Index

absolute regularity condition
16.1.1 Estimation of the
additive outliers
18.1.1.2 Additive Outliers
AFPE
16.2.2 Bandwidth and Lag
agglom algorithm
9.2.1 Agglomerative Hierarchical Methods
$ \alpha$-mixing
16.1.1 Estimation of the
ANOVA
8.1 A Linear Convergence
ASE
16.1.2 Bandwidth Selection
ASEP
16.1.2 Bandwidth Selection
asymptotic final prediction error
see AFPE
asymptotic mean squared error
16.2.2 Bandwidth and Lag
asymptotic MISE
16.2.2 Bandwidth and Lag
average squared error
see ASE
of prediction
see ASEP
backfitting
GAM
7.1.3 Backfitting
GPLM
6.1.2.0.3 Backfitting
bandwidth choice
16.2.2 Bandwidth and Lag
bandwidth selection
8.2.2 Univariate Kernel Density | 16.1.2 Bandwidth Selection | 16.1.2 Bandwidth Selection | 16.2.2 Bandwidth and Lag | 16.2.2 Bandwidth and Lag
cross-validation
16.1.2 Bandwidth Selection
Silverman's rule-of-thumb
16.2.2 Bandwidth and Lag
Bera-Jarque test
16.1.3 Diagnostics
Berkson error
3.2 Nonlinear EIV Models
$ \beta$-mixing
16.1.1 Estimation of the
biplots
correspondence analysis
13.3.6 Biplots
breakdown point
2.1.2 High Breakdown point
CAFPE
16.2.2 Bandwidth and Lag
CART
10. Classification and Regression
density estimation
10.5.3 Density Estimation
example
10.5.1 Simulated Example
growing the tree
10.1 Growing the Tree
plotting the result
10.4 Plotting the Result
pruning the tree
10.2 Pruning the Tree
selecting the final tree
10.3 Selecting the Final
censoring
5.1 Data Structure
classification and regression trees
see CART
cluster analysis
9. Cluster Analysis
average linkage method
9.2.1.3 Average Linkage Method
centroid method
9.2.1.4 Centroid Method
complete linkage method
9.2.1.2 Complete Linkage Method
hierarchical
9.2 Hierarchical Clustering
agglomerative
9.2.1 Agglomerative Hierarchical Methods
divisive
9.2.2 Divisive Hierarchical Methods
median method
9.2.1.5 Median Method
nonhierarchical
9.3 Nonhierarchical Clustering
adaptive K-means
9.3.2 Adaptive K-means Method
fuzzy C-means
9.3.4 Fuzzy C-means Method
hard C-means
9.3.3 Hard C-means Method
K-means
9.3.1 K-means Method
similarity of objects
9.1.2 Similarity of Objects
single linkage method
9.2.1.1 Single Linkage Method
ward method
9.2.1.6 Ward Method
compare two
9.3.4 Fuzzy C-means Method
computation
Nadarya-Watson estimates
16.1.1 Estimation of the
confidence intervals
Nadaraya-Watson estimator
16.1.4 Confidence Intervals
constraints
GPLM
6.4.3 Weights and Offsets
contingency table
13.1 Introduction
controlled-variable model
3.2 Nonlinear EIV Models
correspondence analysis
13. Correspondence Analysis
biplots
13.3.6 Biplots
XploRe implementation
13.2 XploRe Implementation | 13.3.2 Calling the Quantlet
Cox regression
5.3 The Cox Proportional
hypothesis testing
5.3.3 Hypothesis Testing
credit scoring
GPLM
6.2.2 Example
cross-validation
10.3 Selecting the Final | 16.2.2 Bandwidth and Lag
curse of dimensionality
16.2.1 Estimation of the
data preparation
multiple time series
17.1.1 Data Preparation
density estimation
CART
10.5.3 Density Estimation
derivative estimation
16.1.5 Derivative Estimation
diagnostics
flexible time series
16.1.3 Diagnostics
distance
$ L_p$
9.1.1 Distance Measures
Euclidean
9.1.1 Distance Measures
Mahalanobis
9.1.1 Distance Measures
maximum
9.1.1 Distance Measures
distance measures
9.1.1 Distance Measures
DPLS
11. DPLS: Partial Least
computing
11.3 Estimating a DPLS-Model
example
11.4 Example: A Model
overview
11.1 Introduction
theory
11.2 Theoretical Background
dynamic partial least squares
see DPLS
EIV
3. Errors-in-Variables Models
calculation
3.3.3 XploRe Calculation and
linear eiv models
3.1 Linear EIV Models
nonlinear eiv models
3.2 Nonlinear EIV Models
partially linear eiv models
3.3 Partially Linear EIV
regression calibration
3.2.1 Regression Calibration
simulation extrapolation
3.2.2 Simulation Extrapolation
variance of error known
3.3.1 The Variance of
variance of error unknown
3.3.2 The Variance of
vector of explanatory variables
3.1.2 Vector of Explanatory
endogenous variable
4.1 Introduction
error
asymptotic final prediction
see AFPE
asymptotic mean squared
16.2.2 Bandwidth and Lag
average squared
see ASE
of prediction
see ASEP
corrected asymptotid final prediction
see CAFPE
final prediction
see FPE
integrated squared
see ISE
mean integrated square
asymptotic
16.2.2 Bandwidth and Lag
mean integrated squared
see MISE
error model
3.2 Nonlinear EIV Models
errors in variables
see EIV
estimate
leave-one-out cross-validation
16.1.2 Bandwidth Selection
estimation
simultaneous-equations
4.2 Estimation
estimator
local linear
16.1.1 Estimation of the
local quadratic
16.1.5 Derivative Estimation
exogenous regressor
4.1 Introduction
ExploRing Persistence
15. ExploRing Persistence in
$ \phi$-mixing
16.1.1 Estimation of the
final prediction error
see FPE
financial time series
15. ExploRing Persistence in
flexible time series
16. Flexible Time Series | 16.2 Nonlinear Autoregressive Models
bandwidth choice
16.2.2 Bandwidth and Lag
bandwidth selection
16.1.2 Bandwidth Selection
confidence intervals
16.1.4 Confidence Intervals
derivative estimation
16.1.5 Derivative Estimation
diagnostics
16.1.3 Diagnostics | 16.2.3 Plotting and Diagnostics
plot
16.2.3 Plotting and Diagnostics
selection of lags
16.2.2 Bandwidth and Lag
FPE
16.2.2 Bandwidth and Lag | 16.2.2 Bandwidth and Lag
corrected asymptotic
see CAFPE
GAM
6.1.1 Models | 7. Generalized Additive Models | 7.1.1 Models
backfitting
7.1.3 Backfitting
data preparation
7.2 Data Preparation
estimation
7.3 Noninteractive Quantlets for | 7.3.4 Estimating a GAM
interactive
7.4 Interactive Quantlet GAMFIT
marginal integration
7.1.2 Marginal Integration
orthogonal series
7.1.4 Orthogonal Series
testing
7.6 Noninteractive Quantlets for
theory
7.1 Brief Theory
generalized additive models
see GAM
generalized linear model
6. Generalized Partial Linear
generalized partial linear models
see GPLM
GLM
3.2 Nonlinear EIV Models | 6. Generalized Partial Linear
GPLM
6. Generalized Partial Linear | 7. Generalized Additive Models
backfitting
6.1.2.0.3 Backfitting
estimation
6.3 Computing GPLM Estimates | 6.3.1 Estimation
likelihood
6.1.2 Semiparametric Likelihood
models
6.3 Computing GPLM Estimates
output display
6.4.7 Output Modification | 6.5.2 Output Display
profile likelihhood
6.1.2.0.1 Profile Likelihood
specification test
6.5.3 Model selection
Speckman estimator
6.1.2.0.2 Generalized Speckman Estimator
grid
GPLM
6.4.2 Grid and Starting
growth regression
8. Growth Regression and | 8.1 A Linear Convergence
hazard regression
5. Hazard Regression
Cox proportional hazards model
5.3 The Cox Proportional
hypothesis testing
5.3.3 Hypothesis Testing
data structure
5.1 Data Structure
Kaplan-Meier estimator
5.2 Kaplan-Meier Estimates
Hurst coefficient
15.2.1 Hurst Constant
Hurst exponent
14.1 Introduction
income distribution
8. Growth Regression and | 8. Growth Regression and
innovation outliers
18.1.1.3 Innovation Outliers
integrated squared error
see ISE
ISE
16.1.2 Bandwidth Selection
Kalman filter
18.2 Classical Method: Kalman
optimality of
18.2.2 Optimality of the
robust
see robust Kalman filter
Kaplan-Meier estimator
5.2 Kaplan-Meier Estimates
kernel density estimation
multivariate
8.2.3 Multivariate Kernel Density
univariate
8.2.2 Univariate Kernel Density
least median of squares
2.1.2 High Breakdown point
least trimmed squares
see LTS
leave-one-out cross-validation estimate
16.1.2 Bandwidth Selection
likelihood ratio test
GPLM
6.5.3 Model selection
link function
6.1 Estimating GPLMs
local linear estimator
16.1.1 Estimation of the | 16.2.1 Estimation of the
rate of convergence
16.2.1 Estimation of the
variance of
16.1.4 Confidence Intervals
local quadratic estimator
16.1.5 Derivative Estimation
long-memory analysis
14. Long-Memory Analysis
example
15.5 ExploRing the Data
tests
14.2 Model Indepependent Tests | 15.3 Tests for I(0)
long-memory process
14.1 Introduction
spectrum of
14.1 Introduction
LTS
2. Least Trimmed Squares | 2.1.2 High Breakdown point
marginal integration
GAM
7.1.2 Marginal Integration
mean integrated squared error
see MISE
MISE
16.1.2 Bandwidth Selection | 16.2.2 Bandwidth and Lag
asymptotic
16.2.2 Bandwidth and Lag
model
additive partially linear
7.1.1 Models
additive with interaction
7.1.1 Models
aggregate money demand
17. Multiple Time Series
dynamic panel data
12. Uncovered Interest Parity
dynamic partial least squares
see DPLS
generalized additive
see GAM
generalized linear
see GLM | 6. Generalized Partial Linear
generalized partial linear
see GPLM
Klein's
4.1 Introduction
nonlinear autoregressive
see NAR
nonlinear time series
see flexible time series
partial linear
6.1.1 Models
simultaneous-equations
see simultaneous-equations model
vector autoregressive
17. Multiple Time Series
money-demand system
4.3 Application: Money-Demand
multiple time series
17. Multiple Time Series
analysis in XploRe
17.1.2 Starting multi
data preparation
17.1.1 Data Preparation
estimation
17.3.2 Model Estimation
plot of
17.2.1 Plotting the Data
structural analysis
17.4 Structural Analysis
validation
17.3.3 Model Validation
Nadaraya-Watson estimator
16.2.1 Estimation of the
rate of convergence
16.2.1 Estimation of the
variance of
16.1.4 Confidence Intervals
Nadarya-Watson estimator
computation
16.1.1 Estimation of the
NAR
higher order
16.2 Nonlinear Autoregressive Models
neasurement error model
3.2 Nonlinear EIV Models
nonlinear autoregressive model
see NAR
nonlinear time series analysis
see flexible time series
optional parameters
GPLM
6.4 Options
orthogonal series
GAM
7.1.4 Orthogonal Series
outliers
18.1.1 Outliers and Robustness
additive
18.1.1.2 Additive Outliers
innovation
18.1.1.3 Innovation Outliers
other types of
18.1.1.4 Other Types of
output
GPLM
6.4.7 Output Modification | 6.5.2 Output Display
panel data
12. Uncovered Interest Parity
dynamic panel data model
12.4 A Dynamic Panel
fixed effects model
12.3 A Fixed Effects
unit root tests
12.5 Unit Root Tests
plot
CART
10.4 Plotting the Result
flexible time series
16.2.3 Plotting and Diagnostics
multiple time series
17.2.1 Plotting the Data
product kernel
16.2.1 Estimation of the
profile likelihhood
GPLM
6.1.2.0.1 Profile Likelihood
quantile function
1.1 Introduction
conditional
1.2.1 Definitions
quantile regression
1. Quantile Regression
asymptotic normality
1.4.1 Main Asymptotic Results
confidence intervals
1.4.3 Rank Tests
definition
1.2.1 Definitions
equivariance
1.3.1 Equivariance
monotonic transformations
1.3.2 Invariance to Transformations
rank test
1.4.3 Rank Tests
rank test inversion
1.4.3 Rank Tests
robustness
1.3.3 Robustness
statistical inference
1.4 Inference
Wald test
1.4.2 Wald Test
quantile regression process
1.4.1 Main Asymptotic Results
rankscore function
1.4.3 Rank Tests
regression tree
see CART
rIC filter
18.4 The rIC filter
rLS filter
18.3 The rLS filter
robust Kalman filter
18. Robust Kalman Filtering
rIC filter
18.4 The rIC filter
rLS filter
18.3 The rLS filter
rqfit
1.5.1 Quantlet rqfit
rrstest
1.5.2 Quantlet rrstest
simultaneous-equations
computation
4.2.5 Computation
estimation
4.2 Estimation
example
4.2.5 Computation | 4.3 Application: Money-Demand
identification
4.2.1 Identification
Klein's model
4.1 Introduction
three-stage least squares
4.2.4 Three-Stage Least Squares
two-stage least squares
4.2.3 Two-Stage Least Squares
simultaneous-equations model
4. Simultaneuos-Equations Models
singular value decomposition
13.1.1 Singular Value Decomposition
specification test
GPLM
6.5.3 Model selection
Speckman estimator
GPLM
6.1.2.0.2 Generalized Speckman Estimator
start values
GPLM
6.4.2 Grid and Starting
state-space model
18. Robust Kalman Filtering
statistical characteristics
GPLM
6.5.1 Statistical Characteristics
strong mixing
16.1.1 Estimation of the
test
Bera-Jarque
16.1.3 Diagnostics
time series
absolute regularity condition
16.1.1 Estimation of the
$ \alpha$-mixing
16.1.1 Estimation of the
antipersistent
14.1 Introduction
$ \beta$-mixing
16.1.1 Estimation of the
$ \phi$-mixing
16.1.1 Estimation of the
financial
see financial time series
flexible
see flexible time series
fractionally integrated
14.1 Introduction | 15.2.2 Fractional Integration
long-memory
14.1 Introduction | 15.1 Introduction
multiple
see multiple time series
nonlinear
see flexible time series
nonstationary
14.1 Introduction
persistence
15.1 Introduction
strong mixing
16.1.1 Estimation of the
uniform mixing
16.1.1 Estimation of the
uncovered interest parity
12. Uncovered Interest Parity
uniform mixing
16.1.1 Estimation of the
WARPing
16.1.1 Estimation of the | 16.1.5 Derivative Estimation | 16.2.1 Estimation of the
weights
GPLM
6.4.3 Weights and Offsets