Discussion Score-driven models for forecasting by Siem Jan Koopman. Domenico Giannone LUISS University of Rome, ECARES, EIEF and CEPR
|
|
- Augustine Park
- 5 years ago
- Views:
Transcription
1 Discussion Score-driven models for forecasting by Siem Jan Koopman Domenico Giannone LUISS University of Rome, ECARES, EIEF and CEPR 8th ECB Forecasting Workshop European Central Bank, June / 10
2 Generalise Autoregressive Score (GAS) Basic Model Specification y t : data ˆf t : time varying parameter X t : exogenous variables θ: static parameters y t ˆf t p(y t ˆf t, F t ; θ) p q ˆf t+1 = ω + A i ŝ t i+1 + i=1 s t = S t t j=1 t = log p(y t f t ; Λ) f t S t = S(t, ˆf t, F t ; θ) B j ˆft j+1 Usually: S t = αi 1 where I = E( t t) 2 / 10
3 Generalise Autoregressive Score (GAS) The origins: Creal, D., S. J. Koopman, and A. Lucas (2008). A general framework for observation driven time-varying parameter models. Discussion Paper /4, Tinbergen Institute. Harvey, A. C. and T. Chakravarty (2008). Beta-t-(E)GARCH. University of Cambridge, Faculty of Economics, Working paper CWPE The presentation Introduction to GAS (Drew, Koopman, Lucas, 2013) Optimality of score (Blasques, Koopman and Lucas, 2014) Forecasting evidence (Koopman, Lucas and Scharth, 2014) 3 / 10
4 My Quest for the Origins of GAS Siem Jan Koopman: Time series analysis by state space methods Andrew C. Harvey: Forecasting, structural time series models and the Kalman filter The discussion From State Space Models to Score Driven Models Spot the difference Dynamic Factor Models (DFM) Traditional vs Score Driven 4 / 10
5 Linear State space model y t f t N (Λf t, R) f t+1 f t N (Af t, Q) Defining ˆf t+1 := E(f t+1 y t ), we have the Kalman recursion: ˆf t+1 = Aˆf t + AG t v t where v t = (y t Λˆf t ): surprises G t = P t Λ (ΛP t Λ + R) 1 : gain Where the variance of the estimated states is computed from the recursion: E[(ˆf t+1 f t+1 )(ˆf t+1 f t+1 ) ] := P t+1 = AP t A + Q AP t K t Λ 5 / 10
6 Linear State Space model It can be shown that: y t f t N (Λf t, R) f t+1 f t N (Af t, Q) ˆf t+1 = Aˆf t + AG t v t G t v t = (Λ } R{{ 1 Λ} +Pt 1 ) 1 Λ R 1 v }{{} t I t t = log p(y t f t ; Λ) f t, I = E( t t) 6 / 10
7 Kalman and GAS Linear State Space model (Parameter-driven) y t f t N (Λf t, R) f t+1 f t N (Af t, Q) ˆf t+1 = Aˆf t + A(I t + Pt 1 ) 1 t t = log p(y t f t ; Λ) f t, I = E( t t) Generalised Autoregressive Scores (GAS) (Observation-driven) y t ˆf t p(ˆf t ; θ) ˆf t+1 = Aˆf t + α(ĩ t + 0) 1 t t = log p(y t f t ; Λ) f t, I = E( t t) 7 / 10
8 Kalman and GAS Non-Linear State space model (Parameter-driven) y t f t p y f (y t f t ; θ) f t+1 ˆf t p f+ f (f t+1 f t ; θ) Generalised Autoregressive Scores (GAS)(Observation-driven) y t ˆf t p(y t ˆf t ; θ) ˆf t+1 = Aˆf t + α( Ĩ t ) 1 t t = log p(y t f t ; Λ) f t, I = E( t t) 8 / 10
9 Dynamic Factor Model (DFM) Traditional DFM(Engle and Watson, ) y t = Λf t + e t N (0, R) f t+1 = Af t + u t N (0, Q) ˆf t+1 = Aˆf t + AG t (y t Λˆf t ) E[(ˆf t+1 f t+1 )(ˆf t+1 f t+1 ) ] := P t+1 Obs.-driven DFM (Creal, Schwaab, Koopman and Lucas, 2014) y t = Λˆf t + e t N (0, R) ˆf t+1 = Aˆf t + α(λ R 1 Λ) 1 Λ R 1 (y t Λˆf t ) = ˆf t+1 = Aˆf t + α(ˆft ˆf t ) where ˆft = (Λ R 1 Λ) 1 Λ R 1 y t if α = A then ˆf f +1 = Aˆft 9 / 10
10 Conclusions Interesting, Flexible and Useful Approximation of Linear State Space Models Interesting, Flexible and Useful Approximation of Non-Linear State Space Models Very intriguing and Interesting Research Agenda Open Questions (at least for me) Hard to interpret in some instance When is the approximation reasonable? How does it compare with other approximations (e.g. forgetting factor, Koop and Korobolis, 2013) Does it work in forecasting? (See Opschoor et al, 2014) Are computational gains going to remain relevant? Strongly suggest to read the papers 10 / 10
Generalized Autoregressive Score Models
Generalized Autoregressive Score Models by: Drew Creal, Siem Jan Koopman, André Lucas To capture the dynamic behavior of univariate and multivariate time series processes, we can allow parameters to be
More informationGeneralized Autoregressive Score Smoothers
Generalized Autoregressive Score Smoothers Giuseppe Buccheri 1, Giacomo Bormetti 2, Fulvio Corsi 3,4, and Fabrizio Lillo 2 1 Scuola Normale Superiore, Italy 2 University of Bologna, Italy 3 University
More informationA look into the factor model black box Publication lags and the role of hard and soft data in forecasting GDP
A look into the factor model black box Publication lags and the role of hard and soft data in forecasting GDP Marta Bańbura and Gerhard Rünstler Directorate General Research European Central Bank November
More informationTime-Varying Vector Autoregressive Models with Structural Dynamic Factors
Time-Varying Vector Autoregressive Models with Structural Dynamic Factors Paolo Gorgi, Siem Jan Koopman, Julia Schaumburg http://sjkoopman.net Vrije Universiteit Amsterdam School of Business and Economics
More informationAccounting for Missing Values in Score- Driven Time-Varying Parameter Models
TI 2016-067/IV Tinbergen Institute Discussion Paper Accounting for Missing Values in Score- Driven Time-Varying Parameter Models André Lucas Anne Opschoor Julia Schaumburg Faculty of Economics and Business
More informationForecasting economic time series using score-driven dynamic models with mixeddata
TI 2018-026/III Tinbergen Institute Discussion Paper Forecasting economic time series using score-driven dynamic models with mixeddata sampling 1 Paolo Gorgi Siem Jan (S.J.) Koopman Mengheng Li3 2 1: 2:
More informationNonlinear Autoregressive Processes with Optimal Properties
Nonlinear Autoregressive Processes with Optimal Properties F. Blasques S.J. Koopman A. Lucas VU University Amsterdam, Tinbergen Institute, CREATES OxMetrics User Conference, September 2014 Cass Business
More informationShort-term forecasts of GDP from dynamic factor models
Short-term forecasts of GDP from dynamic factor models Gerhard Rünstler gerhard.ruenstler@wifo.ac.at Austrian Institute for Economic Research November 16, 2011 1 Introduction Forecasting GDP from large
More informationLarge time varying parameter VARs for macroeconomic forecasting
Large time varying parameter VARs for macroeconomic forecasting Gianni Amisano (FRB) Domenico Giannone (NY Fed, CEPR and ECARES) Michele Lenza (ECB and ECARES) 1 Invited talk at the Cleveland Fed, May
More informationDynamic and stochastic volatility structures in U.S. inflation: Estimation and signal extraction
Dynamic and stochastic volatility structures in U.S. inflation: Estimation and signal extraction Mengheng Li 1,3,4 and Siem Jan Koopman 1,2,3 1 Department of Econometrics, Vrije Universiteit Amsterdam,
More informationForecasting economic time series using score-driven dynamic models with mixed-data sampling
Forecasting economic time series using score-driven dynamic models with mixed-data sampling Paolo Gorgi a,b Siem Jan Koopman a,b,c Mengheng Li d a Vrije Universiteit Amsterdam, The Netherlands b Tinbergen
More informationTime Varying Transition Probabilities for Markov Regime Switching Models
Time Varying Transition Probabilities for Markov Regime Switching Models Marco Bazzi (a), Francisco Blasques (b) Siem Jan Koopman (b,c), André Lucas (b) (a) University of Padova, Italy (b) VU University
More informationModel-based trend-cycle decompositions. with time-varying parameters
Model-based trend-cycle decompositions with time-varying parameters Siem Jan Koopman Kai Ming Lee Soon Yip Wong s.j.koopman@ klee@ s.wong@ feweb.vu.nl Department of Econometrics Vrije Universiteit Amsterdam
More informationMeasurement Errors and the Kalman Filter: A Unified Exposition
Luiss Lab of European Economics LLEE Working Document no. 45 Measurement Errors and the Kalman Filter: A Unified Exposition Salvatore Nisticò February 2007 Outputs from LLEE research in progress, as well
More informationWORKING PAPER SERIES CONDITIONAL FORECASTS AND SCENARIO ANALYSIS WITH VECTOR AUTOREGRESSIONS FOR LARGE CROSS-SECTIONS NO 1733 / SEPTEMBER 2014
WORKING PAPER SERIES NO 1733 / SEPTEMBER 214 CONDITIONAL FORECASTS AND SCENARIO ANALYSIS WITH VECTOR AUTOREGRESSIONS FOR LARGE CROSS-SECTIONS Marta Bańbura, Domenico Giannone and Michele Lenza In 214 all
More informationUnobserved. Components and. Time Series. Econometrics. Edited by. Siem Jan Koopman. and Neil Shephard OXFORD UNIVERSITY PRESS
Unobserved Components and Time Series Econometrics Edited by Siem Jan Koopman and Neil Shephard OXFORD UNIVERSITY PRESS CONTENTS LIST OF FIGURES LIST OF TABLES ix XV 1 Introduction 1 Siem Jan Koopman and
More informationA score-driven conditional correlation model for noisy and asynchronous data: an application to high-frequency covariance dynamics
A score-driven conditional correlation model for noisy and asynchronous data: an application to high-frequency covariance dynamics Giuseppe Buccheri, Giacomo Bormetti, Fulvio Corsi, Fabrizio Lillo February,
More informationMarta BANBURA Domenico GIANNONE Michele LENZA
214/34 Conditional Forecasts and Scenario Analysis with Vector Autoregressions for Large Cross-Sections Marta BANBURA Domenico GIANNONE Michele LENZA Conditional forecasts and scenario analysis with vector
More informationConditional probabilities for Euro area sovereign default risk
Conditional probabilities for Euro area sovereign default risk Andre Lucas, Bernd Schwaab, Xin Zhang Rare events conference, San Francisco, 28-29 Sep 2012 email: bernd.schwaab@ecb.int webpage: www.berndschwaab.eu
More informationActuarial Studies Seminar Macquarie University 1 December Professor Andrew Harvey
Actuarial Studies Seminar Macquarie University December 00 Professor Andrew Harvey Professor of conometrics, Faculty of conomics, Cambridge University xponential Conditional Volatility Models Abstract:
More informationA note on implementing the Durbin and Koopman simulation smoother
MPRA Munich Personal RePEc Archive A note on implementing the Durbin and Koopman simulation smoother Marek Jarocinski European Central Bank 24. October 2014 Online at http://mpra.ub.uni-muenchen.de/59466/
More informationDynamic Factor Models and Factor Augmented Vector Autoregressions. Lawrence J. Christiano
Dynamic Factor Models and Factor Augmented Vector Autoregressions Lawrence J Christiano Dynamic Factor Models and Factor Augmented Vector Autoregressions Problem: the time series dimension of data is relatively
More informationDNB W O R K ING P A P E R. Nowcasting and forecasting economic growth in the euro area using principal components. No. 415 / February 2014
DNB Working Paper No. 415 / February 2014 Irma Hindrayanto, Siem Jan Koopman and Jasper de Winter DNB W O R K ING P A P E R Nowcasting and forecasting economic growth in the euro area using principal components
More informationA Stochastic Recurrence Equation Approach to Stationarity and phi-mixing of a Class of Nonlinear ARCH Models
TI 2017-072/III Tinbergen Institute Discussion Paper A Stochastic Recurrence Equation Approach to Stationarity and phi-mixing of a Class of Nonlinear ARCH Models Francisco F.) Blasques Marc Nientker2 1
More informationNowcasting and Short-Term Forecasting of Russia GDP
Nowcasting and Short-Term Forecasting of Russia GDP Elena Deryugina Alexey Ponomarenko Aleksey Porshakov Andrey Sinyakov Bank of Russia 12 th ESCB Emerging Markets Workshop, Saariselka December 11, 2014
More informationPart I State space models
Part I State space models 1 Introduction to state space time series analysis James Durbin Department of Statistics, London School of Economics and Political Science Abstract The paper presents a broad
More informationNowcasting the Finnish economy with a large Bayesian vector autoregressive model
Nowcasting the Finnish economy with a large Bayesian vector autoregressive model Itkonen,J. & Juvonen,P. Suomen Pankki Conference on Real-Time Data Analysis, Methods and Applications 19.10.2017 Introduction
More informationWorking Paper Series. A note on implementing the Durbin and Koopman simulation smoother. No 1867 / November Marek Jarocinski
Working Paper Series Marek Jarocinski A note on implementing the Durbin and Koopman simulation smoother No 1867 / November 2015 Note: This Working Paper should not be reported as representing the views
More informationShort Term Forecasts of Euro Area GDP Growth
Short Term Forecasts of Euro Area GDP Growth Elena Angelini European Central Bank Gonzalo Camba Mendez European Central Bank Domenico Giannone European Central Bank, ECARES and CEPR Lucrezia Reichlin London
More information1. Shocks. This version: February 15, Nr. 1
1. Shocks This version: February 15, 2006 Nr. 1 1.3. Factor models What if there are more shocks than variables in the VAR? What if there are only a few underlying shocks, explaining most of fluctuations?
More informationGeneralized Autoregressive Method of Moments *
Generalized Autoregressive Method of Moments * Drew Creal 1, Siem Jan Koopman 2, André Lucas 2, and Marcin Zamojski 2 1 University of Chicago, Booth School of Business 2 VU University Amsterdam and Tinbergen
More informationMaximum likelihood estimation of large factor model on datasets with arbitrary pattern of missing data.
Maximum likelihood estimation of large factor model on datasets with arbitrary pattern of missing data. Marta Bańbura 1 and Michele Modugno 2 Abstract In this paper we show how to estimate a large dynamic
More informationPrediction Using Several Macroeconomic Models
Gianni Amisano European Central Bank 1 John Geweke University of Technology Sydney, Erasmus University, and University of Colorado European Central Bank May 5, 2012 1 The views expressed do not represent
More informationDynamic Models for Volatility and Heavy Tails by Andrew Harvey
Dynamic Models for Volatility and Heavy Tails by Andrew Harvey Discussion by Gabriele Fiorentini University of Florence and Rimini Centre for Economic Analysis (RCEA) Frankfurt, 4-5 May 2012 I enjoyed
More informationForecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis
TI 2012-042/4 Tinbergen Institute Discussion Paper Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis Falk Brauning Siem Jan Koopman Faculty of Economics and Business Administration,
More informationAbstract. Keywords: Factor Models, Forecasting, Large Cross-Sections, Missing data, EM algorithm.
Maximum likelihood estimation of large factor model on datasets with missing data: forecasting euro area GDP with mixed frequency and short-history indicators. Marta Bańbura 1 and Michele Modugno 2 Abstract
More informationMining Big Data Using Parsimonious Factor and Shrinkage Methods
Mining Big Data Using Parsimonious Factor and Shrinkage Methods Hyun Hak Kim 1 and Norman Swanson 2 1 Bank of Korea and 2 Rutgers University ECB Workshop on using Big Data for Forecasting and Statistics
More informationCombining Macroeconomic Models for Prediction
Combining Macroeconomic Models for Prediction John Geweke University of Technology Sydney 15th Australasian Macro Workshop April 8, 2010 Outline 1 Optimal prediction pools 2 Models and data 3 Optimal pools
More informationEuro-indicators Working Group
Euro-indicators Working Group Luxembourg, 9 th & 10 th June 2011 Item 9.4 of the Agenda New developments in EuroMIND estimates Rosa Ruggeri Cannata Doc 309/11 What is EuroMIND? EuroMIND is a Monthly INDicator
More informationCollaborative Nowcasting for Contextual Recommendation
Collaborative for Contextual Recommendation Yu Sun 1, Nicholas Jing Yuan 2, Xing Xie 3, Kieran McDonald 4, Rui Zhang 5 University of Melbourne { 1 sun.y, 5 rui.zhang}@unimelb.edu.au Microsoft Research
More informationCointegration between Trends and Their Estimators in State Space Models and Cointegrated Vector Autoregressive Models
econometrics Article Cointegration between Trends and Their Estimators in State Space Models and Cointegrated Vector Autoregressive Models Søren Johansen * and Morten Nyboe Tabor Department of Economics,
More informationTopics in Forecasting Macroeconomic Time Series
Topics in Forecasting Macroeconomic Time Series ISBN 978 90 3160 477 7 Cover design: Crasborn Graphic Designers bno, Valkenburg a.d. Geul This book is no. 687 of the Tinbergen Institute Research Series,
More information= (, ) V λ (1) λ λ ( + + ) P = [ ( ), (1)] ( ) ( ) = ( ) ( ) ( 0 ) ( 0 ) = ( 0 ) ( 0 ) 0 ( 0 ) ( ( 0 )) ( ( 0 )) = ( ( 0 )) ( ( 0 )) ( + ( 0 )) ( + ( 0 )) = ( + ( 0 )) ( ( 0 )) P V V V V V P V P V V V
More informationTREND ESTIMATION AND THE HODRICK-PRESCOTT FILTER
J. Japan Statist. Soc. Vol. 38 No. 1 2008 41 49 TREND ESTIMATION AND THE HODRICK-PRESCOTT FILTER Andrew Harvey* and Thomas Trimbur** The article analyses the relationship between unobserved component trend-cycle
More informationExpectation Formation and Rationally Inattentive Forecasters
Expectation Formation and Rationally Inattentive Forecasters Javier Turén UCL October 8, 016 Abstract How agents form their expectations is still a highly debatable question. While the existing evidence
More informationNumerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models
Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models Siem Jan Koopman (a) André Lucas (a) Marcel Scharth (b) (a) VU University Amsterdam and Tinbergen Institute, The
More informationEstadística Oficial. Transfer Function Model Identication
Boletín de Estadística e Investigación Operativa Vol 25, No 2, Junio 2009, pp 109-115 Estadística Oficial Transfer Function Model Identication Víctor Gómez Ministerio de Economía y Hacienda B vgomez@sgpgmehes
More informationBEAR 4.2. Introducing Stochastic Volatility, Time Varying Parameters and Time Varying Trends. A. Dieppe R. Legrand B. van Roye ECB.
BEAR 4.2 Introducing Stochastic Volatility, Time Varying Parameters and Time Varying Trends A. Dieppe R. Legrand B. van Roye ECB 25 June 2018 The views expressed in this presentation are the authors and
More informationX t = a t + r t, (7.1)
Chapter 7 State Space Models 71 Introduction State Space models, developed over the past 10 20 years, are alternative models for time series They include both the ARIMA models of Chapters 3 6 and the Classical
More informationBayesian Compressed Vector Autoregressions
Bayesian Compressed Vector Autoregressions Gary Koop a, Dimitris Korobilis b, and Davide Pettenuzzo c a University of Strathclyde b University of Glasgow c Brandeis University 9th ECB Workshop on Forecasting
More informationGENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS
JOURNAL OF APPLIED ECONOMETRICS J. Appl. Econ. 28: 777 795 (2013) Published online 20 January 2012 in Wiley Online Library (wileyonlinelibrary.com).1279 GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS
More informationSiem Jan Koopman Marius Ooms
TI 2004-135/4 Tinbergen Institute Discussion Paper Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models Siem Jan Koopman Marius Ooms Faculty of Economics and Business Administration,
More informationA Practical Guide to State Space Modeling
A Practical Guide to State Space Modeling Jin-Lung Lin Institute of Economics, Academia Sinica Department of Economics, National Chengchi University March 006 1 1 Introduction State Space Model (SSM) has
More informationNOWCASTING GDP IN GREECE: A NOTE ON FORECASTING IMPROVEMENTS FROM THE USE OF BRIDGE MODELS
South-Eastern Europe Journal of Economics 1 (2015) 85-100 NOWCASTING GDP IN GREECE: A NOTE ON FORECASTING IMPROVEMENTS FROM THE USE OF BRIDGE MODELS DIMITRA LAMPROU * University of Peloponnese, Tripoli,
More informationEconometric Forecasting
Robert M Kunst robertkunst@univieacat University of Vienna and Institute for Advanced Studies Vienna September 29, 2014 University of Vienna and Institute for Advanced Studies Vienna Outline Introduction
More informationStock index returns density prediction using GARCH models: Frequentist or Bayesian estimation?
MPRA Munich Personal RePEc Archive Stock index returns density prediction using GARCH models: Frequentist or Bayesian estimation? Ardia, David; Lennart, Hoogerheide and Nienke, Corré aeris CAPITAL AG,
More informationDynamic Factor Models Cointegration and Error Correction Mechanisms
Dynamic Factor Models Cointegration and Error Correction Mechanisms Matteo Barigozzi Marco Lippi Matteo Luciani LSE EIEF ECARES Conference in memory of Carlo Giannini Pavia 25 Marzo 2014 This talk Statement
More informationOutput gap and inflation forecasts in a Bayesian dynamic factor model of the euro area
Output gap and inflation forecasts in a Bayesian dynamic factor model of the euro area Marek Jarociński Michele Lenza Preliminary, this version: September 25, 2015 Abstract We estimate a Bayesian dynamic
More informationSequential Monte Carlo Methods (for DSGE Models)
Sequential Monte Carlo Methods (for DSGE Models) Frank Schorfheide University of Pennsylvania, PIER, CEPR, and NBER October 23, 2017 Some References These lectures use material from our joint work: Tempered
More informationState Space Model, Official Statistics, Bayesian Statistics Introduction to Durbin s paper and related topics
State Space Model, Official Statistics, Bayesian Statistics Introduction to Durbin s paper and related topics Yasuto Yoshizoe Aoyama Gakuin University yoshizoe@econ.aoyama.ac.jp yasuto yoshizoe@post.harvard.edu
More informatione Yield Spread Puzzle and the Information Content of SPF forecasts
Economics Letters, Volume 118, Issue 1, January 2013, Pages 219 221 e Yield Spread Puzzle and the Information Content of SPF forecasts Kajal Lahiri, George Monokroussos, Yongchen Zhao Department of Economics,
More informationNowcasting Norwegian GDP
Nowcasting Norwegian GDP Knut Are Aastveit and Tørres Trovik May 13, 2007 Introduction Motivation The last decades of advances in information technology has made it possible to access a huge amount of
More informationTime-Varying Parameters
Kalman Filter and state-space models: time-varying parameter models; models with unobservable variables; basic tool: Kalman filter; implementation is task-specific. y t = x t β t + e t (1) β t = µ + Fβ
More informationMacroeconomic Forecasting and Structural Change
Macroeconomic Forecasting and Structural Change Antonello D Agostino CBFSAI Luca Gambetti UAB and RECent Domenico Giannone ECB, CEPR and ECARES First Draft: July 2008 This draft: December 2008 Abstract
More informationVolume 30, Issue 3. A note on Kalman filter approach to solution of rational expectations models
Volume 30, Issue 3 A note on Kalman filter approach to solution of rational expectations models Marco Maria Sorge BGSE, University of Bonn Abstract In this note, a class of nonlinear dynamic models under
More informationAn Efficient Ensemble Data Assimilation Approach To Deal With Range Limited Observation
An Efficient Ensemble Data Assimilation Approach To Deal With Range Limited Observation A. Shah 1,2, M. E. Gharamti 1, L. Bertino 1 1 Nansen Environmental and Remote Sensing Center 2 University of Bergen
More informationFaMIDAS: A Mixed Frequency Factor Model with MIDAS structure
FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure Frale C., Monteforte L. Computational and Financial Econometrics Limassol, October 2009 Introduction After the recent financial and economic
More information28 March Sent by to: Consultative Document: Fundamental review of the trading book 1 further response
28 March 203 Norah Barger Alan Adkins Co Chairs, Trading Book Group Basel Committee on Banking Supervision Bank for International Settlements Centralbahnplatz 2, CH 4002 Basel, SWITZERLAND Sent by email
More informationPooling-based data interpolation and backdating
Pooling-based data interpolation and backdating Massimiliano Marcellino IEP - Bocconi University, IGIER and CEPR Revised Version - May 2006 Abstract Pooling forecasts obtained from different procedures
More informationAn inflation-predicting measure of the output gap in the euro area
An inflation-predicting measure of the output gap in the euro area Marek Jarociński Michele Lenza July 9, 26 Abstract Using a small Bayesian dynamic factor model of the euro area we estimate the deviations
More informationTIME-VARYING PARAMETER MODELS FOR DISCRETE VALUED TIME SERIES
TIME-VARYING PARAMETER MODELS FOR DISCRETE VALUED TIME SERIES ISBN 978 90 5170 755 7 Cover design: Crasborn Graphic Designers bno, Valkenburg a.d. Geul Image: Wiki Commons (template) and Marcin Zamojski
More informationForecasting, nowcasting and backcasting Brazilian GDP
Forecasting, nowcasting and backcasting Brazilian GDP Pedro G. Costa Ferreira FGV IBRE Guilherme Branco Gomes FGV EPGE Daiane Marcolino de Mattos FGV IBRE Tiago dos Guaranys Martins FGV EPGE Abstract GDP
More informationResearch Division Federal Reserve Bank of St. Louis Working Paper Series
Research Division Federal Reserve Bank of St Louis Working Paper Series Kalman Filtering with Truncated Normal State Variables for Bayesian Estimation of Macroeconomic Models Michael Dueker Working Paper
More informationeconstor Make Your Publications Visible.
econstor Make Your Publications Visible. A Service of Wirtschaft Centre zbwleibniz-informationszentrum Economics Azam, Kazim; Lucas, Andre Working Paper Mixed Density based Copula Likelihood Tinbergen
More informationECO 513 Fall 2008 C.Sims KALMAN FILTER. s t = As t 1 + ε t Measurement equation : y t = Hs t + ν t. u t = r t. u 0 0 t 1 + y t = [ H I ] u t.
ECO 513 Fall 2008 C.Sims KALMAN FILTER Model in the form 1. THE KALMAN FILTER Plant equation : s t = As t 1 + ε t Measurement equation : y t = Hs t + ν t. Var(ε t ) = Ω, Var(ν t ) = Ξ. ε t ν t and (ε t,
More informationTitle. Description. var intro Introduction to vector autoregressive models
Title var intro Introduction to vector autoregressive models Description Stata has a suite of commands for fitting, forecasting, interpreting, and performing inference on vector autoregressive (VAR) models
More informationAdvances in Nowcasting Economic Activity
Advances in Nowcasting Economic Activity Juan Antoĺın-Díaz 1 Thomas Drechsel 2 Ivan Petrella 3 2nd Forecasting at Central Banks Conference Bank of England November 15, 218 1 London Business School 2 London
More informationBOOTSTRAP PREDICTION INTERVALS IN STATE SPACE MODELS. Alejandro Rodriguez 1 and Esther Ruiz 2
Working Paper 08-11 Departamento de Estadística Statistic and Econometric Series 04 Universidad Carlos III de Madrid March 2008 Calle Madrid, 126 28903 Getafe (Spain) Fax (34-91) 6249849 BOOTSTRAP PREDICTION
More informationDiscussion of Bootstrap prediction intervals for linear, nonlinear, and nonparametric autoregressions, by Li Pan and Dimitris Politis
Discussion of Bootstrap prediction intervals for linear, nonlinear, and nonparametric autoregressions, by Li Pan and Dimitris Politis Sílvia Gonçalves and Benoit Perron Département de sciences économiques,
More informationNowcasting. Domenico Giannone Université Libre de Bruxelles and CEPR
Nowcasting Domenico Giannone Université Libre de Bruxelles and CEPR 3rd VALE-EPGE Global Economic Conference Business Cycles Rio de Janeiro, May 2013 Nowcasting Contraction of the terms Now and Forecasting
More informationA Forty (?) Year Assessment of Forecasting The Boat Race
A Forty (?) Year Assessment of Forecasting The Boat Race Geert Mesters & Siem Jan Koopman Netherlands Institute for the Study of Crime and Law Enforcement (NSCR) VU University Amsterdam & Tinbergen Institute
More informationWORKING PAPER SERIES COMPARING ALTERNATIVE PREDICTORS BASED ON LARGE-PANEL FACTOR MODELS NO 680 / OCTOBER 2006
WORKING PAPER SERIES NO 680 / OCTOBER 2006 COMPARING ALTERNATIVE PREDICTORS BASED ON LARGE-PANEL FACTOR MODELS by Antonello D'Agostino and Domenico Giannone WORKING PAPER SERIES NO 680 / OCTOBER 2006 COMPARING
More informationBayesian Inference for DSGE Models. Lawrence J. Christiano
Bayesian Inference for DSGE Models Lawrence J. Christiano Outline State space-observer form. convenient for model estimation and many other things. Bayesian inference Bayes rule. Monte Carlo integation.
More informationPredicting Mutual Fund Performance
Predicting Mutual Fund Performance Oxford, July-August 2013 Allan Timmermann 1 1 UC San Diego, CEPR, CREATES Timmermann (UCSD) Predicting fund performance July 29 - August 2, 2013 1 / 51 1 Basic Performance
More informationIndependent and conditionally independent counterfactual distributions
Independent and conditionally independent counterfactual distributions Marcin Wolski European Investment Bank M.Wolski@eib.org Society for Nonlinear Dynamics and Econometrics Tokyo March 19, 2018 Views
More information1 Kalman Filter Introduction
1 Kalman Filter Introduction You should first read Chapter 1 of Stochastic models, estimation, and control: Volume 1 by Peter S. Maybec (available here). 1.1 Explanation of Equations (1-3) and (1-4) Equation
More informationForecasting Euro Area Real GDP: Optimal Pooling of Information
Euro Area Business Cycle Network (EABCN) Workshop Using Euro Area Data: Issues and Consequences for Economic Analysis Cambridge, 26-28 March 2008 Hosted by University of Cambridge Forecasting Euro Area
More informationMixed frequency models with MA components
Mixed frequency models with MA components Claudia Foroni Massimiliano Marcellino Dalibor Stevanović December 14, 2017 Abstract Temporal aggregation in general introduces a moving average (MA) component
More informationLong Term Forecasting of El Niño Events via Dynamic Factor Simulations
Long Term Forecasting of El Niño Events via Dynamic Factor Simulations Mengheng Li (a) Siem Jan Koopman (a,b) (a) Vrije Universiteit Amsterdam and Tinbergen Institute, The Netherlands (b) CREATES, Aarhus
More informationSmoothers: Types and Benchmarks
Smoothers: Types and Benchmarks Patrick N. Raanes Oxford University, NERSC 8th International EnKF Workshop May 27, 2013 Chris Farmer, Irene Moroz Laurent Bertino NERSC Geir Evensen Abstract Talk builds
More informationLecture 16: State Space Model and Kalman Filter Bus 41910, Time Series Analysis, Mr. R. Tsay
Lecture 6: State Space Model and Kalman Filter Bus 490, Time Series Analysis, Mr R Tsay A state space model consists of two equations: S t+ F S t + Ge t+, () Z t HS t + ɛ t (2) where S t is a state vector
More informationEstudos e Documentos de Trabalho. Working Papers
Estudos e Documentos de Trabalho Working Papers 13 2009 DYNAMIC FACTOR MODELS WITH JAGGED EDGE PANEL DATA: TAKING ON BOARD THE DYNAMICS OF THE IDIOSYNCRATIC COMPONENTS Maximiano Pinheiro António Rua Francisco
More informationForecasting with High-Dimensional Panel VARs
MPRA Munich Personal RePEc Archive Forecasting with High-Dimensional Panel VARs Gary Koop and Dimitris Korobilis University of Strathclyde, University of Essex December 2015 Online at https://mpra.ub.uni-muenchen.de/84275/
More informationPartially Censored Posterior for Robust and Efficient Risk Evaluation.
Preliminary Draft. Please do not cite, circulate or quote without the authors permission Partially Censored Posterior for Robust and Efficient Risk Evaluation. Agnieszka Borowska (a,b), Lennart Hoogerheide
More informationDSGE Model Forecasting
University of Pennsylvania EABCN Training School May 1, 216 Introduction The use of DSGE models at central banks has triggered a strong interest in their forecast performance. The subsequent material draws
More informationOn the importance of the long-term seasonal component in day-ahead electricity price forecasting. Part II - Probabilistic forecasting
On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Part II - Probabilistic forecasting Rafał Weron Department of Operations Research Wrocław University of
More informationComments on New Approaches in Period Analysis of Astronomical Time Series by Pavlos Protopapas (Or: A Pavlosian Response ) Don Percival
Comments on New Approaches in Period Analysis of Astronomical Time Series by Pavlos Protopapas (Or: A Pavlosian Response ) Don Percival Applied Physics Laboratory Department of Statistics University of
More informationDiscussion of Predictive Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance
Discussion of Predictive Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance by Casarin, Grassi, Ravazzolo, Herman K. van Dijk Dimitris Korobilis University of Essex,
More informationHigher-Order Dynamics in Asset-Pricing Models with Recursive Preferences
Higher-Order Dynamics in Asset-Pricing Models with Recursive Preferences Walt Pohl Karl Schmedders Ole Wilms Dept. of Business Administration, University of Zurich Becker Friedman Institute Computational
More informationForecasting with the Standardized Self-Perturbed Kalman Filter
University of Kent School of Economics Discussion Papers Forecasting with the Standardized Self-Perturbed Kalman Filter Stefano Grassi, Nima Nonejad and Paolo Santucci de Magistris February 214 KDPE 145
More information