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Contemporary issues in economics and econometrics : theory and application
Authors: ---
ISBN: 1843766175 Year: 2004 Publisher: Cheltenham Edward Elgar

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Environmental Econometrics Using Stata
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ISBN: 1597183555 9781597183550 Year: 2021 Publisher: College Station, Texas : Stata Press,

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Econometric modelling with time series : specification, estimation and testing
Authors: --- ---
ISBN: 9780521196604 9780521139816 9781139043205 0521196604 0521139813 113904320X 1139525484 1139526685 1139527878 1139530151 1139531344 1139539531 1283817934 Year: 2013 Publisher: Cambridge : Cambridge University Press,

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This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.


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Bootstrap, the
Authors: --- --- --- --- --- et al.
ISBN: 1529747759 Year: 2020 Publisher: London : SAGE Publications Ltd.,

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The bootstrap is a technique for performing statistical inference. The underlying idea is that most properties of an unknown distribution can be estimated as the same properties of an estimate of that distribution. In most cases, these properties must be estimated by a simulation experiment. The parametric bootstrap can be used when a statistical model is estimated using maximum likelihood since the parameter estimates thus obtained serve to characterise a distribution that can subsequently be used to generate simulated data sets. Simulated test statistics or estimators can then be computed for each of these data sets, and their distribution is an estimate of their distribution under the unknown distribution. The most popular sort of bootstrap is based on resampling the observations of the original data set with replacement in order to constitute simulated data sets, which typically contain some of the original observations more than once, some not at all. A special case of the bootstrap is a Monte Carlo test, whereby the test statistic has the same distribution for all data distributions allowed by the null hypothesis under test. A Monte Carlo test permits exact inference with the probability of Type I error equal to the significance level. More generally, there are two Golden Rules which, when followed, lead to inference that, although not exact, is often a striking improvement on inference based on asymptotic theory. The bootstrap also permits construction of confidence intervals of improved quality. Some techniques are discussed for data that are heteroskedastic, autocorrelated, or clustered.

Keywords

Economics.

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