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[null,"1.","Record Nr.",null,"UNINA9910786998703321"]
[null,null,"Autore",null,"Harvey A. C (Andrew C.)"]
[null,null,"Titolo",null,"Dynamic models for volatility and heavy tails : with applications to financial and economic time series / / Andrew C. Harvey [[electronic resource]]"]
[null,null,"Pubbl/distr/stampa",null,"Cambridge : , : Cambridge University Press, , 2013"]
[null,null,"ISBN",null,"1-107-32712-1"]
[{"$nulls":4},"1-107-33688-0"]
[{"$nulls":4},"1-107-33356-3"]
[{"$nulls":4},"1-107-33522-1"]
[{"$nulls":4},"1-139-54093-9"]
[null,null,"Descrizione fisica",null,"1 online resource (xviii, 261 pages) : digital, PDF file(s)"]
[null,null,"Collana",null,"Econometric Society monographs ; ; 52"]
[null,null,"Disciplina",null,"330.01/5195"]
[null,null,"Soggetti",null,"Econometrics"]
[{"$nulls":4},"Finance - Mathematical models"]
[{"$nulls":4},"Time-series analysis"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Title from publisher's bibliographic system (viewed on 05 Oct 2015)."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. 247-254) and indexes."]
[null,null,"Nota di contenuto",null,"Contents; Preface; Acronyms and Abbreviations; 1 Introduction; 1.1 Unobserved Components and Filters; 1.2 Independence, White Noise and Martingale Differences; 1.2.1 The Law of Iterated Expectations and Optimal Predictions; 1.2.2 Definitions and Properties; 1.3 Volatility; 1.3.1 Stochastic Volatility; 1.3.2 Generalized Autoregressive Conditional Heteroscedasticity; 1.3.3 Exponential GARCH; 1.3.4 Variance, Scale and Outliers; 1.3.5 Location/Scale Models; 1.4 Dynamic Conditional Score Models; 1.5 Distributions and Quantiles; 1.6 Plan of Book; 2 Statistical Distributions and Asymptotic Theory"]
[{"$nulls":4},"2.1 Distributions2.1.1 Student's t Distribution; 2.1.2 General Error Distribution; 2.1.3 Beta Distribution; 2.1.4 Gamma Distribution; 2.2 Maximum Likelihood; 2.2.1 Student's t Distribution; 2.2.2 General Error Distribution; 2.2.3 Gamma Distribution; 2.2.4 Consistency and Asymptotic Normality*; 2.3 Maximum Likelihood Estimation; 2.3.1 An Information Matrix Lemma; 2.3.2 Information Matrix for the First-Order Model; 2.3.3 Information Matrix with the 0=x\"\"010E Parameterization*; 2.3.4 Asymptotic Distribution; 2.3.5 Consistency and Asymptotic Normality*; 2.3.6 Nonstationarity"]
[{"$nulls":4},"2.3.7 Several Parameters2.4 Higher Order Models; 2.5 Tests; 2.5.1 "]
[{"$nulls":4},"Serial Correlation; 2.5.2 Goodness of Fit of Distributions; 2.5.3 Residuals; 2.5.4 Model Fit; 2.6 Explanatory Variables; 3 Location; 3.1 Dynamic Student's t Location Model; 3.2 Basic Properties; 3.2.1 Generalization and Reduced Form; 3.2.2 Moments of the Observations; 3.2.3 Autocorrelation Function; 3.3 Maximum Likelihood Estimation; 3.3.1 Asymptotic Distribution of the Maximum Likelihood Estimator; 3.3.2 Monte Carlo Experiments; 3.3.3 Application to U.S. GDP; 3.4 Parameter Restrictions*"]
[{"$nulls":4},"3.5 Higher Order Models and the State Space Form*3.5.1 Linear Gaussian Models and the Kalman Filter; 3.5.2 The DCS Model; 3.5.3 QARMA Models; 3.6 Trend and Seasonality; 3.6.1 Local Level Model; 3.6.2 Application to Weekly Hours of Employees in U.S. Manufacturing; 3.6.3 Local Linear Trend; 3.6.4 Stochastic Seasonal; 3.6.5 Application to Rail Travel; 3.6.6 QARIMA and Seasonal QARIMA Models*; 3.7 Smoothing; 3.7.1 Weights; 3.7.2 Smoothing Recursions for Linear State Space Models; 3.7.3 Smoothing Recursions for DCS Models; 3.7.4 Conditional Mode Estimation and the Score; 3.8 Forecasting"]
[{"$nulls":4},"3.8.1 QARMA Models3.8.2 State Space Form*; 3.9 Components and Long Memory; 3.10 General Error Distribution; 3.11 Skew Distributions; 3.11.1 How to Skew a Distribution; 3.11.2 Dynamic Skew-t Location Model; 4 Scale; 4.1 Beta-tttt-EGARCH; 4.2 Properties of Stationary Beta-tttt-EGARCH Models; 4.2.1 Exponential GARCH; 4.2.2 Moments; 4.2.3 Autocorrelation Functions of Squares and Powersof Absolute Values; 4.2.4 Autocorrelations and Kurtosis; 4.3 Leverage Effects; 4.4 Gamma-GED-EGARCH; 4.5 Forecasting; 4.5.1 Beta-t-EGARCH; 4.5.2 Gamma-GED-EGARCH; 4.5.3 Integrated Exponential Models"]
[{"$nulls":4},"4.5.4 Predictive Distribution"]
[null,null,null,"Sommario/riassunto","The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling."]
[null,"2.","Record Nr.",null,"UNINA9910791321903321"]
[null,null,"Autore",null,"Goldberg Mike <1956->"]
[null,null,"Titolo",null,"How firms cope with crime and violence : experiences from around the world / / Michael Goldberg, Kwang W. Kim, and Maria Ariani"]
[null,null,"Pubbl/distr/stampa",null,"Washington, DC : , : The World Bank, , [2014]"]
[null,null,"ISBN",null,"1-4648-0102-9"]
[null,null,"Descrizione fisica",null,"1 online resource (xvi, 88 pages) ; ; 26 cm"]
[null,null,"Collana",null,"Directions in development"]
[null,null,"Disciplina",null,"364.2/5"]
[null,null,"Soggetti",null,"Industries"]
[{"$nulls":4},"Industrial location - Social aspects"]
[{"$nulls":4},"Industries - Security measures"]
[{"$nulls":4},"Crime - Economic aspects"]
[{"$nulls":4},"Violence - Economic aspects"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Front Cover; Contents; Foreword; Acknowledgments; About the Authors; Abbreviations; Chapter 1 The Nature, Scale, and Scope of Private Sector Responses to Crime and Violence; Introduction; Study Scope and Methods; Box 1.1 Literature Review on the Role of Firms in Environments Affected by Violence and Conflict; Boxes; Note; References; Chapter 2 Crime, Violence, and the Economy; Factors Contributing to Crime and Violence; Figure 2.1 Countries with the Highest Homicide Rates; Figure 2.2 Homicide Rates in Case Study Countries; Indicators of Crime and Violence; Figures"]
[{"$nulls":4},"Figure 2.3 Homicide Rate Trends in Central America, Selected Countries, 1999-2009 Comparing the Impacts of Crime and Violence; Figure 2.4 Security Constraints and Costs of Doing Business in Case Study Countries Relative to Global Averages; References; Chapter 3 Coping Mechanisms of Private Firms: Analysis of Global Cases; Overview of Case Studies; How Crime and Violence Affect Firms; Table 3.1 Overview of Case Studies for How Firms Cope with Crime and Violence; Tables; Coping Mechanisms; Table 3.2 Matrix of Firm Strategies to Cope with Crime and Violence; Analysis and Lessons Learned"]
[{"$nulls":4},"Policy Implications Notes; References; Chapter 4 World Bank Group Work: From Policies and Research to Operational Initiatives; Growing Focus on Crime and Violence; Private Sector Development (PSD) "]
[{"$nulls":4},"Initiatives; Table 4.1 Recent World Bank CASs and CPSs Addressing Crime and Violence in Latin America and the Caribbean; Non-PSD Initiatives; Box 4.1 Public-Private Dialogue in Investment Climate Interventions; Notes; References; Chapter 5 World Bank Group Support for Private Sector Development in Environments of Crime and Violence; Opportunities for Support; Moving the Agenda Forward"]
[{"$nulls":4},"Operational and Research Issues Notes; References; Case 1: Medellín, Colombia-How the Public and Private Sectors Have Coped with Violence; Chapter 6 Case Studies; Table 6.1 Medellín, Colombia: Summary of Key Crime and Violence Indicators and Coping Mechanisms; Figure 6.1 Homicide Rates in Medellín, Colombia, 1965-2008; Case 2: Rio de Janeiro, Brazil-The Favelas and the Private Sector: An Increasingly Safe Bet?; Table 6.2 Rio de Janeiro: Summary of Key Crime and Violence Indicators and Coping Mechanisms; Case 3: Jamaica-Coping with Violence in Paradise"]
[{"$nulls":4},"Table 6.3 Jamaica: Summary of Key Crime and Violence Indicators and Coping Mechanisms Figure 6.2 Impact of Crime on Selected Business Practices in Jamaica, 2001; Table 6.4 Victimization of Firms, by Sector and Type of Crime in Jamaica, 2001; Figure 6.3 Private Security Costs as Percentage of Firm Revenue, by Firm Size, in Jamaica, 2001; Figure 6.4 Crime Protection Actions by Firms in Jamaica; Figure 6.5 Flankers Peace & Justice Center, Jamaica, Built with Support from Sandals Foundation; Case 4: Mexico-Public-Private Responses to Violence"]
[{"$nulls":4},"Table 6.5 Mexico: Summary of Key Crime and Violence Indicators and Coping Mechanisms"]
[null,null,null,"Sommario/riassunto","Crime and violence inflict high costs on the private sector-costs that are rising globally, according to the World Bank's Enterprise Surveys, discussions with chambers and associations, and the Bank's Country Partnership Strategies, which reference the losses in terms of gross domestic product (GDP). In Latin America and the Caribbean, for example, losses due to crime and violence have been estimated at 9 percent of GDP in Honduras, 7.7 percent in El Salvador, and 3.6 percent in Costa Rica. In sectors such as clothing assembly, international purchasers can shift know-how and capital quickly to"]