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56
Dynamic Bayesian Networks: Representation, Inference and Learning
, 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have bee ..."
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Cited by 598 (3 self)
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Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs
and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete random variable. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linearGaussian. In this thesis, I will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in DBNs, and how to learn DBN models from sequential data.
In particular, the main novel technical contributions of this thesis are as follows: a way of representing
Hierarchical HMMs as DBNs, which enables inference to be done in O(T) time instead of O(T 3), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T) space instead of O(T); a simple way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithm called factored frontier; an analysis of the relationship between the BK algorithm and loopy belief propagation; a way of
applying RaoBlackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization
and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main value of the thesis is its catholic presentation of the field of sequential data modelling.
Has the U.S. Economy Become More Stable? A Bayesian Approach Based on a MarkovSwitching Model of Business Cycle
, 1999
"... We hope to be able to provide answers to the following questions: 1) Has there been a structural break in postwar U.S. real GDP growth toward more stabilization? 2) If so, when would it have been? 3) What's the nature of the structural break? For this purpose, we employ a Bayesian approach to d ..."
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Cited by 322 (13 self)
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We hope to be able to provide answers to the following questions: 1) Has there been a structural break in postwar U.S. real GDP growth toward more stabilization? 2) If so, when would it have been? 3) What's the nature of the structural break? For this purpose, we employ a Bayesian approach to dealing with structural break at an unknown changepoint in a Markovswitching model of business cycle. Empirical results suggest that there has been a structural break in U.S. real GDP growth toward more stabilization, with the posterior mode of the break date around 1984:1. Furthermore, we #nd a narrowing gap between growth rates during recessions and booms is at least as important as a decline in the volatility of shocks. Key Words: Bayes Factor, Gibbs sampling, Marginal Likelihood, MarkovSwitching, Stabilization, Structural Break. JEL Classi#cations: C11, C12, C22, E32. 1. Introduction In the literature, the issue of postwar stabilization of the U.S. economy relative to the prewar period has...
Dynamic Factor Analysis with Non Linear Temporal Aggregation Constraints
 Applied Statistics
, 2006
"... The paper estimates an index of coincident economic indicators for the U.S. economy using time series with different frequencies of observation (monthly and quarterly, possibly with missing values). The model considered is the dynamic factor model proposed by Stock and Watson, specified in the logar ..."
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Cited by 18 (2 self)
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The paper estimates an index of coincident economic indicators for the U.S. economy using time series with different frequencies of observation (monthly and quarterly, possibly with missing values). The model considered is the dynamic factor model proposed by Stock and Watson, specified in the logarithms of the original variables and at the monthly frequency, which poses a problem of temporal aggregation with a nonlinear observational constraint when quarterly time series are included. Our main methodological contribution is to provide an exact solution to this problem, that hinges on conditional mode estimation by extended Kalman filtering and smoothing. On the empirical side the contribution of the paper is to provide monthly estimates of quarterly indicators, among which Gross Domestic Product, that are consistent with the quarterly totals.
ON CURRENCY CRISES AND CONTAGION
, 2000
"... This paper analyzes the role of contagion in the currency crises in emerging markets during the 1990s. It employs a nonlinear Markovswitching model to conduct a systematic comparison and evaluation of three distinct causes of currency crises: contagion, weak economic fundamentals, and sunspots, i. ..."
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Cited by 18 (0 self)
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This paper analyzes the role of contagion in the currency crises in emerging markets during the 1990s. It employs a nonlinear Markovswitching model to conduct a systematic comparison and evaluation of three distinct causes of currency crises: contagion, weak economic fundamentals, and sunspots, i.e. unobservable shifts in agents ’ beliefs. Testing this model empirically through Markovswitching and panel data models reveals that contagiona high degree of real integration and financial interdependence among countriesis a core explanation for recent emerging market crises. The model has a remarkably good predictive power for the 199798 Asian crisis. The findings suggest that in particular the degree of financial interdependence and also real integration among emerging markets are crucial not only in explaining past crises but also in predicting the transmission of future financial crises.
Macroeconomic factors and the correlation of stock and bond returns. Yale ICF Working Paper No
"... This paper examines the correlation between stock and bond returns. It first documents that the major trends in stockbond correlation for G7 countries follow a similar reverting pattern in the past forty years. Next, an asset pricing model is employed to show that the correlation of stock and bond ..."
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Cited by 13 (0 self)
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This paper examines the correlation between stock and bond returns. It first documents that the major trends in stockbond correlation for G7 countries follow a similar reverting pattern in the past forty years. Next, an asset pricing model is employed to show that the correlation of stock and bond returns can be explained by their common exposure to macroeconomic factors. The link between the stockbond correlation and macroeconomic factors is examined using three successively more realistic formulations of asset return dynamics. Empirical results indicate that the major trends in stockbond correlation are determined primarily by uncertainty about expected inflation. Unexpected inflation and the real interest rate are significant to a lesser degree. Forecasting this stockbond correlation using macroeconomic factors also helps improve investors ’ asset allocation decisions. One implication of this link between trends in stockbond correlation and inflation risk is the Murphy’s Law of Diversification: diversification opportunities are least available when they are most needed.
Bayesian Estimation of Dynamic Term Structure Models under Restrictions on Risk Pricing
, 2011
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Global yield curve dynamics and interactions: a dynamic NelsonSiegel approach
 Journal of Econometrics
, 2008
"... Abstract: The popular NelsonSiegel (1987) yield curve is routinely fit to cross sections of intracountry bond yields, and Diebold and Li (2006) have recently proposed a dynamized version. In this paper we extend DieboldLi to a global context, modeling a potentially large set of country yield curv ..."
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Cited by 11 (0 self)
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Abstract: The popular NelsonSiegel (1987) yield curve is routinely fit to cross sections of intracountry bond yields, and Diebold and Li (2006) have recently proposed a dynamized version. In this paper we extend DieboldLi to a global context, modeling a potentially large set of country yield curves in a framework that allows for both global and countryspecific factors. In an empirical analysis of term structures of government bond yields for the Germany, Japan, the U.K. and the U.S., we find that global yield factors do indeed exist and are economically important, generally explaining significant fractions of country yield curve dynamics, with interesting differences across countries.
Markov Switching Models for GDP Growth in a Small Open Economy: The New Zealand Experience
, 2004
"... This paper fits Markov switching models to quarterly New Zealand aggregate GDP growth rates for the period 1978:1 to 2003:2 in order to analyse changes in mean and volatility over time. The models considered are drawn from a simple class of parsimonious, four state, Markov switching models which enc ..."
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Cited by 6 (0 self)
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This paper fits Markov switching models to quarterly New Zealand aggregate GDP growth rates for the period 1978:1 to 2003:2 in order to analyse changes in mean and volatility over time. The models considered are drawn from a simple class of parsimonious, four state, Markov switching models which encompass a wide range of stationary time series behaviour from linear AR(1) models to nonlinear models with persistent cycles and outliers. An overall objective is to use the models to help understand and identify changes in the historical growth performance of New Zealand's small open economy, particularly pre and post wide ranging economic reforms. Conclusions to emerge are that, in contrast to the 1980s, New Zealand GDP growth experienced an unusually long period of time in high growth and low volatility regimes since the early 1990s. In addition, New Zealand does not appear to have experienced the oneoff drop in volatility in the early 1980's that has been commonly reported for other countries.
Estimating the Peace Dividend: The impact of violence on house prices In Northern Ireland
, 2009
"... This paper exploits data on the pattern of violence across regions and over time to estimate the impact of the peace process in Northern Ireland on house prices. We begin with a linear model that estimates the average treatment effect of a conflictrelated killing on house prices –showing a negative ..."
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Cited by 5 (1 self)
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This paper exploits data on the pattern of violence across regions and over time to estimate the impact of the peace process in Northern Ireland on house prices. We begin with a linear model that estimates the average treatment effect of a conflictrelated killing on house prices –showing a negative correlation between house prices and killings. We then develop an approach based on an economic model where the parameters of the statistical process are estimated for a Markov switching model where conflict and peace are treated as a latent state. From this, we are able to construct a measure of the discounted number of killings which is updated in the light of actual killings. This model naturally suggests a heterogeneous effect of killings across space and time which we use to generate estimates of the peace dividend. The economic model suggests a somewhat different pattern of estimates to the linear model. We also show that there is some evidence of spillover effects of violence in adjacent regions.