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3 “Estimating the trend of M3 income velocity underlying the reference value for monetary growth” by
, 2002
"... 411 144 ecb d All rights reserved. Photocopying for educational and noncommercial purposes permitted provided that the source is acknowledged. ISSN 16071484Table of contents Abstract 5 1 Introduction: general aspects of the reference value for monetary growth in the context of the ECB’s monetary p ..."
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Cited by 64 (0 self)
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411 144 ecb d All rights reserved. Photocopying for educational and noncommercial purposes permitted provided that the source is acknowledged. ISSN 16071484Table of contents Abstract 5 1 Introduction: general aspects of the reference value for monetary growth in the context of the ECB’s monetary policy strategy 7 2 A first look at the data 10 2.1 The concept of M3 income velocity and its behaviour in the euro area 10 2.2 Data and aggregation issues 12
Markov Chain Monte Carlo in Conditionally Gaussian State Space Models
 Biometrika
, 1996
"... Introduction Linear Gaussian state space models are used extensively, with unknown parameters usually estimated by maximum likelihood: Wecker & Ansley (1983), Harvey (1989). However, many time series and nonparametric regression applications, such as change point problems, outlier detection and ..."
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Cited by 63 (3 self)
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Introduction Linear Gaussian state space models are used extensively, with unknown parameters usually estimated by maximum likelihood: Wecker & Ansley (1983), Harvey (1989). However, many time series and nonparametric regression applications, such as change point problems, outlier detection and switching regression, require the full generality of the conditionally Gaussian model: Harrison & Stevens (1976), Shumway & Stoffer (1991), West & Harrison (1989), Gordon & Smith (1990). The presence of a large number of indicator variables makes it difficult to estimate conditionally Gaussian models using maximum likelihood, and a Bayesian approach using Markov chain Monte Carlo appears more tractable. We propose a new sampler, which is used to estimate an unknown function nonparametrically when there are jumps in the function and outliers in the observations; it is also applied to a time series change point problem previously discussed by Gordon & Smith (1990). For the first example th
FX Trading and Exchange Rate Dynamics
, 2001
"... This paper provides new perspective on the poor performance of exchange rate models by focusing on the information structure of FX trading. I present a new theoretical model of FX trading that emphasizes the role of incomplete and heterogeneous information. The model shows how an equilibrium distr ..."
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Cited by 49 (7 self)
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This paper provides new perspective on the poor performance of exchange rate models by focusing on the information structure of FX trading. I present a new theoretical model of FX trading that emphasizes the role of incomplete and heterogeneous information. The model shows how an equilibrium distribution of FX transaction prices and orders can arise at each point in time from the optimal trading decisions of dealers. This result motivates an empirical investigation of how the equilibrium distribution of FX prices behaves using a new data set that details trading activity in the FX market. This analysis produces two striking results: (i) Much of the observed shortterm volatility in exchange rates comes from sampling the heterogeneous trading decisions of dealers in an equilibrium distribution that, under normal market conditions, changes comparatively slowly. (ii) In contrast to the assumptions of traditional macro models, public news is rarely the predominant source of exchange rate movements over any horizon.
Particle Filters for State Space Models With the Presence of Static Parameters
, 2002
"... In this paper particle filters for dynamic state space models handling unknown static parameters are discussed. The approach is based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered. Such a marginalization can always be ..."
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Cited by 49 (0 self)
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In this paper particle filters for dynamic state space models handling unknown static parameters are discussed. The approach is based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered. Such a marginalization can always be applied. However, realtime applications are only possible when the distribution of the unknown parameters given both observations and the hidden state vector depends on some lowdimensional sufficient statistics. Such sufficient statistics are present in many of the commonly used state space models. Marginalizing the static parameters avoids the problem of impoverishment which typically occur when static parameters are included as part of the state vector. The filters are tested on several different models, with promising results.
MultiFactor CoxIngersollRoss Models of the Term Structure: Estimates and Tests from a Kalman Filter Model
, 1995
"... This paper presents a method for estimating multifactor versions of the Cox, Ingersoll, Ross (1985b) model of the term structure of interest rates. The fixed parameters in one, two, and three factor models are estimated by applying an approximate maximum likelihood estimator in a statespace model ..."
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Cited by 48 (0 self)
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This paper presents a method for estimating multifactor versions of the Cox, Ingersoll, Ross (1985b) model of the term structure of interest rates. The fixed parameters in one, two, and three factor models are estimated by applying an approximate maximum likelihood estimator in a statespace model using data for the U.S. treasury market. A nonlinear Kalman filter is used to estimate the unobservable factors. Multifactor models are necessary to characterize the changing shape of the yield curve over time, and the statistical tests support the case for two and three factor models. A three factor model would be able to incorporate random variation in short term interest rates, long term rates, and interest rate volatility.
Recursive Monte Carlo filters: Algorithms and theoretical analysis
, 2003
"... powerful tool to perform computations in general state space models. We discuss and compare the accept–reject version with the more common sampling importance resampling version of the algorithm. In particular, we show how auxiliary variable methods and stratification can be used in the accept–rejec ..."
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Cited by 47 (0 self)
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powerful tool to perform computations in general state space models. We discuss and compare the accept–reject version with the more common sampling importance resampling version of the algorithm. In particular, we show how auxiliary variable methods and stratification can be used in the accept–reject version, and we compare different resampling techniques. In a second part, we show laws of large numbers and a central limit theorem for these Monte Carlo filters by simple induction arguments that need only weak conditions. We also show that, under stronger conditions, the required sample size is independent of the length of the observed series. 1. State space and hidden Markov models. A general state space or hidden Markov model consists of an unobserved state sequence (Xt) and an observation sequence (Yt) with the following properties: State evolution: X0,X1,X2,... is a Markov chain with X0 ∼ a0(x)dµ(x) and XtXt−1 = xt−1 ∼ at(xt−1,x)dµ(x). Generation of observations: Conditionally on (Xt), the Yt’s are independent and Yt depends on Xt only with YtXt = xt ∼ bt(xt,y)dν(y). These models occur in a variety of applications. Linear state space models are equivalent to ARMA models (see, e.g., [16]) and have become popular Received January 2003; revised August 2004. AMS 2000 subject classifications. Primary 62M09; secondary 60G35, 60J22, 65C05. Key words and phrases. State space models, hidden Markov models, filtering and smoothing, particle filters, auxiliary variables, sampling importance resampling, central limit theorem. This is an electronic reprint of the original article published by the
A Generative Model for Music Transcription
, 2005
"... In this paper we present a graphical model for polyphonic music transcription. Our model, formulated as a Dynamical Bayesian Network, embodies a transparent and computationally tractable approach to this acoustic analysis problem. An advantage of our approach is that it places emphasis on explicitl ..."
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Cited by 45 (15 self)
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In this paper we present a graphical model for polyphonic music transcription. Our model, formulated as a Dynamical Bayesian Network, embodies a transparent and computationally tractable approach to this acoustic analysis problem. An advantage of our approach is that it places emphasis on explicitly modelling the sound generation procedure. It provides a clear framework in which both high level (cognitive) prior information on music structure can be coupled with low level (acoustic physical) information in a principled manner to perform the analysis. The model is a special case of the, generally intractable, switching Kalman filter model. Where possible, we derive, exact polynomial time inference procedures, and otherwise efficient approximations. We argue that our generative model based approach is computationally feasible for many music applications and is readily extensible to more general auditory scene analysis scenarios.
2002): “Market Timing and Return Prediction Under Model Instability
 Journal of Empirical Finance
"... Despite mounting empirical evidence to the contrary, the literature on predictability of stock returns almost uniformly assumes a timeinvariant relationship between state variables and returns. In this paper we propose a twostage approach for forecasting of financial return series that are subject ..."
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Cited by 45 (9 self)
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Despite mounting empirical evidence to the contrary, the literature on predictability of stock returns almost uniformly assumes a timeinvariant relationship between state variables and returns. In this paper we propose a twostage approach for forecasting of financial return series that are subject to breaks. The first stage adopts a reversed ordered Cusum (ROC) procedure to determine in real time when the most recent break has occurred. In the second stage, postbreak data is used to estimate the parameters of the forecasting model. We compare this approach to existing alternatives for dealing with parameter instability such as the BaiPerron method and the timevarying parameter model. An outofsample forecasting experiment demonstrates considerable gains in market timing precision from adopting the proposed twostage forecasting method.
BUGS for a Bayesian Analysis of Stochastic Volatility Models
, 2000
"... This paper reviews the general Bayesian approach to parameter estimation in stochastic volatility models with posterior computations performed by Gibbs sampling. The main purpose is to illustrate the ease with which the Bayesian stochastic volatility model can now be studied routinely via BUGS (Baye ..."
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Cited by 44 (14 self)
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This paper reviews the general Bayesian approach to parameter estimation in stochastic volatility models with posterior computations performed by Gibbs sampling. The main purpose is to illustrate the ease with which the Bayesian stochastic volatility model can now be studied routinely via BUGS (Bayesian Inference Using Gibbs Sampling), a recently developed, userfriendly, and freely available software package. It is an ideal software tool for the exploratory phase of model building as any modifications of a model including changes of priors and sampling error distributions are readily realized with only minor changes of the code. However, due to the single move Gibbs sampler, convergence can be slow. BUGS automates the calculation of the full conditional posterior distributions using a model representation by directed acyclic graphs. It contains an expert system for choosing an effective sampling method for each full conditional. Furthermore, software for convergence diagnostics and statistical summaries is available for the BUGS output