Coleção Insper Business and Economics Working Papers
URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/5740
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12 resultados
Resultados da Pesquisa
Working Paper Rational Sunspots(2016) Ascari, Guido; Banomolo, Paolo; HEDIBERT FREITAS LOPESThe 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.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.Working Paper A Tutorial on the Computation of Bayes Factor(2014) HEDIBERT FREITAS LOPESWorking Paper Shrinkage priors for linear instrumental variable models with many instruments(2014) Hahn, P. Richard; HEDIBERT FREITAS LOPESWorking 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.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.Working Paper Parsimonious Bayesian Factor Analysis when the Number of Factors is Unknown(2014) Fruhwirth-Schnatter, Sylvia; HEDIBERT FREITAS LOPESWe 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.Working Paper Temporal dependence in extremes with dynamic model(2014) Nascimento, Fernando Ferraz do; Gamerman, Dani; HEDIBERT FREITAS LOPESThis paper is concerned with the analysis of time series data with temporal dependence through extreme events. This is achieved via a model formulation that considers separately the central part and the tail of the distributions, using a two component mixture model. Extremes beyond a thresh old are assumed to follow a generalized Pareto distribution (GPD). Temporal dependence is induced by allowing to GPD parameter to vary with time. Temporal variation and dependence is introduced at a latent level via the novel use of dynamic linear models (DLM). Novelty lies in the time variation of the shape and scale parameter of the resulting distribution. These changes in limiting regimes as time changes reflect better the data behavior, with importante gains in estimation and interpretation. The central part follows a nonparametric mixture approach. The uncertainty about the threshold is explicitly considered. Posterior inference is performed through Markov Chain Monte Carlo (MCMC) methods. A variety of scenarios can be entertained and include the possibility of alternation of presence and absence of a finite upper limit of the distribution for different time periods. Simulations are carried out in order to analyze the performance of our proposed model. We also apply the proposed model to financial time series: returns of Petrobr´as stocks and SP500 index. Results show advantage of our proposal over currently entertained models such as stochastic volatility, with improved estimation of high quantiles and extremes.Working Paper Semi-parametric inference for the means of heavy-tailed distributions(2016) Taddy, Matt; HEDIBERT FREITAS LOPES; Goldberg, David; Gardner, MattHeavy tailed distributions present a tough setting for inference. They are also common in industrial applications, particularly with Internet transaction datasets, and machine learners often analyze such data without considering the biases and risks associated with the misuse of standard tools. This article outlines a procedure for inference about the (possibly conditional) mean of a heavy tailed distribution that combines nonparametric inference for the bulk of the support with parametric inference – motivated from extreme value theory – for the heavy tail. We are able to derive analytic posterior conditional means and variances for the expected value of a heavy tailed distributivo. We also introduce a simple and novel independence Metropolis Hastings algorithm that samples from the distribution for tail parameters via small adjustments to a parametric bootstrap, and through this algorithm are able to provide comparisons between our framework and frequentist semiparametric inference. We also provide a modeling extension that shrinks tails across distributions to an overall background tail. We illustrate on two examples: treatment effect estimation on a set of 72 A/B experiments, and the fitting of regression trees for prediction of user spending. Both use data from tens of millions of users of eBay.com.Working Paper Cholesky Realized Stochasti Volatility Model(2016) Shirota, Shinichiro; Omori, Yashiro; HEDIBERT FREITAS LOPES; Piao, HaixiangMultivariate stochastic volatility models with leverage are expected to play important roles in financial applications such as asset allocation and risk management. However, these models suffer from two major difficulties: (1) there are too many parameters to estimate using only daily asset returns and (2) estimated covariance matrices are not guaranteed to be positive definite. Our approach takes advantage of realized covariances to attain the efficient estimation of parameters by incorporating additional information for the co-volatilities, and considers Cholesky decomposition to guarantee the positive definiteness of the covariance matrices. In this framework, we propose a flexible modeling for stylized facts of financial markets such as dynamic correlations and leverage effects among volatilities. Taking a Bayesian approach, we describe Markov Chain Monte Carlo implementation with a simple but efficient sampling scheme. Our model is applied to nine U.S. stock returns data, and the model comparison is conducted based on portfolio performances.