Nonlinear Financial Econometrics: Forecasting Models, Computational and Bayesian Models

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So Hong Kong University of Science and Technology, China Bayesian analysis, financial time series modeling, market volatility study, risk management. Mark Steel University of Warwick, UK Bayesian inference, models with unobserved heterogeneity, MCMC methods, inference robustness, model choice and Bayesian model averaging, improper and reference priors, mixture modelling, skewness, inference in stochastic processes, spatial statistics, semi- and nonparametric Bayesian, growth theory, stochastic frontier models, contingent valuation, stochastic volatility models. Robert Taylor University of Essex, UK Bootstrap methods for non-stationary time series, co-integration methods, seasonal unit root tests, stationarity tests, stochastic volatility, persistence change testing and structural breaks, financial econometrics.

Peter Winker University of Giessen, Germany Time series modeling, forecasting, model selection, optimization heuristics in statistics and econometrics. Bertrand Clarke University of Nebraska-Lincoln, USA Data mining and machine learning, prediction,statistical techniques for complex or high-dimensional data, model bias and uncertainty. Aurore Delaigle University of Melbourne, Australia Nonparametric estimation, measurement errors, deconvolution problems, functional data analysis.

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John Einmahl Tilburg University, Netherlands Statistics of extremes, empirical processes, multivariate quantiles, empirical likelihood. Marc Hallin Universite Libre de Bruxelles, Belgium Time series, factor models, asymptotic theory of statistical experiments.

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Ivan Kojadinovic University of Pau, France Multivariate analysis, nonparametric statistics, copulas, change-point detection, empirical processes, environmental and financial applications. Christophe Ley Universite Libre de Bruxelles, Belgium Optimal inferential procedures, rank-based procedures, non-Gaussian distributions, directional data, Maximum Likelihood Estimation, Non- and semi-parametric statistics, High-dimensional inferential procedures. Lola Martinez-Miranda University of Granada, Spain Nonparametric estimation, kernel smoothing,non-life insurance,bootstrap, bandwidth.

Domingo Morales University Miguel Hernandez of Elche, Spain Small area estimation, statistical information theory, simulation and resampling methods, survey sampling, asymptotic statistics, statistical models. Davy Paindaveine Universite libre de Bruxelles, Belgium Nonparametric statistics, Statistical depth, Multivariate quantiles, Robust statistics, Rank-based inference, high-dimensional statistics. Igor Pruenster University of Torino, Italy Bayesian asymptotics, Bayesian inference, Bayesian survival analysis, distribution theory, mixture models, predictive inference, random measures, species sampling.

Mattias Villani Linkoping University, Sweden Bayesian inference, machine learning, computational statistics, predictive inference. Lan Wang University of Minnesota, USA High-dimensional data analysis, quantile regression, longitudinal data analysis, survival analysis, hypothesis testing. Alastair Young Imperial College London, United Kingdom Statistical theory, computational statistics, statistical asymptotics and approximation methods, bootstrap, likelihood-based inference.

Helen Zhang University of Arizona, USA Nonparametrics, data smoothing, function estimation, statistical machine learning, high dimensional analysis, biomedical and biological research. For further information please send an email to editorial. The Vuong test for comparing other non-nested models is provided by nonnest2 and specifically for count data regression in pscl. Diagnostic checking : The packages car and lmtest provide a large collection of regression diagnostics and diagnostic tests.

Microeconometrics Generalized linear models GLMs : Many standard microeconometric models belong to the family of generalized linear models and can be fitted by glm from package stats. This includes in particular logit and probit models for modeling choice data and Poisson models for count data. Effects for typical values of regressors in these models can be obtained and visualized using effects.

Marginal effects tables for certain GLMs can be obtained using the margins package. Interactive visualizations of both effects and marginal effects are possible in LinRegInteractive. Bias-reduced GLMs that are robust to complete and quasi-complete separation are provided by brglm. Discrete choice models estimated by simulated maximum likelihood are implemented in Rchoice. Heteroscedastic probit models and other heteroscedastic GLMs are implemented in glmx along with parametric link functions and goodness-of-link tests for GLMs.

Negative binomial GLMs are available via glm. Another implementation of negative binomial models is provided by aod , which also contains other models for overdispersed data. Zero-inflated and hurdle count models are provided in package pscl.

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A reimplementation by the same authors is currently under development in countreg on R-Forge which also encompasses separate functions for zero-truncated regression, finite mixture models etc. Multinomial responses : Multinomial models with individual-specific covariates only are available in multinom from package nnet. An implementation with both individual- and choice-specific variables is mlogit. Generalized multinomial logit models e. A flexible framework of various customizable choice models including multinomial logit and nested logit among many others is implemented in the apollo package.

A Bayesian approach to multinomial probit models is provided by MNP. Various Bayesian multinomial models including logit and probit are available in bayesm. Furthermore, the package RSGHB fits various hierarchical Bayesian specifications based on direct specification of the likelihood function. Ordered responses : Proportional-odds regression for ordered responses is implemented in polr from package MASS.

The package ordinal provides cumulative link models for ordered data which encompasses proportional odds models but also includes more general specifications. Bayesian ordered probit models are provided by bayesm. Censored responses : Basic censored regression models e. Further censored regression models, including models for panel data, are provided in censReg. Censored regression models with conditional heteroscedasticity are in crch.

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Models for sample selection are available in sampleSelection and semiparametric extensions of these are provided by SemiParSampleSel. Package matchingMarkets corrects for selection bias when the sample is the result of a stable matching process e. Truncated responses : crch for truncated and potentially heteroscedastic Gaussian, logistic, and t responses.

Homoscedastic Gaussian responses are also available in truncreg. Fraction and proportion responses : Fractional response models are in frm. Beta regression for responses in 0, 1 is in betareg and gamlss.

High-dimensional fixed effects : Linear models with potentially high-dimensional fixed effects, also for multiple groups, can be fitted by lfe. The corresponding GLMs are covered in alpaca. Stochastic frontier analysis SFA is in frontier and certain special cases also in sfa. The package bayesm implements a Bayesian approach to microeconometrics and marketing.

Estimation and marginal effect computations for multivariate probit models can be carried out with mvProbit. Inference for relative distributions is contained in package reldist. Other implementations are in tsls in package sem , in ivpack , and lfe with particular focus on multiple group fixed effects. The LARF package estimates local average response functions for binary treatments and binary instruments. Dedicated IV panel data models are provided by ivfixed fixed effects and ivpanel between and random effects.

Miscellaneous : REndo fits linear models with endogenous regressor using various latent instrumental variable approaches. Panel data models Panel standard errors : A simple approach for panel data is to fit the pooling or independence model e. Nonlinear Financial Econometrics Forecasting Models Computational and Bayesian M

Different types of clustered, panel, and panel-corrected standard errors are available in sandwich incorporating prior work from multiwayvcov , clusterSEs , pcse , clubSandwich , plm , and geepack , respectively. Linear panel models : plm , providing a wide range of within, between, and random-effect methods among others along with corrected standard errors, tests, etc. Another implementation of several of these models is in Paneldata.

Various dynamic panel models are available in plm and dynamic panel models with fixed effects in OrthoPanels. Within-between or "hybrid" panel models are available in panelr , including multilevel, GEE, and Bayesian estimation of these models. Panel vector autoregressions are implemented in panelvar.

Generalized estimation equations and GLMs : GEE models for panel data or longitudinal data in statistical jargon are in geepack.

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The pglm package provides estimation of GLM-like models for panel data. Mixed effects models : Linear and nonlinear models for panel data and more general multi-level data are available in lme4 and nlme. Instrumental variables : ivfixed and ivpanel , see also above. Heterogeneous time trends : phtt offers the possibility of analyzing panel data with large dimensions n and T and can be considered when the unobserved heterogeneity effects are time-varying.

Miscellaneous : Autocorrelation and heteroscedasticity correction in are available in wahc and panelAR. Threshold regression and unit root tests are in pdR.

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The panel data approach method for program evaluation is available in pampe. Further regression models Nonlinear least squares modeling : nls in package stats. Quantile regression : quantreg including linear, nonlinear, censored, locally polynomial and additive quantile regressions.

Spatial econometric models : The Spatial view gives details about handling spatial data, along with information about regression modeling. In particular, spatial regression models can be fitted using spatialreg and sphet the latter using a GMM approach.