Can Sterilized FX Purchases under Inflation Targeting be Expansionary?
TD n. 589, 01/04/2011
Márcio Garcia.
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TD n. 589, 01/04/2011
Márcio Garcia.
TD n. 588, 01/01/2011
Luiz Aranha Corrêa do Lago, Gustavo Henrique de Barroso Franco.
TD n. 587, 01/12/2010
Rogério Werneck.
TD n. 586, 01/12/2010
João Manoel Pinho de Mello.
TD n. 584, 01/11/2010
Marcelo de Paiva Abreu, Luiz Aranha Corrêa do Lago.
TD n. 581, 01/10/2010
Rodrigo Reis Soares.
TD n. 595, 01/10/2010
Pblicado na Revista de Economia Política v. 32, n.2, p. 264-285, 2012
Márcio Garcia, Pedro Maia da Cunha.
TD n. 583, 01/10/2010
Monica Barros, Marina Figueira de Mello.
TD n. 582, 01/10/2010
Rodrigo Reis Soares.
TD n. 578, 01/10/2010
We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in time series models and apply our modeling framework to daily realized measures of integrated variance. Asymptotic theory for parameter estimation is developed and two model building procedures are proposed. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects in financial volatility. An out-of-sample analysis shows that modeling these effects can improve forecast performance.
A ser publicado em Journal of Business & Economic Statistics
Eric Hillebrand, Marcelo Medeiros.
TD n. 577, 01/10/2010
We derive the asymptotic distribution of the ordinary least squares estimator in a regression
with cointegrated variables under misspecification and/or nonlinearity in the regressors. We show
that, under some circumstances, the order of convergence of the estimator changes and the asymptotic
distribution is non-standard. The t-statistic might also diverge. A simple case arises when the intercept
is erroneously omitted from the estimated model or in nonlinear-in-variables models with endogenous
regressors. In the latter case, a solution is to use an instrumental variable estimator. The core results in
this paper also generalise to more complicated nonlinear models involving integrated time series.
Publicado em Econometic Reviews, v. 33, n.7, p. 713-731, 2014
Marcelo Medeiros, Eduardo F. Mendes, L. Oxley.
TD n. 580, 01/09/2010
João Manoel Pinho de Mello, Pedro Henrique Rosado de Castro.
TD n. 579, 01/09/2010
João Manoel Pinho de Mello.
TD n. 576, 01/09/2010
Nayoung Lee, John Strauss, Geert Ridder.
TD n. 575, 01/09/2010
Jinyong Hahn, Geert Ridder.
TD n. 574, 01/05/2010
João Manoel Pinho de Mello, Márcio Garcia, Christiano Arrigoni Coelho.
TD n. 573, 01/04/2010
Christiano Arrigoni Coelho, João Manoel Pinho de Mello, Bruno Funchal.
TD n. 567, 01/03/2010
In this paper we introduce a linear programming estimator (LPE) for the slope parameter in a constrained linear regression model with a single regressor. The LPE is interesting because it can be superconsistent in the presence of an endogenous regressor and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the LPE. In the first case, the regressor is assumed to be fixed in repeated samples. In the second, the regressor is stochastic and potentially endogenous. For both cases the strong consistency and exact finite-sample distribution of the LPE is established. Conditions under which the LPE is consistent in the presence of serially correlated, heteroskedastic errors are also given. Finally, we describe how the LPE can be extended to the case with multiple regressors and conjecture that the extended estimator is consistent under conditions analogous to the ones given herein. Finite-sample properties of the LPE and extended LPE in comparison to the LSE and instrumental variable estimator (IVE) are investigated in a simulation study. One advantage of the LPE is that it does not require an instrument.
Publicado em Journal of Econometrics, 165, 128-136,2011
Daniel Preve, Marcelo Medeiros.
TD n. 568, 01/03/2010
In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in this paper
Publicado em Journal of Economic Surveys, 25, 6-18,2011
Michael McAller, Marcelo Medeiros.