University of Agricultural Sciences, Bangalore

Includes bibliographical references (pages 642-657) and index.

Cover; Wiley Series in Probability and Statistics; Title Page; Copyright; Dedication; Preface to the Fifth Edition; Preface to the Fourth Edition; Preface to the Third Edition; 1.1 Five Important Practical Problems; 1.2 Stochastic and Deterministic Dynamic Mathematical Models; 1.3 Basic Ideas in Model Building; Appendix A1.1 Use Of The R Software; Exercises; Chapter 1: Introduction; Part One: Stochastic Models and Their Forecasting; Chapter 2: Autocorrelation Function and Spectrum of Stationary Processes; 2.1 Autocorrelation Properties of Stationary Models.

2.2 Spectral Properties of Stationary ModelsAppendix A2.1 Link Between the Sample Spectrum and Autocovariance Function Estimate; Exercises; Chapter 3: Linear Stationary Models; 3.1 General Linear Process; 3.2 Autoregressive Processes; 3.3 Moving Average Processes; 3.4 Mixed Autoregressive-Moving Average Processes; Appendix A3.1 Autocovariances, Autocovariance Generating Function, and Stationarity Conditions for a General Linear Process; Appendix A3.2 Recursive Method for Calculating Estimates of Autoregressive Parameters; Exercises; Chapter 4: Linear Nonstationary Models.

4.1 Autoregressive Integrated Moving Average Processes4.2 Three Explicit Forms for the Arima Model; 4.3 Integrated Moving Average Processes; Appendix A4.1 Linear Difference Equations; Appendix A4.2 IMA(0, 1, 1) Process with Deterministic Drift; Appendix A4.3 Arima Processes with Added Noise; Exercises; Chapter 5: Forecasting; 5.1 Minimum Mean Square Error Forecasts and Their Properties; 5.2 Calculating Forecasts and Probability Limits; 5.3 Forecast Function and Forecast Weights; 5.4 Examples of Forecast Functions and Their Updating.

5.5 Use of State-Space Model Formulation for Exact Forecasting5.6 Summary; Appendix A5.1 Correlation Between Forecast Errors; Appendix A5.2 Forecast Weights for Any Lead Time; Appendix A5.3 Forecasting in Terms of the General Integrated Form; Exercises; Part Two: Stochastic Model Building; Chapter 6: Model Identification; 6.1 Objectives of Identification; 6.2 Identification Techniques; 6.3 Initial Estimates for the Parameters; 6.4 Model Multiplicity; Appendix A6.1 Expected Behavior of the Estimated Autocorrelation Function for a Nonstationary Process; Exercises.

Chapter 7: Parameter Estimation7.1 Study of the Likelihood and Sum-of-Squares Functions; 7.2 Nonlinear Estimation; 7.3 Some Estimation Results for Specific Models; 7.4 Likelihood Function Based on the State-Space Model; 7.5 Estimation Using Bayes' Theorem; Appendix A7.1 Review of Normal Distribution Theory; Appendix A7.2 Review of Linear Least-Squares Theory; Appendix A7.3 Exact Likelihood Function for Moving Average and Mixed Processes; Appendix A7.4 Exact Likelihood Function for an Autoregressive Process; Appendix A7.5 Asymptotic Distribution of Estimators for Autoregressive Models.