Robustness in Econometrics

This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses ap...

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Bibliographic Details
Other Authors: Kreinovich, Vladik (Editor), Sriboonchitta, Songsak (Editor), Huynh, Van-Nam (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2017, 2017
Edition:1st ed. 2017
Series:Studies in Computational Intelligence
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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100 1 |a Kreinovich, Vladik  |e [editor] 
245 0 0 |a Robustness in Econometrics  |h Elektronische Ressource  |c edited by Vladik Kreinovich, Songsak Sriboonchitta, Van-Nam Huynh 
250 |a 1st ed. 2017 
260 |a Cham  |b Springer International Publishing  |c 2017, 2017 
300 |a X, 705 p. 129 illus., 120 illus. in color  |b online resource 
505 0 |a Can bagging improve the forecasting performance of tourism demand models? -- The Role of Asian Credit Default Swap Index in Portfolio Risk Management -- Chinese outbound tourism demand to Singapore, Malaysia and Thailand destinations: A study of political events and holiday impacts -- Forecasting Asian Credit Default Swap spreads: A comparison of multi-regime models -- Forecasting Asian Credit Default Swap spreads: A comparison of multi-regime models -- Effect of Helmet Use on Severity of Head Injuries Using Doubly Robust Estimators -- Forecasting cash holding with cash deposit using time series approaches -- Forecasting GDP Growth in Thailand with Different Leading Indicators using MIDAS regression models -- Testing the Validity of Economic Growth Theories Using Copula-based Seemingly Unrelated Quantile Kink Regression -- Analysis of Global Competitiveness Using Copula-based Stochastic Frontier Kink Model -- Gravity model of trade with Linear Quantile Mixed Models approach --  
505 0 |a Part I Keynote Addresses: Robust Estimation of Heckman Model -- Part II Fundamental Theory: Sequential Monte Carlo Sampling for State Space Models -- Robustness as a Criterion for Selecting a Probability Distribution Under Uncertainty -- Why Cannot We Have a Strongly Consistent Family of Skew Normal (and Higher Order) Distributions -- Econometric Models of Probabilistic Choice: Beyond McFadden’s Formulas -- How to Explain Ubiquity of Constant Elasticity of Substitution (CES) Production and Utility Functions Without Explicitly Postulating CES -- How to Make Plausibility-Based Forecasting More Accurate -- Structural Breaks of CAPM-type Market Model with Heteroskedasticity and Quantile Regression -- Weighted Least Squares and Adaptive Least Squares: Further Empirical Evidence -- Prior-free probabilistic inference for econometricians -- Robustness in Forecasting Future Liabilities in Insurance -- On Conditioning in Multidimensional Probabilistic Models --  
505 0 |a New EstimationMethod for Mixture of Normal Distributions -- EM Estimation for Multivariate Skew Slash Distribution -- Constructions of multivariate copulas -- Plausibility regions on the skewness parameter of skew normal distributions based on inferential models -- International Yield Curve Prediction with Common Functional Principal Component Analysis -- An alternative to p-values in hypothesis testing with applications in model selection of stock price data -- Confidence Intervals for the Common Mean of Several Normal Populations -- A generalized information theoretical approach to Non-linear time series model -- Predictive recursion maximum likelihood of Threshold Autoregressive model -- A multivariate generalized FGM copulas and its application to multiple regression -- Part III Applications: Key Economic Sectors and Their Transitions: Analysis of World Input-Output Network -- Natural Resources, Financial Development and Sectoral Value Added in a Resource Based Economy --  
505 0 |a Stochastic Frontier Model in Financial Econometrics:A Copula-based Approach -- Quantile Forecasting of PM10 Data in Korea based on Time Series Models -- Do We Have Robust GARCH Models under Different Mean Equations: Evidence from Exchange Rates of Thailand? -- Joint Determinants of Foreign Direct Investment (FDI) Inflow in Cambodia: A Panel Co-integration Approach -- The Visitors’ Attitudes and Perceived Value toward Rural Regeneration Community Development of Taiwan -- Analyzing the contribution of ASEAN stock markets to systemic risk -- Estimating Efficiency of Stock Return with Interval Data -- The impact of extreme events on portfolio in financial risk management -- Foreign Direct Investment, Exports and Economic Growth in ASEAN Region: Empirical Analysis from Panel Data -- Author Index 
653 |a Computational intelligence 
653 |a Artificial Intelligence 
653 |a Computational Intelligence 
653 |a Artificial intelligence 
653 |a Econometrics 
700 1 |a Sriboonchitta, Songsak  |e [editor] 
700 1 |a Huynh, Van-Nam  |e [editor] 
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989 |b Springer  |a Springer eBooks 2005- 
490 0 |a Studies in Computational Intelligence 
028 5 0 |a 10.1007/978-3-319-50742-2 
856 4 0 |u https://doi.org/10.1007/978-3-319-50742-2?nosfx=y  |x Verlag  |3 Volltext 
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520 |a This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations