Accounting and Causal Effects: Econometric Challenges
Springer Science & Business Media, Aug 12, 2010 - Business & Economics - 462 pages
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.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Loss functions and estimation
Discrete choice models
Overview of endogeneity
Other editions - View all
2SLS-IV accruals analysis asset asymptotic ATUT average treatment effect Bayesian bias cash flow causal effects certification cost chapter choice models conditional Continuous report precision control function correlation counterfactuals discrete choice discussion draws econometric effect sample statistics endogeneity equation equilibrium estATT estATUT mean expected utility full certification setting Gaussian Halton sequence Heckman Hence heterogeneity homoskedastic identify instrumental variable Jaynes likelihood linear logit marginal treatment effect matrix McMC median minimum nonlinear regression nonparametric regression normally distributed observed binary OLS parameter estimates outcome owners plim posterior distribution Pr(D Pr(s precision but observed probability probit propensity score propensity score matching random variable regressors reported in table selective certification setting Springer Science+Business Media stand.dev standard std.dev stochastic theorem tion treatment effect estimates treatment effect sample Tuebingen IV example unobservable variance vector widgets zero