AMIS 900 Econometric
analysis of accounting choice
Summer 2012-2013
Tuesday May 7, 2013
1:30- in Fisher 400
Topic: dynamic treatment
effects; notes
Tuesday May 14, 2013
1:30- in Fisher 400
Tuesday May 21,
2013 1:30- in Fisher 400
Synthetic controls;
notes
Tuesday May 28,
2013 1:30- in Fisher 400
Objectives:
The objectives are (i) to
build a foundation for meaningful discussions, (ii) to complement econometrics
(and statistics) courses (in my view, coursework provides an opportunity to
survey what's out there – this is an opportunity to develop a deeper
understanding), and (iii) to explore implications for accounting.
Monograph:
Accounting and causal effects: Econometric challenges
Work in progress: Accounting and managerial
experimentation
AAA
August 2012 slides: Accounting and causal effects
Fellingham monograph:
Accounting as an information science
Imbens and WooldridgeÕs NBER
econometric minicourse
Ross, 2011,
The Recovery Theorem.
Dybvig and Ross,
2003, Arbitrage, state prices and portfolio theory, Handbook of the
Economics of Finance.
Beaver, Landsman,
and Owens. Asymmetry in earnings timeliness and persistence: A simultaneous
equations approach, forthcoming Review of Accounting Studies.
Tentative meeting
schedule:
Wednesday at 2:00 pm in the
conference room, 400 Fisher Hall during summer
fall & spring
meeting time to be determined
Outline:
Below is a list of potential
topics for summer seminar. We surely will not cover them all (some will
naturally get combined in the course of our discussions). It goes without
saying, this is an ambitious, time and energy-taxing endeavor, but this seminar
is purely optional (it's an opportunity not an obligation). If you decide to
participate, I would like you to choose a topic and tentative date for
discussion. I believe you'll get more out of seminar if you actively
participate, therefore my plan for this summer is to give you some latitude in
leading the discussion. Don't think you have to exhaust the topic –
that's not possible, many of these topics warrant a course of their own. Do not
expect closure.
There is some coverage of
each of these issues in my monograph, Accounting and
causal effects: Econometric challenges. But don't think you have to
limit yourself to these pages. The monograph is posted below along with some
supplemental material developed (and still under development) subsequent to
completion of the monograph along with some sample R programs.
As you know, linear algebra
is an indispensable tool for these explorations. Amongst the supplemental
materials is an appendix (not listed with the topics below) which includes a
brief survey of some important concepts from linear algebra along with some
examples.
Make use of the best tools at
your disposal (spreadsheets, Mathematica, Matlab, etc.). I find R, the open
source statistical software, to be invaluable especially for computationally
intensive work like McMC simulation. Since it's open source, it evolves rapidly
and many of the contributors have fabulous programming skills that make R
extraordinarily efficient. I plan to post a few sample programs to, hopefully,
ease the learning curve but expect to discover your own tricks for gaining
efficiency (sadly, not my forte).
Treatment effects (a special case of causal effects) receive a
substantial amount of attention in the topics below as well as in the
monograph. Some key features (illustrated in the Tuebingen-style and
supplementary pages examples) are
–omitted,
correlated variables (personally, I devote 99% of my efforts to this problem,
in this case it leads to potential selection bias)
–counterfactuals
& inferring population-level parameters (this distinguishes the treatment
effect problem from, say, a run of the mill endogenous regressor problem and is
a source of controversy among scientists; see Dawid)
–common
support (how confident are you extrapolating outside the relevant range?)
–there
are numerous definitions of treatment effects (average treatment effect,
average treatment effect on the treated, average treatment effect on the
untreated, local average treatment effect, marginal treatment effect,
policy-relevant treatment effect, ambiguity of treatment effects identified by
linear IV, etc.) not all of them are accessible without full support and they
are not all equally well suited to the problem at hand
–choice
of instruments is even more challenging than usual and greater care is required
in interpreting the results
–higher
explanatory power (in either the selection or outcome equations) does not
necessarily lead to a well-identified model
Topics:
Linear models (OLS, GLS, FWL,
etc.)
Equilibrium earnings management
(chapters 2 & 3)
Fixed effects &
differences-in-differences and random effects & random coefficients
Linear instrumental variables
(IV)
Discrete choice models
Nonlinear regression
Maximum likelihood estimation
(James-Stein shrinkage estimators)
Nonparametric &
semiparametric regression
Bootstrapping & posterior
simulation
McMC (Markov chain Monte
Carlo) simulation (chapters 7 & 12 and supplemental materials Bayesian
notes)
Strategic choice models
Treatment effects (chapter 8
Tuebingen-style examples)
Ignorable treatment effects
(chapter 9 Tuebingen-style examples)
Identification
strategies: exogenous dummy variable regression, nonparametric regression,
propensity score & propensity score matching, control functions, regression
discontinuity design, etc. (mean conditional independence (ignorability)
vis-a-vis conditional independence (strong ignorability))
Asset revaluation regulation
(limitations of outcome measures for inferring welfare effects; chapters 2
& 9)
Nonignorable (IV) treatment
effects (chapter 10 Tuebingen-style examples)
Generalized Roy model
interpretation
Identification
strategies: endogenous dummy variable IV regression, propensity score IV,
ordinate control function IV, inverse Mills control function IV, LATE and linear
IV, etc.
IV
control functions & projections examples (ANOVA, ANCOVA to control
functions examples in supplemental materials projections notes)
Continuous treatment &
(correlated) random coefficients
Regulated
report precision (nonignorable versus ignorable identification strategy
effectiveness; chapters 2 & 10)
Marginal treatment effects
Identification and LIV (local
instrumental variables)
Regulated report precision
(apparent nonnormality & marginal treatment effects; chapters 2 & 11)
Bayesian treatment effects
Identification via McMC data
augmentation
Identification & McMC IV
(restricted) data augmentation (supplemental materials Bayesian notes)
Regulated
report precision (marginal & average treatment effects as well as policy-relevant
treatment effects; chapters 2 & 12)
Informed priors &
Bayesian data analysis (probability as logic)
Maximum entropy probability
assignment & Jaynes' widget problem
Conjugate families (supplemental
materials Bayesian notes)
Inverting financial statements
(to recover transactions)
Smooth accruals (valuation and
performance evaluation implications)
Earnings management (stochastic
& selective manipulation)
Quantum production, synergy,
and (product market) strategic disclosure
Other topics of interest to
you
Table of contents
for Accounting and causal effects: Econometric challenges
- including OLS,
GLS, FWL, and IV estimators
4. Loss functions
& estimation
- including MLE, James-Stein shrinkage estimators, and nonlinear regression
- employed as propensity
score in treatment effect analysis
6. Nonparametric
regression models
- employed with
Heckman's MTE
7. Repeated-sampling
inference
- including McMC Bayesian analysis
9. Treatment effects:
ignorability
11. Marginal
treatment effects
12. Bayesian
treatment effects
- including widget
example
- including
inferring transactions from financial statements
- including
convergence in probability, convergence in distribution, and rates of
convergence
Errata
(corrections for the 2010 Springer printed version)
Supplemental materials:
(most of this is work in progress – expect updates)
1. Probability as logic (http://bayes.wustl.edu/etj/science.pdf.html)
2. Accounting and managerial
experimentation
stewardship
and the search for a better Òmouse trapÓ
ANOVA,
ANCOVA, linear regression
double
residual regression (FWL)
ch. 3 classical
causal effects strategies
ignorable
treatment
limited
common support
regression
discontinuity designs
LATE
& 2SLS (linear) IV
control
functions
ch. 4 maximum
entropy distributions
BayesÕ
theorem & consistent reasoning
maximum
entropy probability assignment
posterior
simulation & conjugate families
independent
& conditional posterior simulation
Markov
chain Monte Carlo (McMC) simulation
irreducibility,
time reversibility, & stationarity
Gibbs
sampler
Metropolis-Hastings
(MH) algorithm
data
augmented sampler for Probit
random
walk MH logit
uniform
data augmented Gibbs sampler for logit
ANOVA,
ANCOVA, regression
ch. 9 Bayesian
causal effects strategies
data
augmented Gibbs sampler for treatment effects
data
augmented IV restricted Gibbs sampler for treatment effects
ch. 10 Bayesian
treatment effects without joint distribution of outcomes
ChibÕs
ATE and LATE
extensions
(via bounding) to ATT and ATUT
ch. 11 Partial
identification and missing data
Missing
outcomes
quantiles
expected
values (point and partial identification)
inference
respecting
stochastic dominance
refutability
missing
covariates and outcomes
missing
at random
missing
by choice
stochastic
dominance and treatment effects
Selection
problem
various
instrumental variable strategies (missing at random, statistical independence,
means missing at random, mean independence)
monotone instrumental variable strategies (mean
monotonicity, exogenous treatment selection, monotone treatment selection,
monotone treatment response, MM-MTR, MTR-MTS)
quantile treatment effects (point and partial
identification)
Extrapolation
and the mixing problem
Appendix:
bounds on spreads
linear
algebra basics
fundamental
theorem of linear algebra
subspaces
exactly,
under-, and over-identified
matrix
decomposition
LU
factorization
Cholesky
decomposition
singular
value decomposition (SVD) & pseudo inverse
spectral
decomposition
Gram-Schmidt
orthogonalization
some
determinant identities (& LU factorization)
iterated
expectations
multivariate
normal theory
generalized
least squares (GLS)
two
stage least squares IV (2SLS-IV)
seemingly
unrelated regression (SUR)
maximum
likelihood estimation of discrete choice models
common
distributions
R Statistical Computing
Package
R is a freely available,
open-source version of S/S-plus.
Follow this link: R statistical computing
package
R samples programs
Tutorials:
R tutorial
http://en.wikibooks.org/wiki/R_Programming/
find
your favorite on the web
source
files for data augmented Gibbs sampler bivariate probit:
you
will need to add bayesm library from Cran website if itÕs not including in your
base program
source
files for Metropolis-Hastings bivariate logit (self-contained code and code
utilizing MCMCpack library which you may need to add)
source
files for data augmented Gibbs sampler bivariate selection analysis of
treatment effects:
you
will need to add MASS library from Cran website if itÕs not including in your
base program
Another
Bayesian McMC strategy for identifying treatment effects (perhaps, with panel
data) can be found in ChibÕs papers posted below:
Strategic disclosure
papers include:
Shin, 1993, "News management and the value of firms," RAND Journal of Economics, 25(1), 58-71.
Shin, 1993, "Disclosures and asset returns," Econometrica, 71(1), 105-133.
Shin, 2006, ÒDisclosure risk and price drift,Ó Journal of Accounting Research, 44(2),
351-379.
Arya and Mittendorf, ÒThe interaction between corporate tax structure
and disclosure policy,Ó Annals of Finance,
forthcoming.
Arya and Mittendorf, ÒInput markets and the strategic organization
of the firm,Ó Foundations and Trends in
Accounting, 5(1), 1-97.