- 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
-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
-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
- 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
-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
-mixing
- 16.1.1 Estimation of the
- antipersistent
- 14.1 Introduction
-mixing
- 16.1.1 Estimation of the
-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