Introduction to time series analysis and forecasting : with applications in SAS and SPSS / Robert A. Yaffee with Monnie McGee.

Includes bibliographical references and index.

Preface -- Introduction and Overview: -- Purpose -- Time Series -- Missing Data -- Sample Size -- Representativeness -- Scope of Application -- Stochastic and Deterministic Processes -- Stationarity -- Methodological Approaches -- Importance -- Notation -- Extrapolative and Decomposition Models: -- Introduction -- Goodness-of-Fit Indicators -- Average Techniques -- Exponential Smoothing -- Decomposition Methods -- New Features of Census X-12 -- Introduction of Box-Jenkins Time Series Analysis: -- Introduction -- The importance of Time Series Analysis Modeling -- Limitations -- Assumptions -- Time Series -- Tests for Nonstationarity -- Stabilizing the Variance -- Structural or Regime Stability -- Strict Stationarity -- Implications of Stationarity -- The Basic ARIMA Model: -- Introduction to ARIMA -- Graphical Analysis of Time Series Data -- Basic Formulation of the Autoregressive Integrated Moving Average Model -- The Sample Autocorrelation Function -- The Standard Error of the ACF -- The Bounds of Stationarity and Invertibility -- The Sample Partial Autocorrelation Function -- Bounds of Stationarity and Invertibility Reviewed -- Other Sample Autocorrelation Funcations -- Tentative Identification of Characteristic Patterns of Integrated, Autoregressive, Moving Average, and ARMA Processes -- Seasonal ARIMA Models: -- Cyclicity -- Seasonal Nonstationarity -- Seasonal Differencing -- Multiplicative Seasonal Models -- The Autocorrelation Structure of Seasonal ARIMA Models -- Stationarity and Invertibility of Seasonal ARIMA Model -- A Modeling Strategy for the Seasonal ARIMA Model -- Programming Seasonal Multiplicative Box-Jenkins Models -- Alternative Methods of Modeling Seasonality -- The Question of Deterministic or Stochastic Seasonality -- Estimation and Diagnosis: -- Introduction -- Estimation -- Diagnosis of the Model -- Metadiagnosis and Forecasting: -- Introduction -- Metadiagnosis -- Forecasting with Box-Jenkins Models -- Characteristics of the Optimal Forecast -- Basic Combination of Forecast -- Forecast Evaluation -- Statistical Package Forecast Syntax -- Regression Combination of Forecasts -- Intervention Analysis: -- Introduction: Event Interventions and Their Impacts -- Assumptions of the Event Intervention (Impact Model) -- Impact Analysis Theory -- Significance Tests for Impulse Response Functions -- Modeling Strategies for Impact Analysis -- Programming Impact Analysis -- Applications of Impact Analysis -- Advantages of Intervention Analysis -- Limitations of Intervention Analysis -- Transfer Function Models: -- Definition of a Transfer Function -- Importance -- Theory of the Transfer Function Model -- Modeling Strategies -- Cointegration -- Long-Run and Short-Run Effects in Dynamic Regression -- Basic Characteristics of a Good Time Series Model -- Autoregressive Error Models: -- The Nature of Serial Correlation of Error -- Sources of Autoregressive Error -- Autoregressive Models with Serially Correlated Errors -- Tests for Serial Correlation of Error -- Corrective Algorithms for Regression Models with Autocorrelated Error -- Forecasting with Autocorrelated Error Models -- Programming Regression with Autocorrelated Errors -- Autoregression in Combining Forecasts -- Models with Stochastic Variance -- A Review of Model and Forecast Evaluation: -- Model and Forecat Evaluation -- Model Evaluation -- Comparative Forecast Evaluation -- Comparison of Individual Forecast Methods -- Comparison of Combined Forecast Models -- Power Analysis and Sample Size Determination for Well-Known Time Series Models: -- Census X-11 -- Box-Jenkins Models -- Tests for Nonstationarity -- Intervention Analysis and Transfer Functions -- Regression with Autoregressive Errors -- Conclusion.

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