PPT-Introduction to Time-Varying Effects Models (TVEM)

Slides Courtesy of the Methodology Center at P enn S tate U niversity m ethodologypsuedu Outline Conceptual Introduction to TVEM Types of Questions Data Considerations TVEM Stepbystep. Time-Varying Volatility and ARCH Models. Walter R. Paczkowski . Rutgers . University. 14.1 . The ARCH Model. 14.2 . Time-Varying Volatility. 14. .3 Testing, . Estimating, and Forecasting. 14. .4 . Extensions. Nick Bloom (Stanford & NBER). Harvard, April 23. rd. and 30. th. Talk summarizes . a forthcoming JEP article (& a work-in-progress longer JEL). Talk summarizes . a forthcoming JEP article (& a work-in-progress longer JEL). Pore Expansion (nm/ns). Computation for this work was supported by the University of Southern California Center for High-Performance Computing and Communications . (. http://www.usc.edu/hpcc. /).. Introduction. Michael . Massoglia. Department of Sociology. University of Wisconsin Madison . General Overview. The logic of propensity models. Application based discussion of some of the key features . Emphasis on working understanding use of models . ECML-PKDD 2010, Barcelona, Spain. B. Aditya Prakash. *. ,. . Hanghang. Tong. * ^. , Nicholas . Valler. +. , Michalis Faloutsos. +. , Christos Faloutsos. *. *. Carnegie Mellon University, Pittsburgh USA. . LRI’s. ECO19: . the . ChimERA. . project. Frederik De Laender. , Karel Viaene, . Colin Janssen, Hans . Baveco. , Melissa . Morselli. , . Marco . Scacchi. , Andreas . Focks. , . Antonio Di . Guardo. Nick Bloom (Stanford & NBER). Harvard, April 23. rd. and 30. th. Talk summarizes . a forthcoming JEP article (& a work-in-progress longer JEL). Talk summarizes . a forthcoming JEP article (& a work-in-progress longer JEL). Models for. Count Data. Doctor Visits. Basic Model for Counts of Events. E.g., Visits to site, number of purchases, number of doctor visits. Regression approach. Quantitative outcome measured. Discrete variable, model probabilities. ISBA Lecture on Bayesian Foundations June 25. th. 2012. . Bayesian . Dynamic . Modelling. . . Foundations : History of Dynamic Bayes in Action. Objective. The Los Alamos Sea Ice model has a number of input parameters for which accurate values are not always well established. . We conduct a variance-based sensitivity analysis . of hemispheric sea ice properties to 39 input parameters. The method accounts for non-linear and non-additive effects in the model.. Scale-Varying Triplet Ranking with Classification Loss. for Facial Age Estimation. Woobin Im, Sungeun Hong, Sung-Eui Yoon, Hyun S. Yang. Age Estimation. Estimating age group or age value from face images. Dave Waltham. Outline. C. roll-Milankovitch. cycles. Application to . c. yclostratigraphy. Earth-Moon system evolution. The time-varying drag problem. Time-evolution of CM cycles and their (big) uncertainties. Federal Reserve Bank of St. Louis. Predictions of Short-Term Rates and the Expectations Hypothesis of the Term Structure of Interest Rates. The views are the authors’ and do not necessarily represent the views of the Federal Reserve Bank of St. Louis or the Board of Governors of the Federal Reserve. Anton Esmail-Yakas, 2015. For completion of the 3. rd. year of the Physics with a Year in Europe . Msci. at Imperial College London in relation with . École. . Polytechnique. . Fédérale. de Lausanne as part of the Swiss European Mobility Program..