AMIS 900 Econometric analysis of accounting choice
Summer 2011
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
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.
Tentative schedule: (discussion leader, topic)
starting in August
discussions begin at 1:30 pm in the conference room, 400 Fisher Hall
|
Tuesday |
Thursday |
|
|
week of June 27 |
Schroeder: overview accounting & managerial experimentation ch. 1 & 2 |
Wed. brown bag workshop |
|
week of July 4 |
Schroeder: causal effects accounting & managerial experimentation ch. 3 |
Fellingham: no arbitrage
equilibrium |
|
week of July 11 |
Schroeder: ignorable treatment
effect strategies part 1 ch. 8 & 9 |
Fellingham: quantum information
and production |
|
week of July 18 |
Schroeder: ignorable treatment
effect strategies part 2 ch. 8 & 9 |
out of town Th-Fr |
|
week of July 25 |
out of town Tu-Fr |
out of town Tu-Fr |
|
week of August 1 |
1:30 start time during August Austin: strategic choice ch. 8 |
Mike: Monte Carlo simulation &
bootstrapping ch. 7 |
|
week of August 8 |
Schroeder: IV treatment effect strategies ch. 10 |
Schroeder: Markov chain Monte Carlo (McMC) simulation accounting & managerial experimentation ch. 7 |
|
week of August 15 |
Schroeder: McMC simulation
& treatment effects accounting & managerial experimentation ch. 9 & 10 ch. 12 |
Bret: difference-in-difference
(fixed
effects & treatment effects |
|
week of August 22 |
Minkwan: market-to-book
& accounting |
Schroeder: McMC simulation
& treatment effects accounting & managerial experimentation ch. 9 & 10 ch. 12 |
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)
Strategic
disclosure
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)
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: