| dc.contributor.author | Geweke John | en_US |
| dc.contributor.author | Horowitz Joel | en_US |
| dc.contributor.author | Pesaran Hashem | en_US |
| dc.contributor.editor | Durlauf, SN; Blume, LE | en_US |
| dc.date.accessioned | 2012-03-12T11:24:33Z | |
| dc.date.available | 2012-03-12T11:24:33Z | |
| dc.date.issued | 2008 | en_US |
| dc.identifier | 2008008226 | en_US |
| dc.identifier.citation | Geweke John, Horowitz Joel, and Pesaran Hashem 2008, 'Econometrics', The New Palgrave Dictionary of Economics online, Palgrave Macmillan, Online | en_US |
| dc.identifier.issn | 978-0-333-78676-5 | en_US |
| dc.identifier.other | B3 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10453/17610 | |
| dc.description.abstract | As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly. Major advances have taken place in the analysis of cross-sectional data by means of semiparametric and nonparametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledged and attempts have been made to take it into account either by integrating out its effects or by modelling the sources of heterogeneity when suitable panel data exist. The counterfactual considerations that underlie policy analysis and treatment valuation have been given a more satisfactory foundation. New time-series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Nonlinear econometric techniques are used increasingly in the analysis of cross-section and time-series observations. Applications of Bayesian techniques to econometric problems have been promoted largely by advances in computer power and computational techniques. The use of Bayesian techniques has in turn provided the investigators with a unifying framework where the tasks of forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process, thus providing a basis for â¿¿real time econometricsâ¿¿. | en_US |
| dc.language | en_US | |
| dc.publisher | Palgrave Macmillan | en_US |
| dc.relation.isbasedon | en_US | |
| dc.title | Econometrics | en_US |
| dc.parent | The New Palgrave Dictionary of Economics online | en_US |
| dc.journal.volume | en_US | |
| dc.journal.number | en_US | |
| dc.publocation | Online | en_US |
| dc.identifier.startpage | 1 | en_US |
| dc.identifier.endpage | 32 | en_US |
| dc.cauo.name | BUS.School of Finance and Economics | en_US |
| dc.conference | Verified OK | en_US |
| dc.for | 140300 | en_US |
| dc.personcode | 101228;0000053743;0000053744 | en_US |
| dc.percentage | 000100 | en_US |
| dc.classification.name | Econometrics | en_US |
| dc.classification.type | FOR-08 | en_US |
| dc.edition | 2nd | en_US |
| dc.custom | en_US | |
| dc.date.activity | en_US | |
| dc.location.activity | en_US | |
| dc.description.keywords | acceptance sampling; adaptive expectations hypothesis; ARMA processes; asset pricing models; asset return volatility; auctions; Bachelier, L.; Bayesian computation; Bayesian econometrics; Bayesian inference; Benini, R.; binary logit and probit models; bootstrap; building cycle; bunch maps; causality in economics and econometrics; censored regression models; central limit theorems; cointegration; common factors; conditional hazard functions; conditional mean functions; conditional median functions; confluence analysis; convexity; correlation analysis; Cowles Commission; curse of dimensionality; Davenant, C.; diagnostic tests; discrete choice models; discrete response models; distributed lags; Douglas, P.H.; Duhem¿Quine thesis; duration models; dynamic decision models; dynamic specification; dynamic stochastic general equilibrium models; Econometric Society; econometrics; economic distance; economic laws; Edgeworth expansions; Edgeworth, F. Y.; efficient market hypothesis; Engel curve; error correction models; Euler equations; experimental economics; financial econometrics; Fisher, I.; Fisher, R. A.; fixed effects and random effects; forecast error variances; forecast evaluation; forecasting; Frisch, R. A. K.; full information maximum likelihood; Galton, F.; Gaussian quadrature; generalized method of moments; geometric distributed lag model; Gibbs sampling; Haavelmo, T.; habit persistence; Hastings¿Metropolis algorithm; hedonic prices; homogeneity; Hooker, R.H.; identification; impulse response analysis; indirect utility function; inference; instrumental variables; integration; inventory cycle; joint hypotheses; Juglar cycle; Juglar, C.; k-class estimators; kernel estimators; King, G.; Kitchin, J.; Kondratieff, N.; Koopmans, T. C.; Kuznets, S.; labour market search; Lagrange multiplier; latent variables; least absolute deviations estimators; likelihood ratio; limited information maximum likelihood; linear models; local linear estimation; logit models; long waves; | en_US |
| dc.staffid | University of Minnesota | en_US |