nep-ecm New Economics Papers
on Econometrics
Issue of 2007‒10‒06
nine papers chosen by
Sune Karlsson
Orebro University

  1. Detecting Misspecifications in Autoregressive Conditional Duration Models By Yongmiao Hong; Yoon-Jin Lee
  2. Combining forecasts from nested models By Todd E. Clark; Michael W. McCracken
  3. A Panel-CADF Test for Unit Roots By Costantini, Mauro; Lupi, Claudio; Popp, Stephan
  4. Simplified Implementation of the Heckman Estimator of the Dynamic Probit Model and a Comparison with Alternative Estimators By Wiji Arulampalam; Mark B. Stewart
  5. Discriminating mean and variance shifts By Carlos Santos
  6. A Review of Nonfundamentalness and Identification in Structural VAR Models By Lucia Alessi; Matteo Barigozzi; Marco Capasso
  7. Real-time measurement of business conditions By S. Boragan Aruoba; Francis X. Diebold; Chiara Scotti
  8. Comparing Models for Forecasting the Yield Curve By Marco S. Matsumura; Ajax R. B. Moreira
  9. Counterfactual Distributions with Sample Selection Adjustments: Econometric Theory and an Application to the Netherlands By Jim Albrecht; Aico van Vuuren; Susan Vroman

  1. By: Yongmiao Hong (Cornell University); Yoon-Jin Lee (Indiana University Bloomington)
    Abstract: We propose a new class of specification tests for Autoregressive Conditional Duration (ACD) models. Both linear and nonlinear ACD models are covered, and standardized innovations can have time-varying conditional dispersion and higher order conditional moments of unknown form. No specific estimation method is required, and the tests have a convenient null asymptotic N(0,1) distribution. To reduce the impact of parameter estimation uncertainty in finite samples, we adopt Wooldridge's (1990a) device to our context and justify its validity. Simulation studies show that the finite sample correction gives better sizes in finite samples and are robust to parameter estimation uncertainty. And, it is important to take into account time-varying conditional dispersion and higher order conditional moments in standardized innovations; failure to do so can cause strong overrejection of a correctly specified ACD model. The proposed tests have reasonable power against a variety of popular linear and nonlinear ACD alternatives.
    Keywords: Autoregressive Conditional Duration, Dispersion Clustering, Finite Sample Correction, Generalized Spectral Derivative, Nonlinear Time Series, Parameter Estimation Uncertainty, Wooldridge's Device
    JEL: C4 C2
    Date: 2007–09
  2. By: Todd E. Clark; Michael W. McCracken
    Abstract: Motivated by the common finding that linear autoregressive models forecast better than models that incorporate additional information, this paper presents analytical, Monte Carlo, and empirical evidence on the effectiveness of combining forecasts from nested models. In our analytics, the unrestricted model is true, but as the sample size grows, the data generating process converges to the restricted model. This approach captures the practical reality that the predictive content of variables of interest is often low. We derive MSE-minimizing weights for combining the restricted and unrestricted forecasts. Monte Carlo and empirical analyses verify the practical effectiveness of our combination approach.
    Date: 2007
  3. By: Costantini, Mauro; Lupi, Claudio; Popp, Stephan
    Abstract: In this paper we propose the extension of the covariate-augmented Dickey Fuller (CADF) test for unit roots developed by Hansen (1995} to the panel case. We show that the extension is viable and gives power gains with respect to the time series approach. Particular attention is paid to cross-unit dependence.
    Keywords: Unit root, Panel data, Cross-unit dependence
    JEL: C12 C22 C23
    Date: 2007–09–10
  4. By: Wiji Arulampalam (University of Warwick and IZA); Mark B. Stewart (University of Warwick)
    Abstract: This paper presents a convenient shortcut method for implementing the Heckman estimator of the dynamic random effects probit model using standard software. It then compares the three estimators proposed by Heckman, Orme and Wooldridge based on three alternative approximations, first in an empirical model for the probability of unemployment and then in a set of simple simulation experiments.
    Keywords: dynamic discrete choice models, initial conditions, dynamic probit, panel data
    JEL: C23 C25 C13 C51
    Date: 2007–09
  5. By: Carlos Santos (Faculdade de Economia e Gestão, Universidade Católica Portuguesa (Porto))
    Abstract: A two-stage procedure based on impulse saturation is suggested to distinguish mean and variance shifts. The resulting zero-mean innovation test statistic has a non standard distribution, with a nuisance parameter. Hence, simulation-based critical values are provided for some cases of interest. Monte Carlo evidence reveals the test has good power properties to discriminate mean and variance shifts identified through the impulse saturation break test.
    Keywords: breaks; mean shift; variance shift; impulse saturation; nuisance parameter
    JEL: C12 C15 C22 C52
    Date: 2007–08
  6. By: Lucia Alessi; Matteo Barigozzi; Marco Capasso
    Abstract: We review, under a historical perspective, the developement of the problem of non- fundamentalness of Moving Average (MA) representations of economic models, starting from the work by Hansen and Sargent [1980]. Nonfundamentalness typically arises when agents' information space is larger than the econometrican's one. Therefore it is impos- sible for the latter to use standard econometric techniques, as Vector AutoRegression (VAR), to estimate economic models. We re-state the conditions under which it is pos- sible to invert an MA representation in order to get an ordinary VAR, and we consider how the latter is used in the literature to assess the validity of Dynamic Stochastic Gen- eral Equilibrium models, providing some interesting examples. We believe that possible nonfundamental representations of considered models are too often neglected in the liter- ature. We consider how factor models can be seen as an alternative to VAR for assessing the validity of an economic model without having to deal with the problem of nonfun- damentalness. We then review the works by Lippi and Reichlin [1993] and Lippi and Reichlin [1994] which are the first attempts to give to nonfundamental representations the economic relevance that they deserve, and to outline a method to obtain such repre- sentations starting from an estimated VAR.
    Keywords: Nonfundamentalness, Structural VAR, Dynamic Stochastic General Equilibrium Models, Factor Models
    Date: 2007–10–01
  7. By: S. Boragan Aruoba; Francis X. Diebold; Chiara Scotti
    Abstract: We construct a framework for measuring economic activity in real time (e.g., minute-by-minute), using a variety of stock and flow data observed at mixed frequencies. Specifically, we propose a dynamic factor model that permits exact filtering, and we explore the efficacy of our methods both in a simulation study and in a detailed empirical example.
    Date: 2007
  8. By: Marco S. Matsumura; Ajax R. B. Moreira
    Abstract: The evolution of the yields of different maturities is related and can be described by a reduced number of commom latent factors. Multifactor interest rate models of the finance literature, common factor models of the time series literature and others use this property. Each model has advantages and disadvantages, and it is an empirical matter to evaluate the performance of the approaches. This exercise compares 4 alternative models for the term structure using 3 different markets: the Brazilian domestic and sovereign market and the US market.
    Date: 2006–12
  9. By: Jim Albrecht; Aico van Vuuren; Susan Vroman (Department of Economics, Georgetown University)
    Abstract: Several recent papers use the quantile regression decomposition method of Machado and Mata (2005) to analyze the gender gap in log wages across the distribution. Since employment rates often differ substantially by gender, sample selection is potentially a serious issue for such studies. To address this issue, we extend the Machado-Mata technique to account for selection. In addition, we prove that this procedure yields consistent and asymptotically normal estimates of the quantiles of the counterfactual distribution that it is designed to simulate. We illustrate our approach by analyzing the gender log wage gap between men and women who work full time in the Netherlands. Because the fraction of women working full time in the Netherlands is quite low, this is a case in which sample selection is clearly important. We find a positive selection of women into full-time work and find that about two thirds of this selection is due to observables such as education and experience with the remainder due to unobservables. Our decompositions show that the majority of the gender gap across the log wage distribution is due to differences between men and women in the distributions of returns to labor market characteristics rather than to differences in the distributions of the characteristics themselves. Classification-JEL Codes: C24, J22, J31, J71
    Keywords: Gender, quantile regression, selection
    Date: 2007–07–06

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