nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒04‒23
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Testing for Extreme Volatility Transmission with Realized Volatility Measures By Christophe Boucher; Gilles de Truchis; Elena Dumitrescu; Sessi Tokpavi
  2. High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models - 2nd ed By F. Lilla
  3. The impact of the initial condition on covariate augmented unit root tests By Adrian Nieto
  4. Wild Bootstrap Seasonal Unit Root Tests for Time Series with Periodic Non-Stationary Volatility By Skrobotov Anton; Cavaliere Giuseppe; Taylor Robert
  5. A near optimal test for structural breaks when forecasting under square error loss By Tom Boot; Andreas Pick
  6. A Simple Test on Structural Change in Long-Memory Time Series By Wenger, Kai; Leschinski, Christian; Sibbertsen, Philipp
  7. Confidence Sets for the Break Date in Cointegrating Regressions By Skrobotov Anton; Lanshina T.

  1. By: Christophe Boucher; Gilles de Truchis; Elena Dumitrescu; Sessi Tokpavi
    Abstract: This paper proposes a simple and parsimonious semi-parametric testing procedure for variance transmission. Our test focuses on conditional extreme values of the unobserved process of integrated variance since they are of utmost concern for policy makers due to their sudden and destabilizing effects. The test statistic is based on realized measures of variance and has a convenient asymptotic x2 distribution under the null hypothesis of no Granger causality, which is free of estimation risk. Extensive Monte Carlo simulations show that the test has good small sample size and power properties. An extension to the case of spillovers in quadratic variation is also developed. An empirical application on extreme variance transmission from US to EU equity markets is further proposed. We find that the test performs very well in identifying periods of significant causality in extreme variance, that are subsequently found to be correlated with changes in US monetary policy.
    Keywords: Extreme volatility transmission, Granger causality, Integrated variance, Realized variance, Semi-parametric test, Financial contagion.
    JEL: C12 C32 C58
    Date: 2017
  2. By: F. Lilla
    Abstract: Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem, such as the discreteness of the data, the properties of the trading mechanism and the existence of bid-ask spread. Moreover, these data are not always available and, even if they are, the asset’s liquidity may be not sufficient to allow for frequent transactions. This paper considers different variants of these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumps in prices and leverage effects for volatility. Findings suggest that daily-data models are preferred to HF-data models at 5% and 1% VaR level. Specifically, independently from the data frequency, allowing for jumps in price (or providing fat-tails) and leverage effects translates in more accurate VaR measure.
    JEL: C58 C53 C22 C01 C13
    Date: 2017–04
  3. By: Adrian Nieto
    Abstract: This article assesses the impact of digital television on cognitive development and educational inequality, and studies whether it depends on the educational value of the alternative activities that digital television crowds out. I use heterogeneity in the timing of the television digital switchover in the di erent postcode areas in England to test for the previous. I nd that the digital switchover increased average grades of children by 0.028 standard deviations, contributing to the human capital formation, and that this effect was larger in schools at the bottom of the scores distribution, reducing educational inequality. I also find that the digital switchover decreased the probability of children taking part in harmful activities, and their frequency.
  4. By: Skrobotov Anton (RANEPA); Cavaliere Giuseppe (Department of Statistical Sciences, University of Bologna); Taylor Robert (Essex Business School, University of Essex)
    Abstract: This paper investigates the behaviour of the well-known HEGY (Hylleberg, Engle, Granger and Yoo, 1990, Journal of Econometrics, vol.44, pp.215-238) regression-based seasonal unit root tests in cases where the driving shocks are allowed to display periodic non-stationary volatility and conditional heteroskedasticity. Our set up allows for periodic heteroskedasticity, non-stationary volatility and (seasonal) GARCH as special cases. We show that the limiting null distributions of the HEGY tests depend, in general, on nuisance parameters which derive from the underlying volatility process. Monte Carlo simulations show that the standard HEGY tests can be substantially over-sized in the presence of such effects. As a consequence, we propose bootstrap implementations of the HEGY tests, based around a seasonal block wild bootstrap principle. This is shown to deliver asymptotically pivotal inference under our general conditions on the shocks. Simulation evidence is presented which suggests that our proposed bootstrap tests perform well in practice, largely correcting the size problems seen with the standard HEGY tests even under extreme patterns of heteroskedasticity, yet not losing finite sample relative to the standard HEGY tests.
    Keywords: seasonal unit roots, (periodic) non-stationary volatility, conditional heteroskedasticity, wild bootstrap
    JEL: C12 C22
    Date: 2016
  5. By: Tom Boot (University of Groningen); Andreas Pick (Erasmus University Rotterdam, De Nederlandsche Bank and CESifo Institute)
    Abstract: We propose a near optimal test for structural breaks of unknown timing when the purpose of the analysis is to obtain accurate forecasts under square error loss. A bias-variance trade-off exists under square forecast error loss, which implies that small structural breaks should be ignored. We study critical break sizes, assess the relevance of the break location, and provide a test to determine whether modeling a break will improve forecast accuracy. Asymptotic critical values and near optimality properties are established allowing for a break under the null, where the critical break size varies with the break location. The results are extended to a class of shrinkage forecasts with our test statistic as shrinkage constant. Empirical results on a large number of macroeconomic time series show that structural breaks that are relevant for forecasting occur much less frequently than indicated by existing tests.
    Keywords: structural break test, forecasting, squared error loss
    JEL: C12 C53
    Date: 2017–04–18
  6. By: Wenger, Kai; Leschinski, Christian; Sibbertsen, Philipp
    Abstract: We propose a simple test on structural change in long-range dependent time series. It is based on the idea that the test statistic of the standard CUSUM test retains its asymptotic distribution if it is applied to fractionally differenced data. We prove that our approach is asymptotically valid if the memory is estimated consistently under the null hypothesis. Therefore, the well-known CUSUM test can be used on the differenced data without any further modification. In a simulation study, we compare our test with a CUSUM test on structural change that is specifically constructed for long-memory time series and show that our approach performs well.
    Keywords: Fractional Integration; Structural Breaks; Long Memory
    JEL: C12 C22
    Date: 2017–04
  7. By: Skrobotov Anton (RANEPA); Lanshina T. (Department of Economics, Hitotsubashi University, Japan)
    Abstract: In this paper, we propose constructing confidence sets for a break date in cointegrating regressions by inverting a test for the break location, which is obtained by maximizing the weighted average of power. It is found that the limiting distribution of the test depends on the number of I(1) regressors whose coefficients sustain structural change and the number of I(1) regressors whose coefficients are fixed throughout the sample. By Monte Carlo simulations, we then show that compared with a confidence interval developed by using the existing method based on the limiting distribution of the break point estimator under the assumption of the shrinking shift, the confidence set proposed in the present paper has a more accurate coverage rate, while the length of the confidence set is comparable. By using the method developed in this paper, we then investigate the cointegrating regressions of Russian macroeconomic variables with oil prices with a break.
    Keywords: Confidence interval, structural change, cointegration, Russian economy, oil price
    JEL: C12 C21
    Date: 2016

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