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Author:   Jason Abrevaya
Publisher:   MIT Press Ltd
Imprint:   MIT Press
Weight:   0.369kg
ISBN:  

9780262553360

ISBN 10:   0262553368
Pages:   660
Publication Date:   25 November 2025
Audience:   General/trade ,  General
Format:   Paperback
Publisher's Status:   Active
Availability:   To order  
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Table of Contents

Preface ix Acknowledgments xv 1 The basics of R 1 1.1 Installing R 1 1.2 Arithmetic operations and mathematical functions 2 1.3 Variables and data types 4 1.4 Vectors 9 1.5 Output 18 1.6 Programming 18 1.7 Writing functions 22 1.8 Data frames and file input 24 1.9 Missing values 29 1.10 R packages 30 Exercises 32 2 Introduction to probability theory 37 2.1 Experiments and sample spaces 39 2.2 Events 42 2.3 What is a probability? 45 2.4 Properties of probabilities 51 Exercises 55 3 Conditional probabilities and independence 59 3.1 Definition and properties of conditional probabilities 59 3.2 Multiplication rule and Bayes’ Theorem 60 3.3 Probability tables 63 3.4 Independence 66 3.5 Examples with an infinite number of outcomes 70 Exercises 72 4 Combinatorics (counting methods) 77 4.1 Product rule and sum rule 77 4.2 Permutations and combinations 78 4.3 Probabilities for equally likely choices 81 Exercises 83 5 Economic data and sampling 89 5.1 Types of data 89 5.2 Types of variables 91 5.3 The population and sampling 93 Exercises 96 6 Descriptive statistics and visuals: univariate data 99 6.1 Dataset examples 99 6.2 Categorical data: sample proportions and bar charts 102 6.3 Numerical data: histograms 104 6.4 Numerical data: measures of location 110 6.5 Numerical data: measures of dispersion 116 6.6 Modal outcomes 126 6.7 Linear transformations of univariate data 128 6.8 Time-series plots 133 Exercises 136 7 Descriptive statistics and visuals: bivariate data 143 7.1 Categorical variables 143 7.2 Numerical data: scatter plots, sample covariance and correlation 151 7.3 Correlation is not causation 172 Exercises 173 8 Discrete random variables 179 8.1 Using sample proportions to calculate descriptive statistics 179 8.2 Random variables and discrete random variables 180 8.3 Population descriptive statistics 188 8.4 Multiple discrete random variables 191 8.5 Linear transformations 202 8.6 Linear combination of multiple random variables 204 8.7 Expected values of functions of discrete random variables 207 Exercises 208 9 Models of discrete random variables 215 9.1 Bernoulli random variable 215 9.2 Binomial random variable 216 9.3 Geometric random variable 222 9.4 Negative binomial random variable 224 9.5 Poisson random variable 227 Exercises 230 10 Continuous random variables 237 10.1 Continuous random variables vs. discrete random variables 237 10.2 Probability density function 238 10.3 Cumulative distribution function 243 10.4 Population descriptive statistics 249 10.5 Linear transformations of one random variable 256 10.6 Multiple continuous random variables 258 10.7 Linear transformations and combinations of multiple random variables 268 10.8 Expected values of functions of continuous random variables 273 10.9 Strictly increasing transformations of random variables 275 10.10 Random variables with discrete and continuous outcomes 277 Exercises 278 11 Models of continuous random variables 285 11.1 Normal random variable 285 11.2 Log-normal random variable 297 11.3 Chi-square random variable 301 11.4 Exponential random variable 303 11.5 Mixture of normal random variables 307 Exercises 309 12 Sampling distributions: exact 315 12.1 Sampling distribution of the sample mean 317 12.2 Sampling distribution of the sample variance 322 12.3 Sampling distribution of other statistics 328 Exercises 332 13 Sampling distributions: asymptotic 337 13.1 Asymptotic distribution of the sample mean 337 13.2 Asymptotic distribution of the sample variance 345 13.3 Asymptotic distribution of other statistics 348 Exercises 354 14 Estimation and confidence intervals 359 14.1 Estimation and properties of estimators 359 14.2 Finite-sample confidence intervals: population mean of i.i.d. normal random variables 363 14.3 Asymptotic confidence intervals: population mean of i.i.d. random variables 372 14.4 Asymptotic confidence intervals: parameters with asymptotically normal estimators 377 14.5 Functions of consistent estimators 392 14.6 Asymptotic predictive intervals for continuous random variables 393 Exercises 394 15 The bootstrap 401 15.1 Bootstrap sampling 402 15.2 Bootstrap sampling distribution 405 15.3 Bootstrap standard errors and bootstrap confidence intervals 406 Exercises 414 16 Hypothesis testing 417 16.1 Finite-sample hypothesis testing: population mean of i.i.d. normal random variables 418 16.2 Asymptotic hypothesis testing: parameters with asymptotically normal estimators 429 16.3 Statistical significance versus practical significance 437 16.4 Hypothesis testing for multiple hypotheses: the Wald test 438 Appendix: Details for the Wald test 444 Exercises 450 17 Simple linear regression 455 17.1 The simple linear regression model 455 17.2 The least-squares estimator 460 17.3 Fitted values, estimated residuals, and regression fit 468 17.4 Asymptotic normality and statistical inference 477 17.5 Causality and prediction 487 Exercises 490 18 Multiple linear regression 497 18.1 The multiple linear regression model 497 18.2 The least-squares estimator 499 18.3 Standard errors and confidence intervals 509 18.4 Inference for linear combinations of regression parameters 513 18.5 Hypothesis testing 515 18.6 Modeling approaches and explanatory variables 518 18.7 Log-transformed outcome variable 528 18.8 Asymptotic predictive intervals 530 18.9 Linear probability model 535 Exercises 539 References 544

Reviews

“While there are many books covering introductory probability and statistics for economics/business students, the new text by Jason Abrevaya stands out in three ways. First, the text is more rigorous than most of its competitors. Second, it emphasizes large-sample and simulation methods for statistical inference and thus is compatible with modern practice. And third, it teaches and effectively uses R to give students insight into important (and sometimes confusing) concepts and provides them with practical experience for later empirical work. This is destined to become a leading text in the field.” —Mark W. Watson, Howard Harrison and Gabrielle Snyder Beck Professor of Economics and Public Affairs, Princeton University

Author Information

Jason Abrevaya is Professor of Economics at the University of Texas at Austin and is the holder of the Murray S. Johnson Chair in Economics. He has served on editorial boards for several leading econometrics journals, including the Journal of Econometrics, the Journal of Applied Econometrics, and the Journal of Business and Economic Statistics, and was a founding coeditor of the Journal of Econometric Methods.

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