ISBN: 3540662073
TITLE: XploRe
AUTHOR: Hrdle, W.; Klinke, S.; Mller, M.
TOC:

Preface 15
Part I: First Steps 19
1 Getting Started 21
1.1 Using XploRe 21
1.1.1 Input and Output Windows 21
1.1.2 Simple Computations 22
1.1.3 First Data Analysis 23
1.1.4 Exploring Data 24
1.1.5 Printing Graphics 27
1.2 Quantlet Examples 29
1.2.1 Summary Statistics 30
1.2.2 Histograms 30
1.2.3 2D Density Estimation 31
1.2.4 Interactive Kernel Regression 33
1.3 Getting Help 34
1.4 Basic XploRe Syntax 36
1.4.1 Operators 36
1.4.2 Variables 38
1.4.3 Variable Names 38
1.4.4 Functions 39
1.4.5 Quantlet files 40
2 Descriptive Statistics 43
Marlene Mller
2.1 Data Matrices 43
2.1.1 Creating Data Matrices 44
2.1.2 Loading Data Files 46
2.1.3 Matrix Operations 47
2.2 Computing Statistical Characteristics 49
2.2.1 Minimum and Maximum 50
2.2.2 Mean, Variance and Other Moments 51
2.2.3 Median and Quantiles 52
2.2.4 Covariance and Correlation 55
2.2.5 Categorical Data 57
2.2.6 Missing Values and Infinite Values 59
2.3 Summarizing Statistical Information 63
2.3.1 Summarizing Metric Data 63
2.3.2 Summarizing Categorical Data 66
3 Graphics 69
Sigbert Klinke
3.1 Basic Plotting 70
3.1.1 Plotting a Data Set 70
3.1.2 Plotting a Function 71
3.1.3 Plotting Several Functions 72
3.1.4 Coloring Data Sets 73
3.1.5 Plotting Lines from Data Sets 74
3.1.6 Several Plots 76
3.2 Univariate Graphics 79
3.2.1 Boxplots 80
3.2.2 Dotplots 82
3.2.3 Bar Charts 83
3.2.4 Quantile-Quantile Plots 85
3.2.5 Histograms 86
3.3 Multivariate Graphics 90
3.3.1 Three-Dimensional Plots 91
3.3.2 Surface Plots 92
3.3.3 Contour Plots 92
3.3.4 Sun ower Plots 94
3.3.5 Linear Regression 95
3.3.6 Bivariate Plots 97
3.3.7 Star Diagrams 99
3.3.8 Scatter-Plot Matrices 100
3.3.9 Andrews Curves 102
3.3.10 Parallel Coordinate Plots 103
3.4 Advanced Graphics 105
3.4.1 Moving and Rotating 105
3.4.2 Simple Predefined Graphic Primitives 106
3.4.3 Color Models 108
3.5 Graphic Commands 109
3.5.1 Controlling Data Points 110
3.5.2 Color of Data Points 111
3.5.3 Symbol of DataPoints 113
3.5.4 Size of Data Points 115
3.5.5 Connection of Data Points 116
3.5.6 Label of Data Points 121
3.5.7 Title and Axes Labels 124
3.5.8 Axes Layout 125
4 Regression Methods 129
Jrg Amus
4.1 Simple Linear Regression 131
4.2 Multiple Linear Regression 137
4.3 Nonlinear Regression 143
5 Teachware Quantlets 147
Nathaniel Derby
5.1 Visualizing Data 149
5.2 Random Sampling 150
5.3 The p-Value in Hypothesis Testing 153
5.4 Approximating the Binomial by the Normal Distribution 155
5.5 The Central Limit Theorem 157
5.6 The Pearson Correlation Coecient 159
5.7 Linear Regression 162
Bibliography 165
Part II: Statistical Libraries 167
6 Smoothing Methods 169
Marlene Mller
6.1 Kernel Density Estimation 169
6.1.1 Computational Aspects 171
6.1.2 Computing Kernel Density Estimates 173
6.1.3 Kernel Choice 176
6.1.4 Bandwidth Selection 177
6.1.5 Confidence Intervals and Bands 181
6.2 Kernel Regression 185
6.2.1 Computational Aspects 185
6.2.2 Computing Kernel Regression Estimates 186
6.2.3 Bandwidth Selection 188
6.2.4 Confidence Intervals and Bands 192
6.2.5 Local Polynomial Regression and Derivative Estimation 194
6.3 Multivariate Density and Regression Functions 197
6.3.1 Computational Aspects 197
6.3.2 Multivariate Density Estimation 197
6.3.3 Multivariate Regression 201
Bibliography 203
7 Generalized Linear Models 205
Marlene Mller
7.1 Estimating GLMs 206
7.1.1 Models 206
7.1.2 Maximum-Likelihood Estimation 208
7.2 Computing GLM Estimates 208
7.2.1 Data Preparation 208
7.2.2 Interactive Estimation 209
7.2.3 Noninteractive Estimation 213
7.3 Weights & Constraints 215
7.3.1 Prior Weights 216
7.3.2 Replications in Data 217
7.3.3 Constrained Estimation 217
7.4 Options 218
7.4.1 Setting Options 219
7.4.2 Weights and Offsets 219
7.4.3 Control Parameters 219
7.4.4 Output Modification 221
7.5 Statistical Evaluation and Presentation 221
7.5.1 Statistical Characteristics 221
7.5.2 Output Display 223
7.5.3 Significance of Parameters 223
7.5.4 Likelihood Ratio Tests for Comparing Nested Models 225
7.5.5 Subset Selection 226
Bibliography 228
8 Neural Networks 229
Wolfgang Hrdle and Heiko Lehmann
8.1 Feed-Forward Networks 231
8.2 Computing a Neural Network 232
8.2.1 Controlling the Parameters of the Neural Network 234
8.2.2 The Resulting Neural Network 235
8.3 Running a Neural Network 236
8.3.1 Implementing a Simple Discriminant Analysis 237
8.3.2 Implementing a More Complex Discriminant Analysis 241
Bibliography 246
9 Time Series 247
Petr Franek and Wolfgang Hrdle
9.1 Time Domain and Frequency Domain Analysis 247
9.1.1 Autocovariance and Autocorrelation Function 248
9.1.2 The Periodogram and the Spectrum of a Series 251
9.2 Linear Models 253
9.2.1 Autoregressive Models 253
9.2.2 Autoregressive Moving Average Models 254
9.2.3 Estimating ARMA Processes 256
9.3 Nonlinear Models 259
9.3.1 Several Examples of Nonlinear Models 259
9.3.2 Nonlinearity in the Conditional Second Moments 264
9.3.3 Estimating ARCH Models 266
9.3.4 Testing for ARCH 267
Bibliography 270
10 Kalman Filtering 273
Petr Franek
10.1 State{Space Models 273
10.1.1 Examples of State{Space Models 275
10.1.2 Modeling State{Space Models in XploRe 276
10.2 Kalman Filtering and Smoothing 277
10.3 Parameter Estimation in State{Space Models 280
Bibliography 283
11 Finance 285
Stefan Sperlich and Wolfgang Hrdle
11.1 Outline of the Theory 286
11.1.1 Some History 286
11.1.2 The Black{Scholes Formula 287
11.2 Assets 289
11.2.1 Stock Simulation 290
11.2.2 Stock Estimation 292
11.2.3 Stock Estimation and Simulation 292
11.3 Options 294
11.3.1 Calculation of Option Prices and Implied Volatilities 294
11.3.2 Option Price Determining Factors 298
11.3.3 Greeks 301
11.4 Portfolios and Hedging 304
11.4.1 Calculation of Arbitrage 304
11.4.2 Bull-Call Spreads 305
12 Microeconometrics and Panel Data 307
Jrg Breitung and Axel Werwatz
12.1 Limited-Dependent and Qualitative Dependent Variables 308
12.1.1 Probit, Logit and Tobit 308
12.1.2 Single Index Models 310
12.1.3 Average Derivatives 311
12.1.4 Average Derivative Estimation 312
12.1.5 Weighted Average Derivative Estimation 314
12.1.6 Average Derivatives and Discrete Variables 315
12.1.7 Parametric versus Semiparametric Single Index Models 318
12.2 Multiple Index Models 320
12.2.1 Sliced Inverse Regression 321
12.2.2 Testing Parametric Multiple Index Models 322
12.3 Self-Selection Models 324
12.3.1 Parametric Model 325
12.3.2 Semiparametric Model 327
12.4 Panel Data Analysis 330
12.4.1 The Data Set 333
12.4.2 Time Effects 335
12.4.3 Model Specification 336
12.4.4 Estimation 338
12.4.5 An Example 339
12.5 Dynamic Panel Data Models 343
12.6 Unit Root Tests for Panel Data 347
Bibliography 349
13 Extreme Value Analysis 353
Rolf-Dieter Reiss and Michael Thomas
13.1 Extreme Value Models 354
13.2 Generalized Pareto Distributions 356
13.3 Assessing the Adequacy: Mean Excess Functions 358
13.4 Estimation in EV Models 359
13.4.1 Linear Combination of Ratios of Spacings (LRS) 359
13.4.2 ML Estimator in the EV Model 360
13.4.3 ML Estimator in the Gumbel Model 360
13.5 Fitting GP Distributions to the Upper Tail 361
13.6 Parametric Estimators for GP Models 362
13.6.1 Moment Estimator 363
13.6.2 ML Estimator in the GP Model 364
13.6.3 Pickands Estimator 364
13.6.4 Drees{Pickands Estimator 365
13.6.5 Hill Estimator 366
13.6.6 ML Estimator for Exponential Distributions 366
13.6.7 Selecting a Threshold by Means of a Diagram 367
13.7 Graphical User Interface 368
13.8 Example 369
Bibliography 373
14 Wavelets 375
Yuri Golubev, Wolfgang Hrdle, Zdenek Hlvka, Sigbert Klinke,
Michael H. Neumann and Stefan Sperlich
14.1 Quantlib twave 377
14.1.1 Change Basis 378
14.1.2 Change Function 379
14.1.3 Change View 380
14.2 Discrete Wavelet Transform 381
14.3 Function Approximation 383
14.4 Data Compression 385
14.5 Two Sines 388
14.6 Frequency Shift 389
14.7 Thresholding 392
14.7.1 Hard Thresholding 393
14.7.2 Soft Thresholding 395
14.7.3 Adaptive Thresholding 397
14.8 Translation Invariance 402
14.9 Image Denoising 404
Bibliography 407
Part III: Programming 409
15 Reading and Writing Data 411
Sigbert Klinke, Jrgen Symanzik and Marlene Mller
15.1 Reading and Writing Data Files 411
15.2 Input Format Strings 414
15.3 Output Format Strings 417
15.4 Customizing the Output Window 419
15.4.1 Headline Style 421
15.4.2 Layer Style 422
15.4.3 Line Number Style 424
15.4.4 Value Formats and Lengths 425
15.4.5 Saving Output to a File 426
16 Matrix Handling 429
Yasemin Boztug and Marlene Mller
16.1 Basic Operations 429
16.1.1 Creating Matrices and Arrays 430
16.1.2 Operators for Numeric Matrices 435
16.2 Comparison Operators 440
16.3 Matrix Manipulation 442
16.3.1 Extraction of Elements 442
16.3.2 Matrix Transformation 445
16.4 Sums and Products 448
16.5 Distance Function 450
16.6 Decompositions 451
16.6.1 Spectral Decomposition 451
16.6.2 Singular Value Decomposition 454
16.6.3 LU Decomposition 455
16.6.4 Cholesky Decomposition 456
16.7 Lists 457
16.7.1 Creating Lists 457
16.7.2 Handling Lists 459
16.7.3 Getting Information on Lists 462
17 Quantlets and Quantlibs 465
Wolfgang Hrdle, Zdenek Hlvka and Sigbert Klinke
17.1 Quantlets 465
17.2 Flow Control 476
17.2.1 Local and Global Variables 476
17.2.2 Conditioning 478
17.2.3 Branching 480
17.2.4 While-Loop 481
17.2.5 Do-Loop 482
17.2.6 Optional Input and Output in Procedures 483
17.2.7 Errors and Warnings 486
17.3 User Interaction 488
17.4 APSS 495
17.5 Quantlibs 499
Appendix 503
A Customizing XploRe 505
A.1 XploRe.ini 505
A.1.1 The ini File 505
A.1.2 Composing Paths 507
A.2 startup.xpl 508
B Data Sets 509
B.1 Netincome{Food Expenditures 509
B.2 U.S. Companies 509
B.3 CPS 1985 510
B.4 Boston Housing 510
B.5 Lizard Data 511
B.6 Kyphosis Data 512
B.7 Swiss Bank Notes 513
B.8 Earnings Data 513
B.9 Westwood Data 514
B.10 Pullover Data 514
B.11 Geyser Data 514
Bibliography 515
Index 516
END
