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Sample Surveys in Economics and Finance A Practical Guide (Guillaume Osier).pdf
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抽样调查是经济金融领域重要统计信息来源，能够快速低成本获取感知、预期、态度等其他数据源难以捕捉的主观类信息，基于代表性样本可以对家庭、企业、金融机构等有限总体得到可靠估计。当下行政数字数据发展、调查倦怠、隐私顾虑造成应答率下滑、执行难度上升，但抽样调查并未过时，需要融入多源统计体系发展，可通过混合模式采集、行政数据辅助、AI 等现代数据科学工具优化调查全流程。本书是从设计到分析的实操指南，讲解概率抽样与统计推断理论，兼顾预算、时间、人员、受访者负担等现实约束，面向需要理论基础同时追求实务估计方法的读者。书籍特色：确立抽样调查作为经济金融研究与决策的核心数据源；面向学界与实务人员，兼顾调查设计与分析；结合专用 R 包，用数值实例阐释理论；讲解 AI 等现代工具对调查设计、实施、推断的变革作用。
Sample Surveys in Economics and Finance
Sample surveys are a major source of statistical information in economics and finance. When policymakers, researchers, and analysts need targeted data‑based evidence, surveys can be deployed quickly, at relatively low cost, tailored to specific policy questions and used to capture information that other data sources often miss—subjective dimensions such as perceptions, expectations, values, and attitudes. From economics and finance to social and living conditions, surveys generate robust, decision‑ready insights. And when based on representative samples, they yield accurate estimates for many finite populations, from households and individuals to businesses or financial institutions.
At the same time, the survey environment is changing rapidly. The growth of administrative and digital data sources, combined with survey fatigue and stronger concerns about confidentiality, has led to declining response rates and increasing operational difficulties, challenging the relevance of the survey instrument and raising questions about how surveys should be designed and used.
Even so, surveys are not obsolete, but they must evolve within a multi‑source statistical ecosystem. For example, mixed‑mode data collection can better match respondent preferences; greater use of administrative records can shorten questionnaires and improve precision; and modern data science tools—including AI—can support more efficient production and analysis.
This resource provides a practical guide to survey work from design to analysis. It presents the theoretical foundations of probability‑based sampling and inference, while emphasising the real constraints of survey operations—budget, time, staffing and respondent burden. It is intended for readers who need both methodological grounding and workable approaches for producing accurate estimates in statistical practice.
Key Features:
• Promote sample surveys as a core data source for research and decision‑making in economics and finance.
• Serve as a practical handbook for academics and practitioners, covering both survey design and analysis methods.
• Illustrate key theoretical concepts with numerical applications using dedicated R packages for survey analysis.
• Explain how modern data science tools—including AI—can reshape survey design, implementation and inference in the years ahead.
Preface xv
1 The Central Role of Statistics in Economics and Finance 1
1.1 The Profusion and Diversity of Data Sources 1
1.2 Typology of Statistical Data in Economics and Finance 3
1.3 The European and International Dimension . 7
2 The Importance of Surveys in Economics and Finance 11
2.1 The Rise of Surveys in the Statistical Landscape 11
2.2 Why Surveys Matter in Economics and Finance . 12
2.3 Examples of Surveys in Economics and Finance . 14
2.4 Surveys to Analyse Socio-Economic Inequalities . 16
2.5 Surveys to Understand Subjective Dimensions 18
2.6 Surveys to Analyse Household and Business Access to Finance 21
2.7 Surveys in Labour Economics . 24
2.8 Surveys to Calculate Price Indexes 25
2.9 Surveys to Support Financial Inclusion 27
2.10 Specific Features of Survey Data in Economics and Finance 29
3 Main Concepts and Definitions 35
3.1 Population and Sample, Parameter and Estimator . 35
3.2 Probability and Non-Probability Sampling Designs . 38
3.3 Sample Representativeness . 39
3.4 Sampling Frame and Coverage Errors 40
3.5 Bias, Variance, Standard Error and Mean Square Error 42
3.6 Confidence Intervals 47
3.7 The Bias/Variance Trade-off 49
3.8 The Total Survey Error Framework 52
4 Simple Random Sampling 56
4.1 What is it? . 56
4.2 Inclusion Probabilities . 57
vii
viii Contents
4.3 Estimating Population Totals and Means 57
4.4 Estimating Population Counts, Proportions and Net Changes . 62
4.5 Domain Estimation . 67
4.6 Sampling Algorithms 72
4.7 Software Implementation with the R survey Package 73
5 Stratification 78
5.1 What is it? . 78
5.2 Total, Mean and Proportion Estimators . 79
5.3 Sample Allocation and Optimality 83
5.4 Choosing Stratification Criteria 91
5.5 Software Implementation with the R survey Package 92
6 Unequal Probability Sampling 95
6.1 The Horvitz-Thompson Estimator 95
6.2 The Hansen-Hurwitz Estimator 102
6.3 Software Implementation with the R survey Package 105
7 Multi-Stage Sampling 107
7.1 Introduction and Notations 107
7.2 Estimating a Population Total 107
7.3 Case of Simple Random Sampling at each Stage . 113
7.4 Design Optimality in Multi-Stage Sampling . 114
7.5 Software Implementation with the R survey Package 116
8 Multi-Phase Sampling 119
8.1 Introduction and Notations 119
8.2 The Double-Phase Horvitz-Thompson Estimator 119
8.3 Design Optimality in Multi-Phase Sampling . 123
9 Indirect Sampling 124
9.1 Definition 124
9.2 The Generalised Weight Share Method 126
9.3 Examples 128
9.3.1 Compute Household Weights From Individual
Weights . 128
9.3.2 Dual-Frame Sampling with Telephone Numbers . 129
10 Non-Probability Sampling 130
10.1 Examples of Non-Probability Designs . 131
10.1.1 Quota Sampling 131
10.1.2 Other Examples 134
10.1.3 Access Panels 135
10.2 Inference Under Non-Probability Samples 136
10.2.1 Mass Imputation 138
10.2.2 Inverse Probability Weighting . 140
Contents ix
10.2.3 Doubly Robust Approach . 141
10.2.4 Case of Access Panels . 142
10.3 Software Implementation: The R nonprobsvy package . 143
11 Collecting and Editing Survey Data 148
11.1 Questionnaire Design and Pilot Testing . 148
11.2 Different Modes of Data Collection 149
11.3 Data Editing and Imputation . 151
11.3.1 What is it? . 151
11.3.2 Examples of Data Checks . 152
11.3.3 Imputation of Missing Values . 153
12 The Issue of Unit Non-Response 154
12.1 A Major Challenge for Data Quality . 154
12.2 Dealing With Unit Non-Response . 158
12.3 Variance Estimation Under Unit Non-Response . 160
12.4 Estimating Response Propensities . 162
12.5 Preventing Unit Non-Response 166
13 Using Auxiliary Information to Improve Accuracy 168
13.1 The Importance of Incorporating Auxiliary Information 168
13.2 Examples of Adjusted Estimators . 170
13.2.1 Difference Estimator 170
13.2.2 Ratio Estimator 170
13.2.3 Generalised Regression Estimator . 171
13.2.4 The Post-Stratified Estimator . 172
13.3 Unified Calibration Framework 172
13.4 Software Implementation 177
13.5 Other Operational Aspects 185
13.6 Calibration as a Way to Deal with Unit Non-response . 188
13.7 Calibration and Non-Probability Samples 191
13.8 Penalised Calibration 192
13.9 Weighting as a Trade-off Between Accuracy and Volatility . 193
14 Non-Linear Estimators: Linearisation and Bootstrap 196
14.1 Introduction and Examples 196
14.2 Estimating Non-Linear Parameters 197
14.2.1 Functions of Population Totals 197
14.2.2 Parameters Defined Using an Estimating Equation . 199
14.2.3 Quantiles and Other Rank-Based Parameters 200
14.3 Variance Estimation: The Linearisation Approach 202
14.3.1 The Seminal Approach . 204
14.3.2 Alternative Linearisation Approaches . 206
14.4 Examples of Linearised Estimators 207
14.4.1 Ratio of Two Totals 207
14.4.2 Dispersion 208
x Contents
14.4.3 Theil Index . 210
14.4.4 Distribution Quantiles . 211
14.5 Software Implementation: The R convey Package 212
14.6 Bootstrap and Other Re-sampling Methods . 215
15 Survey Data and Statistical Modelling 217
15.1 The Case of Linear Regression 218
15.1.1 The Theoretical Framework 218
15.1.2 Software Implementation with the R survey Package . 221
15.2 The Case of Logistic Regression 228
15.2.1 The Theoretical Framework 228
15.2.2 Software Implementation with the R survey Package . 229
15.3 The Case of Poisson Regression 231
15.4 To Weight or Not To Weight? . 234
16 Surveys in the Age of Data Science and AI 236
16.1 A More Diverse Statistical Landscape 236
16.2 Data Integration 237
16.2.1 Forecasting Through Micro-simulation Modelling 237
16.2.2 Statistical Matching Between Surveys 238
16.2.3 Distributional Accounts 239
16.3 Uncovering Meaningful Patterns in Survey Data 241
16.3.1 Ridge, LASSO and Elastic Net Regression 242
16.3.2 Random Forests 249
16.4 The Impact of Artificial Intelligence . 253
16.4.1 Investigating New Topics . 254
16.4.2 Creating Survey Questionnaires 254
16.4.3 Testing Survey Questions . 256
16.4.4 Translating Survey Questions . 258
16.4.5 Advanced Textual Analysis 259
16.4.6 Other Possible Applications and Challenges Ahead . 261
17 Final Words: Surveys 2.0 264
Bibliography 271
Index 279
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