Schroeder Accounting and Causal Effects

Econometric Challenges
1. Auflage 2010
ISBN: 978-1-4419-7225-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 5, 475 Seiten

Reihe: Springer Series in Accounting Scholarship

In this book, we synthesize a rich and vast literature on econometric challenges associated with accounting choices and their causal effects. Identi?cation and es- mation of endogenous causal effects is particularly challenging as observable data are rarely directly linked to the causal effect of interest. A common strategy is to employ logically consistent probability assessment via Bayes' theorem to connect observable data to the causal effect of interest. For example, the implications of earnings management as equilibrium reporting behavior is a centerpiece of our explorations. Rather than offering recipes or algorithms, the book surveys our - periences with accounting and econometrics. That is, we focus on why rather than how. The book can be utilized in a variety of venues. On the surface it is geared - ward graduate studies and surely this is where its roots lie. If we're serious about our studies, that is, if we tackle interesting and challenging problems, then there is a natural progression. Our research addresses problems that are not well - derstood then incorporates them throughout our curricula as our understanding improves and to improve our understanding (in other words, learning and c- riculum development are endogenous). For accounting to be a vibrant academic discipline, we believe it is essential these issues be confronted in the undergr- uate classroom as well as graduate studies. We hope we've made some progress with examples which will encourage these developments.

Douglas A. Schroeder has been at The Ohio State University since 1986; previously he was serving as a member of the Purdue University faculty. He has published papers in and served as a reviewer for numerous academic journals. His current scholarly interests involve analysis of accounting choice.

Autoren/Hrsg.

Schroeder, Douglas A

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;List of Tables;15
4;List of Figures;23
5;Introduction;24
5.1;1.1 Problematic illustration;25
5.2;1.2 Jaynes’ desiderata for scientific reasoning;27
5.3;1.3 Overview;30
5.4;1.4 Additional reading;31
6;Accounting choice;32
6.1;2.1 Equilibrium earnings management;33
6.2;2.2 Asset revaluation regulation;35
6.3;2.3 Regulated report precision;37
6.4;2.4 Inferring transactions from financial statements;40
6.5;2.5 Additional reading;41
7;Linear models;42
7.1;3.1 Standard linear model (;42
7.2;3.2 Generalized least squares (;44
7.3;3.3 Tests of restrictions and;45
7.4;(Frisch-Waugh-Lovell);45
7.5;3.4 Fixed and random effects;49
7.6;3.5 Random coefficients;54
7.7;3.6 Ubiquity of the Gaussian distribution;56
7.8;3.7 Interval estimation;59
7.9;3.8 Asymptotic tests of restrictions: Wald,;61
7.10;statistics;61
7.11;3.9 Misspecification and;64
7.12;estimation;64
7.13;3.10 Proxy variables;66
7.14;3.11 Equilibrium earnings management;71
7.15;3.12 Additional reading;77
7.16;3.13 Appendix;78
8;Loss functions and estimation;81
8.1;4.1 Loss functions;81
8.2;4.2 Nonlinear Regression;84
8.3;4.3 Maximum likelihood estimation;87
8.4;4.4 James-Stein shrinkage estimators;92
8.5;4.5 Summary;97
8.6;4.6 Additional reading;98
9;Discrete choice models;99
9.1;5.1 Latent utility index models;99
9.2;5.2 Linear probability models;100
9.3;5.3 Logit (logistic regression) models;100
9.4;5.4 Probit models;108
9.5;5.5 Robust choice models;114
9.6;5.6 Tobit (censored regression) models;116
9.7;5.7 Bayesian data augmentation;116
9.8;5.8 Additional reading;117
10;Nonparametric regression;118
10.1;6.1 Nonparametric (kernel) regression;118
10.2;6.2 Semiparametric regression models;120
10.3;6.3 Specification testing against a general nonparametric benchmark;122
10.4;6.4 Locally linear regression;124
10.5;6.5 Generalized cross-validation (;125
10.6;6.6 Additional reading;126
11;Repeated-sampling inference;127
11.1;7.1 Monte Carlo simulation;128
11.2;7.2 Bootstrap;128
11.3;7.3 Bayesian simulation;131
11.4;7.4 Additional reading;142
12;Overview of endogeneity;143
12.1;8.1 Overview;144
12.2;8.2 Selectivity and treatment effects;167
12.3;8.3 Why bother with endogeneity?;168
12.4;8.4 Discussion and concluding remarks;175
12.5;8.5 Additional reading;175
13;Treatment effects: ignorability;177
13.1;9.1 A prototypical selection setting;177
13.2;9.2 Exogenous dummy variable regression;178
13.3;9.3 Tuebingen-style examples;179
13.4;9.4 Nonparametric identification;184
13.5;9.5 Propensity score approaches;189
13.6;9.6 Propensity score matching;192
13.7;9.7 Asset revaluation regulation example;195
13.8;9.8 Control function approaches;223
13.9;9.9 Summary;224
13.10;9.10 Additional reading;224
14;Treatment effects:;226
14.1;10.1 Setup;226
14.2;10.2 Treatment effects;227
14.3;10.3 Generalized Roy model;229
14.4;10.4 Homogeneous response;230
14.5;10.5 Heterogeneous response and treatment effects;231
14.6;10.6 Continuous treatment;255
14.7;10.7 Regulated report precision;258
14.8;10.8 Summary;292
14.9;10.9 Additional reading;292
15;Marginal treatment effects;293
15.1;11.1 Policy evaluation and policy invariance conditions;293
15.2;11.2 Setup;295
15.3;11.3 Generalized Roy model;295
15.4;11.4 Identification;296
15.5;connections to other treatment effects;298
15.6;11.5;298
15.7;11.6 Comparison of identification strategies;304
15.8;11.7;304
15.9;estimation;304
15.10;11.8 Discrete outcomes;306
15.11;11.9 Distributions of treatment effects;309
15.12;11.10 Dynamic timing of treatment;310
15.13;11.11 General equilibrium effects;311
15.14;11.12 Regulated report precision example;311
15.15;11.13 Additional reading;318
16;Bayesian treatment effects;319
16.1;12.1 Setup;320
16.2;12.2 Bounds and learning;320
16.3;12.3 Gibbs sampler;321
16.4;12.4 Predictive distributions;323
16.5;12.5 Hierarchical multivariate Student t variation;324
16.6;12.6 Mixture of normals variation;324
16.7;12.7 A prototypical Bayesian selection example;325
16.8;12.8 Regulated report precision example;329
16.9;12.9 Probability as logic and the selection problem;348
16.10;12.10 Additional reading;349
17;Informed priors;350
17.1;13.1 Maximum entropy;351
17.2;13.2 Complete ignorance;353
17.3;13.3 A little background knowledge;354
17.4;13.4 Generalization of maximum entropy principle;354
17.5;13.5 Discrete choice model as maximum entropy prior;357
17.6;13.6 Continuous priors;359
17.7;13.7 Variance bound and maximum entropy;368
17.8;13.8 An illustration: Jaynes’ widget problem;372
17.9;13.9 Football game puzzle;387
17.10;13.10 Financial statement example;388
17.11;13.11 Smooth accruals;393
17.12;13.12 Earnings management;399
17.13;distribution;415
17.14;13.13 Jaynes’;415
17.15;13.14 Concluding remarks;418
17.16;13.15 Additional reading;418
17.17;13.16 Appendix;418
18;Asymptotic theory;430
18.1;A.1 Convergence in probability (laws of large numbers);430
18.2;A.2 Convergence in distribution (central limit theorems);434
18.3;A.3 Rates of convergence;439
18.4;A.4 Additional reading;440
19;Bibliography;441
20;Index;460