Preface	6
	Contents	8
	List of Tables	15
	List of Figures	23
	Introduction	24
	1.1 Problematic illustration	25
	1.2 Jaynes’ desiderata for scientific reasoning	27
	1.3 Overview	30
	1.4 Additional reading	31
	Accounting choice	32
	2.1 Equilibrium earnings management	33
	2.2 Asset revaluation regulation	35
	2.3 Regulated report precision	37
	2.4 Inferring transactions from financial statements	40
	2.5 Additional reading	41
	Linear models	42
	3.1 Standard linear model (	42
	3.2 Generalized least squares (	44
	3.3 Tests of restrictions and	45
	(Frisch-Waugh-Lovell)	45
	3.4 Fixed and random effects	49
	3.5 Random coefficients	54
	3.6 Ubiquity of the Gaussian distribution	56
	3.7 Interval estimation	59
	3.8 Asymptotic tests of restrictions: Wald,	61
	statistics	61
	3.9 Misspecification and	64
	estimation	64
	3.10 Proxy variables	66
	3.11 Equilibrium earnings management	71
	3.12 Additional reading	77
	3.13 Appendix	78
	Loss functions and estimation	81
	4.1 Loss functions	81
	4.2 Nonlinear Regression	84
	4.3 Maximum likelihood estimation	87
	4.4 James-Stein shrinkage estimators	92
	4.5 Summary	97
	4.6 Additional reading	98
	Discrete choice models	99
	5.1 Latent utility index models	99
	5.2 Linear probability models	100
	5.3 Logit (logistic regression) models	100
	5.4 Probit models	108
	5.5 Robust choice models	114
	5.6 Tobit (censored regression) models	116
	5.7 Bayesian data augmentation	116
	5.8 Additional reading	117
	Nonparametric regression	118
	6.1 Nonparametric (kernel) regression	118
	6.2 Semiparametric regression models	120
	6.3 Specification testing against a general nonparametric benchmark	122
	6.4 Locally linear regression	124
	6.5 Generalized cross-validation (	125
	6.6 Additional reading	126
	Repeated-sampling inference	127
	7.1 Monte Carlo simulation	128
	7.2 Bootstrap	128
	7.3 Bayesian simulation	131
	7.4 Additional reading	142
	Overview of endogeneity	143
	8.1 Overview	144
	8.2 Selectivity and treatment effects	167
	8.3 Why bother with endogeneity?	168
	8.4 Discussion and concluding remarks	175
	8.5 Additional reading	175
	Treatment effects: ignorability	177
	9.1 A prototypical selection setting	177
	9.2 Exogenous dummy variable regression	178
	9.3 Tuebingen-style examples	179
	9.4 Nonparametric identification	184
	9.5 Propensity score approaches	189
	9.6 Propensity score matching	192
	9.7 Asset revaluation regulation example	195
	9.8 Control function approaches	223
	9.9 Summary	224
	9.10 Additional reading	224
	Treatment effects:	226
	10.1 Setup	226
	10.2 Treatment effects	227
	10.3 Generalized Roy model	229
	10.4 Homogeneous response	230
	10.5 Heterogeneous response and treatment effects	231
	10.6 Continuous treatment	255
	10.7 Regulated report precision	258
	10.8 Summary	292
	10.9 Additional reading	292
	Marginal treatment effects	293
	11.1 Policy evaluation and policy invariance conditions	293
	11.2 Setup	295
	11.3 Generalized Roy model	295
	11.4 Identification	296
	connections to other treatment effects	298
	11.5	298
	11.6 Comparison of identification strategies	304
	11.7	304
	estimation	304
	11.8 Discrete outcomes	306
	11.9 Distributions of treatment effects	309
	11.10 Dynamic timing of treatment	310
	11.11 General equilibrium effects	311
	11.12 Regulated report precision example	311
	11.13 Additional reading	318
	Bayesian treatment effects	319
	12.1 Setup	320
	12.2 Bounds and learning	320
	12.3 Gibbs sampler	321
	12.4 Predictive distributions	323
	12.5 Hierarchical multivariate Student t variation	324
	12.6 Mixture of normals variation	324
	12.7 A prototypical Bayesian selection example	325
	12.8 Regulated report precision example	329
	12.9 Probability as logic and the selection problem	348
	12.10 Additional reading	349
	Informed priors	350
	13.1 Maximum entropy	351
	13.2 Complete ignorance	353
	13.3 A little background knowledge	354
	13.4 Generalization of maximum entropy principle	354
	13.5 Discrete choice model as maximum entropy prior	357
	13.6 Continuous priors	359
	13.7 Variance bound and maximum entropy	368
	13.8 An illustration: Jaynes’ widget problem	372
	13.9 Football game puzzle	387
	13.10 Financial statement example	388
	13.11 Smooth accruals	393
	13.12 Earnings management	399
	distribution	415
	13.13 Jaynes’	415
	13.14 Concluding remarks	418
	13.15 Additional reading	418
	13.16 Appendix	418
	Asymptotic theory	430
	A.1 Convergence in probability (laws of large numbers)	430
	A.2 Convergence in distribution (central limit theorems)	434
	A.3 Rates of convergence	439
	A.4 Additional reading	440
	Bibliography	441
	Index	460