nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒10‒08
eleven papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. An Automated Approach Towards Sparse Single-Equation Cointegration Modelling By Stephan Smeekes; Etienne Wijler
  2. Seasonal adjustment of time series and calendar influence on economic activity By Ante Čobanov
  3. Leverage, asymmetry and heavy tails in the high-dimensional factor stochastic volatility model By Mengheng Li; Marcel Scharth
  4. A structural approach to identify financial transmission in distinguished scenarios of crises By Herwartz, Helmut; Roestel, Jan
  5. Investigating Structural break-GARCH-based Unit root test in US exchange rates By Yaya, OlaOluwa S; Akinlana, Damola M; Ogbonna, Ahamuefula E
  6. Another Look at the Stationarity of Inflation rates in OECD countries: Application of Structural break-GARCH-based unit root tests By Yaya, OlaOluwa S
  7. A sequential panel selection approach to cointegration analysis: An application to Wagner's law for South African provincial data By Vayi, Xolisa; Phiri, Andrew
  8. Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean By Legrand, Romain
  9. Disentangling permanent and transitory monetary shocks with a non-linear Taylor rule By J. A. Lafuente; R. Pérez; J. Ruiz
  10. The evolving impact of global, region-specific and country-specific uncertainty By Haroon Mumtaz; Alberto Musso
  11. Connecting VIX and Stock Index ETF with VAR and Diagonal BEKK By Chia-Lin Chang; Tai-Lin Hsieh; Michael McAleer

  1. By: Stephan Smeekes; Etienne Wijler
    Abstract: In this paper we propose the Single-equation Penalized Error Correction Selector (SPECS) as an automated estimation procedure for dynamic single-equation models with a large number of potentially (co)integrated variables. By extending the classical single-equation error correction model, SPECS enables the researcher to model large cointegrated datasets without necessitating any form of pre-testing for the order of integration or cointegrating rank. We show that SPECS is able to consistently estimate an appropriate linear combination of the cointegrating vectors that may occur in the underlying DGP, while simultaneously enabling the correct recovery of sparsity patterns in the corresponding parameter space. A simulation study shows strong selective capabilities, as well as superior predictive performance in the context of nowcasting compared to high-dimensional models that ignore cointegration. An empirical application to nowcasting Dutch unemployment rates using Google Trends confirms the strong practical performance of our procedure.
    Date: 2018–09
  2. By: Ante Čobanov (The Croatian National Bank, Croatia)
    Abstract: This paper describes the process of the seasonal adjustment of data time series for Croatia, a process that involves cooperation between the Croatian National Bank and the Croatian Bureau of Statistics. The paper shows individual steps of the process, explains calendar effects, describes the revision policy for seasonally adjusted data and presents the seasonal adjustment of selected main monthly indicators of economic activity in the Republic of Croatia: industrial production, the volume of construction works and retail trade turnover. Working-day effect was identified for all indicators; leap year effect was identified for all but the volume of construction works, i.e. the Easter effect for retail trade turnover only. The described assumptions and limitations of the models applied are useful to end-users for the purpose of a better understanding of the published data and their use in further analysis.
    Keywords: seasonal adjustment, working-day effect, leap year effect, Easter effect, calendar effects, JDemetra+
    JEL: C87 C82
    Date: 2018–03
  3. By: Mengheng Li (Economics Discipline Group, University of Technology, Sydney); Marcel Scharth (School of Economics,University of Sydney, Sydney)
    Abstract: We develop a flexible modeling and estimation framework for a high-dimensional factor stochastic volatility (SV) model. Our specification allows for leverage effects, asymmetry and heavy tails across all systematic and idiosyncratic components of the model. This framework accounts for well-documented features of univariate financial time series, while introducing a flexible dependence structure that incorporates tail dependence and asymmetries such as stronger correlations following downturns. We develop an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior simulation based on the particle Gibbs, ancestor sampling, and particle efficient importance sampling methods. We build computationally efficient model selection into our estimation framework to obtain parsimonious specifications in practice. We validate the performance of our proposed estimation method via extensive simulation studies for univariate and multivariate simulated datasets. An empirical study shows that the model outperforms other multivariate models in terms of value-at-risk evaluation and portfolio selection performance for a sample of US and Australian stocks.
    Keywords: Generalised hyperbolic skew Student’s t-distribution; Metropolis-Hastings algorithm; Importance sampling; Particle filter; Particle Gibbs; State space model; Time-varying covariance matrix; Factor model
    JEL: C11 C32 C53 G32
    Date: 2018–08–24
  4. By: Herwartz, Helmut; Roestel, Jan
    Abstract: This paper investigates the propagation of instability through key asset markets of the US financial system - equity, real estate, banking and treasury - between 1/3/2000 and 12/26/2014. For this purpose, we develop an identification method to uncover characteristic financial market interrelations under distinguished scenarios of crises. It refers to the logic behind narrative sign restrictions and allows to extract time varying contemporaneous effects and volatility transmission from conventional reduced form volatility models with dynamic correlations. We find the market value of banking institutions to be highly sensitive to news originating in other markets, with those originating in the real estate market being most important. Under stress, in turn, the banking sector tends to dominate financial market (co)variation, where it exhibits a marked feedback relation with both the real estate and the equity market.
    Keywords: Identification,Contemporaneous effects,Causality,Impulse response analysis,GARCH,Volatility transmission,Financial crises
    JEL: C39 C32 E44 G01
    Date: 2018
  5. By: Yaya, OlaOluwa S; Akinlana, Damola M; Ogbonna, Ahamuefula E
    Abstract: This paper applied a structural break-GARCH-based unit root test in studying the US exchange rates for twenty-two different currencies across America, Europe, Asia-Pacific and Southern Africa. The study employed three different data frequencies – daily, weekly and monthly with a view to understand the dynamics of a high frequency series that is characterized by alternating trend patterns and plausible presence of structural breaks. The chosen sample interval included periods of financial crisis or peculiar events. The exchange rates were found to exhibit ARCH effects at higher lags, thus informing the adaptation of the more parsimonious GARCH process in the residuals in contrast to the white noise disturbance assumption. The non-trended and trended structural break-GARCH-based unit root tests performances were adjudged with other existing tests. With significant break dates, between 2 and 5, the presence or otherwise of a unit root in foreign exchange rate series would be better captured when the inherent heteroscedasticity, trend and structural breaks in foreign exchange rate series are put into consideration
    Keywords: Exchange rate, Heteroscedasticity, Unit root, Structural break
    JEL: C22
    Date: 2017
  6. By: Yaya, OlaOluwa S
    Abstract: This paper re-investigates unit root hypotheses in inflation rates for 21 OECD countries using the newly proposed GARCH-based unit root tests with structural break and trend specifications. The results showed that classical tests over-accept unit roots in inflation rates, whereas these tests are not robust to heteroscedasticity. As observed from the pre-tests, those tests with structural break reject more null hypotheses of unit roots of most inflation series. By applying variants of GARCH-based unit root tests which include those with structural breaks and time trend regression specifications, we found that unit root tests without time trend gave most rejections of the conventional unit root. Thus, care should be taken while applying variants of the new unit root tests on weak trending time series as indicated in this work.
    Keywords: Heteroscedasticity; Inflation rate; Structural breaks; Unit root; OECD countries
    JEL: C2
    Date: 2017
  7. By: Vayi, Xolisa; Phiri, Andrew
    Abstract: This study extends the recently introduced sequential panel selection method (SPSM) to a cointegration framework which is particularly used to investigate Wagner’s law for 9 South African provinces between 2001 and 2016. We note that when applying single country/region estimates we fail to find evidence of cointegration whereas within panel regressions, cointegration effects are present for the entire dataset. In further applying the SPSM we observed significant Wagner’s effects for panels inclusive of Gauteng, Eastern Cape and Kwazulu-Natal provinces and when these provinces are excluded from the panels, cointegration effects are unobserved.
    Keywords: Sequential Panel selection method (SPSM); cointegration; Wagner’s law; Provincial analysis; South Africa.
    JEL: C22 C23 C52 H70
    Date: 2018–09–13
  8. By: Legrand, Romain
    Abstract: Time-varying VAR models have become increasingly popular and are now widely used for policy analysis and forecast purposes. They constitute fundamental tools for the anticipation and analysis of economic crises, which represent rapid shifts in dynamic responses and shock volatility. Yet, despite their flexibility, time-varying VARs remain subject to a number of limitations. On the theoretical side, the conventional random walk assumption used for the dynamic parameters appears excessively restrictive. It also conceals the potential heterogeneities existing between the dynamic processes of different variables. On the application side, the standard two-pass procedure building on the Kalman filter proves excessively complicated and suffers from low efficiency. Based on these considerations, this paper contributes to the literature in four directions: i) it introduces a general time-varying VAR model which relaxes the standard random walk assumption and defines the dynamic parameters as general auto-regressive processes with variable- specific mean values and autoregressive coefficients. ii) it develops an estimation procedure for the model which is simple, transparent and efficient. The procedure requires no sophisticated Kalman filtering methods and reduces to a standard Gibbs sampling algorithm. iii) as an extension, it develops efficient procedures to estimate endogenously the mean values and autoregressive coefficients associated with each variable-specific autoregressive process. iv) through a case study of the Great Recession for four major economies (Canada, the Euro Area, Japan and the United States), it establishes that forecast accuracy can be significantly improved by using the proposed general time-varying model and its extensions in place of the traditional random walk specification.
    Keywords: Time-varyings coefficients; Stochastic volatility; Bayesian methods; Markov Chain Monte Carlo methods; Forecasting; Great Recession
    JEL: C11 C15 C22 E32 F47
    Date: 2018–09–10
  9. By: J. A. Lafuente (Universitat Jaume I .); R. Pérez (Universidad Complutense and ICAE.); J. Ruiz (Universidad Complutense and ICAE.)
    Abstract: This paper provides an estimation method to decompose monetary policy innovations into persistent and transitory components using the non-linear Taylor rule proposed in Andolfatto et al. [Journal of Monetary Economics 55 (2008) 406–422]. In order to use the Kalman filter as the optimal signal extraction technique we use a convenient reformulation for the state equation by allowing expectations play in significant role in explaining the future time evolution of monetary shocks. This alternative formulation allows us to perform the maximum likelihood estimation for all the parameters involved in the monetary policy. Empirical evidence on US monetary policy making is provided for the period 1980-2011. We compare our empirical estimates with those obtained based on the particle filter. While both procedures lead to similar quantitative and qualitative findings, our approach has much less computational cost.
    Keywords: Monetary shocks; Kalman filter; Particle filter; Taylor rule.
    JEL: C22 F31
    Date: 2018–09
  10. By: Haroon Mumtaz (Queen Mary University of London); Alberto Musso (European Central Bank)
    Abstract: We develop a dynamic factor model with time-varying parameters and stochastic volatility, estimate it with several variables for a large number of countries and decompose the variance of each variable in terms of contributions from uncertainty common to all countries (global uncertainty), region-specific uncertainty and country-specific uncertainty. Among other findings, the estimates suggest that global uncertainty plays a primary role in explaining the volatility of inflation, interest rates and stock prices, although to a varying extent over time, while all uncertainty components are found to play a non-negligible role for real economic activity, credit and money for most countries.
    Keywords: Dynamic Factor Model, Time-Varying Parameters, Stochastic Volatility, Uncertainty Shocks, Global Uncertainty
    JEL: C15 C32 E32
    Date: 2018–09–01
  11. By: Chia-Lin Chang (Department of Applied Economics Department of Finance National Chung Hsing University, Taiwan.); Tai-Lin Hsieh (Department of Applied Economics, National Chung Hsing University Taiwan.); Michael McAleer (Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.)
    Abstract: As stock market indexes are not tradeable, the importance and trading volume of Exchange Traded Funds (ETFs) cannot be understated. ETFs track and attempt to replicate the performance of a specific index. Numerous studies have demonstrated a strong relationship between the S&P500 Composite Index and the Volatility Index (VIX), but few empirical studies have focused on the relationship between VIX and ETF returns. The purpose of the paper is to investigate whether VIX returns affect ETF returns by using vector autoregressive (VAR) models to determine whether daily VIX returns with different moving average processes affect ETF returns. The ARCH-LM test shows conditional heteroskedasticity in the estimation of ETF returns, so that the diagonal BEKK model is used to accommodate multivariate conditional heteroskedasticity in the VAR estimates of ETF returns. Daily data on ETF returns that follow different stock indexes in the USA and Europe are used in the empirical analysis, which is presented for the full data set, as well as for the three sub-periods Before, During, and After the Global Financial Crisis. The estimates show that daily VIX returns have: (1) significant negative effects on European ETF returns in the short run; (2) stronger significant effects on single market ETF returns than on European ETF returns; and (3) lower impacts on the European ETF returns than on S&P500 returns. For the European Markets, the estimates of the mean equations tend to differ between the whole sample period and the sub-periods, but the estimates of the matrices A and B in the Diagonal BEKK model are quite similar for the whole sample period and at least two of the three sub-periods. For the US Markets, the estimates of the mean equations also tend to differ between the whole sample period and the sub-periods, but the estimates of the matrices A and B in the Diagonal BEKK model are very similar for the whole sample period and the three sub-periods.
    Keywords: Stock market indexes; Exchange Traded Funds; Volatility Index (VIX); Global Financial Crisis; Vector Autoregressions; Moving Average processes; Conditional Heteroskedasticity; Diagonal BEKK.
    JEL: C32 C58 G12 G15
    Date: 2018–09

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