nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒05‒18
ten papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Nonlinear common trends for the global crude oil market: Markov-switching score-driven models of the multivariate t-distribution By Licht, Adrian; Escribano Saez, Alvaro; Blazsek, Szabolcs Istvan
  2. Dynamic stochastic general equilibrium inference using a score-driven approach By Licht, Adrian; Escribano Saez, Alvaro; Blazsek, Szabolcs Istvan
  3. Fractional trends in unobserved components models By Tobias Hartl; Rolf Tschernig; Enzo Weber
  4. Endogenous Time Variation in Vector Autoregressions By Danilo Leiva-Leon; Luis Uzeda
  5. Forecasting a Nonstationary Time Series with a Mixture of Stationary and Nonstationary Factors as Predictors By Sium Bodha Hannadige; Jiti Gao; Mervyn J. Silvapulle; Param Silvapulle
  6. Nearly Efficient Likelihood Ratio Tests of a Unit Root in an Autoregressive Model of Arbitrary Order By Samuel Brien; Michael Jansson; Morten Ørregaard Nielsen
  7. Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods By Niko Hauzenberger; Florian Huber; Gary Koop
  8. An introduction to time-varying lag autoregression By Franses, Ph.H.B.F.
  9. Volatility Spillover and International Contagion of Housing Bubbles By Bago, Jean-Louis; Akakpo, Koffi; Rherrad, Imad; Ouédraogo, Ernest
  10. Mapping Coupled Time-series Onto Complex Network By Jamshid Ardalankia; Jafar Askari; Somaye Sheykhali; Emmanuel Haven; G. Reza Jafari

  1. By: Licht, Adrian; Escribano Saez, Alvaro; Blazsek, Szabolcs Istvan
    Abstract: Relevant works from the literature on crude oil market use structural vector autoregressive(SVAR) models with several lags to approximate the true model for the variables change in globalcrude oil production, global real economic activity and log real crude oil prices. Those variables involveseasonality, co-integration, structural changes, and outliers. We introduce nonlinear Markov-switchingscore-driven models with common trends of the multivariate t-distribution (MS-Seasonal-t-QVAR), forwhich filters are optimal according to the Kullback-Leibler divergence. We find that MS-Seasonal-t-QVAR provides a better approximation of the true data generating process and more precise short-runand long-run impulse responses than SVAR.
    Keywords: Markov Regime-Switching Models; Outliers And Structural Changes; Nonlinear Co-Integration; Score-Driven Models; Global Crude Oil Market
    JEL: C52 C51 C32
    Date: 2020–05–07
  2. By: Licht, Adrian; Escribano Saez, Alvaro; Blazsek, Szabolcs Istvan
    Abstract: In this paper, the benefits of statistical inference of score-driven state-spacemodels are incorporated into the inference of dynamic stochastic general equilibrium (DSGE)models. We focus on DSGE models, for which a Gaussian ABCD representation exists. Precisionof statistical estimation is improved, by using a score-driven multivariate t-distribution for theerrors. First, the updating term of the transition equation of the ABCD representation isreplaced by the conditional score of the log-likelihood (LL) with respect to location. Second,the time-constant scale parameters of the error terms in the measurement equation of the ABCDrepresentation are replaced by a dynamic parameter that is updated by the conditional score ofthe LL with respect to scale. Impulse response functions (IRFs) and conditions of the maximumlikelihood (ML) estimator are presented. In the empirical application, a benchmark DSGE modelis estimated for real data on US economic output, inflation and interest rate for the period of1954-2019. The score-driven ABCD representation improves the estimation precision of theGaussian ABCD representation. The score-driven ABCD representation with dynamic scaleprovides the best description of the time series data, by identifying a structural change in thesample period and providing the most precise IRF estimates.
    Keywords: Beta-T-Egarch; Generalized Autoregressive Score (Gas); Dynamic Conditional Score (Dcs); Dynamic Stochastic General Equilibrium (Dsge)
    Date: 2020–05–07
  3. By: Tobias Hartl; Rolf Tschernig; Enzo Weber
    Abstract: We develop a generalization of unobserved components models that allows for a wide range of long-run dynamics by modelling the permanent component as a fractionally integrated process. The model does not require stationarity and can be cast in state space form. In a multivariate setup, fractional trends may yield a cointegrated system. We derive the Kalman filter estimator for the common fractionally integrated component and establish consistency and asymptotic (mixed) normality of the maximum likelihood estimator. We apply the model to extract a common long-run component of three US inflation measures, where we show that the $I(1)$ assumption is likely to be violated for the common trend.
    Date: 2020–05
  4. By: Danilo Leiva-Leon; Luis Uzeda
    Abstract: We introduce a new class of time-varying parameter vector autoregressions (TVP-VARs) where the identified structural innovations are allowed to influence — contemporaneously and with a lag — the dynamics of the intercept and autoregressive coefficients in these models. An estimation algorithm and a parametrization conducive to model comparison are also provided. We apply our framework to the US economy. Scenario analysis suggests that the effects of monetary policy on economic activity are larger and more persistent in the proposed models than in an otherwise standard TVP-VAR. Our results also indicate that costpush shocks play an important role in understanding historical changes in inflation persistence.
    Keywords: Econometric and statistical methods; Inflation and prices; Transmission of monetary policy
    JEL: C32 E52
    Date: 2020–05
  5. By: Sium Bodha Hannadige; Jiti Gao; Mervyn J. Silvapulle; Param Silvapulle
    Abstract: This paper develops a method for forecasting a nonstationary time series, such as GDP, using a set of high-dimensional panel data as predictors. To this end, we use what is known as a factor augmented regression [FAR] model that contains a small number of estimated factors as predictors; the factors are estimated using time series data on a large number of potential predictors. The validity of this method for forecasting has been established when all the variables are stationary and also when they are all nonstationary, but not when they consist of a mixture of stationary and nonstationary ones. This paper fills this gap. More specifically, we develop a method for constructing an asymptotically valid prediction interval using the FAR model when the predictors include a mixture of stationary and nonstationary factors; we refer to this as mixture-FAR model. This topic is important because typically time series data on a large number of economic variables is likely to contain a mixture of stationary and nonstationary variables. In a simulation study, we observed that the mixture-FAR performed better than its competitor that requires all the variables to be nonstationary. As an empirical illustration, we evaluated the aforementioned methods for forecasting the nonstationary variables, GDP and Industrial Production [IP], using the quarterly panel data on US macroeconomic variables, known as FRED-D. We observed that the mixture-FAR model proposed in this paper performed better than its aforementioned competitors.
    Keywords: bootstrap,generated factors, panel data, prediction interval.
    JEL: C22 C33 C38 C53
    Date: 2020
  6. By: Samuel Brien; Michael Jansson (UC Berkeley and CREATES); Morten Ørregaard Nielsen (Queen's University and CREATES)
    Abstract: We study large-sample properties of likelihood ratio tests of the unit root hypothesis in an autoregressive model of arbitrary, finite order. Earlier research on this testing problem has developed likelihood ratio tests in the autoregressive model of order one, but resorted to a plug-in approach when dealing with higher-order models. In contrast, we consider the full model and derive the relevant large-sample properties of likelihood ratio tests under a local-to-unity asymptotic framework. As in the simpler model, we show that the full likelihood ratio tests are nearly efficient, in the sense that their asymptotic local power functions are virtually indistinguishable from the Gaussian power envelopes.
    Keywords: Efficiency, Likelihood ratio test, Nuisance parameters, Unit root hypothesis
    JEL: C12 C22
    Date: 2020–05
  7. By: Niko Hauzenberger; Florian Huber; Gary Koop
    Abstract: Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain Monte Carlo (MCMC) methods mean their use is limited to the case where the number of predictors is not too large. In light of these two concerns, this paper proposes a new dynamic shrinkage prior which reflects the empirical regularity that TVPs are typically sparse (i.e. time variation may occur only episodically and only for some of the coefficients). A scalable MCMC algorithm is developed which is capable of handling very high dimensional TVP regressions or TVP Vector Autoregressions. In an exercise using artificial data we demonstrate the accuracy and computational efficiency of our methods. In an application involving the term structure of interest rates in the eurozone, we find our dynamic shrinkage prior to effectively pick out small amounts of parameter change and our methods to forecast well.
    Date: 2020–05
  8. By: Franses, Ph.H.B.F.
    Abstract: This paper introduces a new autoregressive model, with the specific feature that the lag structure can vary over time. More precise, and to keep matters simple, the autoregressive model sometimes has lag 1, and sometimes lag 2. Representation, autocorrelation, specification, inference, and the creation of forecasts are presented. A detailed illustration for annual inflation rates for eight countries in Africa shows the empirical relevance of the new model. Various potential extensions are discussed.
    Keywords: Autoregression, Time-varying lags, Forecasting
    JEL: C22 C53
    Date: 2020–04–01
  9. By: Bago, Jean-Louis; Akakpo, Koffi; Rherrad, Imad; Ouédraogo, Ernest
    Abstract: This paper provides new empirical evidence on housing bubbles timing, volatility spillover and bubbles contagion between Japan and its economics partners, namely, the United States, the Eurozone, and the United Kingdom. First, we apply a generalized sup ADF (GSADF) test developed by Phillips et al. (2015) to quarterly price-to-rent ratio from 1970Q1 to 2018Q4 to detect explosive behaviors in housing prices. Second, we analyze the volatility spillover in housing prices between Japan and its economic partners using the multivariate time-varying DCC-GARCH model developed by Engle (2002). Third, we assess bubbles contagion using the non-parametric model with time-varying coefficients developed by Greenaway-McGrevy and Phillips (2016). We document two historical bubble episodes from 1970 to 2018 in the Japan’s housing market. Moreover, we find evidence of volatility spillover and bubbles contagion between Japan’s real estate market and its most important economic partners during several periods.
    Keywords: Bubble, Contagion, Real estate, Japan, DCC-GARCH
    JEL: C14 G12
    Date: 2020–05
  10. By: Jamshid Ardalankia; Jafar Askari; Somaye Sheykhali; Emmanuel Haven; G. Reza Jafari
    Abstract: For the sake of extracting hidden mutual and coupled information from possibly uncoupled time-series, we explored the profound measures of network science on time-series. Alongside common methods in time-series analysis of coupling between financial and economic markets, mapping coupled time-series onto networks is an outstanding measure to provide insight into hidden aspects embedded in couplings intrinsically. In this manner, we discretize the amplitude of coupled time-series and investigate relative simultaneous locations of the corresponding amplitudes (nodes). The transmissions between simultaneous amplitudes are clarified by edges in the network. In this sense, by segmenting magnitudes, the scaling features, volatilities' size and also the direction of the coupled amplitudes can be described. The frequency of occurrences of the coupled amplitudes is illustrated by the weighted edges, that is to say, some coupled amplitudes in the time-series can be identified as communities in the network. The results show that despite apparently uncoupled joint probabilities, the couplings possess some aspects which diverge from random Gaussian noise. Thereby, with the aid of the network's topological and statistical measurements, we distinguished basic structures of coupling of cross-market networks. Meanwhile, it was discovered that even two possibly known uncoupled markets may possess coupled patterns with each other. Thereby, those markets should be examined as coupled and weakly coupled markets!
    Date: 2020–04

This nep-ets issue is ©2020 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.