12,001 research outputs found
Extracting the Italian output gap: a Bayesian approach
During the last decades particular effort has been directed towards
understanding and predicting the relevant state of the business cycle with the
objective of decomposing permanent shocks from those having only a transitory
impact on real output. This trend--cycle decomposition has a relevant impact on
several economic and fiscal variables and constitutes by itself an important
indicator for policy purposes. This paper deals with trend--cycle decomposition
for the Italian economy having some interesting peculiarities which makes it
attractive to analyse from both a statistic and an historical perspective. We
propose an univariate model for the quarterly real GDP, subsequently extended
to include the price dynamics through a Phillips curve. This study considers a
series of the Italian quarterly real GDP recently released by OECD which
includes both the 1960s and the recent global financial crisis of 2007--2008.
Parameters estimate as well as the signal extraction are performed within the
Bayesian paradigm which effectively handles complex models where the parameters
enter the log--likelihood function in a strongly nonlinear way. A new Adaptive
Independent Metropolis--within--Gibbs sampler is then developed to efficiently
simulate the parameters of the unobserved cycle. Our results suggest that
inflation influences the Output Gap estimate, making the extracted Italian OG
an important indicator of inflation pressures on the real side of the economy,
as stated by the Phillips theory. Moreover, our estimate of the sequence of
peaks and troughs of the Output Gap is in line with the OECD official dating of
the Italian business cycle
Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multi-wavelets
A new time-varying autoregressive (TVAR) modelling approach is proposed for nonstationary signal processing and analysis, with application to EEG data modelling and power spectral estimation. In the new parametric modelling framework, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The time-varying modelling problem is then reduced to regression selection and parameter estimation, which can be effectively resolved by using a forward orthogonal regression algorithm. Two examples, one for an artificial signal and another for an EEG signal, are given to show the effectiveness and applicability of the new TVAR modelling method
Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models
This article introduces two absolutely continuous global-local shrinkage
priors to enable stochastic variable selection in the context of
high-dimensional matrix exponential spatial specifications. Existing approaches
as a means to dealing with overparameterization problems in spatial
autoregressive specifications typically rely on computationally demanding
Bayesian model-averaging techniques. The proposed shrinkage priors can be
implemented using Markov chain Monte Carlo methods in a flexible and efficient
way. A simulation study is conducted to evaluate the performance of each of the
shrinkage priors. Results suggest that they perform particularly well in
high-dimensional environments, especially when the number of parameters to
estimate exceeds the number of observations. For an empirical illustration we
use pan-European regional economic growth data.Comment: Keywords: Matrix exponential spatial specification, model selection,
shrinkage priors, hierarchical modeling; JEL: C11, C21, C5
Model Selection and Adaptive Markov chain Monte Carlo for Bayesian Cointegrated VAR model
This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC)
methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We
replace the popular approach to sampling Bayesian CVAR models, involving griddy
Gibbs, with an automated efficient alternative, based on the Adaptive
Metropolis algorithm of Roberts and Rosenthal, (2009). Developing the adaptive
MCMC framework for Bayesian CVAR models allows for efficient estimation of
posterior parameters in significantly higher dimensional CVAR series than
previously possible with existing griddy Gibbs samplers. For a n-dimensional
CVAR series, the matrix-variate posterior is in dimension , with
significant correlation present between the blocks of matrix random variables.
We also treat the rank of the CVAR model as a random variable and perform joint
inference on the rank and model parameters. This is achieved with a Bayesian
posterior distribution defined over both the rank and the CVAR model
parameters, and inference is made via Bayes Factor analysis of rank.
Practically the adaptive sampler also aids in the development of automated
Bayesian cointegration models for algorithmic trading systems considering
instruments made up of several assets, such as currency baskets. Previously the
literature on financial applications of CVAR trading models typically only
considers pairs trading (n=2) due to the computational cost of the griddy
Gibbs. We are able to extend under our adaptive framework to and
demonstrate an example with n = 10, resulting in a posterior distribution with
parameters up to dimension 310. By also considering the rank as a random
quantity we can ensure our resulting trading models are able to adjust to
potentially time varying market conditions in a coherent statistical framework.Comment: to appear journal Bayesian Analysi
Bayesian Learning and Predictability in a Stochastic Nonlinear Dynamical Model
Bayesian inference methods are applied within a Bayesian hierarchical
modelling framework to the problems of joint state and parameter estimation,
and of state forecasting. We explore and demonstrate the ideas in the context
of a simple nonlinear marine biogeochemical model. A novel approach is proposed
to the formulation of the stochastic process model, in which ecophysiological
properties of plankton communities are represented by autoregressive stochastic
processes. This approach captures the effects of changes in plankton
communities over time, and it allows the incorporation of literature metadata
on individual species into prior distributions for process model parameters.
The approach is applied to a case study at Ocean Station Papa, using Particle
Markov chain Monte Carlo computational techniques. The results suggest that, by
drawing on objective prior information, it is possible to extract useful
information about model state and a subset of parameters, and even to make
useful long-term forecasts, based on sparse and noisy observations
Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation
There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing and parameter and model structural error. This paper presents a novel Markov chain Monte Carlo (MCMC) sampler, entitled differential evolution adaptive Metropolis (DREAM), that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex, high-dimensional sampling problems. This MCMC scheme adaptively updates the scale and orientation of the proposal distribution during sampling and maintains detailed balance and ergodicity. It is then demonstrated how DREAM can be used to analyze forcing data error during watershed model calibration using a five-parameter rainfall-runoff model with streamflow data from two different catchments. Explicit treatment of precipitation error during hydrologic model calibration not only results in prediction uncertainty bounds that are more appropriate but also significantly alters the posterior distribution of the watershed model parameters. This has significant implications for regionalization studies. The approach also provides important new ways to estimate areal average watershed precipitation, information that is of utmost importance for testing hydrologic theory, diagnosing structural errors in models, and appropriately benchmarking rainfall measurement devices
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