LEADER 05508nam 2200685 a 450 001 9910453193503321 005 20200520144314.0 010 $a1-281-93790-8 010 $a9786611937904 010 $a981-277-896-9 035 $a(CKB)1000000000538172 035 $a(EBL)1679390 035 $a(OCoLC)879023568 035 $a(SSID)ssj0000143064 035 $a(PQKBManifestationID)11144463 035 $a(PQKBTitleCode)TC0000143064 035 $a(PQKBWorkID)10109633 035 $a(PQKB)11108990 035 $a(MiAaPQ)EBC1679390 035 $a(WSP)00001848 035 $a(Au-PeEL)EBL1679390 035 $a(CaPaEBR)ebr10255742 035 $a(CaONFJC)MIL193790 035 $a(EXLCZ)991000000000538172 100 $a20080701d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aEconometric forecasting and high-frequency data analysis$b[electronic resource] /$feditors, Roberto S. Mariano, Yiu-Kuen Tse 210 $aHackensack, NJ $cWorld Scientific$dc2008 215 $a1 online resource (200 p.) 225 1 $aLecture notes series,$x1793-0758 ;$vv. 13 300 $aDescription based upon print version of record. 311 $a981-277-895-0 320 $aIncludes bibliographical references. 327 $aCONTENTS; Foreword; Preface; Forecast Uncertainty, its Representation and Evaluation Kenneth F. Wallis; 1. Introduction; 1.1 Motivation; 1.2 Overview; A theoretical illustration; Example; Generalisations; Forecast evaluation; 2. Measuring and reporting forecast uncertainty; 2.1 Model-based measures of forecast uncertainty; The linear regression model; Estimation error in multi-step forecasts; Stochastic simulation in non-linear models; Loss functions; Model uncertainty; 2.2 Empirical measures of forecast uncertainty; 2.3 Reporting forecast uncertainty; Forecast intervals; Density forecasts 327 $aGraphical presentationsAdditional examples; 2.4 Forecast scenarios; 2.5 Uncertainty and disagreement in survey forecasts; 3. Evaluating interval and density forecasts; 3.1 Likelihood ratio tests of interval forecasts; 3.2 Chi-squared tests of interval forecasts; 3.3 Extensions to density forecasts; 3.4 The probability integral transformation; 3.5 The inverse normal transformation; 3.6 The Bank of England's inflation forecasts; 3.7 Comparing density forecasts; 4. Conclusion; References 327 $aThe University of Pennsylvania Models for High-Frequency Macroeconomic and Modeling Lawrence R. Klein and Suleyman Ozmucur1. Introduction; 2. The Methodology of the Current Quarter Model (CQM); 3. The Methodology of the Survey Corner8; 4. Conclusion; References; Forecasting Seasonal Time Series Philip Hans Franses; 1. Introduction; 2. Seasonal Time Series; How do seasonal time series look like?; What do we want to forecast?; Why is seasonal adjustment often problematic?; 3. Basic Models; The deterministic seasonality model; Seasonal random walk; Airline model; Basic structural model 327 $aConclusion4. Advanced Models; Seasonal unit roots; Testing for seasonal unit roots; Seasonal cointegration; Periodic models; Multivariate representation; Conclusion; 5. Recent Advances; Periodic GARCH; 6. Conclusion; References; Car and Affine Processes Christian Gourieroux; 1. Introduction; 2. Compound Autoregressive Processes and A ne Processes; 2.1. The Gaussian Autoregressive Process; 2.2. Definition of a Car Process; 2.3. Marginal Distribution; 2.4. Nonlinear Prediction Formulas; 2.5. Compounding Interpretation; 2.5.1. Integer Autoregressive Process 327 $a2.5.2. Nonnegative Continuous Variables2.6. Continuous Time A ne Processes; 3. Autoregressive Gamma Process; 3.1. Gamma Distribution; 3.1.1. Centered Gamma Distribution; 3.1.2. Noncentered Gamma Distribution; 3.1.3. Change of scale; 3.2. The Autoregressive Gamma Process; 3.3. Nonlinear Prediction Formula; 3.4. Link with the Cox, Ingersoll, Ross Process; 3.5. Extensions; 3.5.1. Autoregressive gamma process of order p; 4. Wishart Autoregressive Process; 4.1. The Outer Product of a Gaussian VAR(1) Process; 4.2. Extension to Stochastic Positive Definite Matrices; 4.3. Conditional Moments 327 $a4.4. Continuous Time Analogue 330 $a This important book consists of surveys of high-frequency financial data analysis and econometric forecasting, written by pioneers in these areas including Nobel laureate Lawrence Klein. Some of the chapters were presented as tutorials to an audience in the Econometric Forecasting and High-Frequency Data Analysis Workshop at the Institute for Mathematical Science, National University of Singapore in May 2006. They will be of interest to researchers working in macroeconometrics as well as financial econometrics. Moreover, readers will find these chapters useful as a guide to the literature as 410 0$aLecture notes series (National University of Singapore. Institute for Mathematical Sciences) ;$vv. 13. 606 $aEconometrics 606 $aFinance$xEconometric models 608 $aElectronic books. 615 0$aEconometrics. 615 0$aFinance$xEconometric models. 676 $a330.0112 701 $aMariano$b Roberto S$0144813 701 $aTse$b Yiu Kuen$f1952-$0614560 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910453193503321 996 $aEconometric forecasting and high-frequency data analysis$92195722 997 $aUNINA