nep-ecm New Economics Papers
on Econometrics
Issue of 2005‒06‒27
seven papers chosen by
Sune Karlsson
Orebro University

  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. Natural Concepts in Macroeconomics By Ray C. Fair
  5. Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing By DUFOUR, Jean-Marie; JOUINI, Tarek
  6. A Portmanteau Test for Serially Correlated Errors in Fixed Effects Models By Atsushi Inoue; Gary Solon
  7. Two-Sample Instrumental Variables Estimators By Atsushi Inoue; Gary Solon

  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: Ray C. Fair (Cowles Foundation, Yale University)
    Abstract: Ragnar Frisch proposed in 1936 a procedure for estimating natural variable values by modifying what are now called structural macroeconometric models. This paper shows that Frisch’s procedure can be used to illuminate natural concepts using today’s models. The procedure also forces one to be precise regarding the assumptions used in moving from a short-run model to a medium-run or long-run model.
    Keywords: Natural concepts, Equilibrium
    JEL: E00
    Date: 2005–06
  5. 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
  6. 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
  7. By: Atsushi Inoue; Gary Solon
    Abstract: Following an influential article by Angrist and Krueger (1992) on two-sample instrumental variables (TSIV) estimation, numerous empirical researchers have applied a computationally convenient two-sample two-stage least squares (TS2SLS) variant of Angrist and Krueger's estimator. In the two-sample context, unlike the single-sample situation, the IV and 2SLS estimators are numerically distinct. Our comparison of the properties of the two estimators demonstrates that the commonly used TS2SLS estimator is more asymptotically efficient than the TSIV estimator and also is more robust to a practically relevant type of sample stratification.
    JEL: C3
    Date: 2005–06

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