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References (41)
-
D. Poirier, M. Intriligator (1979)
Econometric Models, Techniques, and Applications.Journal of the American Statistical Association, 74
-
D. Chotikapanich, W. Griffiths (2000)
Flexible Distributed Lags -
Z. Griliches, G. Maddala, R. Lucas, N. Wallace (1962)
NOTES ON ESTIMATED AGGREGATE QUARTERLY CONSUMPTION FUNCTIONSEconometrica, 30
-
P. Franses, R. Oest (2004)
On the econometrics of the Koyck model -
J. Bentzen, Tom Engsted (1999)
A revival of the autoregressive distributed lag model in estimating energy demand relationshipsEnergy, 26
-
Migon Migon (2000)
The prediction of Brazilian exports using Bayesian forecastingInvestigación Operativa, 9
-
P. Stephen, Brooks, Andrew, Gelman (1998)
General methods for monitoring convergence of iterative simulationsJournal of Computational and Graphical Statistics, 7
-
A. Gelfand, Adrian Smith (1990)
Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 85
-
D. Jorgenson (1966)
Rational Distributed Lag FunctionsEconometrica, 34
-
John Geweke (1998)
Using simulation methods for Bayesian econometric models: inference, development, and communicationStaff Report
-
Yi Huang, F. Dominici, M. Bell (2005)
Bayesian hierarchical distributed lag models for summer ozone exposure and cardio‐respiratory mortalityEnvironmetrics, 16
-
S. Koopman, N. Shephard, J. Doornik (1999)
Statistical algorithms for models in state space using SsfPack 2.2Econometrics Journal, 2
-
S. Chib (1993)
Bayes regression with autoregressive errors : A Gibbs sampling approachJournal of Econometrics, 58
-
Zellner Zellner, Geisel Geisel (1970)
Analysis of distributed lag models with application to the consumption functionEconometrica, 38
-
J. Newhouse, R. Wonnacott, T. Wonnacott (1979)
Econometrics: 2nd Ed -
Shirley Almon (1965)
The Distributed Lag Between Capital Appropriations and ExpendituresEconometrica, 33
-
R. Carter, A. Zellner (2002)
2002-05 The ARAR Error Model for Univariate Time Series and Distributed Lag Models -
D. Spiegelhalter, N. Best, B. Carlin, A. Linde (2002)
Bayesian measures of model complexity and fitJournal of the Royal Statistical Society: Series B (Statistical Methodology), 64
-
V. Chetty (1971)
ESTIMATION OF SOLOW'S DISTRIBUTED LAG MODELSEconometrica, 39
-
A. Zellner, M. Geisel (1970)
Analysis of Distributed Lag Models with Application to Consumption Function EstimationEconometrica, 38
-
H. Migon, D. Gamerman, F. Louzada (1999)
Statistical inference : an integrated approach -
Chris Carter, R. Kohn (1994)
On Gibbs sampling for state space modelsBiometrika, 81
-
R. Engle, C. Granger (1987)
Co-integration and error correction: representation, estimation and testingEconometrica, 55
-
S. Johansen (1991)
Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive ModelsEconometrica, 59
-
Leah Welty, S. Zeger (2005)
Are the acute effects of particulate matter on mortality in the National Morbidity, Mortality, and Air Pollution Study the result of inadequate control for weather and season? A sensitivity analysis using flexible distributed lag models.American journal of epidemiology, 162 1
-
A. Nakamura, E. Berndt (1992)
The Practice of Econometrics: Classic and Contemporary.Journal of the American Statistical Association, 87
-
S. Frühwirth-Schnatter (1995)
Bayesian Model Discrimination and Bayes Factors for Linear Gaussian State Space ModelsJournal of the royal statistical society series b-methodological, 57
-
John Geweke (1999)
Using Simulation Methods for Bayesian Econometric ModelsComputing in Economics and Finance
-
M. West, J. Harrison (1989)
Bayesian forecasting and dynamic models -
T. Lancaster (2004)
An Introduction to Modern Bayesian Econometrics -
Peter Congdon (2002)
Bayesian statistical modellingMeasurement Science and Technology, 13
-
D. Dey, S. Ghosh, B. Mallick (2000)
Generalized Linear Models : A Bayesian Perspective -
D. Gamerman (1998)
Markov chain Monte Carlo for dynamic generalised linear modelsBiometrika, 85
-
Leah Welty, S. Zeger (2005)
Are the Acute Effects of PM10 on Mortality in NMMAPS the Result of Inadequate Control for Weather and Season? A Sensitivity Analysis Using Flexible Distributed Lag Models -
S. Ghosh, A. Gelfand (1998)
Model choice: A minimum posterior predictive loss approach -
Brooks Brooks, Gelman Gelman (1998)
Alternative methods for monitoring convergence of iterative simulationsJournal of Computational and Graphical Statistics, 7
-
R. Carter, A. Zellner (2004)
The ARAR Error Model for Univariate Time Series and Distributed LagStudies in Nonlinear Dynamics & Econometrics, 8
-
Patrick Robinson, A. Zellner (1974)
An Introduction to Bayesian Inference in Econometrics., 137
-
R. Solow (1960)
On a Family of Lag DistributionsEconometrica, 28
-
R. Meyer, Jun Yu (2000)
Bugs for a Bayesian Analysis of Stochastic Volatility ModelsEconometrics eJournal
-
M. West, J. Harrison (1997)
Bayesian forecasting and dynamic models (2nd ed.)Journal of the Operational Research Society, 49
- Publisher
- Wiley
- Copyright
- Copyright © 2006 John Wiley & Sons, Ltd.
- ISSN
- 1524-1904
- eISSN
- 1526-4025
- DOI
- 10.1002/asmb.628
- Publisher site
- See Article on Publisher Site
Abstract
This paper aims to show to practitioners how flexible and straightforward the implementation of the Bayesian paradigm can be for distributed lag models within the Bayesian dynamic linear model framework. Distributed lag models are of importance when it is believed that a covariate at time t, say Xt, causes an impact on the mean value of the response variable, Yt. Moreover, it is believed that the effect of X on Y persists for a period and decays to zero as time passes by. There are in the literature many different models that deal with this kind of situation. This paper aims to review some of these proposals and show that under some fairly simple reparametrization they fall into a particular case of a class of Dynamic Linear Models (DLM), the transfer functions models. Inference is performed following the Bayesian paradigm. Samples from the joint posterior distribution of the unknown quantities of interest are easily obtained through the use of Markov chain Monte Carlo (MCMC) methods. The computation is simplified by the use of the software WinBugs. As an example, a consumption function is analysed using the Koyck transformation and the transfer function models. Then a comparison is made with classical cointegration techniques. Copyright © 2006 John Wiley & Sons, Ltd.
Journal
Applied Stochastic Models in Business and Industry – Wiley
Published: Mar 1, 2006