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Next
1. Principles of econometrics [2011]
 Hill, R. Carter.
 4th ed.  Hoboken, NJ : Wiley, c2011.
 Description
 Book — xxvi, 758 p. : ill. ; 27 cm.
 Summary

 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, GoodnessofFit 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 TimeVarying 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.
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 Online
2. Applied Nonparametric Econometrics [2015]
 Henderson, Daniel J., author.
 Cambridge : Cambridge University Press, 2015.
 Description
 Book — 1 online resource (378 pages) : digital, PDF file(s).
 Summary

 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.
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 Abingdon, Oxon ; New York, NY : Routledge, 2019.
 Description
 Book — 1 online resource (381 pages)
 Summary

 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 QuantileHeterogeneous 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 spotfutures noarbitrage 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 ChavezCasillas
 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 Highfrequencybased Measures Improve Conditional Covariance Forecasts?, Denisa BanulescuRadu, 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 quasilikelihood analysis, Yuta Koike, Nakahiro Yoshida
 13. The LogGARCH Model via ARMA Representations, Genaro Sucarrat
 Index.
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 Chicago ; London : The University of Chicago Press, 2022
 Description
 Book — xiii, 721 pages : illustrations (black and white) ; 24 cm
 Summary

 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 betweenfirm 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, 19952016 / 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 classbased approach / Pirmin Fessler, Martin Schürz
 Social security wealth, inequality, and lifecycle 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, JeanWilliam Laliberté
 Inequality of opportunity for income in Denmark and the United States : a comparison based on administrative data / Pablo A. Mitnik, anneLine 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 halfcentury 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, 19291940 / 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 macromicro 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
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 Online
 Thrane, Christer, author.
 Abingdon, Oxon ; New York, NY : Routledge, 2023
 Description
 Book — 1 online resource (vii, 255 pages) : illustrations
 Summary

"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, covid19 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
6. Analysis of panel data [2022]
 Hsiao, Cheng, 1943 author.
 Fourth edition.  Cambridge ; New York, NY : Cambridge University Press, 2022.
 Description
 Book — 1 online resource.
 Summary

 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 crosssectional dependent data
 12. Program evaluation
 13. Varying coefficients models
 14. Big data analysis.
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 Hoboken, New Jersey : John Wiley & Sons, Inc, 2014.
 Description
 Book — 1 online resource.
 Summary

 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 GLMMAR(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: NonNormality, 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 nonBayesian 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 HeavyTailed 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.
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8. Causal inference [2023]
 Rosenbaum, Paul R., author.
 Cambridge, Massachusetts : The MIT Press, [2023]
 Description
 Book — 1 online resource
 Summary

"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
 Harding, Don, author. Author http://id.loc.gov/vocabulary/relators/aut
 Princeton, NJ : Princeton University Press, [2016]
 Description
 Book — 1 online resource (232 p.) : 20 line illus. 18 tables Digital: text file; PDF.
 Summary

 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. ModelBased 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
10. HighFrequency Financial Econometrics [2014]
 AïtSahalia, Yacine, author. Author http://id.loc.gov/vocabulary/relators/aut
 Course Book  Princeton, NJ : Princeton University Press, [2014]
 Description
 Book — 1 online resource (688 p.) : 35 line illus. 3 tables Digital: text file; PDF.
 Summary

 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. HighFrequency 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. Cojumps
 Appendix A. Asymptotic Results for Power Variations
 Appendix B. Miscellaneous Proofs
 Bibliography
 Index
11. Bayesian Estimation of DSGE Models [2016]
 Herbst, Edward P., author. Author http://id.loc.gov/vocabulary/relators/aut
 Princeton, NJ : Princeton University Press, [2015]
 Description
 Book — 1 online resource (296 p.) : 34 line illus. 23 tables Digital: text file; PDF.
 Summary

 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. MetropolisHastings 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
 Terrell, Dek.
 Bingley : Emerald Publishing Limited, 2020.
 Description
 Book — 1 online resource (468 p.).
 Summary

 Introduction
 Dek Terrell, Tong Li and M. Hashem Pesaran Chapter 1. Correction for the Asymptotical Bias of the ArellanoBond 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 JeanMichel 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
 JeanJacques Laffont, Isabelle Perrigne, Michel Simioni, and Quang Vuong Chapter 10. Bayesian Estimation of Linear Sum Assignment Problems
 YuWei 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 CrossSectional 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.
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(source: Nielsen Book Data)
 Steland, Ansgar.
 Chichester, West Sussex, United Kingdom : Wiley, 2012.
 Description
 Book — 1 online resource.
 Summary

 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 OnePeriod 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 Noarbitrage and martingale measures 55 2.6 Arbitragefree 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 MultiPeriod Model 139 4.1 Definitions and preliminaries 139 4.2 Selffinancing trading strategies 140 4.3 Noarbitrage and martingale measures 143 4.4 European claims on arbitragefree markets 146 4.5 The martingale representation theorem in discrete time 150 4.6 The CoxRossRubinstein binomial model 151 4.7 The BlackScholes 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 Selfsimilarity 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 BlackScholesModel 227 7.1 The model and first properties 227 7.2 Girsanov's theorem 233 7.3 Equivalent martingale measure 237 7.4 Arbitragefree pricing and hedging claims 238 7.5 The delta hedge 241 7.6 Timedependent volatility 242 7.7 The generalized BlackScholes model 244 7.8 Notes and further reading 246
 8 Limit Theory for DiscreteTime 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 Changepoint 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.
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14. Stata : a really short introduction [2019]
 Bittmann, Felix, author.
 Berlin ; Boston : De Gruyter Oldenbourg, [2019]
 Description
 Book — 1 online resource (170 pages)
 Summary

 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
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 Angrist, Joshua David, author.
 Princeton ; Oxford : Princeton University Press, [2015]
 Description
 Book — xv, 282 pages : illustrations ; 22 cm
 Summary

 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.
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 Online
Business Library
Business Library  Status 

Stacks  Request (opens in new tab) 
HB139 .A53984 2015  CHECKEDOUT 
 Watson, Patrick (Patrick Kent)
 Kingston : Univeersity Of West Indies Press, 2010.
 Description
 Book — 1 online resource
 Watson, Patrick (Patrick Kent)
 Kingston : Univeersity Of West Indies Press, 2010.
 Description
 Book — 1 online resource
 Bierens, Herman J., 1943
 Cambridge, UK ; New York : Cambridge University Press, 2005.
 Description
 Book — 1 online resource (xvii, 323 pages) : illustrations
 Summary

 Part I. Probability and Measure: 1. The Texas lotto
 2. Quality control
 3. Why do we need sigmaalgebras of events?
 4. Properties of algebras and sigmaalgebras
 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 sigmaalgebras
 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 opnotations
 52. Asymptotic normality of Mestimators
 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.
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19. A companion to theoretical econometrics [2003]
 Malden, MA : Blackwell Pub., 2003.
 Description
 Book — 1 online resource (xvii, 709 pages) : illustrations.
 Summary

 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. FloresLagunes
 Selfselection / Lungfei 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 / JeanMarie 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 nearnonstationarity / Eric Ghysels, Denise R. Osborn and Paulo M.M. Rodrigues
 Vector autoregressions / Helmut Lütkepohl.
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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 uptodate research in areas not usually covered by standard econometric texts. * Focuses on the foundations of econometrics. * Integrates realworld topics encountered by professionals and practitioners. * Draws on uptodate research in areas not covered by standard econometrics texts. * Organized to provide clear, accessible information and point to further readings.
(source: Nielsen Book Data)
 Granger, C. W. J. (Clive William John), 19342009.
 Cambridge ; New York : Cambridge University Press, 2001.
 Description
 Book — 1 online resource (2 volumes in 1) : illustrations.
 Summary

 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 datafiltering process? E. Ghysels and P. L. Siklos
 Part III. Nonlinearity: 6. Nonlinear 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 extendedmemory 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? Shortrun forecasts of electricity loads and peaks R. Ramanathan, R. F. Engle, F. VahidAraghi and C. Brace. Volume II: Part I. Causality: 1. Investigating causal relations by econometric models and crossspectral 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. CoIntegration and errorcorrection: 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 longmemory components in Cointegrated Systems J. Gonzalo
 13. Separation in cointegrated systems and persistenttransitory 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 longmemory Time Series models and fractional differencing R. Joyeux
 18. Longmemory 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)
(source: Nielsen Book Data)
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