nep-ets New Economics Papers
on Econometric Time Series
Issue of 2005‒12‒20
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. GMM with many weak moment conditions By Whitney Newey; Frank Windmeijer
  2. On the Estimation and Inference of a Panel Cointegration Model with Cross-Sectional Dependence By Jushan Bai; Chihwa Kao
  3. The Myth of Long-Horizon Predictability By Jacob Boudoukh; Matthew Richardson; Robert Whitelaw
  4. Reduced-Rank Identification of Structural Shocks in VARs By Yuriy Gorodnichenko
  5. Testing the Markov property with ultra-high frequency financial data By Matos, Joao Amaro de; Fernandes, Marcelo

  1. By: Whitney Newey (Institute for Fiscal Studies and Massachussets Institute of Technology); Frank Windmeijer (Institute for Fiscal Studies and University of Bristol)
    Abstract: Using many moment conditions can improve efficiency but makes the usual GMM inferences inaccurate. Two step GMM is biased. Generalized empirical likelihood (GEL) has smaller bias but the usual standard errors are too small. In this paper we use alternative asymptotics, based on many weak moment conditions, that addresses this problem. This asymptotics leads to improved approximations in overidentified models where the variance of the derivative of the moment conditions is large relative to the squared expected value of the moment conditions and identification is not too weak. We obtain an asymptotic variance for GEL that is larger than the usual one and give a "sandwich" estimator of it. In Monte Carlo examples we find that this variance estimator leads to a better Gaussian approximation to t-ratios in a range of cases. We also show that Kleibergen (2005) K statistic is valid under these asymptotics. We also compare these results with a jackknife GMM estimator, finding that GEL is asymptotically more efficient under many weak moments.
    Keywords: GMM, Continuous Updating, Many Moments, Variance Adjustment
    JEL: C12 C13 C23
    Date: 2005–12
  2. By: Jushan Bai (Department of Economics, New York University, New York, NY 10003, and Department of Economics, Tsinghua University, Beijing 10084, China); Chihwa Kao (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020)
    Abstract: Most of the existing literature on panel data cointegration assumes cross-sectional independence, an assumption that is difficult to satisfy. This paper studies panel cointegration under cross-sectional dependence, which is characterized by a factor structure. We derive the limiting distribution of a fully modified estimator for the panel cointegrating coefficients. We also propose a continuous-updated fully modified (CUP-FM) estimator). Monte Carlo results show that the CUP-FM estimator has better small sample properties than the two-step FM (2S-FM) and OLS estimators.
    Keywords: panel data cointegration, cross-sectional independence, cross-sectional dependence, continuous updated fully modified (CUP-FM) estimator, Monte Carlo results, two-step FM (2S-FM) estimator, OLS estimator
    JEL: C13 C15 C23
    Date: 2005–12
  3. By: Jacob Boudoukh; Matthew Richardson; Robert Whitelaw
    Abstract: The prevailing view in finance is that the evidence for long-horizon stock return predictability is significantly stronger than that for short horizons. We show that for persistent regressors, a characteristic of most of the predictive variables used in the literature, the estimators are almost perfectly correlated across horizons under the null hypothesis of no predictability. For example, for the persistence levels of dividend yields, the analytical correlation is 99% between the 1- and 2-year horizon estimators and 94% between the 1- and 5-year horizons, due to the combined effects of overlapping returns and the persistence of the predictive variable. Common sampling error across equations leads to ordinary least squares coefficient estimates and R2s that are roughly proportional to the horizon under the null hypothesis. This is the precise pattern found in the data. The asymptotic theory is corroborated, and the analysis extended by extensive simulation evidence. We perform joint tests across horizons for a variety of explanatory variables, and provide an alternative view of the existing evidence.
    JEL: G12 G10 C32
    Date: 2005–12
  4. By: Yuriy Gorodnichenko (University of Michigan)
    Abstract: This paper integrates imposing a factor structure on residuals in vector autoregressions (VARs) into structural VAR analysis. Identification, estimation and testing procedures are discussed. The paper applies this approach to the well-known problem of studying the effects of monetary policy in open economy VAR models. The use of factor structure in identifying structural shocks is shown to resolve three long-standing puzzles in VAR literature. First, the price level does not increase in response to a monetary tightening. Second, the exchange rate appreciates on impact and then gradually depreciates. Hence, no price level and exchange rate puzzles are found. Third, monetary policy shocks are much less volatile than suggested by standard VAR identification schemes. In addition, the paper suggests that the apparent weak contemporaneous cross-variable responses and strong own responses in structural VARs can be an artifact of identifying assumptions and vanish after imposing a factor structure on the shocks.
    Keywords: Vector autoregressions, identification, factor structure, monetary policy
    JEL: E52 C32
    Date: 2005–12–15
  5. By: Matos, Joao Amaro de; Fernandes, Marcelo
    Abstract: This paper develops a framework to nonparametrically test whether discretevalued irregularly-spaced financial transactions data follow a Markov process. For that purpose, we consider a specific optional sampling in which a continuous-time Markov process is observed only when it crosses some discrete level. This framework is convenient for it accommodates not only the irregular spacing of transactions data, but also price discreteness. Under such an observation rule, the current price duration is independent of previous price durations given the current price realization. A simple nonparametric test then follows by examining whether this conditional independence property holds. Finally, we investigate whether or not bid-ask spreads follow Markov processes using transactions data from the New York Stock Exchange. The motivation lies on the fact that asymmetric information models of market microstructures predict that the Markov property does not hold for the bid-ask spread. The results are mixed in the sense that the Markov assumption is rejected for three out of the five stocks we have analyzed.
    Keywords: Bid-ask spread, nonparametric testing, price durations, Markov property, ultra-high frequency data
    JEL: C14 C52 G10 G19
    Date: 2004

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