Subject Index#
Numerals are chapter numbers. Preface and Road Map name the front matter; App. 8 and App. 11 name the two chapter appendices. Entries that carry a numbered definition, proposition, or theorem link to it directly. Cross-references read see and see also.
A#
absolute continuity of alternative probability models — 8
act (decision theory) — 10
additive functional — Definition 4.1, 4
decomposition of — Proposition 4.1, 4
martingale component, construction — 4
exponential of — 8
stochastic volatility example — Example 4.1
-
score process as — Theorem 7.1
admissibility of decision rules — 10
Bayesian solutions and — Proposition 10.1, Proposition 10.2
complete class theorems — 10
-
aversion — 10
interval — Remark 8.8
distinguished from risk and misspecification — 10
aperiodicity — 2, Theorem 2.2
Arrow securities, recovering transition probabilities from — 8
asset pricing
cash flows — 8
conditional moment restrictions — Example 13.2
long-term risk-return tradeoff — 8
marginal valuations as — 12
B#
balanced growth, stochastic — 3
Bayesian decision theory — 10
conditional problem — 10
threshold rule for model selection — Remark 7.3
belief distortion — 8
beliefs of agents inside a model — 1, 8
heterogeneous — Example 8.4, 11
survival of investors with distorted — Example 8.4
Birkhoff’s theorem — Theorem 1.2, 1
Borel set — 1
Brownian motion
C#
canonical construction of a probability space — 1
cash flows, valuation of stochastic — 8
central limit theorem
for martingales (Gordin) — Proposition 3.2, 3
for GMM estimators — 13
for score processes — Theorem 7.1
-
unknown autoregressive coefficient — Example 8.8
unknown volatility — Example 8.9
climate change, social cost of — 12
cointegration — 3
commitment, dynamic robust decision problem under — 10
conditional expectation
conjugate prior — 6
co-states, relation to stochastic responses — 12
D#
E#
efficiency bound, GMM — 13
eigenfunction
principal — 8, Theorem 8.1
unit eigenvalue — 2, Proposition 2.1
second solution and infinite values — 8
elasticity, shock — 9
continuous time — 9
exposure versus price — 9
recursive utility economy — 11
vector autoregression — Example 9.1
entropy, see Chernoff entropy; relative entropy
ergodic decomposition — 1, Proposition 1.2
ergodicity — 1, Proposition 1.1
of Markov processes — 2, Proposition 2.4
of vector autoregressions — 2
failure of — Example 2.5, Example 2.6
Euler equation — 4, Example 13.2
exchangeability — 1
experimentation, absence of — 10
F#
G#
Gaussian, see normal distribution
generalized method of moments (GMM) — 13
Gibbs sampling — 6
Gordin’s theorem — Proposition 3.2
Gordon growth model, stochastic — 8
growth
H#
I#
identification
partial — 8, Remark 8.8
of shocks in a structural VAR — 6
impulse response function — 5
causal readings of — 5
local projections — 5
as limit of shock elasticities — Example 9.1
indirect inference — 13
information state vector — 7
innovation process, Kalman — 6
instrumental variables, nonlinear — Example 13.1
intertemporal elasticity of substitution — 11
differing from unity — 12
invariant event — 1
irreducibility — 2
Ito’s formula — 11
J#
K#
L#
Law of Large Numbers — 1, Theorem 1.2
least squares
likelihood function, distinguished from prior — 10
likelihood ratio process — 7, 7
almost-sure convergence to zero — Proposition 7.3
as multiplicative martingale — Definition 7.1
for two autoregressions — Example 7.2
limiting empirical measure — 1
local projection — 5
long memory — 3
Lyapunov equation, discrete — 2
M#
Malliavin calculus — 5
marginal valuation — 12
Markov chain, finite-state — Example 2.2, 2
multiplicative functional on — Example 8.5
Markov process — 2
constituents — 2
stationary distribution — Definition 2.1
transition distribution — 2
martingale
measure-preserving transformation — 1, Theorem 1.1
misspecification, model — 10
moment restrictions
conditional — Example 13.2, 13
unconditional — Example 13.1
moving-average representation — Example 1.8, 3
continuous-time counterpart — 5
non-invertible — Example 6.1
multiplicative functional — Definition 8.1, 8
factorization of — Theorem 8.1, 8
perturbation of — 9
three primitive types — 8
worked example — 8
multiplicative martingale — Definition 7.1
as change of measure — Proposition 7.1
peculiar sample-path behavior — 8
Murphy’s law, stochastic version — 11
N#
O#
P#
Q#
quadratic approximation of state dynamics — 4
R#
S#
score process — Definition 7.2, 7
self-confirming equilibrium — 1
semigroup of operators — 8
shock elasticity, see elasticity, shock
skip sampling — 2
smooth ambiguity preferences — 10
social cost of carbon — 12
social value of research and development — 12
spectral density estimation — 13
state vector — 2
statistical model — Definition 1.6, 1
stationary distribution — Definition 2.1, 2
stationary increments — 3
Markovian — 4
stationary stochastic process — 1
stochastic stability — Definition 8.2, 8
uniqueness of the factorization — Theorem 8.2
stochastic volatility — Example 4.1, Example 8.7
strong contraction — Definition 2.6, 2
submartingale — Proposition 7.2
supermartingale — Proposition 7.2
survey forecasts — 8
T#
U#
V#
variance multiplier — 11
variational preferences — 10
variational process, see stochastic response
vector autoregression — Example 2.1, 2