nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒09‒17
eleven papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. A Class of Time-Varying Parameter Structural VARs for Inference under Exact or Set Identification By Bognanni, Mark
  2. Identification of structural multivariate GARCH models By HAFNER Christian,; HERWARTZ Helmut,; MAXAND Simone,
  3. Analytic Moments for GARCH Processes By Carol Alexander; Emese Lazar; Silvia Stanescu
  4. Bayesian Estimation of Fractionally Integrated Vector Autoregressions and an Application to Identified Technology Shocks By Ross Doppelt; Keith O'Hara
  5. A further look at Modified ML estimation of the panel AR(1) model with fixed effects and arbitrary initial conditions. By Kruiniger, Hugo
  6. Hamiltonian Sequential Monte Carlo with Application to Consumer Choice Behavior By Martin Burda; Remi Daviet
  7. Testing for bubbles in cryptocurrencies with time-varying volatility By HAFNER Christian,
  8. Emergence of Turbulent Epochs in Oil Prices By Josselin Garnier; Knut Solna
  9. What Makes Commodity Prices Move Together? An Answer From A Dynamic Factor Model By Esposti, Roberto
  10. Oil Price Fluctuations and Exchange Rate Dynamics in the MENA Region: Evidence from Non-Causality-in- Variance and Asymmetric Non-Causality Tests By Ridha Nouira; Thouraya Hadj Amor; Christophe Rault
  11. The Bayesian MS-GARCH model and Value-at-Risk in South African agricultural commodity price markets By Shiferaw, Y.

  1. By: Bognanni, Mark (Federal Reserve Bank of Cleveland)
    Abstract: This paper develops a new class of structural vector autoregressions (SVARs) with time-varying parameters, which I call a drifting SVAR (DSVAR). The DSVAR is the first structural time-varying parameter model to allow for internally consistent probabilistic inference under exact—or set—identification, nesting the widely used SVAR framework as a special case. I prove that the DSVAR implies a reduced-form representation, from which structural inference can proceed similarly to the widely used two-step approach for SVARs: beginning with estimation of a reduced form and then choosing among observationally equivalent candidate structural parameters via the imposition of identifying restrictions. In a special case, the implied reduced form is a tractable known model for which I provide the first algorithm for Bayesian estimation of all free parameters. I demonstrate the framework in the context of Baumeister and Peersman’s (2013b) work on time variation in the elasticity of oil demand.
    Keywords: structural vector autoregressions; time-varying parameters; Gibbs sampling; stochastic volatility; Bayesian inference;
    JEL: C11 C15 C32 C52 E3 E4 E5
    Date: 2018–09–11
  2. By: HAFNER Christian, (CORE and ISBA, UCLouvain); HERWARTZ Helmut, (University of Goettingen); MAXAND Simone, (University of Helsinki)
    Abstract: Multivariate GARCH models are widely used to model volatility and correlation dynamics of nancial time series. These models are typically silent about the transmission of implied orthogonalized shocks to vector returns. We propose a loss statistic to discriminate in a data-driven way between alternative structural assumptions about the transmission scheme. In its structural form, a four dimensional system comprising US and Latin American stock market returns points to a substantial volatility transmission from the US to the Latin American markets. The identified structural model improves the estimation of classical measures of portfolio risk, as well as corresponding variations.
    Keywords: structural innovations; identifying assumptions; MGARCH; portfolio risk; volatility transmission
    JEL: C32 G15
    Date: 2018–07–25
  3. By: Carol Alexander; Emese Lazar; Silvia Stanescu
    Abstract: Conditional returns distributions generated by a GARCH process, which are important for many applications in market risk assessment and portfolio optimization, are typically generated via simulation. This paper extends previous research on analytic moments of GARCH returns distributions in several ways: we consider a general GARCH model -- the GJR specification with a generic innovation distribution; we derive analytic expressions for the first four conditional moments of the forward return, of the forward variance, of the aggregated return and of the aggregated variance -- corresponding moments for some specific GARCH models largely used in practice are recovered as special cases; we derive the limits of these moments as the time horizon increases, establishing regularity conditions for the moments of aggregated returns to converge to normal moments; and we demonstrate empirically that some excellent approximate predictive distributions can be obtained from these analytic moments, thus precluding the need for time-consuming simulations.
    Date: 2018–08
  4. By: Ross Doppelt (Penn State); Keith O'Hara (New York University)
    Abstract: We introduce a new method for Bayesian estimation of fractionally integrated vector autoregressions (FIVARs). The FIVAR, which nests a standard VAR as a special case, allows each series to exhibit long memory, meaning that low frequencies can play a dominant role — a salient feature of many macroeconomic and financial time series. Although the parameter space is typically high-dimensional, our inferential procedure is computationally tractable and relatively easy to implement. We apply our methodology to the identification of technology shocks, an empirical problem in which business-cycle predictions depend on carefully accounting for low-frequency fluctuations.
    Date: 2018
  5. By: Kruiniger, Hugo
    Abstract: In this paper we consider two kinds of generalizations of Lancaster's (Review of Economic Studies, 2002) Modified ML estimator (MMLE) for the panel AR(1) model with fixed effects and arbitrary initial conditions and possibly covariates when the time dimension, T, is fixed. When the autoregressive parameter ρ=1, the limiting modified profile log-likelihood function for this model has a stationary point of inflection and ρ is first-order underidentified but second-order identified. We show that the generalized MMLEs exist w.p.a.1 and are uniquely defined w.p.1. and consistent for any value of ρ≥-1. When ρ=1, the rate of convergence of the MMLEs is N^{1/4}, where N is the cross-sectional dimension of the panel. We then develop an asymptotic theory for GMM estimators when one of the parameters is only second-order identified and use this to derive the limiting distributions of the MMLEs. They are generally asymmetric when ρ=1. One kind of generalized MMLE depends on a weight matrix W_{N} and we show that a suitable choice of W_{N} yields an asymptotically unbiased MMLE. We also show that Quasi LM tests that are based on the modified profile log-likelihood and use its expected rather than observed Hessian, with an additional modification for ρ=1, and confidence regions that are based on inverting these tests have correct asymptotic size in a uniform sense when |ρ|≤1. Finally, we investigate the finite sample properties of the MMLEs and the QLM test in a Monte Carlo study.
    Keywords: dynamic panel data, expected Hessian, fixed effects, Generalized Method of Moments (GMM), inflection point, Modified Maximum Likelihood, Quasi LM test, second-order identification, singular information matrix, weak moment conditions.
    JEL: C11 C12 C13 C23
    Date: 2018–06–16
  6. By: Martin Burda; Remi Daviet
    Abstract: Practical use of nonparametric Bayesian methods requires the availability of efficient algorithms for implementation for posterior inference. The inherently serial nature of Markov Chain Monte Carlo (MCMC) imposes limitations on its efficiency and scalability. In recent years there has been a surge of research activity devoted to developing alternative implementation methods that target parallel computing environments. Sequential Monte Carlo (SMC), also known as a particle filter, has been gaining popularity due to its desirable properties. SMC uses a genetic mutation-selection sampling approach with a set of particles representing the posterior distribution of a stochastic process. We propose to enhance the performance of SMC by utilizing Hamiltonian transition dynamics in the particle transition phase, in place of random walk used in the previous literature. We call the resulting procedure Hamiltonian Sequential Monte Carlo (HSMC). Hamiltonian transition dynamics has been shown to yield superior mixing and convergence properties relative to random walk transition dynamics in the context of MCMC procedures. The rationale behind HSMC is to translate such gains to the SMC environment. We apply both SMC and HSMC to a panel discrete choice model with a nonparametric distribution of unobserved individual heterogeneity. We contrast both methods in terms of convergence properties and show the favorable performance of HSMC.
    Keywords: Particle filtering, Bayesian nonparametrics, mixed panel logit, discrete choice
    JEL: C11 C14 C15 C23 C25
    Date: 2018–09–12
  7. By: HAFNER Christian, (CORE and ISBA, UCLouvain)
    Abstract: The recent evolution of cryptocurrencies has been characterized by bubble-like behavior and extreme volatility. While it is difficult to assess an intrinsic value to a specific cryptocurrency, one can employ recently proposed bubble tests that rely on recursive applications of classical unit root tests. This paper extends this approach to the case where volatility is time varying, assuming a deterministic long-run component that may take into account a decrease of unconditional volatility when the cryptocurrency matures with a higher market dissemination. Volatility also includes a stochastic short-run component to capture volatility clustering. The wild bootstrap is shown to correctly adjust the size properties of the bubble test, which retains good power properties. In an empirical application using eleven of the largest cryptocurrencies and the CRIX index, the general evidence in favor of bubbles is confirmed, but much less pronounced than under constant volatility.
    Keywords: cryptocurrencies; speculative bubbles; wild bootstrap; volatility
    JEL: C14 C43 Z11
    Date: 2018–07–25
  8. By: Josselin Garnier; Knut Solna
    Abstract: Oil price data have a complicated multi-scale structure that may vary with time. We use time-frequency analysis to identify the main features of these variations and, in particular, the regime shifts. The analysis is based on a wavelet-based decomposition and analysis of the associated scale spectrum. The joint estimation of the local Hurst exponent and volatility is the key to detect and identify regime shifting and switching of the oil price. The framework involves in particular modeling in terms of a process of `multi-fractional' type so that both the roughness and the volatility of the price process may vary with time. Special epochs then emerge as a result of these degrees of freedom, moreover, as a result of the special type of spectral estimator used. These special epochs are discussed and related to historical events. Some of them are not detected by standard analysis based on maximum likelihood estimation. The paper presents a novel algorithm for robust detection of such special epochs and multi-fractional behavior in financial or other types of data. In the financial context insight about such behavior of the asset price is important to evaluate financial contracts involving the asset.
    Date: 2018–08
  9. By: Esposti, Roberto
    Abstract: This paper aims to investigate the common movement of commodity prices. Two alternative hypotheses explaining the origin and nature of this common movement are put forward: the interdependence and the common latent factor hypotheses. This latter is assessed by specifying a DF/FAVAR model whose latent factors move around a zero-mean short-term level and a non-stationary long-run equilibrium level, respectively. Four heterogeneous and mostly unrelated commodities are considered (crude oil, copper, wheat, beef). Using IMF monthly prices over the 1980:1-2016:4 period, a Kalman Filter ML estimation is performed and results suggest that, beside the increasing price volatility, the last decade experienced a significant rise of the long-term equilibrium price. Some implications of this major result are also discussed
    Keywords: Marketing
    Date: 2017–08–29
  10. By: Ridha Nouira; Thouraya Hadj Amor; Christophe Rault
    Abstract: The aim of this paper is to investigate the exchange rate consequences of oil-price fluctuations and to test for the dynamics of oil price volatility by examining interactions between oil market and exchange rate in selected MENA countries (Egypt, Jordan, Morocco, Qatar, Saudi Arabia, Tunisia, and UAE). Using daily time series data covering the period from January 1, 2001 to December 29, 2017, we implement the test for asymmetric non-causality of Hatemi-J (2012), the asymmetric generalized impulse response functions of Hatemi-J (2014), and the test for noncausality-in-variance of Hafner and Herwartz (2006) to examine the presence of volatility spillover between oil prices and exchange rates return series. The econometric investigation reveals in particular that i) when prices are rising in Tunisia and Saudi Arabia, oil prices cause change in exchange rates, and ii) there is significant evidence of volatility spillovers from oil markets to exchange rate markets in the selected MENA countries. These findings have important implications both from the investor's and from the policy-maker's perspective.
    Keywords: oil price shocks, exchange rate volatility, asymmetric causality test, asymmetric generalized impulsion functions, causality-in-variance tests, MENA countries
    JEL: F31 G01 Q43
    Date: 2018
  11. By: Shiferaw, Y.
    Abstract: The core objective of this paper is to examine the relationship between the prices of agricultural commodities with the oil price, gas price, coal price and exchange rate (USD/Rand). In addition, the paper tries to fit an appropriate model that best describes the log return price volatility and estimate Value-at- Risk (VaR). The data used in this study are the daily returns of agricultural commodity prices from 02 January 2007 to 31st October 2016. The paper applies the three-state Markov-switching (MS) regression, the standard single-regime GARCH, and the Markov-switching GARCH (MS-GARCH) models. To choose the best fit model, the log-likelihood function, Akaike information criterion (AIC), Bayesian information criterion (BIC) and deviance information criterion (DIC) are employed under different distributions for innovations. The results indicate that the price of agricultural commodities was found to be significantly associated with the price of coal, the price of natural gas, price of oil and exchange rate. Moreover, for most of the agricultural commodities considered in this paper, the MS-GARCH models under the MCMC approach outperformed the standard single regime GARCH models in measuring VaR. In conclusion, this paper provided a practical guide for modelling agricultural commodity prices by MS regression and MSGARCH processes.
    Keywords: Agricultural and Food Policy, International Development
    Date: 2018–07

This nep-ets issue is ©2018 by Jaqueson K. Galimberti. 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.