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• Machine generated contents note: Chapter 1 An Introduction to Econometrics.
• A Probability Primer.
• Chapter 2 The Simple Linear Regression Model.
• Chapter 3 Interval Estimation and Hypothesis Testing.
• Chapter 4 Prediction, Goodness-of-Fit and Modeling Issues.
• Chapter 5 The Multiple Regression Model.
• Chapter 6 Further Inference in the Multiple Regression Model.
• Chapter 7 Using Indicator Variables.
• Chapter 8 Heteroskedasticity.
• Chapter 9 Regression with Time Series Data: Stationary Variables.
• Chapter 10 Random Regressors and Moment Based Estimation.
• Chapter 11 Simultaneous Equations Models.
• Chapter 12 Regression with Time Series Data: Nonstationary Variables.
• Chapter 13 Vector Error Correction and Vector Autoregressive Models.
• Chapter 14 Time-Varying Volatility and ARCH Models.
• Chapter 15 Panel Data Models.
• Chapter 16 Qualitative and Limited Dependent Variable Models.
• Appendix A Mathematical Tools.
• Appendix B Probability Concepts.
• Appendix C Review of Statistical Inference.

Designed to arm finance professionals with an understanding of why econometrics is necessary, this book also provides them with a working knowledge of basic econometric tools. The fourth edition has been thoroughly updated to reflect the current state of economic and financial markets. New discussions are presented on Kennel Density Fitting and the analysis of treatment effects. A new summary of probability and statistics has been added. In addition, numerous new end-of-chapter questions and problems have been integrated throughout the chapters. This will help finance professionals apply basic econometric tools to modeling, estimation, inference, and forecasting through real world problems.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Univariate density estimation
• 3. Multivariate density estimation
• 4. Inference about the density
• 5. Regression
• 6. Testing in regression
• 7. Smoothing discrete variables
• 8. Regression with discrete covariates
• 9. Semiparametric methods
• 10. Instrumental variables
• 11. Panel data
• 12. Constrained estimation and inference
• Bibliography
• Index.
• (source: Nielsen Book Data)

The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls.
(source: Nielsen Book Data)

• About the Editors
• List of Contributors
• Introduction
• Part 1: Commodities Finance
• 1. Long Memory and Asymmetry in Commodity Returns and Risk: The Role of Term Spread, Steven J. Cochran, Iqbal Mansur and Babatunde Odusami
• 2. The Quantile-Heterogeneous Autoregressive Model of Realized Volatility: New Evidence from Commodity Markets, Konstantin Kick and Robert Maderitsch
• 3. The Importance of Rollover in Commodity Returns using PARCH models, M.G. Karanasos, P. D. Koutroumpis, Z. N. P. Margaronis and R. B. Nath
• Part 2: Mathematical Stochastical Finance
• 4. Variance and Volatility Swaps and Futures Pricing for Stochastic Volatility Models, Anatoliy Swishchuk, Zijia Wang
• 5. A nonparametric ACD model, Antonio Cosma, Fausto Galli
• 6. Sovereign debt crisis and economic growth: new evidence for the euro area, Iuliana Matei
• 7. On the spot-futures no-arbitrage relations in commodity markets, Rene Aid, Luciano Campi, Delphine Lautier
• 8. Compound Hawkes Processes in Limit Order Books, Anatoliy Swishchuk, Bruno Remillard, Robert Elliott, Jonathan Chavez-Casillas
• Part 3: Financial Volatility and Covariance Modelling
• 9. Models with Multiplicative Decomposition of Conditional Variances and Correlations, Cristina Amado, Annastiina Silvennoinen, Timo Terasvirta
• 10. Do High-frequency-based Measures Improve Conditional Covariance Forecasts?, Denisa Banulescu-Radu, Elena Dumitrescu
• 11. Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of the US Banking Sector, Gianluca Cubadda, Alain Hecq, Antonio Riccardo
• 12. Covariance estimation and quasi-likelihood analysis, Yuta Koike, Nakahiro Yoshida
• 13. The Log-GARCH Model via ARMA Representations, Genaro Sucarrat
• Index.
• (source: Nielsen Book Data)

This book provides an up-to-date series of advanced chapters on applied financial econometric techniques pertaining the various fields of commodities finance, mathematics & stochastics, international macroeconomics and financial econometrics. Financial Mathematics, Volatility and Covariance Modelling: Volume 2 provides a key repository on the current state of knowledge, the latest debates and recent literature on financial mathematics, volatility and covariance modelling. The first section is devoted to mathematical finance, stochastic modelling and control optimization. Chapters explore the recent financial crisis, the increase of uncertainty and volatility, and propose an alternative approach to deal with these issues. The second section covers financial volatility and covariance modelling and explores proposals for dealing with recent developments in financial econometrics This book will be useful to students and researchers in applied econometrics; academics and students seeking convenient access to an unfamiliar area. It will also be of great interest established researchers seeking a single repository on the current state of knowledge, current debates and relevant literature.
(source: Nielsen Book Data)

• Introduction / Raj Chetty, John N. Friedman, Janet C. Gornick, Barry Johnson, and Arthur Kennickell
• I. Income inequality. In search of the roots of American inequality exceptionalism : an analysis based on Luxembourg Income Study (LIS) data / Janet C. Gornick, Branko Milanovic, Nathaniel Johnson
• Rising between-firm inequality and declining labor market fluidity : evidence of a changing job ladder / John Haltiwanger, James R. Spletzer
• United States earnings dynamics : inequality, mobility, and volatility / Kevin L. McKinney, John M. Abowd, John Sabelhaus
• Evidence from unique Swiss tax data on the composition and joint distribution of income and wealth / Isabel Z. Martinez
• II. Wealth inequality. The wealth of generations, with special attention to the Millennials / William G. Gale, Hilary Gelfond, Jason J. Fichtner, Benjamin H. Harris
• Wealth transfers and net wealth at death : evidence from the Italian inheritance tax records, 1995-2016 / Paolo Acciari, Salvatore Morelli
• On the distribution of estates and the distribution fo wealth : evidence from the dead / Yonatan Berman and Salvatore Morelli
• Structuring the analysis of wealth inequality using the functions of wealth : a class-based approach / Pirmin Fessler, Martin Schürz
• Social security wealth, inequality, and life-cycle saving / John Sabelhaus, Alice Henriques Volz
• III. Income and wealth mobility. Parental education and the rising transmission of income between generations / Marie Connolly, Catherine Haeck, Jean-William Laliberté
• Inequality of opportunity for income in Denmark and the United States : a comparison based on administrative data / Pablo A. Mitnik, anne-Line Helsø, Victoria L. Bryant
• Presence and persistence of poverty in US tax data / Jeff Larrimore, Jacob Mortenson, David Splinter
• Intergenerational home ownership in France over the twentieth century / Bertrand Garbinti, Frédérique Savignac
• Inequality and mobility over the past half-century using income, consumption, and wealth / Jonathan D. Fisher, David S. Johnson
• IV. Mitigating inequality. The accuracy of tax imputations : estimating tax liabilities and credits using linked survey and administrative data / Bruce D. Meyer, Derek Wu, Grace Finley, Patrick Langetieg, Carla Medalia, Mark Payne, Alan Plumley
• Geographic inequality in social provision : variation across the US states / Sarah K. Bruch, Janet C. Gornick, Joseph van der Naald
• Inequality and the safety net in American cities through the income distribution, 1929-1940 / James Feigenbaum, Price Fishback, Keoka Grayson
• The EITC and linking data for examining multigenerational effects / Randall Akee, Maggie R. Jones, Emilia Simeonova
• Part V. Distributional national accounts. Distributing personal income : trends over time / Dennis Fixler, Marina Gindelsky, David S. Johnson
• Developing indicators of inequality and poverty consistent with national accounts / Richard Tonkin, Sean White, Sofiya Stoyanova, Aly Youssef, Sunny Valentineo Sidhu, Chris Payne
• Distributional national accounts : a macro-micro approach to inequality in Germany / Stefan Bach, Charlotte Bartels, Theresa Neef
• The distributional financial accounts of the United States / Michael Batty, Jesse Bricker, Joseph Briggs, Sarah Friedman, Danielle Nemschoff, Eric Nielsen, Kamila Sommer, and Alice Henriques Volz
• Using tax data to better capture top incomes in official UK income inequality statistics / Dominic Webber, Richard Tonkin, Martin Shine

A collection of twenty-three studies that explore the latest developments in the analysis of income and wealth distribution and mobility. Economic research is increasingly focused on inequality in the distribution of personal resources and outcomes. One aspect of inequality is mobility: are individuals locked into their respective places in this distribution? To what extent do circumstances change, either over the lifecycle or across generations? Research not only measures inequality and mobility, but also analyzes the historical, economic, and social determinants of these outcomes and the effect of public policies. This volume explores the latest developments in the analysis of income and wealth distribution and mobility. The collection of twenty-three studies is divided into five sections. The first examines observed patterns of income inequality and shifts in the distribution of earnings and in other factors that contribute to it. The next examines wealth inequality, including a substantial discussion of the difficulties of defining and measuring wealth. The third section presents new evidence on the intergenerational transmission of inequality and the mechanisms that underlie it. The next section considers the impact of various policy interventions that are directed at reducing inequality. The final section addresses the challenges of combining household-level data, potentially from multiple sources such as surveys and administrative records, and aggregate data to study inequality, and explores ways to make survey data more comparable with national income accounts data. .
(source: Nielsen Book Data)

"Doing Statistical Analysis looks at three kinds of statistical research questions - descriptive, associational and inferential - and shows students how to conduct statistical analyses and interpret the results. Keeping equations to a minimum, it uses a conversational style and relatable examples such as football, covid-19 and tourism, to aid understanding. Each chapter contains practice exercises, and a section showing students how to reproduce the statistical results in the book using Stata and SPSS. Digital supplements consist of data sets in Stata, SPSS and Excel, practical videos explaining how to do basic analysis, and a test bank for instructors. Its accessible approach means this is the ideal textbook for undergraduate students across the social and behavioural sciences needing to build their confidence with statistical analysis"-- Provided by publisher

• Preface
• 1. Introduction
• 2. Static models with additive effects
• 3. Dynamic models with additive effects
• 4. Static simultaneous models with additive effects
• 5. Dynamic system
• 6. Qualitative choice models
• 7. Limited dependent and sample section models
• 8. Some nonlinear models
• 9. Miscellaneous topics
• 10. Interactive effects models
• 11. Spatial models and cross-sectional dependent data
• 12. Program evaluation
• 13. Varying coefficients models
• 14. Big data analysis.
• (source: Nielsen Book Data)

Now in its fourth edition, this comprehensive introduction of fundamental panel data methodologies provides insights on what is most essential in panel literature. A capstone to the forty-year career of a pioneer of panel data analysis, this new edition's primary contribution will be the coverage of advancements in panel data analysis, a statistical method widely used to analyze two or higher-dimensional panel data. The topics discussed in early editions have been reorganized and streamlined to comprehensively introduce panel econometric methodologies useful for identifying causal relationships among variables, supported by interdisciplinary examples and case studies. This book, to be featured in Cambridge's Econometric Society Monographs series, has been the leader in the field since the first edition. It is essential reading for researchers, practitioners and graduate students interested in the analysis of microeconomic behavior.
(source: Nielsen Book Data)

• List of Figures iii
• 1 Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics 1 Zack W. Almquist and Carter T. Butts
• 1.1 Introduction 2
• 1.2 Statistical Models for Social Network Data 2
• 1.3 Dynamic Network Logistic Regression with Vertex Dynamics 11
• 1.4 Empirical Examples and Simulation Analysis 14
• 1.5 Discussion 29
• 1.6 Conclusion 30
• 2 Ethnic Minority Rule and Civil War: A Bayesian Dynamic Multilevel Analysis 39 Xun Pang
• 2.1 Introduction: Ethnic Minority Rule and Civil War 40
• 2.2 EMR: Grievance and Opportunities of Rebellion 41
• 2.3 Bayesian GLMM-AR(p) Model 42
• 2.4 Variables, Model and Data 47
• 2.5 Empirical Results and Interpretation 49
• 2.6 Civil War: Prediction 54
• 2.7 Robustness Checking: Alternative Measures of EMR 59
• 2.8 Conclusion 60
• References 62
• 3 Bayesian Analysis of Treatment Effect Models 67 Mingliang Li and Justin L. Tobias
• 3.1 Introduction 68
• 3.2 Linear Treatment Response Models Under Normality 69
• 3.3 Nonlinear Treatment Response Models 73
• 3.4 Other Issues and Extensions: Non-Normality, Model Selection and Instrument Imperfection 78
• 3.5 Illustrative Application 84
• 3.6 Conclusion 89
• 4 Bayesian Analysis of Sample Selection Models 95 Martijn van Hasselt
• 4.1 Introduction 95
• 4.2 Univariate Selection Models 97
• 4.3 Multivariate Selection Models 101
• 4.4 Semiparametric Models 111
• 4.5 Conclusion 114
• References 114
• 5 Modern Bayesian Factor Analysis 117 Hedibert Freitas Lopes
• 5.1 Introduction 117
• 5.2 Normal linear factor analysis 119
• 5.3 Factor stochastic volatility 125
• 5.4 Spatial factor analysis 128
• 5.5 Additional developments 133
• 5.6 Modern non-Bayesian factor analysis 136
• 5.7 Final remarks 137
• 6 Estimation of stochastic volatility models with heavy tails and serial dependence 159 Joshua C.C. Chan and Cody Y.L. Hsiao
• 6.1 Introduction 159
• 6.2 Stochastic Volatility Model 160
• 6.3 Moving Average Stochastic Volatility Model 168
• 6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions 173
• References 178
• 7 From the Great Depression to the Great Recession: A Modelbased Ranking of U.S. Recessions 181 Rui Liu and Ivan Jeliazkov
• 7.1 Introduction 181
• 7.2 Methodology 183
• 7.3 Results 188
• 7.4 Conclusions 191
• Appendix: Data 192
• References 192
• 8 What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models 201 Paskalis Glabadanidis
• 8.1 Introduction 202
• 8.2 Methodology 204
• 8.3 Data 205
• 8.4 Empirical Results 206
• 8.5 Concluding Remarks 212
• 9 Stochastic Search For Price Insensitive Consumers 227 Eric Eisenstat
• 9.1 Introduction 228
• 9.2 Random utility models in marketing applications 230
• 9.3 The censored mixing distribution in detail 234
• 9.4 Reference price models with price thresholds 240
• 9.5 Conclusion 244
• References 245
• 10 Hierarchical Modeling of Choice Concentration of US Households 249 Karsten T. Hansen, Romana Khan and Vishal Singh
• 10.1 Introduction 250
• 10.2 Data Description 252
• 10.3 Measures of Choice Concentration 252
• 10.4 Methodology 254
• 10.5 Results 256
• 10.6 Interpreting 260
• 10.7 Decomposing the effects of time, number of decisions and concentration preference 263
• 10.8 Conclusion 265
• References 267
• 11 Approximate Bayesian inference in models defined through estimating equations 269
• 11.1 Introduction 269
• 11.2 Examples 271
• 11.3 Frequentist estimation 273
• 11.4 Bayesian estimation 276
• 11.5 Simulating from the posteriors 281
• 11.6 Asymptotic theory 283
• 11.7 Bayesian validity 285
• 11.8 Application 286
• 11.9 Conclusions 288
• 12 Reacting to Surprising Seemingly Inappropriate Results 295 Dale J. Poirier
• 12.1 Introduction 295
• 12.2 Statistical Framework 296
• 12.3 Empirical Illustration 300
• 12.4 Discussion 301
• References 301
• 13 Identification and MCMC estimation of bivariate probit models with partial observability 303 Ashish Rajbhandari
• 13.1 Introduction 303
• 13.2 Bivariate Probit Model 305
• 13.3 Identification in a partially observable model 307
• 13.4 Monte Carlo Simulations 308
• 13.5 Bayesian Methodology 309
• 13.6 Application 312
• 13.7 Conclusion 315
• Chapter Appendix 316
• References 317
• 14 School Choice Effects in Tokyo Metropolitan Area: A Bayesian Spatial Quantile Regression Approach 321 Kazuhiko Kakamu and Hajime Wago
• 14.1 Introduction 321
• 14.2 The Model 323
• 14.3 Posterior Analysis 325
• 14.4 Empirical Analysis 326
• 14.5 Conclusions 330.
• (source: Nielsen Book Data)

Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
(source: Nielsen Book Data)

"Causality is central to the understanding and use of data; without an understanding of cause and effect relationships, we cannot use data to answer important questions in medicine and many other fields"-- Provided by publisher

• Frontmatter
• Contents
• Series Editors' Introduction
• Preface
• Chapter 1. Overview
• Chapter 2. Methods for Describing Oscillations, Fluctuations, and Cycles in Univariate Series
• Chapter 3. Constructing Reference Cycles with Multivariate Information
• Chapter 4. Model-Based Rules for Describing Recurrent Events
• Chapter 5. Measuring Recurrent Event Features in Univariate Data
• Chapter 6. Measuring Synchronization of Recurrent Events in Multivariate Data
• Chapter 7. Accounting for Observed Cycle Features with a Range of Statistical Models
• Chapter 8. Using the Recurrent Event Binary States to Examine Economic Modeling Issues
• Chapter 9. Predicting Turning Points and Recessions
• References
• Index

The global financial crisis highlighted the impact on macroeconomic outcomes of recurrent events like business and financial cycles, highs and lows in volatility, and crashes and recessions. At the most basic level, such recurrent events can be summarized using binary indicators showing if the event will occur or not. These indicators are constructed either directly from data or indirectly through models. Because they are constructed, they have different properties than those arising in microeconometrics, and how one is to use them depends a lot on the method of construction.This book presents the econometric methods necessary for the successful modeling of recurrent events, providing valuable insights for policymakers, empirical researchers, and theorists. It explains why it is inherently difficult to forecast the onset of a recession in a way that provides useful guidance for active stabilization policy, with the consequence that policymakers should place more emphasis on making the economy robust to recessions. The book offers a range of econometric tools and techniques that researchers can use to measure recurrent events, summarize their properties, and evaluate how effectively economic and statistical models capture them. These methods also offer insights for developing models that are consistent with observed financial and real cycles.This book is an essential resource for students, academics, and researchers at central banks and institutions such as the International Monetary Fund

• Frontmatter
• Contents
• Preface
• Notation
• Part I. Preliminary Material
• Chapter 1. From Diffusions to Semimartingales
• Chapter 2. Data Considerations
• Part II. Asymptotic Concepts
• Introduction
• Chapter 3. Introduction to Asymptotic Theory: Volatility Estimation for a Continuous Process
• Chapter 4. With Jumps: An Introduction to Power Variations
• Chapter 5. High-Frequency Observations: Identifiability and Asymptotic Efficiency
• Part III. Volatility
• Introduction
• Chapter 6. Estimating Integrated Volatility: The Base Case with No Noise and Equidistant Observations
• Chapter 7. Volatility and Microstructure Noise
• Chapter 8. Estimating Spot Volatility
• Chapter 9. Volatility and Irregularly Spaced Observations
• Part IV. Jumps
• Introduction
• Chapter 10. Testing for Jumps
• Chapter 11. Finer Analysis of Jumps: The Degree of Jump Activity
• Chapter 12. Finite or Infinite Activity for Jumps?
• Chapter 13. Is Brownian Motion Really Necessary?
• Chapter 14. Co-jumps
• Appendix A. Asymptotic Results for Power Variations
• Appendix B. Miscellaneous Proofs
• Bibliography
• Index

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis.Yacine Aït-Sahalia and Jean Jacod cover the mathematical foundations of stochastic processes, describe the primary characteristics of high-frequency financial data, and present the asymptotic concepts that their analysis relies on. Aït-Sahalia and Jacod also deal with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As they demonstrate, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes.Aït-Sahalia and Jacod approach high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike

• Frontmatter
• Contents
• Figures
• Tables
• Series Editors' Introduction
• Preface
• Part I. Introduction to DSGE Modeling and Bayesian Inference
• 1. DSGE Modeling
• 2. Turning a DSGE Model into a Bayesian Model
• 3. A Crash Course in Bayesian Inference
• Part II. Estimation of Linearized DSGE Models
• 4. Metropolis-Hastings Algorithms for DSGE Models
• 5. Sequential Monte Carlo Methods
• 6. Three Applications
• Part III. Estimation of Nonlinear DSGE Models
• 7. From Linear to Nonlinear DSGE Models
• 8. Particle Filters
• 9. Combining Particle Filters with MH Samplers
• 10. Combining Particle Filters with SMC Samplers
• Appendix A. Model Descriptions
• Appendix B. Data Sources
• Bibliography
• Index

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations.Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions

• Introduction
• Dek Terrell, Tong Li and M. Hashem Pesaran Chapter 1. Correction for the Asymptotical Bias of the Arellano-Bond type GMM Estimation of Dynamic Panel Models
• Yonghui Zhang and Qiankun Zhou Chapter 2. Testing Convergence using HAR Inference
• Peter Phillips, Jianning Kong and Donggyu Sul Chapter 3. Bayesian Estimation of Linear Sum Assignment Problems
• Yubo Tao and Jun Yu Chapter 4. A VAR Approach to Forecasting Multivariate Long Memory Processes Subject to Structural Breaks
• Cindy S.H. Wang and Shui Ki Wan Chapter 5. Identifying Global and National Output and Fiscal Policy Shocks Using a GVAR
• Alexander Chudik, M. Hashem Pesaran and Kamiar Mohaddes Chapter 6. The Determinents of Health Care Expenditure and Trends: A Semiparametric Panal Data Analysis of OECD Countries
• Ming Kong, Jiti Gao and Xueyan Zhao Chapter 7. Growth empirics: a Bayesian semiparametric model with random coefficients for a panel of OECD Countries
• Badi H. Baltagi, Georges Bresson and Jean-Michel Etienne Chapter 8. Robust Estimation and Inference for Importance Sampling Estimators with Infinite Variance
• Joshua Chan, Chenghan Hou and Thomas Tao Yang Chapter 9. Econometrics of Scoring Auctions
• Jean-Jacques Laffont, Isabelle Perrigne, Michel Simioni, and Quang Vuong Chapter 10. Bayesian Estimation of Linear Sum Assignment Problems
• Yu-Wei Hsieh and Matthew Shum Chapter 11. The Mode is the Message: Using Predata as Exclusion Restrictions to Evaluate Survey Design
• Heng Chen, Geoffrey Dunbar, and Q. Rallye Shen Chapter 12. Estimating Peer Effects on Career Choice: A Spatial Multinomial Logit Approach
• Bolun Li, Robin Sickles and Jenny Williams Chapter 13. Mortgage Portfolio Diversification in the Presence of Cross-Sectional and Spatial Dependence
• Timothy Dombrowski, R. Kelley Pace and Rajesh Narayanan Chapter 14. An Econometrician's Perspective on Big Data
• Cheng Hsiao Hsiao Chapter 15. Comments on 'An Econometrician's Perspective on Big Data'
• Thomas Fomby Chapter 16. Comments on 'An Econometrician's Perspective on Big Data' by Cheng Hsiao
• Georges Bresson.
• (source: Nielsen Book Data)

Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao. In the first few chapters of this book, new theoretical panel and time series results are presented, exploring JIVE estimators, HAC, HAR and various sandwich estimators, as well as asymptotic distributions for using information criteria to distinguish between the unit root model and explosive models. Other chapters address topics such as structural breaks or growth empirics; auction models; and semiparametric methods testing for common vs. individual trends. Three chapters provide novel empirical approaches to applied problems, such as estimating the impact of survey mode on responses, or investigating how cross-sectional and spatial dependence of mortgages varies by default rates and geography. In the final chapters, Cheng Hsiao offers a forward-focused discussion of the role of big data in economics. For any researcher of econometrics, this is an unmissable volume of the most current and engaging research in the field.
(source: Nielsen Book Data)

• Preface xi Acknowledgements xv
• 1 Elementary Financial Calculus 1 1.1 Motivating Examples 1 1.2 Cashflows, interest rates, prices and returns 2 1.3 Elementary statistical analysis of returns 11 1.4 Financial instruments 28 1.5 A Primer on Option Pricing 32 1.6 Notes and further reading 42
• 2 Arbitrage Theory for the One-Period Model 45 2.1 Definitions and preliminaries 45 2.2 Linear pricing measures 47 2.3 More on arbitrage 50 2.4 Separation theorems in Rn 52 2.5 No-arbitrage and martingale measures 55 2.6 Arbitrage-free pricing of contingent claims 63 2.7 Construction of MartingaleMeasures: General Case 68 2.8 Complete financial markets 71 2.9 Notes and further reading 74
• 3 Financial Models in Discrete Time 75 3.1 Adapted stochastic processes in discrete time 77 3.2 Martingales and martingale differences 81 3.3 Stationarity 97 3.4 Linear Processes and ARMA Models 106 3.5 The frequency domain 118 3.6 Estimation of ARMA processes 126 3.7 (G)ARCH models 127 3.8 Long memory series 133 3.9 Notes and further reading 137
• 4 Arbitrage Theory for the Multi-Period Model 139 4.1 Definitions and preliminaries 139 4.2 Self-financing trading strategies 140 4.3 No-arbitrage and martingale measures 143 4.4 European claims on arbitrage-free markets 146 4.5 The martingale representation theorem in discrete time 150 4.6 The Cox-Ross-Rubinstein binomial model 151 4.7 The Black-Scholes formula 156 4.8 American options and contingent claims 161 4.9 Notes and further reading 165
• 5 Brownian Motion and Related Processes in Continuous Time 167 5.1 Preliminaries 167 5.2 Brownian Motion 170 5.3 Continuity and differentiability 181 5.4 Self-similarity and fractional Brownian motion 183 5.5 Counting processes 184 5.6 Levy processes 188 5.7 Notes and further reading 190
• 6 Ito Calculus 191 6.1 Total and quadratic variation 191 6.2 Stochastic Stieltjes integration 196 6.3 The Ito integral 199 6.4 Quadratic covariation 211 6.5 Ito's formula 212 6.6 Ito processes 215 6.7 Diffusion processes and ergodicity 222 6.8 Numerical approximations and statistical estimation 223 6.9 Notes and further reading 225
• 7 The Black-Scholes-Model 227 7.1 The model and first properties 227 7.2 Girsanov's theorem 233 7.3 Equivalent martingale measure 237 7.4 Arbitrage-free pricing and hedging claims 238 7.5 The delta hedge 241 7.6 Time-dependent volatility 242 7.7 The generalized Black-Scholes model 244 7.8 Notes and further reading 246
• 8 Limit Theory for Discrete-Time Processes 249 8.1 Limit theorems for correlated time series 250 8.2 A regression model for financial time series 259 8.3 Limit theorems for martingale difference 263 8.4 Asymptotics 268 8.5 Density estimation and nonparametric regression 272 8.6 The CLT for linear processes 287 8.7 Mixing Processes 290 8.8 Limit Theorems for Mixing Processes 297 8.9 Notes and further reading 306
• 9 Special Topics 309 9.1 Copulas - and the 2008 financial crisis 309 9.2 Local linear nonparametric regression 322 9.3 Change-point detection and monitoring 333 9.4 Unit roots and random walk 345 9.5 Notes and further reading 363 A Appendix A 365 A.1 (Stochastic) Landau Symbols 365 A.2 Bochner's Lemma 366 A.3 Conditional Expectation 367 A.4 Inequalities 368 A.5 Random Series 369 A.6 Local martingales in discrete time 369 Appendix B Weak Convergence and Central Limit Theorems 371 B.1 Convergence in distribution 371 B.2 Weak convergence 372 B.3 Prohorov's theorem 377 B.4 Sufficient criteria 379 B.5 More on Skorohod spaces 381 B.6 Central Limit Theorems for Martingale Differences 381 B.7 Functional central limit theorems 382 B.8 Strong Approximations 384 References 386 Index 409.
• (source: Nielsen Book Data)

Mathematical finance has grown into a huge area of research which requires a lot of care and a large number of sophisticated mathematical tools. Mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, it considers various aspects of the application of statistical methods in finance and illustrates some of the many ways that statistical tools are used in financial applications. Financial Statistics and Mathematical Finance: Provides an introduction to the basics of financial statistics and mathematical finance. Explains the use and importance of statistical methods in econometrics and financial engineering. Illustrates the importance of derivatives and calculus to aid understanding in methods and results. Looks at advanced topics such as martingale theory, stochastic processes and stochastic integration. Features examples throughout to illustrate applications in mathematical and statistical finance. Is supported by an accompanying website featuring R code and data sets. Financial Statistics and Mathematical Finance introduces the financial methodology and the relevant mathematical tools in a style that is both mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, both graduate students and researchers in statistics, finance, econometrics and business administration will benefit from this book.
(source: Nielsen Book Data)

• Intro; Contents; List of Notes;
• 1. Introduction;
• 2. The first steps;
• 3. Cleaning and preparing data;
• 4. Describing data;
• 5. Introduction to causal analysis;
• 6. Regression analysis;
• 7. Regression diagnostics;
• 8. Logistic regression;
• 9. Matching;
• 10. Reporting results;
• 11. Writing a seminar paper;
• 12. The next steps; References; Copyright; Index

Stata is one of the most popular statistical software in the world and suited for all kinds of users, from absolute beginners to experienced veterans. This book offers a clear and concise introduction to the usage and the workflow of Stata. Included topics are importing and managing datasets, cleaning and preparing data, creating and manipulating variables, producing descriptive statistics and meaningful graphs as well as central quantitative methods, like linear (OLS) and binary logistic regressions and matching. Additional information about diagnostical tests ensures that these methods yield valid and correct results that live up to academic standards. Furthermore, users are instructed how to export results that can be directly used in popular software like Microsoft Word for seminar papers and publications. Lastly, the book offers a short yet focussed introduction to scientific writing, which should guide readers through the process of writing a first quantitative seminar paper or research report. The book underlines correct usage of the software and a productive workflow which also introduces aspects like replicability and general standards for academic writing. While absolute beginners will enjoy the easy to follow point-and-click interface, more experienced users will benefit from the information about do-files and syntax which makes Stata so popular. Lastly, a wide range of user-contributed software ("Ados") is introduced which further improves the general workflow and guarantees the availability of state of the art statistical methods.
(source: Nielsen Book Data)

• List of figures
• List of tables
• Introduction
• Randomized trials
• Regression
• Instrumental variables
• Regression discontinuity designs
• Differences in differences
• The wages of schooling
• Abbreviations and acronyms
• Empirical notes
• Acknowledgments
• Index.

Applied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu-themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstrates why econometrics is exciting and useful. The five most valuable econometric methods, or what the authors call the Furious Five--random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences--are illustrated through well-crafted real-world examples (vetted for awesomeness by Kung Fu Panda's Jade Palace). Does health insurance make you healthier? Randomized experiments provide answers. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Regression analysis and a regression discontinuity design reveal the surprising truth. When private banks teeter, and depositors take their money and run, should central banks step in to save them? Differences-in-differences analysis of a Depression-era banking crisis offers a response. Could arresting O. J. Simpson have saved his ex-wife's life? Instrumental variables methods instruct law enforcement authorities in how best to respond to domestic abuse. Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect. * Shows why econometrics is important* Explains econometric research through humorous and accessible discussion* Outlines empirical methods central to modern econometric practice* Works through interesting and relevant real-world examples.
(source: Nielsen Book Data)

• Part I. Probability and Measure: 1. The Texas lotto
• 2. Quality control
• 3. Why do we need sigma-algebras of events?
• 4. Properties of algebras and sigma-algebras
• 5. Properties of probability measures
• 6. The uniform probability measures
• 7. Lebesque measure and Lebesque integral
• 8. Random variables and their distributions
• 9. Density functions
• 10. Conditional probability, Bayes's rule, and independence
• 11. Exercises: A. Common structure of the proofs of Theorems 6 and 10, B. Extension of an outer measure to a probability measure
• Part II. Borel Measurability, Integration and Mathematical Expectations: 12. Introduction
• 13. Borel measurability
• 14. Integral of Borel measurable functions with respect to a probability measure
• 15. General measurability and integrals of random variables with respect to probability measures
• 16. Mathematical expectation
• 17. Some useful inequalities involving mathematical expectations
• 18. Expectations of products of independent random variables
• 19. Moment generating functions and characteristic functions
• 20. Exercises: A. Uniqueness of characteristic functions
• Part III. Conditional Expectations: 21. Introduction
• 22. Properties of conditional expectations
• 23. Conditional probability measures and conditional independence
• 24. Conditioning on increasing sigma-algebras
• 25. Conditional expectations as the best forecast schemes
• 26. Exercises
• A. Proof of theorem 22
• Part IV. Distributions and Transformations: 27. Discrete distributions
• 28. Transformations of discrete random vectors
• 29. Transformations of absolutely continuous random variables
• 30. Transformations of absolutely continuous random vectors
• 31. The normal distribution
• 32. Distributions related to the normal distribution
• 33. The uniform distribution and its relation to the standard normal distribution
• 34. The gamma distribution
• 35. Exercises: A. Tedious derivations
• B. Proof of theorem 29
• Part V. The Multivariate Normal Distribution and its Application to Statistical Inference: 36. Expectation and variance of random vectors
• 37. The multivariate normal distribution
• 38. Conditional distributions of multivariate normal random variables
• 39. Independence of linear and quadratic transformations of multivariate normal random variables
• 40. Distribution of quadratic forms of multivariate normal random variables
• 41. Applications to statistical inference under normality
• 42. Applications to regression analysis
• 43. Exercises
• A. Proof of theorem 43
• Part VI. Modes of Convergence: 44. Introduction
• 45. Convergence in probability and the weak law of large numbers
• 46. Almost sure convergence, and the strong law of large numbers
• 47. The uniform law of large numbers and its applications
• 48. Convergence in distribution
• 49. Convergence of characteristic functions
• 50. The central limit theorem
• 51. Stochastic boundedness, tightness, and the Op and op-notations
• 52. Asymptotic normality of M-estimators
• 53. Hypotheses testing
• 54. Exercises: A. Proof of the uniform weak law of large numbers
• B. Almost sure convergence and strong laws of large numbers
• C. Convergence of characteristic functions and distributions
• Part VII. Dependent Laws of Large Numbers and Central Limit Theorems: 55. Stationary and the world decomposition
• 56. Weak laws of large numbers for stationary processes
• 57. Mixing conditions
• 58. Uniform weak laws of large numbers
• 59. Dependent central limit theorems
• 60. Exercises: A. Hilbert spaces
• Part VIII. Maximum Likelihood Theory
• 61. Introduction
• 62. Likelihood functions
• 63. Examples
• 64. Asymptotic properties if ML estimators
• 65. Testing parameter restrictions
• 66. Exercises.
• (source: Nielsen Book Data)

This book is intended for use in a rigorous introductory PhD level course in econometrics, or in a field course in econometric theory. It covers the measure-theoretical foundation of probability theory, the multivariate normal distribution with its application to classical linear regression analysis, various laws of large numbers, central limit theorems and related results for independent random variables as well as for stationary time series, with applications to asymptotic inference of M-estimators, and maximum likelihood theory. Some chapters have their own appendices containing the more advanced topics and/or difficult proofs. Moreover, there are three appendices with material that is supposed to be known. Appendix I contains a comprehensive review of linear algebra, including all the proofs. Appendix II reviews a variety of mathematical topics and concepts that are used throughout the main text, and Appendix III reviews complex analysis. Therefore, this book is uniquely self-contained.
(source: Nielsen Book Data)

• Artificial regressions / Russell Davidson and James G. MacKinnon
• General hypothesis testing / Anil K. Bera and Gamini Premaratne
• Serial correlation / Maxwell L. King
• Heteroskedasticity / William E. Griffiths
• Seemingly unrelated regression / Denzil G. Fiebig
• Simultaneous equation model estimators: statistical properties and practical implications / Roberto S. Mariano
• Identification in parametric models / Paul Bekker and Tom Wansbeek
• Measurement error and latent variables / Tom Wansbeek and Erik Meijer
• Diagnostic testing / Jeffrey M. Wooldridge
• Basic elements of asymptotic theory / Benedikt M. Pötscher and Ingmar R. Prucha
• Generalized method of moments / Alastair R. Hall
• Collinearity / R. Carter Hill and Lee C. Adkins
• Nonnested hypothesis testing: an overview / M. Hashem Pesaran and Melvyn Weeks
• Spatial econometrics / Luc Anselin
• Essentials of count data regression / A. Colin Cameron and Pravin K. Trivedi
• Panel data models / Cheng Hsiao
• Qualitative response models / G.S. Maddala and A. Flores-Lagunes
• Self-selection / Lung-fei Lee
• Random coefficient models / P.A.V.B. Swamy and George S. Tavlas
• Nonparametric kernel methods of estimation and hypothesis testing / Aman Ullah
• Durations / Christian Gouriéroux and Joann Jasiak
• Simulation based inference for dynamic multinomial choice models / John Geweke, Daniel Hauser and Michael Keane
• Monte Carlo test methods in econometrics / Jean-Marie Dufour and Lydia Khalaf
• Bayesian analysis of stochastic frontier models / Gary Koop and Mark F.J. Steel
• Parametric and nonparametric tests of limited domain and ordered hypotheses in economics / Esfandiar Maasoumi
• Spurious regressions in econometrics / Clive W.J. Granger
• Forecasting economic time series / James H. Stock
• Time series and dynamic models / Aris Spanos
• Unit roots / Herman J. Bierens
• Cointegration / Juan J. Dolado, Jesús Gonzalo and Francesc Marmol
• Seasonal nonstationarity and near-nonstationarity / Eric Ghysels, Denise R. Osborn and Paulo M.M. Rodrigues
• Vector autoregressions / Helmut Lütkepohl.

A Companion to Theoretical Econometrics provides a comprehensive reference to the basics of econometrics. It focuses on the foundations of the field and at the same time integrates popular topics often encountered by practitioners. The chapters are written by international experts and provide up-to-date research in areas not usually covered by standard econometric texts. This book is an exceptional reference for readers who require quick access to the foundation theories in this field. Chapters are organized to provide clear information and to point to further readings on the subject. Important topics covered include: serial correlation heteroskedasticity nonparametric and semiparametric models count and panel data regression models spatial correlation.
(source: Nielsen Book Data)
A Companion to Theoretical Econometrics provides a comprehensive reference to the basics of econometrics. This companion focuses on the foundations of the field and at the same time integrates popular topics often encountered by practitioners. The chapters are written by international experts and provide up--to--date research in areas not usually covered by standard econometric texts. * Focuses on the foundations of econometrics. * Integrates real--world topics encountered by professionals and practitioners. * Draws on up--to--date research in areas not covered by standard econometrics texts. * Organized to provide clear, accessible information and point to further readings.
(source: Nielsen Book Data)

• Volume I: Introduction to Volumes I and II
• 1. A profile: the ET Interview: Professor Clive Granger
• Part I. Spectral Analysis: 2. Spectral analysis of New York Stock Market prices O. Morgenstern
• 3. The typical spectral shape of an eonomic variable
• Part II. Seasonality: 4. Seasonality: causation, interpretation and implications A. Zellner
• 5. Is seasonal adjustment a linear or nonlinear data-filtering process? E. Ghysels and P. L. Siklos
• Part III. Nonlinearity: 6. Non-linear Time Series Modeling A. Anderson
• 7. Using the correlation exponent to decide whether an economic series is chaotic T. Liu and W. P. Heller
• 8. Testing for neglected nonlinearity in Time Series Models: a comparison of neural network methods and alternative tests
• 9. Modeling nonlinear relationships between extended-memory variables
• 10. Semiparametric estimates of the relation between weather and electricity sales R. F. Engle, J. Rice and A. Weiss
• Part IV. Methodology: 11. Time Series Modeling and interpretation M. J. Morris
• 12. On the invertibility of Time Series Models A. Anderson
• 13. Near normality and some econometric models
• 14. The Time Series approach to econometric model building P. Newbold
• 15. Comments on the evaluation of policy models
• 16. Implications of aggregation with common factors
• Part V. Forecasting: 17. Estimating the probability of flooding on a tidal river
• 18. Prediction with a generalized cost of error function
• 19. Some comments on the evaluation of economic forecasts P. Newbold
• 20. The combination of forecasts
• 21. Invited review: combining forecasts - twenty years later
• 22. The combination of forecasts using changing weights M. Deutsch and T. Terasvirta
• 23. Forecasting transformed series
• 24. Forecasting white noise A. Zellner
• 25. Can we improve the perceived quality of economic forecasts? Short-run forecasts of electricity loads and peaks R. Ramanathan, R. F. Engle, F. Vahid-Araghi and C. Brace. Volume II: Part I. Causality: 1. Investigating causal relations by econometric models and cross-spectral methods
• 2. Testing for causality
• 3. Some recent developments in a concept of causality
• 4. Advertising and aggregate consumption: an analysis of causality R. Ashley and R. Schmalensee
• Part II. Integration and Cointegration: 5. Spurious regressions in econometrics
• 6. Some properties of time series data and their use in econometric model specification
• 7. Time series analysis of error correction models A. A. Weiss
• 8. Co-Integration and error-correction: representation, estimation and testing
• 9. Developments in the study of cointegrated economic variables
• 10. Seasonal integration and cointegration S. Hylleberg, R. F. Engle and B. S. Yoo
• 11. A cointegration analysis of Treasury Bill yields A. D. Hall and H. M. Anderson
• 12. Estimation of common long-memory components in Cointegrated Systems J. Gonzalo
• 13. Separation in cointegrated systems and persistent-transitory decompositions N. Haldrup
• 14. Nonlinear transformations of Integrated Time Series J. Hallman
• 15. Long Memory Series with attractors J. Hallman
• 16. Further developments in the study of cointegrated variables N. R. Swanson
• Part III. Long Memory: 17. An introduction to long-memory Time Series models and fractional differencing R. Joyeux
• 18. Long-memory relationships and the aggregation of dynamic models
• 19. A long memory property of stock market returns and a new model Z. Ding and R. F. Engle.
• (source: Nielsen Book Data)

This two-volume set of books in the Econometric Society Monographs series (ESM numbers 32 and 33), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in these volumes explore topics in spectral analysis, seasonality, nonlinearity, methodology, forecasting, causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.
(source: Nielsen Book Data)