XploRe : Application Guide : Application Guide - W. HÃ¤rdle

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XploRe : Application Guide

Application Guide

By: W. HÃ¤rdle, Z. Hlavka, S. Klinke

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CD-ROM | 1 December 2000

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532 Pages

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24.13 x 16.51 x 3.81

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Most statistical applications involve computational work with data stored on a computer. The mechanics of interaction with the data is a function of the sta- tistical computing environment. This application guide is intended for slightly experienced statisticians in computer-aided data analysis who desire to learn advanced applications in various fields of statistics. The prerequisities for XploRe-the statistic computing environment-are an introductory course in statistics or mathematics. This book is designed as an e-book which means that the text contained in here is also available as an integrated document in HTML and PDF format. The reader of this application guide should therefore be familiar with the basics of Acrobat Reader and of HTML browsers in order to profit from direct computing possibilities within this document. The quantlets presented here may be used together with the academic edi- tion of XploRe (http: //www.i-xplore.de) or via the XploRe Quantlet Client (XQC) on http: //www.xplore-stat.de. The book comes together with a CD- Rom that contains the XploRe Quantlet Server (XQS) and the full Auto Pilot Support System (APSS). With this e-book bundle one may directly try the application without being dependent on a specific software version. The quantlets described in the book can be accessed via the links included All executable quantlets are denoted by the symbol . Some in the text.

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You Can Find This Book In

Non-Fiction Mathematics Probability & Statistics Economics Econometrics Economic Statistics Economic Theory & Philosophy Accounting & Finance Finance Computing & I.T.Business Applications Mathematical & Statistical Software

Databases Data Capture & Analysis

Regression Models	p. 17
Quantile Regression	p. 19
Introduction	p. 19
Quantile Regression	p. 22
Definitions	p. 23
Computation	p. 26
Essential Properties	p. 28
Equivariance	p. 28
Invariance to Transformations	p. 28
Robustness	p. 29
Inference	p. 32
Main Asymptotic Results	p. 33
Wald Test	p. 34
Rank Tests	p. 36
Description of Quantlets	p. 41
Quantlet rqfit	p. 41
Quantlet rrstest	p. 45
Bibliography	p. 47
Least Trimmed Squares	p. 49
Robust Regression	p. 49
Introduction	p. 49
High Breakdown point Estimators	p. 52
Least Trimmed Squares	p. 54
Definition	p. 54
Computation	p. 56
Supplementary Remarks	p. 58
Choice of the Trimming Constant	p. 58
LTS as a Diagnostic Tool	p. 59
High Subsample Sensitivity	p. 60
Bibliography	p. 62
Errors-in-Variables Models	p. 65
Linear EIV Models	p. 65
A Single Explanatory Variable	p. 67
Vector of Explanatory Variables	p. 78
Nonlinear EIV Models	p. 83
Regression Calibration	p. 85
Simulation Extrapolation	p. 87
Partially Linear EIV Models	p. 89
The Variance of Error Known	p. 89
The Variance of Error Unknown	p. 90
XploRe Calculation and Practical Data	p. 91
Bibliography	p. 95
Simultaneous-Equations Models	p. 97
Introduction	p. 97
Estimation	p. 98
Identification	p. 98
Some Notation	p. 99
Two-Stage Least Squares	p. 100
Three-Stage Least Squares	p. 101
Computation	p. 103
Application: Money-Demand	p. 108
Bibliography	p. 114
Hazard Regression	p. 115
Data Structure	p. 116
Kaplan-Meier Estimates	p. 121
The Cox Proportional Hazards Model	p. 126
Estimating the Regression Coefficients	p. 127
Estimating the Hazard and Survival Functions	p. 131
Hypothesis Testing	p. 137
Example: Length of Stay in Nursing Homes	p. 141
Bibliography	p. 143
Generalized Partial Linear Models	p. 145
Estimating GPLMs	p. 145
Models	p. 146
Semiparametric Likelihood	p. 146
Data Preparation	p. 150
General	p. 150
Example	p. 151
Computing GPLM Estimates	p. 152
Estimation	p. 153
Estimation in Expert Mode	p. 156
Options	p. 158
Setting Options	p. 159
Grid and Starting Values	p. 159
Weights and Offsets	p. 160
Control Parameters	p. 161
Model Parameters	p. 162
Specification Test	p. 162
Output Modification	p. 162
Statistical Evaluation and Presentation	p. 163
Statistical Characteristics	p. 163
Output Display	p. 164
Model selection	p. 165
Generalized Additive Models	p. 171
Brief Theory	p. 172
Models	p. 172
Marginal Integration	p. 173
Backfitting	p. 174
Orthogonal Series	p. 175
Data Preparation	p. 176
Noninteractive Quantlets for Estimation	p. 176
Estimating an AM	p. 177
Estimating an APLM	p. 179
Estimating an AM and APLM	p. 181
Estimating a GAM	p. 184
Estimating a GAPLM	p. 186
Estimating Bivariate Marginal Influence	p. 189
Estimating an AM with Interaction Terms	p. 191
Estimating an AM Using Marginal Integration	p. 195
Interactive Quantlet GAMFIT	p. 197
How to Append Optional Parameters	p. 203
Noninteractive Quantlets for Testing	p. 205
Component Analysis in APL Models	p. 206
Testing for Interaction	p. 208
Testing for Interaction	p. 210
Odds and Ends	p. 213
Special Properties of GAM Quantlib Quantlets	p. 213
Estimation on Principal Component by PCAD	p. 213
Application for Real Data	p. 215
Bibliography	p. 220
Data Exploration	p. 221
Growth Regression and Counterfactual Income Dynamics	p. 223
A Linear Convergence Equation	p. 224
Counterfactual Income Dynamics	p. 226
Sources of the Growth Differential With Respect to a Hypothetical Average Economy	p. 226
Univariate Kernel Density Estimation and Bandwidth Selection	p. 227
Multivariate Kernel Density Estimation	p. 234
Cluster Analysis	p. 239
Introduction	p. 239
Distance Measures	p. 240
Similarity of Objects	p. 242
Hierarchical Clustering	p. 243
Agglomerative Hierarchical Methods	p. 244
Divisive Hierarchical Methods	p. 258
Nonhierarchical Clustering	p. 262
K-means Method	p. 263
Adaptive K-means Method	p. 265
Hard C-means Method	p. 267
Fuzzy C-means Method	p. 269
Bibliography	p. 278
Classification and Regression Trees	p. 281
Growing the Tree	p. 281
Pruning the Tree	p. 283
Selecting the Final Tree	p. 284
Plotting the Result of CART	p. 285
Examples	p. 287
Simulated Example	p. 287
Boston Housing Data	p. 290
Density Estimation	p. 296
DPLS: Partial Least Squares Program	p. 305
Introduction	p. 305
Theoretical Background	p. 307
The Dynamic Path Model DPLS	p. 307
PLS Estimation with Dynamic Inner Approximation	p. 307
Prediction and Goodness of Fit	p. 309
Estimating a DPLS-Model	p. 311
The Computer Program DPLS	p. 311
Creating design-matrices	p. 311
Estimating with DPLS	p. 314
Measuring the Forecasting Validity	p. 317
Example: A Model for German Share Prices	p. 318
The General Path Model	p. 318
Manifest Variables and Sources of Data	p. 319
Empirical Results	p. 321
Bibliography	p. 322
Uncovered Interest Parity	p. 323
The Uncovered Interest Parity	p. 323
The Data	p. 325
A Fixed Effects Model	p. 327
A Dynamic Panel Data Model	p. 330
Unit Root Tests for Panel Data	p. 332
Conclusions	p. 335
Macro Data	p. 336
Bibliography	p. 336
Correspondence Analysis	p. 339
Introduction	p. 339
Singular Value Decomposition	p. 339
Coordinates of Factors	p. 340
XploRe Implementation	p. 341
Example: Eye-Hair	p. 341
Description of Data	p. 341
Calling the Quantlet	p. 342
Documentation of Results	p. 343
Eigenvalues	p. 343
Contributions	p. 343
Biplots	p. 347
Brief Remark	p. 350
Example: Media	p. 350
Description of the Data Set	p. 350
Calling the Quantlet	p. 352
Brief Interpretation	p. 352
Bibliography	p. 358
Dynamic Statistical Systems	p. 359
Long-Memory Analysis	p. 361
Introduction	p. 361
Model Indepependent Tests for I(0) against I(d)	p. 363
Robust Rescaled Range Statistic	p. 364
The KPSS Statistic	p. 366
The Rescaled Variance V/S Statistic	p. 367
Nonparametric Test for I(0)	p. 369
Semiparametric Estimators in the Spectral Domain	p. 371
Log-periodogram Regression	p. 371
Semiparametric Gaussian Estimator	p. 373
Bibliography	p. 374
ExploRing Persistence in Financial Time Series	p. 377
Introduction	p. 377
Hurst and Fractional Integration	p. 379
Hurst Constant	p. 379
Fractional Integration	p. 379
Tests for I(0) against fractional alternatives	p. 380
Semiparametric estimation of difference parameter d	p. 380
ExploRing the Data	p. 381
Typical Spectral Shape	p. 381
Typical Distribution: Mean, Variance, Skewness and Kurtosis	p. 383
The Data	p. 384
The Quantlets	p. 386
The Results	p. 388
Equities	p. 388
Exchange	p. 389
Practical Considerations	p. 390
Risk and Volatility	p. 390
Estimating and Forecasting of Asset Prices	p. 391
Portfolio Allocation Strategy	p. 391
Diversification and Fractional Cointegration	p. 392
MMAR and FIGARCH	p. 392
Conclusion	p. 393
Bibliography	p. 393
Flexible Time Series Analysis	p. 397
Nonlinear Autoregressive Models of Order One	p. 398
Estimation of the Conditional Mean	p. 398
Bandwidth Selection	p. 407
Diagnostics	p. 410
Confidence Intervals	p. 413
Derivative Estimation	p. 417
Nonlinear Autoregressive Models of Higher Order	p. 420
Estimation of the Conditional Mean	p. 421
Bandwidth and Lag Selection	p. 427
Plotting and Diagnostics	p. 437
Estimation of the Conditional Volatility	p. 440
Multiple Time Series Analysis	p. 459
Getting Started	p. 459
Data Preparation	p. 460
Starting multi	p. 460
Preliminary Analysis	p. 461
Plotting the Data	p. 462
Data Transformation	p. 463
Specifying a VAR Model	p. 465
Process Order	p. 466
Model Estimation	p. 468
Model Validation	p. 470
Structural Analysis	p. 477
Impulse Response Analysis	p. 477
Confidence Intervals for Impulse Responses	p. 480
Robust Kalman Filtering	p. 483
State-Space Models and Outliers	p. 483
Outliers and Robustness Problems	p. 484
Examples of AO's and IO's	p. 488
Problem Setup	p. 490
Classical Method: Kalman Filter	p. 491
Table of Contents provided by Publisher. All Rights Reserved.

XploRe : Application Guide

Application Guide

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