Forecasting, time series, and regression : an applied approach
Forecasting, time series, and regression : an applied approach
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发表时间：
2005
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通讯作者：
B. Bowerman;R. T. O'connell;A. Koehler
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作者：
B. Bowerman;R. T. O'connell;A. Koehler
第一部分：基础统计学的介绍和回顾。1. 预测导论。预测和数据。预测方法。预测中的错误。选择预测技术。定量预测技术综述。基本统计概念。人群。概率。随机样本和样本统计。连续概率分布。正态概率分布。t分布，f分布，卡方分布。总体均值的置信区间。总体均值的假设检验。练习。第二部分：回归分析。3. 简单线性回归。简单线性回归模型。最小二乘点估计。点估计和点预测。模型假设和标准误差。检验斜率和y轴截距的重要性。置信区间和预测区间。简单决定系数和相关系数。模型的F检验。练习。4。多元线性回归。线性回归模型。最小二乘估计，点估计和预测。均方误差和标准误差。模型效用：R2、调整后的R2和总体F检验。检验自变量的显著性。置信区间和预测区间。二次回归模型。交互。用虚拟变量对定性自变量建模。部分F检验：检验回归模型的一部分的显著性。练习。5。模型建立和残差分析。模型建立与多重共线性效应。简单回归中的残差分析。多元回归中的残差分析。用于检测外围和有影响的观测值的诊断。练习。第三部分：时间序列回归、分解方法和指数平滑。6. 时间序列回归。用多项式函数建模趋势。检测自相关。季节变化的类型。利用假变量和三角函数模拟季节变化。增长曲线。处理一阶自相关。练习。7。分解方法。乘法分解。添加剂分解。X-12-ARIMA季节调整方法。练习。8。指数平滑法。简单的指数平滑。跟踪信号。霍尔特趋势修正指数平滑。Holt-Winters方法。阻尼趋势和其他指数平滑方法。指数平滑和预测区间模型。练习。第四部分：BOX-JENKINS方法论。9. 非季节性Box-Jenkins模型及其初步鉴定。平稳与非平稳时间序列。样本自相关和部分自相关函数：SAC和SPAC。非季节建模与预报导论。非季节性Box-Jenkins模型的初步鉴定。练习。10。非季节性Box-Jenkins模型的估计、诊断检查和预测。估计。诊断检查。预测。案例研究。指数平滑的Box-Jenkins实现。练习。11。Box-Jenkins季节模型。季节时间序列转化为平稳时间序列。季节性建模和预测的三个例子。时间序列回归中的Box-Jenkins误差项模型。练习。12。高级Box-Jenkins建模。一般季节性模型和暂定鉴定指南。干预模型。建立传递函数模型的程序。练习。附录A：统计表附录B：矩阵代数的回归计算。矩阵和向量。矩阵的转置。矩阵的和与差。矩阵乘法。单位矩阵。线性相关和线性无关。矩阵的逆。最小二乘点估计。无法解释的变异和可以解释的变异。b.距离值。使用平方项。使用交互术语。使用虚拟变量。回归参数线性组合估计的标准误差。练习。附录C：参考文献。
Part I: INTRODUCTION AND REVIEW OF BASIC STATISTICS. 1. An Introduction to Forecasting. Forecasting and Data. Forecasting Methods. Errors in Forecasting. Choosing a Forescasting Technique. An Overview of Quantitative Forecasting Techniques. 2. Basic Statistical Concepts. Populations. Probability. Random Samples and Sample Statistics. Continuous Probability Distributions. The Normal Probability Distribution. The t-Distribution, the F-Distribution, the Chi-Square Distribution. Confidence Intervals for a Population Mean. Hypothesis Testing for a Population Mean. Exercises. Part II: REGRESSION ANALYSIS. 3. Simple Linear Regression. The Simple Linear Regression Model. The Least Squares Point Estimates. Point Estimates and Point Predictions. Model Assumptions and the Standard Error. Testing the Significance of the Slope and y Intercept. Confidence and Prediction Intervals. Simple Coefficients of Determination and Correlation. An F Test for the Model. Exercises. 4. Multiple Linear Regression. The Linear Regression Model. The Least Squares Estimates, and Point Estimation and Prediction. The Mean Square Error and the Standard Error. Model Utility: R2, Adjusted R2, and the Overall F Test. Testing the Significance of an Independent Variable. Confidence and Prediction Intervals. The Quadratic Regression Model. Interaction. Using Dummy Variables to Model Qualitative Independent Variables. The Partial F Test: Testing the Significance of a Portion of a Regression Model. Exercises. 5. Model Building and Residual Analysis. Model Building and the Effects of Multicollinearity. Residual Analysis in Simple Regression. Residual Analysis in Multiple Regression. Diagnostics for Detecting Outlying and Influential Observations. Exercises. Part III: TIME SERIES REGRESSION, DECOMPOSITION METHODS, AND EXPONENTIAL SMOOTHING. 6. Time Series Regression. Modeling Trend by Using Polynomial Functions. Detecting Autocorrelation. Types of Seasonal Variation. Modeling Seasonal Variation by Using Dummy Variables and Trigonometric Functions. Growth Curves. Handling First-Order Autocorrelation. Exercises. 7. Decomposition Methods. Multiplicative Decomposition. Additive Decomposition. The X-12-ARIMA Seasonal Adjustment Method. Exercises. 8. Exponential Smoothing. Simple Exponential Smoothing. Tracking Signals. Holts Trend Corrected Exponential Smoothing. Holt-Winters Methods. Damped Trends and Other Exponential Smoothing Methods. Models for Exponential Smoothing and Prediction Intervals. Exercises. Part IV: THE BOX-JENKINS METHODOLOGY. 9. Nonseasonal Box-Jenkins Modeling and Their Tentative Identification. Stationary and Nonstationary Time Series. The Sample Autocorrelation and Partial Autocorrelation Functions: The SAC and SPAC. An Introduction to Nonseasonal Modeling and Forecasting. Tentative Identification of Nonseasonal Box-Jenkins Models. Exercises. 10. Estimation, Diagnostic Checking, and Forecasting for Nonseasonal Box-Jenkins Models. Estimation. Diagnostic Checking. Forecasting. A Case Study. Box-Jenkins Implementation of Exponential Smoothing. Exercises. 11. Box-Jenkins Seasonal Modeling. Transforming a Seasonal Time Series into a Stationary Time Series. Three Examples of Seasonal Modeling and Forecasting. Box-Jenkins Error Term Models in Time Series Regression. Exercises. 12. Advanced Box-Jenkins Modeling. The General Seasonal Model and Guidelines for Tentative Identificatino. Intervention Models. A Procedure for Building a Transfer Function Model. Exercises. Appendix A: Statistical Tables Appendix B: Matrix Algebra for Regression Calculations. Matrices and Vectors. The Transpose of a Matrix. Sums and Differences of Matrices. Matrix Multiplication. The Identity Matrix. Linear Dependence and Linear Independence. The Inverse of a Matrix. The Least Squares Point Esimates. The Unexplained Variation and Explained Variation. The Standard Error of the Estimate b. The Distance Value. Using Squared Terms. Using Interaction Terms. Using Dummy Variable. The Standard Error of the Estimate of a Linear Combination of Regression Parameters. Exercises. Appendix C: References.