nep-ets New Economics Papers
on Econometric Time Series
Issue of 2005‒06‒27
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Estimation and inference in dynamic unbalanced panel data models with a small number of individuals By Giovanni S.F. Bruno
  2. A Simple Approach to the Parametric Estimation of Potentially Nonstationary Diffusions By Federico M. Bandi; Peter C.B. Phillips
  3. A Two-Stage Realized Volatility Approach to the Estimation for Diffusion Processes from Discrete Observations By Peter C.B. Phillips; Jun Yu
  4. Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing By DUFOUR, Jean-Marie; JOUINI, Tarek
  5. A Portmanteau Test for Serially Correlated Errors in Fixed Effects Models By Atsushi Inoue; Gary Solon
  6. Dating Business Cycle Turning Points By Marcelle Chauvet; James D. Hamilton

  1. By: Giovanni S.F. Bruno (IEP, Università Bocconi, Milano)
    Abstract: This study describes a new Stata routine that computes bias-corrected LSDV estimators and thier bootstrap variance-covariance matrix for dynamic (possibly) unbalanced panel data models. A Monte Carlo analysis is carried out to evaluate the finite-sample performance of the bias corrected LSDV estimators in comparison to the original LSDV estimators and three popular N-consistent estimators: Arellano-Bond, Anderson-Hsiao and Blundell-Bond. Results strongly support the bias-corrected LSDV estimators according to bias and root mean squared error criteria when the number of individuals is small.
    Keywords: Bias approximation; Unbalanced panels; Dynamic Panel data; LSDV estimator; Monte Carlo experiment; Bootstrap variance-covariance
    JEL: C23 C15
    Date: 2005–05
  2. By: Federico M. Bandi; Peter C.B. Phillips (Cowles Foundation, Yale University)
    Abstract: A simple and robust approach is proposed for the parametric estimation of scalar homogeneous stochastic differential equations. We specify a parametric class of diffusions and estimate the parameters of interest by minimizing criteria based on the integrated squared difference between kernel estimates of the drift and diffusion functions and their parametric counterparts. The procedure does not require simulations or approximations to the true transition density and has the simplicity of standard nonlinear least-squares methods in discrete-time. A complete asymptotic theory for the parametric estimates is developed. The limit theory relies on infill and long span asymptotics and is robust to deviations from stationarity, requiring only recurrence.
    Keywords: Diffusion, Drift, Local time, Parametric estimation, Semimartingale, Stochastic differential equation
    JEL: C14 C22
    Date: 2005–06
  3. By: Peter C.B. Phillips (Cowles Foundation, Yale University); Jun Yu
    Abstract: This paper motivates and introduces a two-stage method for estimating diffusion processes based on discretely sampled observations. In the first stage we make use of the feasible central limit theory for realized volatility, as recently developed in Barndorff-Nielsen and Shephard (2002), to provide a regression model for estimating the parameters in the diffusion function. In the second stage the in-fill likelihood function is derived by means of the Girsanov theorem and then used to estimate the parameters in the drift function. Consistency and asymptotic distribution theory for these estimates are established in various contexts. The finite sample performance of the proposed method is compared with that of the approximate maximum likelihood method of Ait-Sahalia (2002).
    Keywords: Maximum likelihood, Girsnov theorem, Discrete sampling, Continuous record, Realized volatility
    JEL: C13 C22 E43 G13
    Date: 2005–06
  4. By: DUFOUR, Jean-Marie; JOUINI, Tarek
    Abstract: Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.
    Keywords: Vector autoregression ; VAR ; exact test ; Monte Carlo test ; maximized Monte Carlo test ; bootstra; Granger causality ; order selection ; nonstationary model ; macroeconomics ; money and income ; interest rate ; inflation
    JEL: C32 C12 C15 E4 E5
    Date: 2005
  5. By: Atsushi Inoue; Gary Solon
    Abstract: We propose a portmanteau test for serial correlation of the error term in a fixed effects model. The test is derived as a conditional Lagrange multiplier test, but it also has a straightforward Wald test interpretation. In Monte Carlo experiments, the test displays good size and power properties.
    JEL: C23
    Date: 2005–06
  6. By: Marcelle Chauvet; James D. Hamilton
    Abstract: This paper discusses formal quantitative algorithms that can be used to identify business cycle turning points. An intuitive, graphical derivation of these algorithms is presented along with a description of how they can be implemented making very minimal distributional assumptions. We also provide the intuition and detailed description of these algorithms for both simple parametric univariate inference as well as latent-variable multiple-indicator inference using a state-space Markov-switching approach. We illustrate the promise of this approach by reconstructing the inferences that would have been generated if parameters had to be estimated and inferences drawn based on data as they were originally released at each historical date. Waiting until one extra quarter of GDP growth is reported or one extra month of the monthly indicators released before making a call of a business cycle turning point helps reduce the risk of misclassification. We introduce two new measures for dating business cycle turning points, which we call the %u201Cquarterly real-time GDP-based recession probability index%u201D and the %u201Cmonthly real-time multiple-indicator recession probability index%u201D that incorporate these principles. Both indexes perform quite well in simulation with real-time data bases. We also discuss some of the potential complicating factors one might want to consider for such an analysis, such as the reduced volatility of output growth rates since 1984 and the changing cyclical behavior of employment. Although such refinements can improve the inference, we nevertheless find that the simpler specifications perform very well historically and may be more robust for recognizing future business cycle turning points of unknown character.
    JEL: E32
    Date: 2005–06

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