Accounting and Causal Effects

Accounting and Causal Effects PDF Author: Douglas A Schroeder
Publisher: Springer Science & Business Media
ISBN: 1441972250
Category : Business & Economics
Languages : en
Pages : 475

Get Book Here

Book Description
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.

Accounting and Causal Effects

Accounting and Causal Effects PDF Author: Douglas A Schroeder
Publisher: Springer Science & Business Media
ISBN: 1441972250
Category : Business & Economics
Languages : en
Pages : 475

Get Book Here

Book Description
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.

Causality in a Social World

Causality in a Social World PDF Author: Guanglei Hong
Publisher: John Wiley & Sons
ISBN: 1118332563
Category : Mathematics
Languages : en
Pages : 443

Get Book Here

Book Description
Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory. Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.

Handbook of Causal Analysis for Social Research

Handbook of Causal Analysis for Social Research PDF Author: Stephen L. Morgan
Publisher: Springer Science & Business Media
ISBN: 9400760949
Category : Social Science
Languages : en
Pages : 423

Get Book Here

Book Description
What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of these effects? What complications do the interactions of individuals create for these techniques? When can mixed methods of analysis be used to deepen causal accounts? Must causal claims include generative mechanisms, and how effective are empirical methods designed to discover them? The Handbook of Causal Analysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development.

Causal Inferences in Capital Markets Research

Causal Inferences in Capital Markets Research PDF Author: Iván Marinovic
Publisher:
ISBN: 9781680831603
Category : Causation
Languages : en
Pages : 360

Get Book Here

Book Description
This monograph promotes a broad interdisciplinary debate about causality and the role of causal inference in the social sciences. It allows researchers and Ph.D students in accounting/social sciences to acquire a deeper understanding of the notion of causality and the nature, limits, and scope of empirical research in the social sciences.

The Estimation of Causal Effects by Difference-in-difference Methods

The Estimation of Causal Effects by Difference-in-difference Methods PDF Author: Michael Lechner
Publisher: Foundations and Trends(r) in E
ISBN: 9781601984982
Category : Business & Economics
Languages : en
Pages : 72

Get Book Here

Book Description
This monograph presents a brief overview of the literature on the difference-in-difference estimation strategy and discusses major issues mainly using a treatment effect perspective that allows more general considerations than the classical regression formulation that still dominates the applied work.

The Effect

The Effect PDF Author: Nick Huntington-Klein
Publisher: CRC Press
ISBN: 1000509141
Category : Business & Economics
Languages : en
Pages : 646

Get Book Here

Book Description
Extensive code examples in R, Stata, and Python Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions An easy-to-read conversational tone Up-to-date coverage of methods with fast-moving literatures like difference-in-differences

Econometric Identification of Causal Effects

Econometric Identification of Causal Effects PDF Author: Sanjay Kallapur
Publisher:
ISBN:
Category :
Languages : en
Pages : 26

Get Book Here

Book Description
It is well known that causal inference relies on untestable a-priori causal assumptions. Identification refers to whether a causal relationship can be inferred from observed statistical associations; it requires an understanding of what statistical associations are induced by those causal assumptions. Since the assumptions are untestable, a transparent description of their statistical consequences helps the readers. However, the relation between causal assumptions and their induced statistical associations may not be obvious. In this paper I describe a technique known as Directed Acyclical Graphs or Graphical Bayesian Network or Graphical Causal Models. The technique was developed in the computer science literature in the 1980s (Pearl 2009) although it has antecedents in path analysis developed by Philip and Sewall Wright beginning in the 1920s (Wright 1921). In addition to describing the technique, I illustrate its application to a case study of a research issue in auditing.

An Extended Class of Instrumental Variables for the Estimation of Causal Effects

An Extended Class of Instrumental Variables for the Estimation of Causal Effects PDF Author: Karim Chalak
Publisher:
ISBN:
Category :
Languages : en
Pages :

Get Book Here

Book Description


Causal Inference in Statistics, Social, and Biomedical Sciences

Causal Inference in Statistics, Social, and Biomedical Sciences PDF Author: Guido W. Imbens
Publisher: Cambridge University Press
ISBN: 0521885884
Category : Business & Economics
Languages : en
Pages : 647

Get Book Here

Book Description
This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide

Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide PDF Author: Agency for Health Care Research and Quality (U.S.)
Publisher: Government Printing Office
ISBN: 1587634236
Category : Medical
Languages : en
Pages : 236

Get Book Here

Book Description
This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)