Nonparametric Econometrics pdf epub mobi txt 电子书 下载 2026
出版者:Princeton University Press
作者:Qi Li
出品人:
页数:768
译者:
出版时间:2006-12-17
价格:USD 130.00
装帧:Hardcover
isbn号码:9780691121611
丛书系列:
图书标签:
- Econometrics
- Nonparametric
- 美國
- 經濟學
- 李其
- 計量經濟學
- 算法
- 中國
- Econometrics
- Nonparametric Methods
- Statistical Inference
- Econometric Theory
- Data Analysis
- Quantitative Economics
- Applied Econometrics
- Regression Analysis
- Time Series Analysis
- Causal Inference
下载链接在页面底部
具体描述
Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. "Nonparametric Econometrics" fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data-nominal and ordinal - in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types -continuous, nominal, and ordinal - within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. "Nonparametric Econometrics" covers all the material necessary to understand and apply nonparametric methods for real-world problems.
经济计量学中的非参数方法：一种探索性视角 经济计量学，作为一门连接经济理论与现实数据的桥梁，其核心在于构建和检验模型以理解经济现象背后的规律。传统的经济计量方法，往往依赖于对数据生成过程的严格假设，例如线性关系、误差项的正态性以及参数的固定不变性。然而，现实世界的经济数据往往复杂而多变，充满了非线性和结构性变化，简单地套用参数化模型，可能无法捕捉到数据的全貌，甚至产生误导性的结论。正是在这样的背景下，经济计量学中的非参数方法应运而生，为研究者提供了一种更灵活、更具探索性的工具箱。 非参数经济计量方法，顾名思义，其最大的特点在于对模型形式的限制较少，甚至可以说是“无所为而无不为”。与那些试图精确估计一系列特定参数（如回归系数、方差）的参数模型不同，非参数方法更侧重于从数据本身出发，揭示变量之间的潜在关系，而无需预先设定这些关系的具体函数形式。这就像一位侦探，不是带着预设的嫌疑人名单去审问，而是仔细观察现场的每一个线索，然后根据线索的指向来推断真相。 这种“无假设”的自由度，使得非参数方法在处理复杂经济现象时显得尤为得力。例如，在分析收入与消费的关系时，我们可能并不确信这种关系是简单的线性，而是可能随着收入水平的变化而发生弯曲，甚至出现拐点。参数化模型可能需要我们预先尝试各种非线性函数（如二次方、三次项），并进行模型选择，而非参数方法则可以直接通过平滑技术，将数据中蕴含的这种非线性特征“描绘”出来，从而更直观地理解收入变化对消费的影响模式。 非参数方法的核心技术之一是核密度估计 (Kernel Density Estimation)。试想一下，如果我们想了解某个经济变量（如失业率）的分布情况，而不是仅仅计算均值和方差，核密度估计可以帮助我们“绘制”出这个变量的概率密度函数。它通过在每个数据点处放置一个“核函数”（一个光滑的、以数据点为中心的函数），然后将这些核函数加权求和，最终得到一个光滑的、连续的密度估计。这个光滑的曲线能够清晰地展现出数据的分布形态，例如是否存在多峰、偏度以及异常值等，这些信息对于理解经济系统的内在动态至关重要。 另一个重要的工具是局部多项式回归 (Local Polynomial Regression)，也称为LOESS (Locally Estimated Scatterplot Smoothing) 或 LOWESS (Locally Weighted Scatterplot Smoothing)。与全局的参数化回归不同，局部多项式回归在估计每个数据点的响应变量值时，只考虑该点附近的数据。它通过在每个点附近构建一个局部模型（通常是多项式），然后用加权最小二乘法进行拟合，其中离当前点越近的数据点拥有越大的权重。这种方法能够有效地捕捉数据中的局部变化和非线性趋势，生成一条平滑的回归曲线，而无需事先假定全局的函数形式。举个例子，如果我们要分析技术进步对生产率的影响，考虑到不同行业、不同发展阶段的技术采纳速度和效果可能存在显著差异，局部多项式回归就能帮助我们更精细地刻画这种局部动态。 样条回归 (Spline Regression) 也是非参数方法中的一个重要分支。样条函数是由一系列多项式片段拼接而成，并且在拼接处（称为节点）具有一定阶数的连续性。这种结构使得样条函数能够同时具备多项式函数的灵活性和局部控制性，从而能够很好地拟合具有复杂形状的数据。通过调整节点的数量和位置，我们可以让样条函数在数据的关键转折点上表现出更精细的拟合。例如，在分析广告支出对销售额的影响时，广告效果可能存在饱和效应，即达到一定程度后，增加广告投入的回报会逐渐递减。样条函数能够灵活地捕捉这种非线性的回报递减现象。 此外，核回归 (Kernel Regression) 是一种更基础但同样强大的非参数回归技术。它通过对周围数据点进行加权平均来估计目标点的响应变量值，权重由核函数决定。其思想与核密度估计类似，都是利用核函数来衡量邻近观测值的重要性。虽然其理论基础相对简单，但在实践中，核回归可以用来估计期望值函数，从而揭示变量之间的潜在关系。 非参数方法在经济计量学中的应用范围极其广泛。在时间序列分析领域，它们可以用来捕捉经济数据中非线性的趋势、季节性以及随机波动，例如，利用非参数方法来检测经济周期中的结构性变化，或者捕捉金融市场中价格波动的非高斯性。在面板数据分析中，非参数方法可以用来估计个体效应的非参数形式，或者捕捉跨个体之间的异质性关系，比如分析不同国家或地区在同一经济政策下的反应差异。在因果推断领域，非参数匹配方法（如核匹配、局部多项式匹配）可以用来更有效地估计处理效应，因为它们不需要对处理组和控制组的条件期望函数做严格的参数化假设。 然而，非参数方法并非没有挑战。“维数诅咒” (Curse of Dimensionality) 是一个普遍存在的问题。随着解释变量数量的增加，要获得足够密集的数据来可靠地进行局部估计，所需的样本量会呈指数级增长，这使得非参数方法在处理高维数据时可能变得不切实际。此外，非参数模型通常缺乏明确的参数解释，这使得研究者在解释结果时需要更加谨慎。例如，我们可能能够估计出收入与消费之间存在一个复杂的非线性关系，但要给出关于“边际消费倾向”的具体数值，非参数方法可能不如参数模型直接。 模型选择和诊断在非参数经济计量学中也至关重要。由于模型形式是数据驱动的，如何选择合适的平滑参数（如带宽、节点数量）直接影响到估计结果的质量。过小的平滑参数可能导致模型对数据“过度拟合”，捕捉到过多的随机噪音，而过大的平滑参数则可能导致模型“欠拟合”，无法充分捕捉数据的真实结构。因此，研究者需要利用交叉验证、信息准则等工具来选择最佳的平滑参数，并通过残差分析、拟合优度检验等方式来诊断模型的拟合效果。 尽管存在挑战，非参数经济计量方法所提供的灵活性和探索性，使其成为现代经济计量学研究中不可或缺的工具。它们鼓励研究者在进行实证分析时，不局限于先验的理论框架，而是更加开放地从数据中学习，发现那些隐藏在复杂经济现象背后的规律。通过对非参数方法的深入理解和熟练运用，研究者能够更准确地刻画经济变量之间的复杂关系，更深入地揭示经济运行的内在机制，从而为经济政策的制定和经济理论的发展提供更坚实的实证基础。它代表着一种更加拥抱不确定性、更加尊重数据本身所蕴含信息的分析范式，是通往更深刻经济理解的一条重要路径。
作者简介
Qi Li is Professor of Economics and Hugh Roy Cullen Professor in Liberal Arts at Texas A&M University. Jeffrey Scott Racine is Professor of Economics, Professor in the Graduate Program in Statistics, and Senator William McMaster Chair in Econometrics at McMaster University.
目录信息
TABLE OF CONTENTS:
Preface xvii
PART I: Nonparametric Kernel Methods 1
Chapter 1: Density Estimation 3
1.1 Univariate Density Estimation 4
1.2 Univariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods 14
1.3 Univariate Bandwidth Selection: Cross-Validation ZMethods 15
1.3.1 Least Squares Cross-Validation 15
1.3.2 Likelihood Cross-Validation 18
1.3.3 An Illustration of Data-Driven Bandwidth Selection 19
1.4 Univariate CDF Estimation 19
1.5 Univariate CDF Bandwidth Selection: Cross- Validation Methods 23
1.6 Multivariate Density Estimation 24
1.7 Multivariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods 26
1.8 Multivariate Bandwidth Selection: Cross-Validation Methods 27
1.8.1 Least Squares Cross-Validation 27
1.8.2 Likelihood Cross-Validation 28
1.9 Asymptotic Normality of Density Estimators 28
1.10 Uniform Rates of Convergence 30
1.11 Higher Order Kernel Functions 33
1.12 Proof of Theorem 1.4 (Uniform Almost Sure Convergence) 35
1.13 Applications 40
1.13.1 Female Wage Inequality 41
1.13.2 Unemployment Rates and City Size 43
1.13.3 Adolescent Growth 44
1.13.4 Old Faithful Geyser Data 44
1.13.5 Evolution of Real Income Distribution in Italy, 1951-1998 45
1.14 Exercises 47
Chapter 2: Regression 57
2.1 Local Constant Kernel Estimation 60
2.1.1 Intuition Underlying the Local Constant Kernel Estimator 64
2.2 Local Constant Bandwidth Selection 66
2.2.1 Rule-of-Thumb and Plug-In Methods 66
2.2.2 Least Squares Cross-Validation 69
2.2.3 AICc 72
2.2.4 The Presence of Irrelevant Regressors 73
2.2.5 Some Further Results on Cross-Validation 78
2.3 Uniform Rates of Convergence 78
2.4 Local Linear Kernel Estimation 79
2.4.1 Local Linear Bandwidth Selection: Least Squares Cross-Validation 83
2.5 Local Polynomial Regression (General pth Order) 85
2.5.1 The Univariate Case 85
2.5.2 The Multivariate Case 88
2.5.3 Asymptotic Normality of Local Polynomial Estimators 89
2.6 Applications 92
2.6.1 Prestige Data 92
2.6.2 Adolescent Growth 92
2.6.3 Inflation Forecasting and Money Growth 93
2.7 Proofs 97
2.7.1 Derivation of (2.24) 98
2.7.2 Proof of Theorem 2.7 100
2.7.3 Definitions of Al,p+1 and Vl Used in Theorem 2.10 106
2.8 Exercises 108
Chapter 3: Frequency Estimation with Mixed Data 115
3.1 Probability Function Estimation with Discrete Data 116
3.2 Regression with Discrete Regressors 118
3.3 Estimation with Mixed Data: The Frequency Approach 118
3.3.1 Density Estimation with Mixed Data 118
3.3.2 Regression with Mixed Data 119
3.4 Some Cautionary Remarks on Frequency Methods 120
3.5 Proofs 122
3.5.1 Proof of Theorem 3.1 122
3.6 Exercises 123
Chapter 4: Kernel Estimation with Mixed Data 125
4.1 Smooth Estimation of Joint Distributions with Discrete Data 126
4.2 Smooth Regression with Discrete Data 131
4.3 Kernel Regression with Discrete Regressors: The Irrelevant Regressor Case 134
4.4 Regression with Mixed Data: Relevant Regressors 136
4.4.1 Smooth Estimation with Mixed Data 136
4.4.2 The Cross-Validation Method 138
4.5 Regression with Mixed Data: Irrelevant Regressors 140
4.5.1 Ordered Discrete Variables 144
4.6 Applications 145
4.6.1 Food-Away-from-Home Expenditure 145
4.6.2 Modeling Strike Volume 147
4.7 Exercises 150
Chapter 5: Conditional Density Estimation 155
5.1 Conditional Density Estimation: Relevant Variables 155
5.2 Conditional Density Bandwidth Selection 157
5.2.1 Least Squares Cross-Validation: Relevant Variables 157
5.2.2 Maximum Likelihood Cross-Validation: Relevant Variables 160
5.3 Conditional Density Estimation: Irrelevant Variables 162
5.4 The Multivariate Dependent Variables Case 164
5.4.1 The General Categorical Data Case 167
5.4.2 Proof of Theorem 5.5 168
5.5 Applications 171
5.5.1 A Nonparametric Analysis of Corruption 171
5.5.2 Extramarital Affairs Data 172
5.5.3 Married Female Labor Force Participation 175
5.5.4 Labor Productivity 177
5.5.5 Multivariate Y Conditional Density Example: GDP Growth and Population Growth Conditional on OECD Status 178
5.6 Exercises 180
Chapter 6: Conditional CDF and Quantile Estimation 181
6.1 Estimating a Conditional CDF with Continuous
Covariates without Smoothing the Dependent Variable 182
6.2 Estimating a Conditional CDF with Continuous Covariates Smoothing the Dependent Variable 184
6.3 Nonparametric Estimation of Conditional Quantile Functions 189
6.4 The Check Function Approach 191
6.5 Conditional CDF and Quantile Estimation with Mixed Discrete and Continuous Covariates 193
6.6 A Small Monte Carlo Simulation Study 196
6.7 Nonparametric Estimation of Hazard Functions 198
6.8 Applications 200
6.8.1 Boston Housing Data 200
6.8.2 Adolescent Growth Charts 202
6.8.3 Conditional Value at Risk 202
6.8.4 Real Income in Italy, 1951-1998 206
6.8.5 Multivariate Y Conditional CDF Example: GDP Growth and Population Growth Conditional on OECD Status 206
6.9 Proofs 209
6.9.1 Proofs of Theorems 6.1, 6.2, and 6.4 209
6.9.2 Proofs of Theorems 6.5 and 6.6 (Mixed Covariates Case) 214
6.10 Exercises 215
PART II: Semiparametric Methods 219
Chapter 7: Semiparametric Partially Linear Models 221
7.1 Partially Linear Models 222
7.1.1 Identification of 222
7.2 Robinson's Estimator 222
7.2.1 Estimation of the Nonparametric Component 228
7.3 Andrews's MINPIN Method 230
7.4 Semiparametric Efficiency Bounds 233
7.4.1 The Conditionally Homoskedastic Error Case 233
7.4.2 The Conditionally Heteroskedastic Error Case 235
7.5 Proofs 238
7.5.1 Proof of Theorem 7.2 238
7.5.2 Verifying Theorem 7.3 for a Partially Linear Model 244
7.6 Exercises 246
Chapter 8: Semiparametric Single Index Models 249
8.1 Identification Conditions 251
8.2 Estimation 253
8.2.1 Ichimura's Method 253
8.3 Direct Semiparametric Estimators for 258
8.3.1 Average Derivative Estimators 258
8.3.2 Estimation of g() 262
8.4 Bandwidth Selection 263
8.4.1 Bandwidth Selection for Ichimura's Method 263
8.4.2 Bandwidth Selection with Direct Estimation Methods 265
8.5 Klein and Spady's Estimator 266
8.6 Lewbel's Estimator 267
8.7 Manski's Maximum Score Estimator 269
8.8 Horowitz's Smoothed Maximum Score Estimator 270
8.9 Han's Maximum Rank Estimator 270
8.10 Multinomial Discrete Choice Models 271
8.11 Ai's Semiparametric Maximum Likelihood Approach 272
8.12 A Sketch of the Proof of Theorem 8.1 275
8.13 Applications 277
8.13.1 Modeling Response to Direct Marketing Catalog Mailings 277
8.14 Exercises 281
Chapter 9: Additive and Smooth (Varying) Coefficient Semiparametric Models 283
9.1 An Additive Model 283
9.1.1 The Marginal Integration Method 284
9.1.2 A Computationally Efficient Oracle Estimator 286
9.1.3 The Ordinary Backfitting Method 289
9.1.4 The Smoothed Backfitting Method 290
9.1.5 Additive Models with Link Functions 295
9.2 An Additive Partially Linear Model 297
9.2.1 A Simple Two-Step Method 299
9.3 A Semiparametric Varying (Smooth) Coefficient Model 301
9.3.1 A Local Constant Estimator of the Smooth Coefficient Function 302
9.3.2 A Local Linear Estimator of the Smooth Coefficient Function 303
9.3.3 Testing for a Parametric Smooth Coefficient Model 306
9.3.4 Partially Linear Smooth Coefficient Models 308
9.3.5 Proof of Theorem 9.3 310
9.4 Exercises 312
Chapter 10: Selectivity Models 315
10.1 Semiparametric Type-2 Tobit Models 316
10.2 Estimation of a Semiparametric Type-2 Tobit Model 317
10.2.1 Gallant and Nychka's Estimator 318
10.2.2 Estimation of the Intercept in Selection Models 319
10.3 Semiparametric Type-3 Tobit Models 320
10.3.1 Econometric Preliminaries 320
10.3.2 Alternative Estimation Methods 323
10.4 Das, Newey and Vella's Nonparametric Selection Model 328
10.5 Exercises 330
Chapter 11: Censored Models 331
11.1 Parametric Censored Models 332
11.2 Semiparametric Censored Regression Models 334
11.3 Semiparametric Censored Regression Models with Nonparametric Heteroskedasticity 336
11.4 The Univariate Kaplan-Meier CDF Estimator 338
11.5 The Multivariate Kaplan-Meier CDF Estimator 341
11.5.1 Nonparametric Regression Models with Random Censoring 343
11.6 Nonparametric Censored Regression 345
11.6.1 Lewbel and Linton's Approach 345
11.6.2 Chen, Dahl and Khan's Approach 346
11.7 Exercises 348
III Consistent Model Specification Tests 349
Chapter 12: Model Specification Tests 351
12.1 A Simple Consistent Test for Parametric Regression Functional Form 354
12.1.1 A Consistent Test for Correct Parametric Functional Form 355
12.1.2 Mixed Data 360
12.2 Testing for Equality of PDFs 362
12.3 More Tests Related to Regression Functions 365
12.3.1 Härdle and Mammen's Test for a Parametric Regression Model 365
12.3.2 An Adaptive and Rate Optimal Test 367
12.3.3 A Test for a Parametric Single Index Model 369
12.3.4 A Nonparametric Omitted Variables Test 370
12.3.5 Testing the Significance of Categorical Variables 375
12.4 Tests Related to PDFs 378
12.4.1 Testing Independence between Two Random Variables 378
12.4.2 A Test for a Parametric PDF 380
12.4.3 A Kernel Test for Conditional Parametric Distributions 382
12.5 Applications 385
12.5.1 Growth Convergence Clubs 385
12.6 Proofs 388
12.6.1 Proof of Theorem 12.1 388
12.6.2 Proof of Theorem 12.2 389
12.6.3 Proof of Theorem 12.5 389
12.6.4 Proof of Theorem 12.9 391
12.7 Exercises 394
Chapter 13: Nonsmoothing Tests 397
13.1 Testing for Parametric Regression Functional Form 398
13.2 Testing for Equality of PDFs 401
13.3 A Nonparametric Significance Test 401
13.4 Andrews's Test for Conditional CDFs 402
13.5 Hong's Tests for Serial Dependence 404
13.6 More on Nonsmoothing Tests 408
13.7 Proofs 409
13.7.1 Proof of Theorem 13.1 409
13.8 Exercises 410
PART IV: Nonparametric Nearest Neighbor and Series Methods 413
Chapter 14: K-Nearest Neighbor Methods 415
14.1 Density Estimation: The Univariate Case 415
14.2 Regression Function Estimation 419
14.3 A Local Linear k-nn Estimator 421
14.4 Cross-Validation with Local Constant k-nn Estimation 422
14.5 Cross-Validation with Local Linear k-nn Estimation 425
14.6 Estimation of Semiparametric Models with k-nn Methods 427
14.7 Model Specification Tests with k-nn Methods 428
14.7.1 A Bootstrap Test 431
14.8 Using Different k for Different Components of x 432
14.9 Proofs 432
14.9.1 Proof of Theorem 14.1 435
14.9.2 Proof of Theorem 14.5 435
14.9.3 Proof of Theorem 14.10 440
14.10 Exercises 444
Chapter 15: Nonparametric Series Methods 445
15.1 Estimating Regression Functions 446
15.1.1 Convergence Rates 449
15.2 Selection of the Series Term K 451
15.2.1 Asymptotic Normality 453
15.3 A Partially Linear Model 454
15.3.1 An Additive Partially Linear Model 455
15.3.2 Selection of Nonlinear Additive Components 461
15.3.3 Estimating an Additive Model with a Known Link Function 463
15.4 Estimation of Partially Linear Varying Coefficient Models 466
15.4.1 Testing for Correct Parametric Regression Functional Form 471
15.4.2 A Consistent Test for an Additive Partially Linear Model 474
15.5 Other Series-Based Tests 479
15.6 Proofs 480
15.6.1 Proof of Theorem 15.1 480
15.6.2 Proof of Theorem 15.3 484
15.6.3 Proof of Theorem 15.6 488
15.6.4 Proof of Theorem 15.9 492
15.6.5 Proof of Theorem 15.10 497
15.7 Exercises 502
PART V: Time Series, Simultaneous Equation, and Panel Data Models 503
Chapter 16: Instrumental Variables and Efficient Estimation of Semiparametric Models 505
16.1 A Partially Linear Model with Endogenous Regressors in the Parametric Part 505
16.2 A Varying Coefficient Model with Endogenous Regressors in the Parametric Part 509
16.3 Ai and Chen's Efficient Estimator with Conditional Moment Restrictions 511
16.3.1 Estimation Procedures 511
16.3.2 Asymptotic Normality for 513
16.3.3 A Partially Linear Model with the Endogenous Regressors in the Nonparametric Part 515
16.4 Proof of Equation (16.16) 517
16.5 Exercises 520
Chapter 17: Endogeneity in Nonparametric Regression Models 521
17.1 A Nonparametric Model 521
17.2 A Triangular Simultaneous Equation Model 522
17.3 Newey-Powell Series-Based Estimator 527
17.4 Hall and Horowitz's Kernel-Based Estimator 529
17.5 Darolles, Florens and Renault's Estimator 532
17.6 Exercises 533
Chapter 18: Weakly Dependent Data 535
18.1 Density Estimation with Dependent Data 537
18.1.1 Uniform Almost Sure Rate of Convergence 541
18.2 Regression Models with Dependent Data 541
18.2.1 The Martingale Difference Error Case 541
18.2.2 The Autocorrelated Error Case 544
18.2.3 One-Step-Ahead Forecasting 546
18.2.4 d-Step-Ahead Forecasting 547
18.2.5 Estimation of Nonparametric Impulse Response Functions 548
18.3 Semiparametric Models with Dependent Data 551
18.3.1 A Partially Linear Model with Dependent
Data 551
18.3.2 Additive Regression Models 552
18.3.3 Varying Coefficient Models with Dependent Data 553
18.4 Testing for Serial Correlation in Semiparametric Models 554
18.4.1 The Test Statistic and Its Asymptotic
Distribution 554
18.4.2 Testing Zero First Order Serial Correlation 555
18.5 Model Specification Tests with Dependent Data 556
18.5.1 A Kernel Test for Correct Parametric Regression Functional Form 556
18.5.2 Nonparametric Significance Tests 557
18.6 Nonsmoothing Tests for Regression Functional Form 558
18.7 Testing Parametric Predictive Models 559
18.7.1 In-Sample Testing of Conditional CDFs 559
18.7.2 Out-of-Sample Testing of Conditional CDFs 562
18.8 Applications 564
18.8.1 Forecasting Short-Term Interest Rates 564
18.9 Nonparametric Estimation with Nonstationary Data 566
18.10 Proofs 567
18.10.1 Proof of Equation (18.9) 567
18.10.2 Proof of Theorem 18.2 569
18.11 Exercises 572
Chapter 19: Panel Data Models 575
19.1 Nonparametric Estimation of Panel Data Models: Ignoring the Variance Structure 576
19.2 Wang's Efficient Nonparametric Panel Data Estimator 578
19.3 A Partially Linear Model with Random Effects 584
19.4 Nonparametric Panel Data Models with Fixed Effects 586
19.4.1 Error Variance Structure Is Known 587
19.4.2 The Error Variance Structure Is Unknown 590
19.5 A Partially Linear Model with Fixed Effects 592
19.6 Semiparametric Instrumental Variable Estimators 594
19.6.1 An Infeasible Estimator 594
19.6.2 The Choice of Instruments 595
19.6.3 A Feasible Estimator 597
19.7 Testing for Serial Correlation and for Individual Effects in Semiparametric Models 599
19.8 Series Estimation of Panel Data Models 602
19.8.1 Additive Effects 602
19.8.2 Alternative Formulation of Fixed Effects 604
19.9 Nonlinear Panel Data Models 606
19.9.1 Censored Panel Data Models 607
19.9.2 Discrete Choice Panel Data Models 614
19.10 Proofs 618
19.10.1 Proof of Theorem 19.1 618
19.10.2 Leading MSE Calculation of Wang's Estimator 621
19.11 Exercises 624
Chapter 20: Topics in Applied Nonparametric Estimation 627
20.1 Nonparametric Methods in Continuous-Time Models 627
20.1.1 Nonparametric Estimation of Continuous-Time Models 627
20.1.2 Nonparametric Tests for Continuous-Time Models 632
20.1.3 Ait-Sahalia's Test 632
20.1.4 Hong and Li's Test 633
20.1.5 Proofs 636
20.2 Nonparametric Estimation of Average Treatment Effects 639
20.2.1 The Model 640
20.2.2 An Application: Assessing the Efficacy of Right Heart Catheterization 642
20.3 Nonparametric Estimation of Auction Models 645
20.3.1 Estimation of First Price Auction Models 645
20.3.2 Conditionally Independent Private Information Auctions 648
20.4 Copula-Based Semiparametric Estimation of Multivariate Distributions 651
20.4.1 Some Background on Copula Functions 651
20.4.2 Semiparametric Copula-Based Multivariate Distributions 652
20.4.3 A Two-Step Estimation Procedure 653
20.4.4 A One-Step Efficient Estimation Procedure 655
20.4.5 Testing Parametric Functional Forms of a Copula 657
20.5 A Semiparametric Transformation Model 659
20.6 Exercises 662
A Background Statistical Concepts 663
1.1 Probability, Measure, and Measurable Space 663
1.2 Metric, Norm, and Functional Spaces 672
1.3 Limits and Modes of Convergence 680
1.3.1 Limit Supremum and Limit Infimum 680
1.3.2 Modes of Convergence 681
1.4 Inequalities, Laws of Large Numbers, and Central Limit Theorems 688
1.5 Exercises 694
Bibliography 697
Author Index 737
Subject Index 744
· · · · · · (收起)
Preface xvii
PART I: Nonparametric Kernel Methods 1
Chapter 1: Density Estimation 3
1.1 Univariate Density Estimation 4
1.2 Univariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods 14
1.3 Univariate Bandwidth Selection: Cross-Validation ZMethods 15
1.3.1 Least Squares Cross-Validation 15
1.3.2 Likelihood Cross-Validation 18
1.3.3 An Illustration of Data-Driven Bandwidth Selection 19
1.4 Univariate CDF Estimation 19
1.5 Univariate CDF Bandwidth Selection: Cross- Validation Methods 23
1.6 Multivariate Density Estimation 24
1.7 Multivariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods 26
1.8 Multivariate Bandwidth Selection: Cross-Validation Methods 27
1.8.1 Least Squares Cross-Validation 27
1.8.2 Likelihood Cross-Validation 28
1.9 Asymptotic Normality of Density Estimators 28
1.10 Uniform Rates of Convergence 30
1.11 Higher Order Kernel Functions 33
1.12 Proof of Theorem 1.4 (Uniform Almost Sure Convergence) 35
1.13 Applications 40
1.13.1 Female Wage Inequality 41
1.13.2 Unemployment Rates and City Size 43
1.13.3 Adolescent Growth 44
1.13.4 Old Faithful Geyser Data 44
1.13.5 Evolution of Real Income Distribution in Italy, 1951-1998 45
1.14 Exercises 47
Chapter 2: Regression 57
2.1 Local Constant Kernel Estimation 60
2.1.1 Intuition Underlying the Local Constant Kernel Estimator 64
2.2 Local Constant Bandwidth Selection 66
2.2.1 Rule-of-Thumb and Plug-In Methods 66
2.2.2 Least Squares Cross-Validation 69
2.2.3 AICc 72
2.2.4 The Presence of Irrelevant Regressors 73
2.2.5 Some Further Results on Cross-Validation 78
2.3 Uniform Rates of Convergence 78
2.4 Local Linear Kernel Estimation 79
2.4.1 Local Linear Bandwidth Selection: Least Squares Cross-Validation 83
2.5 Local Polynomial Regression (General pth Order) 85
2.5.1 The Univariate Case 85
2.5.2 The Multivariate Case 88
2.5.3 Asymptotic Normality of Local Polynomial Estimators 89
2.6 Applications 92
2.6.1 Prestige Data 92
2.6.2 Adolescent Growth 92
2.6.3 Inflation Forecasting and Money Growth 93
2.7 Proofs 97
2.7.1 Derivation of (2.24) 98
2.7.2 Proof of Theorem 2.7 100
2.7.3 Definitions of Al,p+1 and Vl Used in Theorem 2.10 106
2.8 Exercises 108
Chapter 3: Frequency Estimation with Mixed Data 115
3.1 Probability Function Estimation with Discrete Data 116
3.2 Regression with Discrete Regressors 118
3.3 Estimation with Mixed Data: The Frequency Approach 118
3.3.1 Density Estimation with Mixed Data 118
3.3.2 Regression with Mixed Data 119
3.4 Some Cautionary Remarks on Frequency Methods 120
3.5 Proofs 122
3.5.1 Proof of Theorem 3.1 122
3.6 Exercises 123
Chapter 4: Kernel Estimation with Mixed Data 125
4.1 Smooth Estimation of Joint Distributions with Discrete Data 126
4.2 Smooth Regression with Discrete Data 131
4.3 Kernel Regression with Discrete Regressors: The Irrelevant Regressor Case 134
4.4 Regression with Mixed Data: Relevant Regressors 136
4.4.1 Smooth Estimation with Mixed Data 136
4.4.2 The Cross-Validation Method 138
4.5 Regression with Mixed Data: Irrelevant Regressors 140
4.5.1 Ordered Discrete Variables 144
4.6 Applications 145
4.6.1 Food-Away-from-Home Expenditure 145
4.6.2 Modeling Strike Volume 147
4.7 Exercises 150
Chapter 5: Conditional Density Estimation 155
5.1 Conditional Density Estimation: Relevant Variables 155
5.2 Conditional Density Bandwidth Selection 157
5.2.1 Least Squares Cross-Validation: Relevant Variables 157
5.2.2 Maximum Likelihood Cross-Validation: Relevant Variables 160
5.3 Conditional Density Estimation: Irrelevant Variables 162
5.4 The Multivariate Dependent Variables Case 164
5.4.1 The General Categorical Data Case 167
5.4.2 Proof of Theorem 5.5 168
5.5 Applications 171
5.5.1 A Nonparametric Analysis of Corruption 171
5.5.2 Extramarital Affairs Data 172
5.5.3 Married Female Labor Force Participation 175
5.5.4 Labor Productivity 177
5.5.5 Multivariate Y Conditional Density Example: GDP Growth and Population Growth Conditional on OECD Status 178
5.6 Exercises 180
Chapter 6: Conditional CDF and Quantile Estimation 181
6.1 Estimating a Conditional CDF with Continuous
Covariates without Smoothing the Dependent Variable 182
6.2 Estimating a Conditional CDF with Continuous Covariates Smoothing the Dependent Variable 184
6.3 Nonparametric Estimation of Conditional Quantile Functions 189
6.4 The Check Function Approach 191
6.5 Conditional CDF and Quantile Estimation with Mixed Discrete and Continuous Covariates 193
6.6 A Small Monte Carlo Simulation Study 196
6.7 Nonparametric Estimation of Hazard Functions 198
6.8 Applications 200
6.8.1 Boston Housing Data 200
6.8.2 Adolescent Growth Charts 202
6.8.3 Conditional Value at Risk 202
6.8.4 Real Income in Italy, 1951-1998 206
6.8.5 Multivariate Y Conditional CDF Example: GDP Growth and Population Growth Conditional on OECD Status 206
6.9 Proofs 209
6.9.1 Proofs of Theorems 6.1, 6.2, and 6.4 209
6.9.2 Proofs of Theorems 6.5 and 6.6 (Mixed Covariates Case) 214
6.10 Exercises 215
PART II: Semiparametric Methods 219
Chapter 7: Semiparametric Partially Linear Models 221
7.1 Partially Linear Models 222
7.1.1 Identification of 222
7.2 Robinson's Estimator 222
7.2.1 Estimation of the Nonparametric Component 228
7.3 Andrews's MINPIN Method 230
7.4 Semiparametric Efficiency Bounds 233
7.4.1 The Conditionally Homoskedastic Error Case 233
7.4.2 The Conditionally Heteroskedastic Error Case 235
7.5 Proofs 238
7.5.1 Proof of Theorem 7.2 238
7.5.2 Verifying Theorem 7.3 for a Partially Linear Model 244
7.6 Exercises 246
Chapter 8: Semiparametric Single Index Models 249
8.1 Identification Conditions 251
8.2 Estimation 253
8.2.1 Ichimura's Method 253
8.3 Direct Semiparametric Estimators for 258
8.3.1 Average Derivative Estimators 258
8.3.2 Estimation of g() 262
8.4 Bandwidth Selection 263
8.4.1 Bandwidth Selection for Ichimura's Method 263
8.4.2 Bandwidth Selection with Direct Estimation Methods 265
8.5 Klein and Spady's Estimator 266
8.6 Lewbel's Estimator 267
8.7 Manski's Maximum Score Estimator 269
8.8 Horowitz's Smoothed Maximum Score Estimator 270
8.9 Han's Maximum Rank Estimator 270
8.10 Multinomial Discrete Choice Models 271
8.11 Ai's Semiparametric Maximum Likelihood Approach 272
8.12 A Sketch of the Proof of Theorem 8.1 275
8.13 Applications 277
8.13.1 Modeling Response to Direct Marketing Catalog Mailings 277
8.14 Exercises 281
Chapter 9: Additive and Smooth (Varying) Coefficient Semiparametric Models 283
9.1 An Additive Model 283
9.1.1 The Marginal Integration Method 284
9.1.2 A Computationally Efficient Oracle Estimator 286
9.1.3 The Ordinary Backfitting Method 289
9.1.4 The Smoothed Backfitting Method 290
9.1.5 Additive Models with Link Functions 295
9.2 An Additive Partially Linear Model 297
9.2.1 A Simple Two-Step Method 299
9.3 A Semiparametric Varying (Smooth) Coefficient Model 301
9.3.1 A Local Constant Estimator of the Smooth Coefficient Function 302
9.3.2 A Local Linear Estimator of the Smooth Coefficient Function 303
9.3.3 Testing for a Parametric Smooth Coefficient Model 306
9.3.4 Partially Linear Smooth Coefficient Models 308
9.3.5 Proof of Theorem 9.3 310
9.4 Exercises 312
Chapter 10: Selectivity Models 315
10.1 Semiparametric Type-2 Tobit Models 316
10.2 Estimation of a Semiparametric Type-2 Tobit Model 317
10.2.1 Gallant and Nychka's Estimator 318
10.2.2 Estimation of the Intercept in Selection Models 319
10.3 Semiparametric Type-3 Tobit Models 320
10.3.1 Econometric Preliminaries 320
10.3.2 Alternative Estimation Methods 323
10.4 Das, Newey and Vella's Nonparametric Selection Model 328
10.5 Exercises 330
Chapter 11: Censored Models 331
11.1 Parametric Censored Models 332
11.2 Semiparametric Censored Regression Models 334
11.3 Semiparametric Censored Regression Models with Nonparametric Heteroskedasticity 336
11.4 The Univariate Kaplan-Meier CDF Estimator 338
11.5 The Multivariate Kaplan-Meier CDF Estimator 341
11.5.1 Nonparametric Regression Models with Random Censoring 343
11.6 Nonparametric Censored Regression 345
11.6.1 Lewbel and Linton's Approach 345
11.6.2 Chen, Dahl and Khan's Approach 346
11.7 Exercises 348
III Consistent Model Specification Tests 349
Chapter 12: Model Specification Tests 351
12.1 A Simple Consistent Test for Parametric Regression Functional Form 354
12.1.1 A Consistent Test for Correct Parametric Functional Form 355
12.1.2 Mixed Data 360
12.2 Testing for Equality of PDFs 362
12.3 More Tests Related to Regression Functions 365
12.3.1 Härdle and Mammen's Test for a Parametric Regression Model 365
12.3.2 An Adaptive and Rate Optimal Test 367
12.3.3 A Test for a Parametric Single Index Model 369
12.3.4 A Nonparametric Omitted Variables Test 370
12.3.5 Testing the Significance of Categorical Variables 375
12.4 Tests Related to PDFs 378
12.4.1 Testing Independence between Two Random Variables 378
12.4.2 A Test for a Parametric PDF 380
12.4.3 A Kernel Test for Conditional Parametric Distributions 382
12.5 Applications 385
12.5.1 Growth Convergence Clubs 385
12.6 Proofs 388
12.6.1 Proof of Theorem 12.1 388
12.6.2 Proof of Theorem 12.2 389
12.6.3 Proof of Theorem 12.5 389
12.6.4 Proof of Theorem 12.9 391
12.7 Exercises 394
Chapter 13: Nonsmoothing Tests 397
13.1 Testing for Parametric Regression Functional Form 398
13.2 Testing for Equality of PDFs 401
13.3 A Nonparametric Significance Test 401
13.4 Andrews's Test for Conditional CDFs 402
13.5 Hong's Tests for Serial Dependence 404
13.6 More on Nonsmoothing Tests 408
13.7 Proofs 409
13.7.1 Proof of Theorem 13.1 409
13.8 Exercises 410
PART IV: Nonparametric Nearest Neighbor and Series Methods 413
Chapter 14: K-Nearest Neighbor Methods 415
14.1 Density Estimation: The Univariate Case 415
14.2 Regression Function Estimation 419
14.3 A Local Linear k-nn Estimator 421
14.4 Cross-Validation with Local Constant k-nn Estimation 422
14.5 Cross-Validation with Local Linear k-nn Estimation 425
14.6 Estimation of Semiparametric Models with k-nn Methods 427
14.7 Model Specification Tests with k-nn Methods 428
14.7.1 A Bootstrap Test 431
14.8 Using Different k for Different Components of x 432
14.9 Proofs 432
14.9.1 Proof of Theorem 14.1 435
14.9.2 Proof of Theorem 14.5 435
14.9.3 Proof of Theorem 14.10 440
14.10 Exercises 444
Chapter 15: Nonparametric Series Methods 445
15.1 Estimating Regression Functions 446
15.1.1 Convergence Rates 449
15.2 Selection of the Series Term K 451
15.2.1 Asymptotic Normality 453
15.3 A Partially Linear Model 454
15.3.1 An Additive Partially Linear Model 455
15.3.2 Selection of Nonlinear Additive Components 461
15.3.3 Estimating an Additive Model with a Known Link Function 463
15.4 Estimation of Partially Linear Varying Coefficient Models 466
15.4.1 Testing for Correct Parametric Regression Functional Form 471
15.4.2 A Consistent Test for an Additive Partially Linear Model 474
15.5 Other Series-Based Tests 479
15.6 Proofs 480
15.6.1 Proof of Theorem 15.1 480
15.6.2 Proof of Theorem 15.3 484
15.6.3 Proof of Theorem 15.6 488
15.6.4 Proof of Theorem 15.9 492
15.6.5 Proof of Theorem 15.10 497
15.7 Exercises 502
PART V: Time Series, Simultaneous Equation, and Panel Data Models 503
Chapter 16: Instrumental Variables and Efficient Estimation of Semiparametric Models 505
16.1 A Partially Linear Model with Endogenous Regressors in the Parametric Part 505
16.2 A Varying Coefficient Model with Endogenous Regressors in the Parametric Part 509
16.3 Ai and Chen's Efficient Estimator with Conditional Moment Restrictions 511
16.3.1 Estimation Procedures 511
16.3.2 Asymptotic Normality for 513
16.3.3 A Partially Linear Model with the Endogenous Regressors in the Nonparametric Part 515
16.4 Proof of Equation (16.16) 517
16.5 Exercises 520
Chapter 17: Endogeneity in Nonparametric Regression Models 521
17.1 A Nonparametric Model 521
17.2 A Triangular Simultaneous Equation Model 522
17.3 Newey-Powell Series-Based Estimator 527
17.4 Hall and Horowitz's Kernel-Based Estimator 529
17.5 Darolles, Florens and Renault's Estimator 532
17.6 Exercises 533
Chapter 18: Weakly Dependent Data 535
18.1 Density Estimation with Dependent Data 537
18.1.1 Uniform Almost Sure Rate of Convergence 541
18.2 Regression Models with Dependent Data 541
18.2.1 The Martingale Difference Error Case 541
18.2.2 The Autocorrelated Error Case 544
18.2.3 One-Step-Ahead Forecasting 546
18.2.4 d-Step-Ahead Forecasting 547
18.2.5 Estimation of Nonparametric Impulse Response Functions 548
18.3 Semiparametric Models with Dependent Data 551
18.3.1 A Partially Linear Model with Dependent
Data 551
18.3.2 Additive Regression Models 552
18.3.3 Varying Coefficient Models with Dependent Data 553
18.4 Testing for Serial Correlation in Semiparametric Models 554
18.4.1 The Test Statistic and Its Asymptotic
Distribution 554
18.4.2 Testing Zero First Order Serial Correlation 555
18.5 Model Specification Tests with Dependent Data 556
18.5.1 A Kernel Test for Correct Parametric Regression Functional Form 556
18.5.2 Nonparametric Significance Tests 557
18.6 Nonsmoothing Tests for Regression Functional Form 558
18.7 Testing Parametric Predictive Models 559
18.7.1 In-Sample Testing of Conditional CDFs 559
18.7.2 Out-of-Sample Testing of Conditional CDFs 562
18.8 Applications 564
18.8.1 Forecasting Short-Term Interest Rates 564
18.9 Nonparametric Estimation with Nonstationary Data 566
18.10 Proofs 567
18.10.1 Proof of Equation (18.9) 567
18.10.2 Proof of Theorem 18.2 569
18.11 Exercises 572
Chapter 19: Panel Data Models 575
19.1 Nonparametric Estimation of Panel Data Models: Ignoring the Variance Structure 576
19.2 Wang's Efficient Nonparametric Panel Data Estimator 578
19.3 A Partially Linear Model with Random Effects 584
19.4 Nonparametric Panel Data Models with Fixed Effects 586
19.4.1 Error Variance Structure Is Known 587
19.4.2 The Error Variance Structure Is Unknown 590
19.5 A Partially Linear Model with Fixed Effects 592
19.6 Semiparametric Instrumental Variable Estimators 594
19.6.1 An Infeasible Estimator 594
19.6.2 The Choice of Instruments 595
19.6.3 A Feasible Estimator 597
19.7 Testing for Serial Correlation and for Individual Effects in Semiparametric Models 599
19.8 Series Estimation of Panel Data Models 602
19.8.1 Additive Effects 602
19.8.2 Alternative Formulation of Fixed Effects 604
19.9 Nonlinear Panel Data Models 606
19.9.1 Censored Panel Data Models 607
19.9.2 Discrete Choice Panel Data Models 614
19.10 Proofs 618
19.10.1 Proof of Theorem 19.1 618
19.10.2 Leading MSE Calculation of Wang's Estimator 621
19.11 Exercises 624
Chapter 20: Topics in Applied Nonparametric Estimation 627
20.1 Nonparametric Methods in Continuous-Time Models 627
20.1.1 Nonparametric Estimation of Continuous-Time Models 627
20.1.2 Nonparametric Tests for Continuous-Time Models 632
20.1.3 Ait-Sahalia's Test 632
20.1.4 Hong and Li's Test 633
20.1.5 Proofs 636
20.2 Nonparametric Estimation of Average Treatment Effects 639
20.2.1 The Model 640
20.2.2 An Application: Assessing the Efficacy of Right Heart Catheterization 642
20.3 Nonparametric Estimation of Auction Models 645
20.3.1 Estimation of First Price Auction Models 645
20.3.2 Conditionally Independent Private Information Auctions 648
20.4 Copula-Based Semiparametric Estimation of Multivariate Distributions 651
20.4.1 Some Background on Copula Functions 651
20.4.2 Semiparametric Copula-Based Multivariate Distributions 652
20.4.3 A Two-Step Estimation Procedure 653
20.4.4 A One-Step Efficient Estimation Procedure 655
20.4.5 Testing Parametric Functional Forms of a Copula 657
20.5 A Semiparametric Transformation Model 659
20.6 Exercises 662
A Background Statistical Concepts 663
1.1 Probability, Measure, and Measurable Space 663
1.2 Metric, Norm, and Functional Spaces 672
1.3 Limits and Modes of Convergence 680
1.3.1 Limit Supremum and Limit Infimum 680
1.3.2 Modes of Convergence 681
1.4 Inequalities, Laws of Large Numbers, and Central Limit Theorems 688
1.5 Exercises 694
Bibliography 697
Author Index 737
Subject Index 744
· · · · · · (收起)
读后感
评分
我预计这本书流行不起来，原因很简单，写得太复杂。证明全整高维，首先高维模型的东西在现实中其实没啥用，其次，高维问题只是一维或二维的推广，完全可以放到exercise里面，所以这本书估计看的人不会太多。难的问题可以写得让人容易接受，可惜这本书没有做到。 另外一个缺点是...
评分我预计这本书流行不起来，原因很简单，写得太复杂。证明全整高维，首先高维模型的东西在现实中其实没啥用，其次，高维问题只是一维或二维的推广，完全可以放到exercise里面，所以这本书估计看的人不会太多。难的问题可以写得让人容易接受，可惜这本书没有做到。 另外一个缺点是...
评分我预计这本书流行不起来，原因很简单，写得太复杂。证明全整高维，首先高维模型的东西在现实中其实没啥用，其次，高维问题只是一维或二维的推广，完全可以放到exercise里面，所以这本书估计看的人不会太多。难的问题可以写得让人容易接受，可惜这本书没有做到。 另外一个缺点是...
评分我预计这本书流行不起来，原因很简单，写得太复杂。证明全整高维，首先高维模型的东西在现实中其实没啥用，其次，高维问题只是一维或二维的推广，完全可以放到exercise里面，所以这本书估计看的人不会太多。难的问题可以写得让人容易接受，可惜这本书没有做到。 另外一个缺点是...
评分我预计这本书流行不起来，原因很简单，写得太复杂。证明全整高维，首先高维模型的东西在现实中其实没啥用，其次，高维问题只是一维或二维的推广，完全可以放到exercise里面，所以这本书估计看的人不会太多。难的问题可以写得让人容易接受，可惜这本书没有做到。 另外一个缺点是...
用户评价
评分
我花了很长时间寻找一本能真正系统讲解现代微观经济学理论的书籍，最终选择了这本《现代经济学原理》。它最大的亮点在于其严谨的逻辑框架和对行为经济学最新进展的整合。不同于很多传统教材，这本书在均衡分析部分，并没有满足于静态的分析，而是花费了大量篇幅去探讨动态博弈的策略演化，这对于理解市场结构的长期演变至关重要。作者在阐述效用最大化时，非常巧妙地引入了心理学视角，讨论了禀赋效应和损失厌恶如何扭曲消费者的选择边界。章节之间的衔接处理得极为流畅，前面对消费者剩余的讨论，自然而然地引出了对外部性的讨论，为后续的福利经济学奠定了坚实的基础。我尤其喜欢它在每一章末尾设置的“前沿思考”栏目，它们通常涉及一些尚未完全解决的学术难题，极大地激发了我的批判性思维，让我不再满足于接受既定结论，而是开始主动质疑和探索。
评分我对宏观经济学一直抱有敬畏之心，总觉得里面的模型太过宏大，难以把握。然而，这本《全球宏观经济学：从理论到政策》彻底改变了我的看法。作者采用了“自下而上”的教学策略，先从简单的异质性代理人模型入手，逐步构建出包含财政和货币政策相互作用的动态随机一般均衡（DSGE）模型。这种循序渐进的方式，极大地降低了理解复杂机制的门槛。书中对财政政策的代际影响分析尤为精彩，它清晰地展示了当前赤字是如何在代际之间转移负担的，配以清晰的IS-LM-FEER框架的扩展应用，让人茅塞顿开。更难能可贵的是，它没有将发达经济体作为唯一的分析对象，而是花了不少篇幅讨论了新兴市场在资本管制和汇率波动下的宏观政策选择，这极大地拓宽了我的全球视野。整本书的结构设计，仿佛是在引导读者一步步攀登思想的高峰，每一步都有清晰的脚印可循。
评分这本《计量经济学导论》简直是打开了我对这个学科的全新认知。作者以一种极其直观且引人入胜的方式，将那些原本晦涩难懂的理论概念一一剖析开来，仿佛在进行一场精心编排的思维漫步。特别是它在处理时间序列分析时的那种细腻笔触，让我这个初学者也能领会到模型设定的精髓所在。书中没有过多纠缠于那些繁复的数学推导，而是将重点放在了经济学直觉与实际应用之间搭建桥梁上。例如，在解释内生性问题时，作者没有直接抛出复杂的工具变量估计公式，而是通过一个贴近生活的例子，让我们深刻理解为什么传统 OLS 会失效，以及我们应该如何修正。这种叙事风格，让学习过程不再是枯燥的知识灌输，而更像是一场充满发现的旅程。我特别欣赏它在处理数据可视化和报告解读上的详尽指导，这对于未来从事政策分析或市场研究的人来说，是无价之宝。可以说，这本书成功地将计量经济学从象牙塔中拉了出来，让它变得触手可及且充满力量。
评分这本《金融市场与机构分析》绝对是为金融从业者量身定做的教科书。它的深度和广度都令人印象深刻，尤其是在描述债券定价模型和衍生品结构设计时，展现出了惊人的专业水准。这本书的叙述方式非常务实，每一个理论推导后面紧跟着的就是实际市场中的案例应用。我曾尝试阅读一些侧重于理论证明的金融著作，结果往往是高不成低不就，但这本书不同，它平衡得恰到好处。对于期权定价中的波动率微笑现象，作者不仅解释了它存在的事实，更深入剖析了支撑这些现象背后的交易者行为和市场微结构因素。对于那些希望深入了解资产证券化流程和风险隔离机制的人来说，书中的那一章简直就是一本操作手册。它没有使用过于华丽的辞藻，而是用精准、量化的语言构建了一个清晰的金融世界图景，读完后，我对现代金融体系的复杂运作有了更深刻、更踏实的理解。
评分这本书《高级统计推断方法》的阅读体验，是一次对思维耐力的终极考验，但回报是巨大的。它毫不留情地将读者推向了统计学方法论的最前沿。书中关于贝叶斯方法与频率学派方法的深入辩论，不仅仅停留在哲学层面，而是通过具体的计算示例展示了两者在处理小样本问题时的实际差异。我尤其对作者在处理高维数据降维技术（如主成分回归与因子分析）时所展现的数学功底印象深刻，他对这些方法背后的假设条件和适用范围做了极其审慎的论证。阅读过程中，我不得不经常停下来，拿起笔进行反复的验算，因为它对读者的数学基础要求极高，任何基础知识的薄弱都会导致理解上的巨大障碍。对于那些期望从“会用”统计软件到“理解”统计模型背后的数学原理的科研人员来说，这本书是不可替代的工具书。它不是用来快速查阅的，而是需要沉下心来，逐字逐句进行消化的经典之作，它训练的不仅仅是技能，更是严谨的科学精神。
评分看了作者写的一个小册子primer，感觉条理很清楚。
评分看了作者写的一个小册子primer，感觉条理很清楚。
评分看了作者写的一个小册子primer，感觉条理很清楚。
评分看了作者写的一个小册子primer，感觉条理很清楚。
评分看了作者写的一个小册子primer，感觉条理很清楚。
相关图书
本站所有内容均为互联网搜索引擎提供的公开搜索信息，本站不存储任何数据与内容，任何内容与数据均与本站无关，如有需要请联系相关搜索引擎包括但不限于百度，google,bing,sogou 等
© 2026 book.wenda123.org All Rights Reserved. 图书目录大全 版权所有