nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒11‒05
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. A Bias-Corrected Method of Moments Approach to Estimation of Dynamic Short-T Panels By Alexander Chudik; M. Hashem Pesaran
  2. Testing the CVAR in the fractional CVAR model By Søren Johansen; Morten Ørregaard Nielsen
  3. When does information on forecast variance improve the performance of a combined forecast? By Conrad, Christian
  4. Long Memory and Data Frequency in Financial Markets By Guglielmo Maria Caporale; Luis A. Gil-Alana; Alex Plastun

  1. By: Alexander Chudik; M. Hashem Pesaran
    Abstract: This paper contributes to the GMM literature by introducing the idea of self-instrumenting target variables instead of searching for instruments that are uncorrelated with the errors, in cases where the correlation between the target variables and the errors can be derived. The advantage of the proposed approach lies in the fact that, by construction, the instruments have maximum correlation with the target variables and the problem of weak instrument is thus avoided. The proposed approach can be applied to estimation of a variety of models such as spatial and dynamic panel data models. In this paper we focus on the latter and consider both univariate and multivariate panel data models with short time dimension. Simple Bias-corrected Methods of Moments (BMM) estimators are proposed and shown to be consistent and asymptotically normal, under very general conditions on the initialization of the processes, individual-speci.c e¤ects, and error variances allowing for heteroscedasticity over time as well as cross-sectionally. Monte Carlo evidence document BMM.s good small sample performance across di¤erent experimental designs and sample sizes, including in the case of experiments where the system GMM estimators are inconsistent. We also .nd that the proposed estimator does not su¤er size distortions and has satisfactory power performance as compared to other estimators.
    Keywords: short-t dynamic panels, GMM, weak instrument problem, quadratic moment conditions, panel VARs, Monte Carlo evidence
    JEL: C12 C13 C23
    Date: 2017
  2. By: Søren Johansen (University of Copenhagen and CREATES); Morten Ørregaard Nielsen (Queen's University and CREATES)
    Abstract: We consider the fractional cointegrated vector autoregressive (CVAR) model of Johansen and Nielsen (2012a) and show that the test statistic for the ususal CVAR model is asymptotically chi-squared distributed. Because the usual CVAR model lies on the boundary of the parameter space for the fractional CVAR in Johansen and Nielsen (2012a), the analysis requires the study of the fractional CVAR model on a slightly larger parameter space so that the CVAR model lies in the interior. This in turn implies some further analysis of the asymptotic properties of the fractional CVAR model.
    Keywords: cointegration, fractional integration, likelihood inference, vector autoregressive model
    JEL: C32
    Date: 2017–10
  3. By: Conrad, Christian
    Abstract: We show that the consensus forecast can be biased if some forecasters minimize an asymmetric loss function and the DGP features conditional heteroscedasticity. The time-varying bias depends on the variance of the process. As a consequence, the information from the ex-ante variation of forecasts can be used to improve the predictive accuracy of the combined forecast. Forecast survey data from the Euro area and the U.S. confirm the implications of the theoretical model.
    JEL: C51 C53
    Date: 2017
  4. By: Guglielmo Maria Caporale; Luis A. Gil-Alana; Alex Plastun
    Abstract: This paper investigates persistence in financial time series at three different frequencies (daily, weekly and monthly). The analysis is carried out for various financial markets (stock markets, FOREX, commodity markets) over the period from 2000 to 2016 using two different long memory approaches (R/S analysis and fractional integration) for robustness purposes. The results indicate that persistence is higher at lower frequencies, for both returns and their volatility. This is true of the stock markets (both developed and emerging) and partially of the FOREX and commodity markets examined. Such evidence against the random walk behavior implies predictability and is inconsistent with the Efficient Market Hypothesis (EMH), since abnormal profits can be made using specific option trading strategies (butterfly, straddle, strangle, iron condor, etc.).
    Keywords: persistence, long memory, R/S analysis, fractional integration
    JEL: C22 G12
    Date: 2017

This nep-ets issue is ©2017 by Yong Yin. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.