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Generalized method of moments
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1	Is the New Keynesian Phillips Curve Flat?∗ Keith Kuester Federal Reserve Bank of Philadelphia Gernot J. M¨uller Goethe University Frankfurt Add to Reading List Source URL: www.keithkuester.eu Language: English - Date: 2009-01-08 10:05:48 Economy Economics Macroeconomics New Keynesian economics Regression analysis Keynesian economics Unemployment Monetary economics Phillips curve Nominal rigidity Autocorrelation Generalized method of moments
2	On the finite sample properties of pre-test estimators of spatial models Gianfranco Piras∗ Ingmar R. Prucha† Add to Reading List Source URL: rri.wvu.edu Language: English - Date: 2013-10-02 12:09:44 Statistics Estimation theory Regression analysis Statistical theory Parametric statistics Signal processing Ordinary least squares Autocorrelation Estimator Generalized method of moments Robust statistics Linear regression
3	Ann Inst Stat Math:469–490 DOIs10463Second-order asymptotic comparison of the MLE and MCLE of a natural parameter for a truncated exponential family of distributions Add to Reading List Source URL: www.ism.ac.jp Language: English - Date: 2016-06-28 03:34:43 Statistics Estimation theory Statistical theory Maximum likelihood estimation Efficient estimator Efficiency Estimator Asymptotic theory Ole Barndorff-Nielsen Likelihood function Two-step M-estimators involving MLE Generalized method of moments
4	CAM Centre for Applied Microeconometrics Department of Economics University of Copenhagen http://www.econ.ku.dk/CAM/ Add to Reading List Source URL: www.econ.ku.dk Language: English - Date: 2011-10-17 17:38:09 Estimation theory Statistics Statistical theory M-estimators Statistical inference Regression analysis Maximum likelihood estimation Estimator Extremum estimator Generalized method of moments Loss function Efficiency
5	Appendix to The Life-Cycle and the Business-Cycle of Wage Risk - Cross-Country Comparisons Christian Bayer Falko Juesseny Universität Bonn and IGIER Add to Reading List Source URL: www.wiwi.uni-bonn.de Language: English - Date: 2011-01-04 04:34:31 Statistics Estimation theory Generalized method of moments Variance Covariance British Household Panel Survey Estimator M-estimator
6	University of Hawai`i at Mānoa Department of Economics Working Paper Series Saunders Hall 542, 2424 Maile Way, Honolulu, HIPhone: ( Add to Reading List Source URL: www.economics.hawaii.edu Language: English - Date: 2016-01-22 21:37:07 Statistics Estimation theory Regression analysis Time series models Simultaneous equation methods Statistical models Vector autoregression Autoregressive model Instrumental variable Ordinary least squares Generalized method of moments Variance
7	Dynare Working Papers Series http://www.dynare.org/wp/ Indirect Likelihood Inference Michael Creel Add to Reading List Source URL: www.dynare.org Language: English - Date: 2011-07-11 10:23:40 Estimation theory Statistical theory Estimator Maximum likelihood estimation Bias of an estimator M-estimator Extremum estimator Generalized method of moments Bayes estimator Efficiency Asymptotic theory Loss function
8	Tilburg University Testing for a Threshold in Models with Endogenous Regressors Rothfelder, Mario; Boldea, Otilia Document version: Add to Reading List Source URL: pure.uvt.nl Language: English - Date: 2016-08-03 04:50:02 Estimation theory Econometrics Regression analysis Instrumental variable M-estimators Generalized method of moments Simultaneous equations model
9	Estimating a High-Frequency NewKeynesian Phillips Curve by Steffen Ahrens and Stephen Sacht No. 1686 \| November 2012 Kiel Institute for the World Economy, Hindenburgufer 66, 24105 Kiel, Germany Add to Reading List Source URL: www.macroeconomics.tu-berlin.de Language: English - Date: 2014-09-03 07:01:50 Estimation theory New Keynesian economics Nominal rigidity Phillips curve Generalized method of moments Real versus nominal value Macroeconomics Monetary policy Inflation Consumer price index Economics Substitution
10	Christiano FINC 520, Spring 2008 Homework 6, due Thursday, MayConsider the iid Normal stochastic process, {xt } , with Ext = μ and E (xt − μ)2 = σ 2 . Let μl = E (xt − μ)l denote the lth moment about the Add to Reading List Source URL: faculty.wcas.northwestern.edu Language: English - Date: 2008-05-17 12:29:25 Probability distributions Normal distribution Statistical theory Estimation theory Econometrics Bootstrapping T-statistic Generalized method of moments Sampling distribution Skewness Multivariate normal distribution Errors and residuals

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