Mudanças de regimes na função de reação do Banco Central do Brasil : uma abordagem utilizando Markov Regime Switching

O presente trabalho busca identificar a ocorrência, duração e probabilidades de transição de diferentes regimes na condução da política monetária no Brasil a partir da implantação do sistema de metas de inflação em 1999. A estimação da função de reação do Banco Central do Brasil é realizada a partir de uma Regra de Taylor forward looking para uma economia aberta, onde utilizamos a metodologia Markov Regime Switching para caracterizar de forma endógena os diferentes regimes de política monetária. Os resultados obtidos indicam a ocorrência de três regimes distintos de política monetária a partir da implantação do sistema de metas de inflação no Brasil. O primeiro regime ocorre durante 21% do período estudado e se caracteriza pela não aderência ao princípio de Taylor e discricionariedade da autoridade monetária, que reage demonstrando maior sensibilidade ao hiato do produto. O segundo regime é o de maior duração, ocorre durante 67% do período estudado, e se caracteriza pela aderência ao princípio de Taylor e equilíbrio nos pesos atribuídos pelo Banco Central tanto ao hiato do produto como ao desvio das expectativas de inflação com relação à meta. Já o terceiro regime ocorre durante 12% do período estudado e se caracteriza não somente pela aderência ao princípio de Taylor, como também por uma maior aversão ao desvio das expectativas de inflação com relação à meta.

Green-Markov models - new optimization strategies: a case study for user allocation in co-channel macro/femto networks

ABSTRACT: The femtocell concept aims to combine fixed-line broadband access with mobile telephony using the deployment of low-cost, low-power third and fourth generation base stations in the subscribers' homes. While the self-configuration of femtocells is a plus, it can limit the quality of service (QoS) for the users and reduce the efficiency of the network, based on outdated allocation parameters such as signal power level. To this end, this paper presents a proposal for optimized allocation of users on a co-channel macro-femto network, that enable self-configuration and public access, aiming to maximize the quality of service of applications and using more efficiently the available energy, seeking the concept of Green networking. Thus, when the user needs to connect to make a voice or a data call, the mobile phone has to decide which network to connect, using the information of number of connections, the QoS parameters (packet loss and throughput) and the signal power level of each network. For this purpose, the system is modeled as a Markov Decision Process, which is formulated to obtain an optimal policy that can be applied on the mobile phone. The policy created is flexible, allowing different analyzes, and adaptive to the specific characteristics defined by the telephone company. The results show that compared to traditional QoS approaches, the policy proposed here can improve energy efficiency by up to 10%.

USE OF HIDDEN MARKOV MODELS FOR QTL MAPPING

An important aspect of the QTL mapping problem is the treatment of missing genotype data. If complete genotype data were available, QTL mapping would reduce to the problem of model selection in linear regression. However, in the consideration of loci in the intervals between the available genetic markers, genotype data is inherently missing. Even at the typed genetic markers, genotype data is seldom complete, as a result of failures in the genotyping assays or for the sake of economy (for example, in the case of selective genotyping, where only individuals with extreme phenotypes are genotyped). We discuss the use of algorithms developed for hidden Markov models (HMMs) to deal with the missing genotype data problem.

Checking Assumptions in Latent Class Regression Models via a Markov Chain Monte Carlo Estimation Approach: An Application to Depression and Socio-Economic Status

Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.

A comparison of Markov and generalized linear models of hospital mortality

This paper reports a comparison of three modeling strategies for the analysis of hospital mortality in a sample of general medicine inpatients in a Department of Veterans Affairs medical center. Logistic regression, a Markov chain model, and longitudinal logistic regression were evaluated on predictive performance as measured by the c-index and on accuracy of expected numbers of deaths compared to observed. The logistic regression used patient information collected at admission; the Markov model was comprised of two absorbing states for discharge and death and three transient states reflecting increasing severity of illness as measured by laboratory data collected during the hospital stay; longitudinal regression employed Generalized Estimating Equations (GEE) to model covariance structure for the repeated binary outcome. Results showed that the logistic regression predicted hospital mortality as well as the alternative methods but was limited in scope of application. The Markov chain provides insights into how day to day changes of illness severity lead to discharge or death. The longitudinal logistic regression showed that increasing illness trajectory is associated with hospital mortality. The conclusion is reached that for standard applications in modeling hospital mortality, logistic regression is adequate, but for new challenges facing health services research today, alternative methods are equally predictive, practical, and can provide new insights. ^

Code-switching: a touchstone of models of bilingual language production

The goal of the present thesis was to investigate the production of code-switched utterances in bilinguals’ speech production. This study investigates the availability of grammatical-category information during bilingual language processing. The specific aim is to examine the processes involved in the production of Persian-English bilingual compound verbs (BCVs). A bilingual compound verb is formed when the nominal constituent of a compound verb is replaced by an item from the other language. In the present cases of BCVs the nominal constituents are replaced by a verb from the other language. The main question addressed is how a lexical element corresponding to a verb node can be placed in a slot that corresponds to a noun lemma. This study also investigates how the production of BCVs might be captured within a model of BCVs and how such a model may be integrated within incremental network models of speech production. In the present study, both naturalistic and experimental data were used to investigate the processes involved in the production of BCVs. In the first part of the present study, I collected 2298 minutes of a popular Iranian TV program and found 962 code-switched utterances. In 83 (8%) of the switched cases, insertions occurred within the Persian compound verb structure, hence, resulting in BCVs. As to the second part of my work, a picture-word interference experiment was conducted. This study addressed whether in the case of the production of Persian-English BCVs, English verbs compete with the corresponding Persian compound verbs as a whole, or whether English verbs compete with the nominal constituents of Persian compound verbs only. Persian-English bilinguals named pictures depicting actions in 4 conditions in Persian (L1). In condition 1, participants named pictures of action using the whole Persian compound verb in the context of its English equivalent distractor verb. In condition 2, only the nominal constituent was produced in the presence of the light verb of the target Persian compound verb and in the context of a semantically closely related English distractor verb. In condition 3, the whole Persian compound verb was produced in the context of a semantically unrelated English distractor verb. In condition 4, only the nominal constituent was produced in the presence of the light verb of the target Persian compound verb and in the context of a semantically unrelated English distractor verb. The main effect of linguistic unit was significant by participants and items. Naming latencies were longer in the nominal linguistic unit compared to the compound verb (CV) linguistic unit. That is, participants were slower to produce the nominal constituent of compound verbs in the context of a semantically closely related English distractor verb compared to producing the whole compound verbs in the context of a semantically closely related English distractor verb. The three-way interaction between version of the experiment (CV and nominal versions), linguistic unit (nominal and CV linguistic units), and relation (semantically related and unrelated distractor words) was significant by participants. In both versions, naming latencies were longer in the semantically related nominal linguistic unit compared to the response latencies in the semantically related CV linguistic unit. In both versions, naming latencies were longer in the semantically related nominal linguistic unit compared to response latencies in the semantically unrelated nominal linguistic unit. Both the analysis of the naturalistic data and the results of the experiment revealed that in the case of the production of the nominal constituent of BCVs, a verb from the other language may compete with a noun from the base language, suggesting that grammatical category does not necessarily provide a constraint on lexical access during the production of the nominal constituent of BCVs. There was a minimal context in condition 2 (the nominal linguistic unit) in which the nominal constituent was produced in the presence of its corresponding light verb. The results suggest that generating words within a context may not guarantee that the effect of grammatical class becomes available. A model is proposed in order to characterize the processes involved in the production of BCVs. Implications for models of bilingual language production are discussed.

Empirical Analysis of Credit Risk Regime Switching and Temporal Conditional Default Correlation in Credit Default Swap Valuation: The Market liquidity effect

In this paper, we extend the debate concerning Credit Default Swap valuation to include time varying correlation and co-variances. Traditional multi-variate techniques treat the correlations between covariates as constant over time; however, this view is not supported by the data. Secondly, since financial data does not follow a normal distribution because of its heavy tails, modeling the data using a Generalized Linear model (GLM) incorporating copulas emerge as a more robust technique over traditional approaches. This paper also includes an empirical analysis of the regime switching dynamics of credit risk in the presence of liquidity by following the general practice of assuming that credit and market risk follow a Markov process. The study was based on Credit Default Swap data obtained from Bloomberg that spanned the period January 1st 2004 to August 08th 2006. The empirical examination of the regime switching tendencies provided quantitative support to the anecdotal view that liquidity decreases as credit quality deteriorates. The analysis also examined the joint probability distribution of the credit risk determinants across credit quality through the use of a copula function which disaggregates the behavior embedded in the marginal gamma distributions, so as to isolate the level of dependence which is captured in the copula function. The results suggest that the time varying joint correlation matrix performed far superior as compared to the constant correlation matrix; the centerpiece of linear regression models.

Estimating parameters in Markov models for longitudinal studies with missing data or surrogate outcomes

The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis. ^ This dissertation research proposes methods to analyze longitudinal data (1) that have categorical outcome with missing observations or (2) that use complete or incomplete surrogate observations to analyze the categorical latent outcome. For (1), different missing mechanisms were considered for empirical studies using methods that include EM algorithm, Monte Carlo EM and a procedure that is not a data augmentation method. For (2), the hidden Markov model with the forward-backward procedure was applied for parameter estimation. This method was also extended to cover the computation of standard errors. The proposed methods were demonstrated by the Schizophrenia example. The relevance of public health, the strength and limitations, and possible future research were also discussed. ^

Analysing time series structure with hidden Markov models

This paper consides the problem of extracting the relationships between two time series in a non-linear non-stationary environment with Hidden Markov Models (HMMs). We describe an algorithm which is capable of identifying associations between variables. The method is applied both to synthetic data and real data. We show that HMMs are capable of modelling the oil drilling process and that they outperform existing methods.

Time delay estimation with hidden Markov models

Most traditional methods for extracting the relationships between two time series are based on cross-correlation. In a non-linear non-stationary environment, these techniques are not sufficient. We show in this paper how to use hidden Markov models to identify the lag (or delay) between different variables for such data. Adopting an information-theoretic approach, we develop a procedure for training HMMs to maximise the mutual information (MMI) between delayed time series. The method is used to model the oil drilling process. We show that cross-correlation gives no information and that the MMI approach outperforms maximum likelihood.

Dynamical local models for segmentation and prediction of financial time series

In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data.