nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒09‒10
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Trend-cycle-seasonal interactions: identification and estimation By Irma Hindrayanto; Jan P.A.M. Jacobs; Denise R. Osborn; Jing Tian
  2. Volatility spillovers and heavy tails: a large t-Vector AutoRegressive approach By Luca Barbaglia; Christophe Croux; Ines Wilms
  3. Time-Varying Extreme Value Dependence with Application to Leading European Stock Markets By Daniela Castro Camilo; Miguel de Carvalho; Jennifer Wadsworth
  4. Tensor Representation in High-Frequency Financial Data for Price Change Prediction By Dat Tran Thanh; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
  5. Confidence Sets for the Date of a Mean Shift at the End of a Sample By KUROZUMI, Eiji
  6. Asymptotic properties of QMLE for periodic asymmetric strong and semi-strong GARCH models. By Bibi, Abdelouahab; Ghezal, Ahmed
  7. Fully Bayesian Analysis of SVAR Models under Zero and Sign Restrictions By Kocięcki, Andrzej
  8. Co-integration and control: assessing the impact of events using time series data By Harvey, A.; Thiele, S.

  1. By: Irma Hindrayanto; Jan P.A.M. Jacobs; Denise R. Osborn; Jing Tian
    Abstract: Economists typically use seasonally adjusted data in which the assumption is imposed that seasonality is uncorrelated with trend and cycle. The importance of this assumption has been highlighted by the Great Recession. The paper examines an unobserved components model that permits non-zero correlations between seasonal and nonseasonal shocks. Identification conditions for estimation of the parameters are discussed from the perspectives of both analytical and simulation results. Applications to UK household consumption expenditures and US employment reject the zero correlation restrictions and also show that the correlation assumptions imposed have important implications about the evolution of the trend and cycle in the post-Great Recession period.
    Keywords: Trend-cycle-seasonal decomposition, unobserved components, seasonal adjustment, employment, Great Recession
    JEL: C22 E24 E32 E37 F01
    Date: 2017–08
  2. By: Luca Barbaglia; Christophe Croux; Ines Wilms
    Abstract: Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could affect the volatility of another asset tomorrow. These lagged effects among volatilities - which we call volatility spillovers - are studied using the Vector AutoRegressive (VAR) model. We account for the possible fat-tailed distribution of the VAR model errors using a VAR model with errors following a multivariate Student t-distribution with unknown degrees of freedom. Moreover, we study volatility spillovers among a large number of assets. To this end, we use penalized estimation of the VAR model with t-distributed errors. We study volatility spillovers among energy, biofuel and agricultural commodities and reveal bidirectional volatility spillovers between energy and biofuel, and between energy and agricultural commodities.
    Keywords: Commodities, Forecasting, Multivariate t-distribution, Vector AutoRegressive model, Volatility spillover
    Date: 2017–08
  3. By: Daniela Castro Camilo; Miguel de Carvalho; Jennifer Wadsworth
    Abstract: Extremal dependence between international stock markets is of particular interest in today's global financial landscape. However, previous studies have shown this dependence is not necessarily stationary over time. We concern ourselves with modeling extreme value dependence when that dependence is changing over time, or other suitable covariate. Working within a framework of asymptotic dependence, we introduce a regression model for the angular density of a bivariate extreme value distribution that allows us to assess how extremal dependence evolves over a covariate. We apply the proposed model to assess the dynamics governing extremal dependence of some leading European stock markets over the last three decades, and find evidence of an increase in extremal dependence over recent years.
    Date: 2017–09
  4. By: Dat Tran Thanh; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
    Abstract: Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.
    Date: 2017–09
  5. By: KUROZUMI, Eiji
    Abstract: This paper proposes constructing a confidence set for the date of a structural change at the end of a sample in a mean shift model. While the break fraction, the ratio of the number of observations before the break to the sample size, is typically assumed to take a value in the (0, 1) open interval, we consider the case where a permissible break date is included in a fixed number of observations at the end of the sample and thus the break fraction approaches one as the sample size goes to infinity. We propose inverting the test for the break date to construct a confidence set, while critical values are obtained by using the subsampling method. By using Monte Carlo simulations, we show that the confidence set proposed in this paper can control the coverage rate in finite samples well, while the average length of the confidence set is comparable to existing methods based on asymptotic theory with a fixed break fraction in the (0, 1) interval.
    Keywords: structural change, coverage rate, subsampling method
    JEL: C12 C15 C22
    Date: 2017–09
  6. By: Bibi, Abdelouahab; Ghezal, Ahmed
    Abstract: In this paper, we propose a natural extension of time-invariant coefficients threshold GARCH (TGARCH) processes to periodically time-varying coefficients (PTGARCH) one. So some theoretical probabilistic properties of such models are discussed, in particular, we establish firstly necessary and sufficient conditions which ensure the strict stationarity and ergodicity (in periodic sense) solution of PTGARCH. Secondary, we extend the standard results for the limit theory of the popular quasi-maximum likelihood estimator (QMLE) for estimating the unknown parameters of the model. More precisely, the strong consistency and the asymptotic normality of QMLE are studied in cases when the innovation process is an i.i.d (Strong case) and/or is not (Semi-strong case). The finite-sample properties of QMLE are illustrated by a Monte Carlo study. Our proposed model is applied to model the exchange rates of the Algerian Dinar against the U.S-dollar and the single European currency (Euro).
    Keywords: Periodic asymmetric GARCH model, Stationarity, Strong consistency, Asymptotic normality.
    JEL: C13
    Date: 2017–09–04
  7. By: Kocięcki, Andrzej
    Abstract: The paper proposes the methodologically sound method to deal with set identified Structural VAR (SVAR) models under zero and sign restrictions. What distinguishes our method from that proposed by Arias, Rubio-Ramírez and Waggoner (2016) is that we isolated many special cases for which we arrive at more efficient algorithms to draw from the posterior. We illustrate our approach with the help of two serious empirical examples. First of all we challenge the output puzzle found by Uhlig (2005). Second, we check the robustness of the results given by Beaudry et al. (2014) concerning impact of optimism shocks on economy.
    Keywords: Set identified Structural VAR, Sign restrictions, Monetary policy, Bayesian
    JEL: C11 C18 C3 E5 E52
    Date: 2017–08–23
  8. By: Harvey, A.; Thiele, S.
    Abstract: Control groups can sometimes provide counterfactual evidence for assessing the impact of an event or policy change on a target variable. Fitting a time series model to target and control series offers potential gains over a direct comparison between the target and a weighted average of the controls. More importantly it highlights the assumptions underlying methods such as difference in differences and synthetic control and in doing so suggests ways in which these assumptions may be tested. Our focus is on time series models that are both simple and transparent. Potential gains from fitting such models are analysed and their relative performance is investigated using examples taken from the literature, including the effect of the California smoking law of 1988 and German re-unification. At the same time, some of the draw-backs to current methodology become apparent. It is argued that a time series strategy for the selection of a valid set of controls is to be preferred to one based on data-driven regression methods.
    Keywords: common trends, difference in differences, intervention analysis, stationarity tests, synthetic control, unobserved components
    Date: 2017–08–30

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.