TY  - BOOK
AU  - Hendry,David F.
AU  - Nielsen,Bent
TI  - Econometric Modeling: A Likelihood Approach 
SN  - 9781400845651
AV  - HB141 
U1  - 330.015195 
PY  - 2012///]
CY  - Princeton, NJ : 
PB  - Princeton University Press, 
KW  - Econometric models
KW  - Econometrics
KW  - BUSINESS & ECONOMICS / Econometrics
KW  - bisacsh
KW  - Accuracy and precision
KW  - Asymptotic distribution
KW  - Autocorrelation
KW  - Autoregressive conditional heteroskedasticity
KW  - Autoregressive model
KW  - Bayesian statistics
KW  - Bayesian
KW  - Bernoulli distribution
KW  - Bias of an estimator
KW  - Calculation
KW  - Central limit theorem
KW  - Chow test
KW  - Cointegration
KW  - Conditional expectation
KW  - Conditional probability distribution
KW  - Confidence interval
KW  - Confidence region
KW  - Correlation and dependence
KW  - Correlogram
KW  - Count data
KW  - Cross-sectional data
KW  - Cross-sectional regression
KW  - Distribution function
KW  - Dummy variable (statistics)
KW  - Econometric model
KW  - Empirical distribution function
KW  - Equation
KW  - Error term
KW  - Estimation
KW  - Estimator
KW  - Exogeny
KW  - Exploratory data analysis
KW  - F-distribution
KW  - F-test
KW  - Fair coin
KW  - Forecast error
KW  - Forecasting
KW  - Granger causality
KW  - Heteroscedasticity
KW  - Inference
KW  - Instrumental variable
KW  - Joint probability distribution
KW  - Law of large numbers
KW  - Least absolute deviations
KW  - Least squares
KW  - Likelihood function
KW  - Likelihood-ratio test
KW  - Linear regression
KW  - Logistic regression
KW  - Lucas critique
KW  - Marginal distribution
KW  - Markov process
KW  - Mathematical optimization
KW  - Maximum likelihood estimation
KW  - Model selection
KW  - Monte Carlo method
KW  - Moving-average model
KW  - Multiple correlation
KW  - Multivariate normal distribution
KW  - Nonparametric regression
KW  - Normal distribution
KW  - Normality test
KW  - One-Tailed Test
KW  - Opportunity cost
KW  - Orthogonalization
KW  - P-value
KW  - Parameter
KW  - Partial correlation
KW  - Poisson regression
KW  - Probability
KW  - Probit model
KW  - Quantile
KW  - Quantity
KW  - Quasi-likelihood
KW  - Random variable
KW  - Regression analysis
KW  - Residual sum of squares
KW  - Round-off error
KW  - Seemingly unrelated regressions
KW  - Selection bias
KW  - Simple linear regression
KW  - Skewness
KW  - Standard deviation
KW  - Standard error
KW  - Stationary process
KW  - Statistic
KW  - Student's t-test
KW  - Sufficient statistic
KW  - Summary statistics
KW  - T-statistic
KW  - Test statistic
KW  - Time series
KW  - Type I and type II errors
KW  - Unit root test
KW  - Unit root
KW  - Utility
KW  - Variable (mathematics)
KW  - Variance
KW  - Vector autoregression
KW  - White test
N1  - Frontmatter --; Contents --; Preface --; Data and software --; Chapter One. The Bernoulli model --; Chapter Two. Inference in the Bernoulli model --; Chapter Three. A first regression model --; Chapter Four. The logit model --; Chapter Five. The two-variable regression model --; Chapter Six. The matrix algebra of two-variable regression --; Chapter Seven. The multiple regression model --; Chapter Eight. The matrix algebra of multiple regression --; Chapter Nine. Mis-specification analysis in cross sections --; Chapter Ten. Strong exogeneity --; Chapter Eleven. Empirical models and modeling --; Chapter Twelve. Autoregressions and stationarity --; Chapter Thirteen. Mis-specification analysis in time series --; Chapter Fourteen. The vector autoregressive model --; Chapter Fifteen. Identification of structural models --; Chapter Sixteen. Non-stationary time series --; Chapter Seventeen. Cointegration --; Chapter Eighteen. Monte Carlo simulation experiments --; Chapter Nineteen. Automatic model selection --; Chapter Twenty. Structural breaks --; Chapter Twenty One. Forecasting --; Chapter Twenty Two. The way ahead --; References --; Author index --; Subject index; restricted access
N2  - Econometric 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
UR  - https://doi.org/10.1515/9781400845651?locatt=mode:legacy
UR  - https://www.degruyter.com/isbn/9781400845651
UR  - https://www.degruyter.com/document/cover/isbn/9781400845651/original
ER  -