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| 008 | 221201t20122007nju fo d z eng d | ||
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_a9781400845651 _qPDF |
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| 024 | 7 |
_a10.1515/9781400845651 _2doi |
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| 035 | _a(DE-B1597)9781400845651 | ||
| 035 | _a(DE-B1597)642796 | ||
| 040 |
_aDE-B1597 _beng _cDE-B1597 _erda |
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| 050 | 4 | _aHB141 | |
| 072 | 7 |
_aBUS021000 _2bisacsh |
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| 082 | 0 | 4 | _a330.015195 |
| 084 | _aonline - DeGruyter | ||
| 100 | 1 |
_aHendry, David F. _eautore |
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| 245 | 1 | 0 |
_aEconometric Modeling : _bA Likelihood Approach / _cDavid F. Hendry, Bent Nielsen. |
| 264 | 1 |
_aPrinceton, NJ : _bPrinceton University Press, _c[2012] |
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| 264 | 4 | _c©2007 | |
| 300 |
_a1 online resource (384 p.) : _b50 line illus. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | 0 |
_tFrontmatter -- _tContents -- _tPreface -- _tData and software -- _tChapter One. The Bernoulli model -- _tChapter Two. Inference in the Bernoulli model -- _tChapter Three. A first regression model -- _tChapter Four. The logit model -- _tChapter Five. The two-variable regression model -- _tChapter Six. The matrix algebra of two-variable regression -- _tChapter Seven. The multiple regression model -- _tChapter Eight. The matrix algebra of multiple regression -- _tChapter Nine. Mis-specification analysis in cross sections -- _tChapter Ten. Strong exogeneity -- _tChapter Eleven. Empirical models and modeling -- _tChapter Twelve. Autoregressions and stationarity -- _tChapter Thirteen. Mis-specification analysis in time series -- _tChapter Fourteen. The vector autoregressive model -- _tChapter Fifteen. Identification of structural models -- _tChapter Sixteen. Non-stationary time series -- _tChapter Seventeen. Cointegration -- _tChapter Eighteen. Monte Carlo simulation experiments -- _tChapter Nineteen. Automatic model selection -- _tChapter Twenty. Structural breaks -- _tChapter Twenty One. Forecasting -- _tChapter Twenty Two. The way ahead -- _tReferences -- _tAuthor index -- _tSubject index |
| 506 | 0 |
_arestricted access _uhttp://purl.org/coar/access_right/c_16ec _fonline access with authorization _2star |
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| 520 | _aEconometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointegrated systems. In each setting, a statistical model is constructed to explain the observed variation in the data, with estimation and inference based on the likelihood function. Substantive issues are always addressed, showing how both statistical and economic assumptions can be tested and empirical results interpreted. Important empirical problems such as structural breaks, forecasting, and model selection are covered, and Monte Carlo simulation is explained and applied. Econometric Modeling is a self-contained introduction for advanced undergraduate or graduate students. Throughout, data illustrate and motivate the approach, and are available for computer-based teaching. Technical issues from probability theory and statistical theory are introduced only as needed. Nevertheless, the approach is rigorous, emphasizing the coherent formulation, estimation, and evaluation of econometric models relevant for empirical research. | ||
| 538 | _aMode of access: Internet via World Wide Web. | ||
| 546 | _aIn English. | ||
| 588 | 0 | _aDescription based on online resource; title from PDF title page (publisher's Web site, viewed 01. Dez 2022) | |
| 650 | 0 | _aEconometric models. | |
| 650 | 0 | _aEconometrics. | |
| 650 | 7 |
_aBUSINESS & ECONOMICS / Econometrics. _2bisacsh |
|
| 653 | _aAccuracy and precision. | ||
| 653 | _aAsymptotic distribution. | ||
| 653 | _aAutocorrelation. | ||
| 653 | _aAutoregressive conditional heteroskedasticity. | ||
| 653 | _aAutoregressive model. | ||
| 653 | _aBayesian statistics. | ||
| 653 | _aBayesian. | ||
| 653 | _aBernoulli distribution. | ||
| 653 | _aBias of an estimator. | ||
| 653 | _aCalculation. | ||
| 653 | _aCentral limit theorem. | ||
| 653 | _aChow test. | ||
| 653 | _aCointegration. | ||
| 653 | _aConditional expectation. | ||
| 653 | _aConditional probability distribution. | ||
| 653 | _aConfidence interval. | ||
| 653 | _aConfidence region. | ||
| 653 | _aCorrelation and dependence. | ||
| 653 | _aCorrelogram. | ||
| 653 | _aCount data. | ||
| 653 | _aCross-sectional data. | ||
| 653 | _aCross-sectional regression. | ||
| 653 | _aDistribution function. | ||
| 653 | _aDummy variable (statistics). | ||
| 653 | _aEconometric model. | ||
| 653 | _aEmpirical distribution function. | ||
| 653 | _aEquation. | ||
| 653 | _aError term. | ||
| 653 | _aEstimation. | ||
| 653 | _aEstimator. | ||
| 653 | _aExogeny. | ||
| 653 | _aExploratory data analysis. | ||
| 653 | _aF-distribution. | ||
| 653 | _aF-test. | ||
| 653 | _aFair coin. | ||
| 653 | _aForecast error. | ||
| 653 | _aForecasting. | ||
| 653 | _aGranger causality. | ||
| 653 | _aHeteroscedasticity. | ||
| 653 | _aInference. | ||
| 653 | _aInstrumental variable. | ||
| 653 | _aJoint probability distribution. | ||
| 653 | _aLaw of large numbers. | ||
| 653 | _aLeast absolute deviations. | ||
| 653 | _aLeast squares. | ||
| 653 | _aLikelihood function. | ||
| 653 | _aLikelihood-ratio test. | ||
| 653 | _aLinear regression. | ||
| 653 | _aLogistic regression. | ||
| 653 | _aLucas critique. | ||
| 653 | _aMarginal distribution. | ||
| 653 | _aMarkov process. | ||
| 653 | _aMathematical optimization. | ||
| 653 | _aMaximum likelihood estimation. | ||
| 653 | _aModel selection. | ||
| 653 | _aMonte Carlo method. | ||
| 653 | _aMoving-average model. | ||
| 653 | _aMultiple correlation. | ||
| 653 | _aMultivariate normal distribution. | ||
| 653 | _aNonparametric regression. | ||
| 653 | _aNormal distribution. | ||
| 653 | _aNormality test. | ||
| 653 | _aOne-Tailed Test. | ||
| 653 | _aOpportunity cost. | ||
| 653 | _aOrthogonalization. | ||
| 653 | _aP-value. | ||
| 653 | _aParameter. | ||
| 653 | _aPartial correlation. | ||
| 653 | _aPoisson regression. | ||
| 653 | _aProbability. | ||
| 653 | _aProbit model. | ||
| 653 | _aQuantile. | ||
| 653 | _aQuantity. | ||
| 653 | _aQuasi-likelihood. | ||
| 653 | _aRandom variable. | ||
| 653 | _aRegression analysis. | ||
| 653 | _aResidual sum of squares. | ||
| 653 | _aRound-off error. | ||
| 653 | _aSeemingly unrelated regressions. | ||
| 653 | _aSelection bias. | ||
| 653 | _aSimple linear regression. | ||
| 653 | _aSkewness. | ||
| 653 | _aStandard deviation. | ||
| 653 | _aStandard error. | ||
| 653 | _aStationary process. | ||
| 653 | _aStatistic. | ||
| 653 | _aStudent's t-test. | ||
| 653 | _aSufficient statistic. | ||
| 653 | _aSummary statistics. | ||
| 653 | _aT-statistic. | ||
| 653 | _aTest statistic. | ||
| 653 | _aTime series. | ||
| 653 | _aType I and type II errors. | ||
| 653 | _aUnit root test. | ||
| 653 | _aUnit root. | ||
| 653 | _aUtility. | ||
| 653 | _aVariable (mathematics). | ||
| 653 | _aVariance. | ||
| 653 | _aVector autoregression. | ||
| 653 | _aWhite test. | ||
| 700 | 1 |
_aNielsen, Bent _eautore |
|
| 850 | _aIT-RoAPU | ||
| 856 | 4 | 0 | _uhttps://doi.org/10.1515/9781400845651?locatt=mode:legacy |
| 856 | 4 | 0 | _uhttps://www.degruyter.com/isbn/9781400845651 |
| 856 | 4 | 2 |
_3Cover _uhttps://www.degruyter.com/document/cover/isbn/9781400845651/original |
| 942 | _cEB | ||
| 999 |
_c206821 _d206821 |
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