Artigos em Andamento [Working Paper]

URI permanente desta comunidadehttps://repositorio.insper.edu.br/handle/11224/3232

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2009 - 200932010 - 201619

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Idioma: search.filters.langiso.InglêsÁrea do Conhecimento: search.filters.subjectcnpq.Ciências Exatas e da Terra

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Agora exibindo 1 - 10 de 22
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    Working Paper
    Rational Sunspots
    (2016) Ascari, Guido; Banomolo, Paolo; HEDIBERT FREITAS LOPES
    The instability of macroeconomic variables is usually ruled out by rational expectations. We propose a generalization of the rational expectations framework to estimate possible temporary unstable paths. Our approach yields drifting parameters and stochastic volatility. The methodology allows the data to choose between diferent possible alternatives: determinacy, indeterminacy and instability. We apply our methodology to US inflation dynamics in the '70s through the lens of a simple New Keynesian model. When unstable RE paths are allowed, the data unambiguously select them to explain the stagflation period in the '70s. Thus, our methodology suggests that US inflation dynamics in the '70s is better described by unstable rational equilibrium paths.
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    Working Paper
    Dynamics in two networks based on stocks of the US stock market
    (2014) Sandoval Junior, Leonidas
    We follow the main stocks belonging to the New York Stock Exchange and to Nasdaq from 2003 to 2012, through years of normality and of crisis, and study the dynamics of networks built on two measures expressing relations between those stocks: correlation, which is symmetric and measures how similar two stocks behave, and Transfer Entropy, which is non-symmetric and measures the influence of the time series of one stock onto another in terms of the information that the time series of one stock transmits to the time series of another stock. The two measures are used in the creation of two networks that evolve in time, revealing how the relations between stocks and industrial sectors changed in times of crisis. The two networks are also used in conjunction with a dynamic model of the spreading of volatility in order to detect which are the stocks that are most likely to spread crises, according to the model. This information may be used in the building of policies aiming to reduce the effect of financial crises.
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    Working Paper
    Sequential bayesian learning for stochastic volatility with variance-gamma jumps in return
    (2014) Warty, Samir P.; HEDIBERT FREITAS LOPES; Polson, Nicholas G.
    In this work, we investigate sequential Bayesian estimation for inference of stochastic volatility with variance-gamma jumps in returns (SVVG). We develop an estimation algorithm that adapts the sequential learning auxiliary particle filter proposed by Carvalho, Johannes, Lopes, and Polson (2010) to SVVG. Simulation evidence and empirical estimation results indicate that this approach is able to filter latent variances, identify latent jumps in returns, and provide sequential learning about the static parameters of SVVG. We demonstrate comparative performance of the sequential algorithm and offline Markov Chain Monte Carlo in synthetic and real data applications.
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    Working Paper
    A Tutorial on the Computation of Bayes Factor
    (2014) HEDIBERT FREITAS LOPES
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    Working Paper
    Shrinkage priors for linear instrumental variable models with many instruments
    (2014) Hahn, P. Richard; HEDIBERT FREITAS LOPES
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    Working Paper
    Parsimony Inducing Priors for Large Scale State-Space Models
    (2014) HEDIBERT FREITAS LOPES; McCulloch, Robert E.; Tsay, Ruey S.
    State-space models are commonly used in the engineering, economic, and statistical literatures. They are flexible and encompass many well-known statistical models, including random coefficient autoregressive models and dynamic factor models. Bayesian analysis of state-space models has attracted much interest in recent years. However, for large scale models, prior specification becomes a challenging issue in Bayesian inference. In this paper, we propose a flexible prior for state-space models. The proposed prior is a mixture of four commonly entertained models, yet achieves parsimony in high-dimensional systems. Here “parsimony” is represented by the idea that in a largesystem, some states may not be time-varying. Simulation and simple examples are used throughout to demonstrate the performance of the proposed prior. As an application, we consider the time-varying conditional covariance matrices of daily log returns of 94 components of the S&P 100 index, leading to a state-space model with 94×95/2=4,465 time-varying states. Our model for this large system enables us to use parallel computing.
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    Working Paper
    Particle Learning for Fat-tailed Distributions
    (2014) HEDIBERT FREITAS LOPES; Polson, Nicholas G.
    It is well-known that parameter estimates and forecasts are sensitive to assumptions about the tail behavior of the error distribution. In this paper we develop an approach to sequential inference that also simultaneously estimates the tail of the accompanying error distribution. Our simulation-based approach models errors with a tν-distribution and, as new data arrives, we sequentially compute the marginal posterior distribution of the tail thickness. Our method naturally incorporates fat-tailed error distributions and can be extended to other data features such as stochastic volatility. We show that the sequential Bayes factor provides an optimal test of fat-tails versus normality. We provide an empirical and theoretical analysis of the rate of learning of tail thickness under a default Jeffreys prior. We illustrate our sequential methodology on the British pound/US dollar daily exchange rate data and on data from the 2008-2009 credit crisis using daily S&P500 returns. Our method naturally extends to multivariate and dynamic panel data.
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    Working Paper
    Parsimonious Bayesian Factor Analysis when the Number of Factors is Unknown
    (2014) Fruhwirth-Schnatter, Sylvia; HEDIBERT FREITAS LOPES
    We introduce a new and general set of identifiability conditions for factor models which handles the ordering problem associated with current common practice. In addition, the new class of parsimonious Bayesian factor analysis leads to a factor loading matrix representation which is an intuitive and easy to implement factor selection scheme. We argue that the structuring the factor loadings matrix is in concordance with recent trends in applied factor analysis. Our MCMC scheme for posterior inference makes several improvements over the existing alternatives while outlining various strategies for conditional posterior inference in a factor selection scenario. Four applications, two based on synthetic data and two based on well known real data, are introduced to illustrate the applicability and generality of the new class of parsimonious factor models, as well as to highlight features of the proposed sampling schemes.
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    Working Paper
    Weak Approximations for Wiener Functionals
    (2012) Leão, Dorival; Ohashi, Alberto Masayoshi Faria
    In this paper we introduce a simple space-filtration discretization scheme on Wiener space which allows us to study weak decompositions and smooth explicit approximations for a large class of Wiener functionals. We show that any Wiener functional has an underlying robust semimartingale skeleton which under mild conditions converges to it. The discretization is given in terms of discrete-jumping filtrations which allow us to approximate non-smooth processes by means of a stochastic derivative operator on the Wiener space. As a by-product, we provide a robust semimartingale approxi mation for weak Dirichlet-type processes. The underlying semimartingale skeleton is intrinsically constructed in such way that all the relevant structure is amenable to a robust numerical scheme. In order to illustrate the results, we provide an easily implementable approxi mation scheme for the classical Clark-Ocone formula in full generality. Unlike in previous works, our methodology does not assume an underlying Markovian structure and does not require Malliavin weights. We conclude by proposing a method that enables us to compute optimal stopping times for possibly non Markovian systems arising e.g. from the fractional Brownian motion.
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    Working Paper
    On the Discrete Cramér-von Mises Statistics under Random Censorship
    (2012) Leão, Dorival; Ohashi, Alberto Masayoshi Faria
    In this work, nonparametric log-rank-type statistical tests are introduced in order to verify homogeneity of purely discrete variables subject to arbitrary right-censoring for infinitely many categories. In particular, the Cram´er-von Mises test statistics for discrete models under censoring is established. In order to introduce the test, we develop the weighted log-rank statistics in a general multivariate discrete setup which complements previous fundamental results of Gill [13] and Andersen et al. [5]. Due to the presence of persistent jumps over the unbounded set of categories, the asymptotic distribution of the test is not distribution-free. The statistical test for a large class of weighted processes is described as a weighted series of independent chi-squared variables whose weights can be consistently estimated. Moreover, the associated limiting covariance operator can be infinite-dimensional which allows us to deal consistently with an infinite survival time typically founded in long-term survival analysis such as cure-rate models. The test is consistent to any alternative hypothesis and, in particular, it allows us to deal with crossing hazard functions. We also provide a simulation study in order to illustrate the theoretical results.