Monthly Scientific American New Scientist Salon and major news media. Professor Hammond currently serves in policy advisory roles on the Food and Nutrition Board of the U S. National Academies of Science as a Special Government Employee at the FDA Center for Tobacco. Products as a federally appointed member of the Advisory Council on Minority Health and Health. Disparities at the National Institutes of Health NIH and as a member of the Lancet Commission on. Obesity He has participated in five large NIH funded modeling networks using complex systems tools. MIDAS focused on communicable disease NICH focused on health disparities ENVISION within the. National Collaborative on Childhood Obesity Research ECHO the NIH early childhood longitudinal. cohort programme and SCTC the State and Community Tobacco Control network Hammond has. taught at Harvard School of Public Health University of Michigan School of Public Health and the NIH. and is on the editorial board of the journals Behavioral Science and Policy and Childhood Obesity He is. currently a visiting fellow at the Center for Research and Interdisciplinarity CRI at Paris Descartes. University through June 2019, Elena Rovenskaya Programme Director Advanced Systems Analysis IIASA. Towards a systems perspective on national well being. Policy planning in modern states increasingly recognises that national economic growth does not fully. represent citizen s well being Macro economic accounting especially GDP as the most commonly used. measure of growth does not cover all the dimensions of a nation s progress towards well being Even. though economic welfare is one of the key prerequisites of citizens well being there is a need for more. comprehensive richer and more direct measures that can directly target well being for efficient. policymaking OECD Better Life Index that combines a wide variety of metrics from economy to housing. and health being a prominent example of going beyond just GDP IIASA in collaboration with Israel s. National Economic Council has launched an exploratory project to aiming at enhancing our systemic. understanding of national well being We attempt to reveal and analyse underlying mechanisms and. causal links as well as external drivers and uncertainties which all together form the national well being. The enhanced systemic understanding is expected inter alia to shed light on what factors are critical in. defining national well being and what critical feedback loops policy makers should pay attention to We. employ systems mapping as the main methodology in this project In the current first stage of the. analysis we identify main components of the national well being system as well as causal relationships. between them We work across four resources for future well being natural capital economic capital. social capital and human capital We rely on an extensive literature review to define the systems. boundaries relevant elements and connections and complement it by participatory modelling exercises. with experts and stakeholders in Israel As a by product of this activity systems mapping exercise also. facilitates achieving a mutual understanding between stakeholders with different views on the matter. Elena Rovenskaya is the Programme Director of the Advanced Systems Analysis ASA Programme at. the International Institute for Applied Systems Analysis IIASA Her scientific interests lie in the fields of. optimization decision sciences and mathematical modeling of complex socio environmental systems. Dr Rovenskaya graduated in 2003 from the Faculty of Physics Lomonosov Moscow State University. Russia She received her PhD in 2006 from the Faculty of Computational Mathematics and Cybernetics. of the same university The title of her PhD thesis was On solving the problem of finding the optimal. compatibility parameter value for a class of equations in a normalized space Since 2006 she has been. working at the Faculty of Computational Mathematics and Cybernetics as a researcher since 2013 on. leave Also since 2006 she has been collaborating with IIASA In 2013 she was appointed the ASA. Program Director In this function Dr Rovenskaya is leading a team of 35 scientists who focus the latest. NEW APPROACHES TO ECONOMIC CHALLENGES NAEC, developments in applied mathematics and modeling to develop test and make available new. quantitative and qualitative methods to address problems that arise in the policy analysis of complex. socio environmental systems ASA activities focus on methods used to support decisions in the presence. of uncertain and volatile input data ambiguous stakeholder interests and complex underlying systems. 14 30 16 00 Session 7 Young Researchers, Moderator Alan Kirman Chief Advisor to the NAEC Initiative. Eugenio Caverzasi Post Doctoral Researcher Universit Politecnica delle. Inequality and Finance in a Rent Economy, The present paper aims at offering a contribution to the understanding of the interactions betweenfinance. and inequality We investigate the ways through which income and wealth inequality may haveinfluenced. the development of modern financial systems in advanced economies the US economy firstand foremost. and how modern financial systems have then fed back on income and wealth distribution We focus in. particular on securitisation and on the production of complex structured financial products We analyse. this topic by elaborating a hybrid Agent Based Stock Flow Consistent AB SFC macroeconomic model. encompassing heterogenous i e households and aggregate sectors Theinnovative hybrid approach. allows to exploit the strengths of both modelling methods while maintainingthe economic intuition clear. The SFC approach provides a thorough representation of sectors balancesheets evolution and. interrelations While the AB approach allows to study the evolution of distributionaldynamics and the. endogenous unfolding of the events from the micro to the macro level Our findingssuggest that the. increase in economic growth favoured by the higher levels of credit supply coming with securitisation. may determine a more unequal and financially unstable economic system We also findthat a lower. degree of tax progressiveness and wider wage inequality further polarise income and wage distribution. and reduce economic growth, Eugenio Caverzasi is a Pos Doctoral Researcher at the Department of Economics and Social Sciences.
of the Universit Politecnica delle Marche Universit degli Studi di Ancona His research interests. include Agent based models Financialisation Monetary Keynesianism Post Keynesian Stock Flow. Consistent Modelling and projects on Macro Finance Financial Firms and Shadow Banking He has a. PhD in Economics from the Universit degli Studi di Pavia. Amir Sani Researcher,A Resting Time Policy for the Limit order Book. Regulation must ensure markets are efficient fair orderly and transparent through policies that minimise. market malpractice Gaming the limit order book through flash orders wash trades spoofing and quote. stuffing compromises market integrity Existing policy options attempt to limit these abusive practices by. regulating the entire universe of traders potentially impacting efficient market transactions The policy. challenges in regulating these predatory activities while maintaining efficient markets is exacerbated by. technological advancements in algorithmic and high frequency trading Regulatory policies must adapt to. this technological innovation to create a level playing field One popular policy option is to institute. minimum resting times on all trades which promise to reduce volatility increase liquidity and minimise. these predatory gaming strategies Unfortunately minimum resting times may cause wider spreads and. a reduction in displayed liquidity Here we present a resting time policy based on an Agent Based Limit. Order Book Model that reduces predatory gaming strategies while minimising volatility and maximising. Amir Sani is a Machine learning researcher working on surrogate models adaptive sampling and. forecasting He is a post doctoral researcher at the Centre d Economie de la Sorbonne Paris and. Imperial College London He has over 15 years of business and investment experience and has advised. several startups that have reached sustained profitability His current consulting work focuses on machine. learning solutions to tough business and IT problems that lack direct solutions and require careful thought. Guido de Blasio Economist Directorate General for Economics Statistics and. Research Bank of Italy, Machine learning in the service of policy targeting The case of public credit guarantees. We use Machine Learning ML predictive tools to propose a policy assignment rule designed to increase. the effectiveness of a public guarantee programme We apply ML on credit register data to predict not. NEW APPROACHES TO ECONOMIC CHALLENGES NAEC, only the firms probability of default but also their chance to be financially constrained The study. elaborates on the case of Italy s Guarantee Fund and demonstrates by means of ex post evaluation. methods that the programme effectiveness can be increased by targeting firms predicted to be both. creditworthy and credit constrained We discuss the problems in using ML for the implementation of public. policies such as transparency and manipulation, Guido de Blasio is Deputy Division Chief in the Regional Analysis Division of the Directorate General for. Economics Statistics and Research at the Bank of Italy He has held visiting fellow and researcher. positions at the London School of Economics the International Monetary Fund Georgetown University. and the World Bank He has a PhD in Economics from the University of Siena Florence and Pisa and a. Masters in Banking and Finance from the Uiversity of Siena Post graduate School of Banking His areas. of expertise include Regional science and urban economics Place based policies Applied econometrics. and machine learning South of Italy, Ermanno Catullo Researcher Universit Politecnica delle Marche.
Forecasting in a complex environment maching learning sales expectations in a SFC. Agent based simulation model, Adopting machine learning techniques even in a complex economic system it is possible to model agents. with expectations that are not bi ased and show a certain degree of accuracy We analyse the micro and. macro e ects of introducing into an agent based simulation model rms that are able to formulate e ective. sales forecasts The model reproducesa simulated economy where macro dynamics are the results of. the inter action of agents households rms and banks following adaptive rules in a stock ow consistent. setting Caiani et al 2018 Firms make expectations on the variations of their sales in order to ori entate. production and price decisions We tested di erent computational methods to make sales forecasts a. genetic algorithm GA an autoregressive model AR and a naive approach where predictions are equal. to the previous realisation The GA and the AR methods are able to provide expectations with average. errors that converge to zero and with a good level of accuracy Firms that adopt these last two predictive. methods GA and AR are able to increase their pro ts without augmenting their riskiness failure rate. However on the aggregate level higher rm pro tability is associated with higher mark ups that results. in a weaker dynamic of real wages in both the GA and AR implementations Thus the wage shareshrinks. a ecting negatively the aggregate demand In the long run higher pro ts come at the price of higher. unemployment levels and lower output growth The model could be feasible to test the relations between. policy interventions and agents expectations, Ermanno Catullo is an Associate Researcher in Economics and Statistics in the Department of. Economics and Social Sciences of the Universit Politecnica delle Marche Italy He specialises in Applied. Economics Agent Based Modelling and Agent Based Computational Economics He graduated in. development economics from the University of Florence he has a Master Coripe in Polictical Economy. and and received his doctorate in Economics from the University of Torino He has been a Researcher at. the Institute for Foreign Trade ICE and worked at the IRER Institute of Research of the Lombardy. Jannes Klaas Quantative Researcher University of Oxford Said Business. If stress tests are predictable are they still useful. Stress tests have become an important part of banking regulation Given that enough stress tests have. been run since their introduction it has become feasible to train advanced machine learning algorithms. to predict their outcomes A key finding has been that the pass fail outcome of a stress test is predictable. from a small number of macroeconomic variables as well as variables concerning an institutions financial. health Our work examines how well previously proposed machine learning systems generalise by testing. models trained on 2011 and 2014 data on the 2016 and 2018 stress test We examine how models for. predicting stress tests compare to models predicting bank failure andother financial crisis forecasting. models We then conduct an error analysis to find quantitative andqualitative similarities in institutions. whose stress tests outcomes were not correctly predicted from the proposed variables Using this data. we examine which new information a stress test delivers that was not predictable from existing knowledge. We discuss if stress tests reveal enough new information to be useful or if they could be replaced with. much cheaper and much more frequent machine learning driven assessment of a banks performance in. an adverse scenario Our research addresses the question of how machine learning can be used to. improve regulatory instruments policy and macroeconomic forecasting to prevent financial crises.


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