C.4 General lagged-variable autoregressive model. B.8 Lagged-variable autoregressive models. B.4 First-order autoregressive linear model. B.3 Maximum likelihood estimation method. Appendix A: Models for a single time series. 11.6 The local polynomial Kernel fit regression. 11.5 Nonparametric regression based on a time series. 11.1 What is the nonparametric data analysis. 9.7 Illustrative examples based on the Demo.wf1. 8.4 ARCH models with exogenous variables. 7.9 Further extension of the instrumental models. 7.8 Multivariate instrumental models based on the US_DPOC. 7.7 Instrumental seemingly causal models. 7.6 Instrumental models with time-related effects. 7.5 Selected cases based on the US_DPOC data. 7.4 System equation with instrumental variables. 7.2 Should we apply instrumental models? 7.3 Residual analysis in developing instrumental models. 5.5 Cases based on the US domestic price of copper. 5.2 Specific cases of growth curve models. 4.9 Additional selected seemingly causal models. 4.8 General discontinuous seemingly causal models. 4.7 Seemingly causal models with dummy variables. 4.5 System equations based on trivariate time series. 4.2 Statistical analysis based on a single time series. 3.10 Multivariate models by states and time periods. 3.9 General two-piece models with time-related effects. 3.8 Generalized discontinuous models with trend. 3.6 Alternative discontinuous growth models. 3.5 Discontinuous translog linear AR(1) growth models. 3.4 Two-piece polynomial bounded growth models. 2.16 Generalized multivariate models with time-related effects. 2.15 Alternative multivariate models with trend. 2.13 Generalized multivariate models with trend. 2.10 Alternative univariate growth models. 2.8 Growth models with exogenous variables. 2.6 Lagged variables or autoregressive growth models. 2.5 Bounded autoregressive growth models. 1.1 What is the EViews workfile? 1.2 Basic options in EViews. 1 EViews workfile and descriptive data analysis. Statistics, life sciences, and social science students, as well as applied researchers, will also find this book an invaluable resource.ĭimensions: 23.3 x 15.2 x 3.7 centimeters (1.04 kg) The author: Provides step-by-step directions on how to apply EViews software to time series data analysis Offers guidance on how to develop and evaluate alternative empirical models, permitting the most appropriate to be selected without the need for computational formulae Examines a variety of times series models, including continuous growth, discontinuous growth, seemingly causal, regression, ARCH, and GARCH as well as a general form of nonlinear time series and nonparametric models Gives over 250 illustrative examples and notes based on the author's own empirical findings, allowing the advantages and limitations of each model to be understood Describes the theory behind the models in comprehensive appendices Provides supplementary information and data sets An essential tool for advanced undergraduate and graduate students taking finance or econometrics courses. The procedures introduced are easily extendible to cross-section data sets. Rich in examples and with an emphasis on how to develop acceptable statistical models, Time Series Data Analysis Using EViews is a perfect complement to theoretical books presenting statistical or econometric models for time series data. Do you want to recognize the most suitable models for analysis of statistical data sets? This book provides a hands-on practical guide to using the most suitable models for analysis of statistical data sets using EViews - an interactive Windows-based computer software program for sophisticated data analysis, regression, and forecasting - to define and test statistical hypotheses.
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