Leseprobe
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Econometric Modeling
David F. Hendry, Bent Nielsen
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Sozialwissenschaften, Recht, Wirtschaft / Wirtschaft
Beschreibung
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.
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Dummy variable (statistics), Instrumental variable, Model selection, Probability, Forecast error, Variable (mathematics), Estimation, Least squares, Forecasting, Student's t-test, Type I and type II errors, Stationary process, Estimator, Correlation and dependence, Skewness, Regression analysis, T-statistic, Maximum likelihood estimation, Law of large numbers, Central limit theorem, Quantity, Parameter, Accuracy and precision, Bayesian, Random variable, Monte Carlo method, Partial correlation, Joint probability distribution, Statistic, Asymptotic distribution, Econometric model, Error term, Time series, Unit root, Autoregressive model, Likelihood function, Lucas critique, Test statistic, Probit model, Fair coin, Multiple correlation, One-Tailed Test, Equation, Inference, Normality test, Conditional expectation, Unit root test, Marginal distribution, Bias of an estimator, Granger causality, Likelihood-ratio test, Count data, Quantile, Autoregressive conditional heteroskedasticity, Heteroscedasticity, Conditional probability distribution, Confidence interval, Bernoulli distribution, Normal distribution, White test, Chow test, Logistic regression, Correlogram, Empirical distribution function, Nonparametric regression, Variance, Autocorrelation, Cointegration, Distribution function, Standard error