3SLS three-stage least squares
a.s. almost sure
ACD Autoregressive Conditional Duration
ADF Augmented Dickey–Fuller
AE asymptotically equivalent
AIC Aikake’s information criteria
AIMA asymptotically ideal model
AIMSE average integrated mean square error
AR autoregressive
AR(1) first-order autoregressive
ARCH autoregressive conditional heteroskedasticity
ARFIMA autoregressive fractionally integrated moving average
ARIMA autoregressive integrated moving average
ARMA autoregressive moving average
BDS Brock, Dechert, and Scheinkman
BIC Bayesian information criteria
BKW Belsley, Kuh, and Welsch
BLUE best, linear unbiased estimator
BMC bound Monte Carlo
CAPM capital asset pricing model
CAPS consistent adjusted least squares
CDF (or cdf) cumulative distribution function
CES constant elasticity of substitution
CFI comparative fit index
CG matrix matrix of contributions to the gradient
CI confidence interval
CLT central limit theorem
CM conditional moment
CME conditional mean encompassing
CMT conditional moment test
CPI consumer price index
CPS current population survey
CUAN consistent and uniformly asymptotic normal
DEA data envelopment analysis
DF Dickey–Fuller
DGLS dynamic generalized least squares
DGM data generating mechanism
DGP data generating process
DHF Dickey, Hasza, and Fuller
DLR double-length artificial regression
DOLS dynamic ordinary least squares
DW Durbin–Watson
DWH Durbin–Wu–Hausman
EBA elimination-by-aspects
ECM expectation conditional maximization
EM expectation maximization
EPE estimated prediction error
ESS explained sums of squares
ESSR restricted sum of squares
ESSU unrestricted error sum of squares
EWMA exponentially weighted moving average
FCLT functional central limit theorem
FGLS feasible generalized least squares
FIML full information maximum likelihood
FIVE full information instrumental variables efficient
FM-OLS fully modified ordinary least squares estimator
FSD first-order stochastic dominate
FWL Frisch–Waugh–Lovell
GARCH generalized autoregressive conditional heteroskedastic
GEV generalized extreme value
GHK Geweke, Hajivassiliou, and Keane
GHM Gouriéoux, Holly, and Montfort
GIS geographic information systems
GL generalized Lorenz
GLM generalized linear model
GLN Ghysels, Lee, and Noh
GLS generalized least squares
GML generalized maximum likelihood
GMM generalized method of moments
GNR Gauss–Newton regression
GSUR generalized seemingly unrelated regression
HAC heteroskedasticity and autocorrelation consistent
HEBA hierarchical elimination-by-aspects
HEGY Hylleberg, Engle, Granger, and Yoo
H–K Honoré and Kyriazidou
HRGNR heteroskedasticity-robust Gauss–Newton regression
i.p. in probability
IC information criteria
ID independently distributed
IIA independence of irrelevant alternatives
IID independently identically distributed
IIV iterated instrumental variable
ILS indirect least squares
IM information matrix
IMSE integrated mean square error
INAR integer autoregressive
IP industrial production
IV instrumental variable
JB Jarque–Bera
KLIC Kullback–Leibler information criterion
KPSS Kwiatkowski, Phillips, Schmidt, and Shin
KS Kolmogorov–Smirnov
KT Kuhn–Tucker
LBI locally best invariant
LCLS local constant least squares
LEF linear exponential family
LI limited information
LIML limited information maximum likelihood
LIVE limited information instrumental variables efficient
LL local linear
LLLS local linear least squares
LLN law of large numbers
LLS local least squares
LM Lagrange multiplier
LMC local Monte Carlo
LMP locally most powerful
LMPU locally most powerful unbiased
LPLS local polynomial least squares
LR likelihood ratio
LS least squares
LSE least squares estimation
LSTAR logistic smooth transition autoregression
M2SLS modified two-stage least squares
MA moving average
MA(1) first-order moving average
MC Monte Carlo
MCMC Markov Chain Monte Carlo
MD martingale difference
MDML multivariate dynamic linear
MIMIC multiple indicators-multiple causes
ML maximum likelihood
MLE maximum likelihood estimation
MLR multivariate linear regression
MM method of moments
MMC maximized Monte Carlo
MML maximum marginal likelihood
MNL multinomial logit
MNP multinomial probit
MP most powerful
MS maximum score
MSE mean square error
MSFE mean squared forecast error
MSL method of simulated likelihood
MSM method of simulated moments
MSS method of simulated scores
NB negative binomial
NFI normed fit index
NLS nonlinear least squares
NMNL nested multinomial logit
NN neural network
N–P Neyman–Pearson
NPRSS nonparametric residual sum of squares
N–W Nadaraya–Watson
NYSE New York Stock Exchange
OLS ordinary least squares
OPG outer-product-of-the-gradient
PDF probability distribution function
PLS predictive least squares
PML pseudo-ML
PP Phillips–Perron
PR probabilistic reduction
PRSS parametric residual sum of squares
psd positive semi-definite
PSP partial sum process
QML quasi-ML
QP quadratic programming
QRM qualitative response model
RCM random coefficient models
RESET regression error specification test
RIS recursive importance sampling
RLS restricted least squares
RMSE root mean squared error
RMSFE root mean squared forecast error
RNI relative noncentrality index
RRR reduced rank regression
RS Rao’s score
RSS residual sum of squares
s/n signal-to-noise
SA simulated annealing
SAR spatial autoregressive
SD stochastic dominance
SEM simultaneous equations model
SET score encompassing test
SMA spatial moving average
SML simulated maximum likelihood
SNP semi-nonparametric
SP semiparametric
SSD second-order stochastic dominate
SSE sum of square error
SSR sum of squared residuals
STAR smooth transition autoregression
SUR(E) seemingly unrelated regression
SVD Stochastic Volatility Duration
TAR transition autoregression
TSD third-order stochastic
UI union intersection
UL uniform linear
ULLN uniform law of large numbers
UMP uniformly most powerful
UMPI uniformly most powerful invariant
UMPU uniformly most powerful unbiased
VAR vector autoregression
VECM vector error correction model
VIF variance-inflation factor
VNM von Neumann–Morgenstern
W Wald
WET Wald encompassing test
wrt with respect to
(摘自A Companion to Theoretical Econometrics)