LEADER 05349nam 2200649 a 450 001 9910876825903321 005 20200520144314.0 010 $a1-283-44612-X 010 $a9786613446121 010 $a1-118-15052-X 010 $a1-118-15078-3 035 $a(CKB)2550000000079837 035 $a(EBL)836551 035 $a(OCoLC)774272128 035 $a(SSID)ssj0000590472 035 $a(PQKBManifestationID)11941231 035 $a(PQKBTitleCode)TC0000590472 035 $a(PQKBWorkID)10671636 035 $a(PQKB)10813865 035 $a(MiAaPQ)EBC836551 035 $a(EXLCZ)992550000000079837 100 $a19910312d1991 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aForecasting with dynamic regression models /$fAlan Pankratz 210 $aNew York $cJohn Wiley & Sons$d1991 215 $a1 online resource (410 p.) 225 1 $aWiley series in probability and mathematical statistics. Applied probability and statistics,$x0271-6356 300 $a"A Wiley-Interscience publication." 311 $a0-471-61528-5 320 $aIncludes bibliographical references and index. 327 $aForecasting with Dynamic Regression Models; Contents; Preface; Chapter 1 Introduction and Overview; 1.1 Related Time Series; 1.2 Overview: Dynamic Regression Models; 1.3 Box and Jenkins' Modeling Strategy; 1.4 Correlation; 1.5 Layout of the Book; Questions and Problems; Chapter 2 A Primer on ARIMA Models; 2.1 Introduction; 2.2 Stationary Variance and Mean; 2.3 Autocorrelation; 2.4 Five Stationary ARIMA Processes; 2.5 ARIMA Modeling in Practice; 2.6 Backshift Notation; 2.7 Seasonal Models; 2.8 Combined Nonseasonal and Seasonal Processes; 2.9 Forecasting; 2.10 Extended Autocorrelation Function 327 $a2.11 Interpreting ARIMA Model ForecastsQuestions and Problems; Case 1 Federal Government Receipts (ARIMA); Chapter 3 A Primer on Regression Models; 3.1 Two Types of Data; 3.2 The Population Regression Function (PRF) with One Input; 3.3 The Sample Regression Function (SRF) with One Input; 3.4 Properties of the Least-Squares Estimators; 3.5 Goodness of Fit (R2); 3.6 Statistical Inference; 3.7 Multiple Regression; 3.8 Selected Issues in Regression; 3.9 Application to Time Series Data; Questions and Problems; Case 2 Federal Government Receipts (Dynamic Regression); Case 3 Kilowatt-Hours Used 327 $aChapter 4 Rational Distributed Lag Models4.1 Linear Distributed Lag Transfer Functions; 4.2 A Special Case: The Koyck Model; 4.3 Rational Distributed Lags; 4.4 The Complete Rational Form DR Model and Some Special Cases 163; Questions and Problems; Chapter 5 Building Dynamic Regression Models: Model Identification; 5.1 Overview; 5.2 Preliminary Modeling Steps; 5.3 The Linear Transfer Function (LTF) Identification Method; 5.4 Rules for Identifying Rational Distributed Lag Transfer Functions; Questions and Problems; Appendix 5A The Corner Table 327 $aAppendix 5B Transfer Function Identification Using Prewhitening and Cross CorrelationsChapter 6 Building Dynamic Regression Models: Model Checking, Reformulation and Evaluation; 6.1 Diagnostic Checking and Model Reformulation; 6.2 Evaluating Estimation Stage Results; Questions and Problems; Case 4 Housing Starts and Sales; Case 5 Industrial Production, Stock Prices, and Vendor Performance; Chapter 7 Intervention Analysis; 7.1 Introduction; 7.2 Pulse Interventions; 7.3 Step Interventions; 7.4 Building Intervention Models; 7.5 Multiple and Compound Interventions; Questions and Problems 327 $aCase 6 Year-End LoadingChapter 8 Intervention and Outlier Detection and Treatment; 8.1 The Rationale for Intervention and Outlier Detection; 8.2 Models for Intervention and Outlier Detection; 8.3 Likelihood Ratio Criteria; 8.4 An Iterative Detection Procedure; 8.5 Application; 8.6 Detected Events Near the End of a Series; Questions and Problems; Appendix 8A BASIC Program to Detect AO, LS, and IO Events; Appendix 8B Specifying IO Events in the SCA System; Chapter 9 Estimation and Forecasting; 9.1 DR Model Estimation; 9.2 Forecasting; Questions and Problems 327 $aAppendix 9A A BASIC Routine for Computing the Nonbiasing Factor in (9.2.24) 330 $aOne of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies. 410 0$aWiley series in probability and mathematical statistics.$pApplied probability and statistics. 606 $aTime-series analysis 606 $aRegression analysis 606 $aPrediction theory 615 0$aTime-series analysis. 615 0$aRegression analysis. 615 0$aPrediction theory. 676 $a519.5/5 700 $aPankratz$b Alan$f1944-$089085 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910876825903321 996 $aForecasting with dynamic regression models$9437968 997 $aUNINA