author = { DeJong, David N. AND Dave, Chetan },

title = { Structural Macroeconometrics Second Edition },

publisher = {Princeton University Press},

publisher = {},

isbn = {9781400840502},

keywords = { Business enterprises , Business , Macroeconomics Econometric models , Macroeconomics , BUSINESS & ECONOMICS / Economics / Macroeconomics , Forecast error , Forecasting , Fourier transform , Household , Identity matrix , Implementation , Importance sampling , Inference , Iteration , Jacobian matrix and determinant , Kalman filter , Lag operator , Likelihood function , Likelihood-ratio test , Linear combination , Linearization , Log-linear model , Loss function , Mathematical optimization , Measurement , Methodology , Nonlinear system , Normal distribution , Notation , Null hypothesis , Numerical analysis , Observable variable , Observable , Observational error , Parameter , Parametrization , Particle filter , Percentage , Point estimation , Posterior probability , Prediction , Preference (economics) , Pricing , Prior probability , Probability , Production function , Random variable , Requirement , Risk aversion , Scientific notation , Seasonal adjustment , Special case , Square root , Standard deviation , Standard error , State variable , State-space representation , Stationary process , Statistic , Statistical hypothesis testing , Stochastic process , Subset , Summary statistics , Taylor series , Textbook , Theory , Time series , Trade-off , Trend line (technical analysis) , Uncertainty , Unit root , Utility , Variable (mathematics) , Variance , Vector autoregression , Accuracy and precision , Addition , Algorithm , Approximation , Autocorrelation , Autocovariance , Autoregressive model , Autoregressive–moving-average model , Bayes' rule , Bayes' theorem , Bayesian inference , Bayesian , Business cycle , Calculation , Cobb–Douglas production function , Coefficient , Conditional probability , Covariance matrix , Data set , Degrees of freedom (statistics) , Derivative , Dividend , Dynamic programming , Eigenvalues and eigenvectors , Equation , Equity premium puzzle , Estimation , Estimator , Expected value , Finite difference },

year = {2011},

year = {, ©2012},

abstract = {Frontmatter},

abstract = {Contents},

abstract = {Preface},

abstract = {Preface to the First Edition},

abstract = {Part I Introduction},

abstract = {Chapter 1 Background and Overview},

abstract = {Chapter 2 Casting Models in Canonical Form},

abstract = {Chapter 3 DSGE Models: Three Examples},

abstract = {Part II Model Solution Techniques},

abstract = {Chapter 4 Linear Solution Techniques},

abstract = {Chapter 5 Nonlinear Solution Techniques},

abstract = {Part III Data Preparation and Representation},

abstract = {Chapter 6 Removing Trends and Isolating Cycles},

abstract = {Chapter 7 Summarizing Time Series Behavior When All Variables Are Observable},

abstract = {Chapter 8 State-Space Representations},

abstract = {Part IV Monte Carlo Methods},

abstract = {Chapter 9 Monte Carlo Integration: The Basics},

abstract = {Chapter 10 Likelihood Evaluation and Filtering in State-Space Representations Using Sequential Monte Carlo Methods},

abstract = {Chapter 11 Calibration},

abstract = {Chapter 12 Matching Moments},

abstract = {Chapter 13 Maximum Likelihood},

abstract = {Chapter 14 Bayesian Methods},

abstract = {References},

abstract = {Index},

address = { Princeton, NJ , },

url = { http://slubdd.de/katalog?TN_libero_mab2 }