Computational Methods in Statistics and EconometricsReflecting current technological capacities and analytical trends, Computational Methods in Statistics and Econometrics showcases Monte Carlo and nonparametric statistical methods for models, simulations, analyses, and interpretations of statistical and econometric data. The author explores applications of Monte Carlo methods in Bayesian estimation, state space modeling, and bias correction of ordinary least squares in autoregressive models. The book offers straightforward explanations of mathematical concepts, hundreds of figures and tables, and a range of empirical examples. A CD-ROM packaged with the book contains all of the source codes used in the text. |
Contents
| 32 | |
| 35 | |
XVI | 37 |
XVII | 40 |
XVIII | 41 |
XIX | 43 |
XX | 44 |
XXI | 46 |
XXII | 51 |
XXIII | 54 |
XXIV | 57 |
XXV | 58 |
XXVI | 59 |
XXVII | 60 |
XXVIII | 62 |
XXIX | 66 |
XXX | 67 |
XXXI | 68 |
XXXII | 74 |
XXXIII | 77 |
XXXIV | 82 |
XXXV | 83 |
XXXVI | 85 |
XXXVII | 87 |
XLI | 88 |
XLII | 92 |
XLIII | 99 |
XLIV | 100 |
XLV | 101 |
XLVI | 103 |
XLVII | 105 |
XLVIII | 106 |
XLIX | 109 |
L | 116 |
LI | 119 |
LII | 124 |
LIII | 126 |
LIV | 128 |
LV | 129 |
LVI | 130 |
LVII | 131 |
LVIII | 133 |
LIX | 134 |
LX | 135 |
LXI | 136 |
LXII | 138 |
LXIII | 139 |
LXIV | 140 |
LXV | 142 |
LXVI | 143 |
LXVII | 144 |
LXVIII | 145 |
LXIX | 146 |
LXX | 149 |
LXXI | 151 |
LXXII | 155 |
LXXIII | 157 |
LXXIV | 160 |
LXXVI | 165 |
LXXVII | 167 |
LXXVIII | 173 |
LXXIX | 175 |
LXXX | 179 |
LXXXII | 180 |
LXXXIV | 184 |
LXXXV | 185 |
LXXXVI | 186 |
LXXXVII | 190 |
LXXXVIII | 191 |
LXXXIX | 195 |
XC | 196 |
XCI | 199 |
XCII | 202 |
XCIII | 203 |
XCIV | 210 |
XCV | 212 |
XCVI | 214 |
XCVII | 216 |
XCVIII | 217 |
CVII | 255 |
CX | 257 |
CXI | 258 |
CXII | 259 |
CXIII | 261 |
CXV | 262 |
CXVI | 265 |
CXVII | 267 |
CXVIII | 275 |
CXIX | 276 |
CXX | 277 |
CXXI | 278 |
CXXIII | 282 |
CXXIV | 289 |
CXXV | 290 |
CXXVI | 293 |
CXXIX | 294 |
CXXX | 298 |
CXXXI | 300 |
| 301 | |
CXXXIII | 302 |
CXXXIV | 307 |
CXXXV | 310 |
CXXXVI | 315 |
CXXXVIII | 319 |
CXXXIX | 321 |
CXLII | 323 |
CXLIV | 324 |
CXLV | 332 |
CXLVI | 334 |
CXLVII | 336 |
CXLVIII | 338 |
CXLIX | 340 |
CL | 343 |
CLI | 345 |
CLII | 346 |
CLIII | 347 |
CLIV | 349 |
CLV | 350 |
CLVI | 359 |
CLVIII | 360 |
CLIX | 367 |
CLX | 372 |
CLXI | 376 |
CLXII | 379 |
CLXIII | 381 |
CLXIV | 382 |
CLXV | 384 |
CLXVI | 390 |
CLXVII | 391 |
CLXVIII | 399 |
CLXIX | 401 |
CLXXII | 402 |
CLXXIII | 403 |
CLXXIV | 406 |
CLXXV | 407 |
CLXXVII | 413 |
CLXXVIII | 414 |
CLXXIX | 417 |
CLXXX | 419 |
CLXXXI | 420 |
CLXXXII | 433 |
CLXXXIII | 439 |
CLXXXIV | 440 |
CLXXXV | 446 |
CLXXXVI | 447 |
CLXXXVII | 448 |
CLXXXVIII | 450 |
CLXXXIX | 453 |
CXCII | 454 |
CXCIV | 458 |
CXCV | 461 |
CXCVI | 472 |
CXCVII | 482 |
CXCVIII | 483 |
CXCIX | 483 |
CC | 483 |
| 487 | |
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Common terms and phrases
a₁ acceptance probability alpha B₁ B₂ Bayesian beta binomial BMLE call urnd(ix computational degrees of freedom denotes discrete random variable Empirical Sizes error term exponential Figure filtering follows Gibbs sampler given H₁ importance resampling iy,k,rn iy,rn joint density Kurtosis likelihood function Lines M2SE maximum likelihood estimator method Metropolis-Hastings algorithm moment-generating function Monte Carlo multivariate Nikkei stock average noncentral nonparametric tests normal distribution normal random draws null hypothesis obtained OLSE permutation probability density function random number random variable recursive regression coefficient rejection sampling represented return end RMSE Sample Powers sampling density score test shown Simulation smoothing snrnd snrnd(ix source code space model standard error standard normal random subroutine Table Tanizaki target density test statistic theorem u₁ unbiased estimator uniform distribution uniform random draw urnd utilized Wilcoxon test X₁ y₁ zero σ²
Popular passages
Page 1 - An experiment is any procedure that has a random output, ie, the results of the experiment occur randomly. The sample space of an experiment is the set of all possible outcomes. For example: The sample space for the experiment of tossing two coins and noting heads (H) or tails (T) of each coin is...