nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒12‒03
seven papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. A Time-Varying Parameter Model for Local Explosions By Francisco (F.) Blasques; Siem Jan (S.J.) Koopman; Marc Nientker
  2. Hurst exponents and delampertized fractional Brownian motions By Matthieu Garcin
  3. Inference in Bayesian Proxy-SVARs By Arias, Jonas E.; Rubio-Ramirez, Juan F.; Waggoner, Daniel F.
  4. Likelihood based inference for an Identifiable Fractional Vector Error Correction Model By Federico Carlini; Katarzyna (K.A.) Lasak
  5. Long-Memory Modeling and Forecasting: Evidence from the U.S. Historical Series of Inflation By Heni Boubaker; Giorgio Canarella; Rangan Gupta; Stephen M. Miller
  6. Generalized Dynamic Factor Models and Volatilities: Consistency, Rates, and Prediction Intervals By Matteo Barigozzi; Marc Hallin
  7. Spillovers from US monetary policy: Evidence from a time-varying parameter GVAR model By Crespo Cuaresma, Jesus; Doppelhofer, Gernot; Feldkircher, Martin; Huber, Florian

  1. By: Francisco (F.) Blasques (VU Amsterdam); Siem Jan (S.J.) Koopman (VU Amsterdam); Marc Nientker (VU Amsterdam)
    Abstract: Locally explosive behavior is observed in many economic and financial time series when bubbles are formed. We introduce a time-varying parameter model that is capable of describing this behavior in time series data. Our proposed model can be used to predict the emergence, existence and burst of bubbles. We adopt a flexible observation driven model specification that allows for different bubble shapes and behavior. We establish stationarity, ergodicity, and bounded moments of the data generated by our model. Furthermore, we obtain the consistency and asymptotic normality of the maximum likelihood estimator. Given the parameter estimates, our filter is capable of extracting the unobserved bubble process from observed data. We study finite-sample properties of our estimator through a Monte Carlo simulation study. Finally, we show that our model compares well with noncausal models in a financial application concerning the Bitcoin/US dollar exchange rate.
    Keywords: bubbles; observation driven models; noncausal models; stationary; ergodic; consistency; asymptotic normality; exchange rates
    JEL: C22 C58 G10
    Date: 2018–11–16
  2. By: Matthieu Garcin (Research Center - Léonard de Vinci Pôle Universitaire - De Vinci Research Center)
    Abstract: The inverse Lamperti transform of a fractional Brownian motion is a stationary process. We determine the empirical Hurst exponent of such a composite process with the help of a regression of the log absolute moments of its increments, at various scales, on the corresponding log scales. This perceived Hurst exponent underestimates the Hurst exponent of the underlying fractional Brownian motion. We thus encounter some time series having a perceived Hurst exponent lower than 1/2, but an underlying Hurst exponent higher than 1/2. This paves the way for short-and medium-term forecasting. Indeed, in such series, mean reversion predominates at high scales, whereas persistence is overriding at lower scales. We propose a way to characterize the Hurst horizon, namely a limit scale between these opposite behaviours. We show that the delampertized fractional Brownian motion, which mixes persistence and mean reversion, is relevant for financial time series, in particular for high-frequency foreign exchange rates. In our sample, the empirical Hurst horizon is always above 1 hour and 23 minutes.
    Keywords: fractional Brownian motion,Hurst exponent,Lamperti transform,Ornstein-Uhlenbeck process,foreign exchange rates
    Date: 2018–11–12
  3. By: Arias, Jonas E. (Federal Reserve Bank of Philadelphia); Rubio-Ramirez, Juan F. (Federal Reserve Bank of Atlanta); Waggoner, Daniel F. (Federal Reserve Bank of Atlanta)
    Abstract: Motivated by the increasing use of external instruments to identify structural vector autoregressions SVARs), we develop algorithms for exact finite sample inference in this class of time series models, commonly known as proxy SVARs. Our algorithms make independent draws from the normal-generalized-normal family of conjugate posterior distributions over the structural parameterization of a proxy-SVAR. Importantly, our techniques can handle the case of set identification and hence they can be used to relax the additional exclusion restrictions unrelated to the external instruments often imposed to facilitate inference when more than one instrument is used to identify more than one equation as in Mertens and Montiel-Olea (2018).
    Keywords: SVARs; External Instruments; Importance Sampler
    JEL: C15 C32
    Date: 2018–11–05
  4. By: Federico Carlini (USI, Lugano); Katarzyna (K.A.) Lasak (University of Amsterdam)
    Abstract: We consider the Fractional Vector Error Correction model proposed in Avarucci (2007), which is characterized by a richer lag structure than the models proposed in Granger (1986) and Johansen (2008, 2009). In particular, we discuss the properties of the model of Avarucci (2007) (FECM) in comparison to the model of Johansen (2008, 2009) (FCVAR). Both models generate the same class of processes, but the properties of the two models are different. First, opposed to the model of Johansen (2008, 2009), the model of Avarucci has a convenient nesting structure, which allows for testing the number of lags and the cointegration rank exactly in the same way as in the standard I(1) cointegration framework of Johansen (1995) and hence might be attractive for econometric practice. Second, we find that the model of Avarucci (2007) is almost free from identification problems, contrary to the model of Johansen (2008, 2009) and Johansen and Nielsen (2012), which identification problems are discussed in Carlini and Santucci de Magistris (2017). However, due to a larger number of parameters, the estimation of the FECM model of Avarucci (2007) turns out to be more complicated. Therefore, we propose a 4-step estimation procedure for this model that is based on the switching algorithm employed in Carlini and Mosconi (2014), together with the GLS procedure of Mosconi and Paruolo (2014). We check the performance of the proposed estimation procedure in finite samples by means of a Monte Carlo experiment and we prove the asymptotic distribution of the estimators of all the parameters. The solution of the model has been previously derived in Avarucci (2007), while testing for the rank has been discussed in Lasak and Velasco (for cointegration strength >0.5) and Avarucci and Velasco (for cointegration strength
    Keywords: Error correction model; Gaussian VAR model; Fractional Cointegration; Estimation algorithm; Maximum likelihood estimation; Switching Algorithm; Reduced Rank Regression.
    JEL: C13 C32
    Date: 2018–11–16
  5. By: Heni Boubaker (International University of Rabat, BEAR LAB, Technopolis Rabat-Shore Rocade-Sale, Morocco); Giorgio Canarella (University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Stephen M. Miller (University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada, USA)
    Abstract: We report the results of applying semi-parametric long-memory estimators to the historical monthly series of U.S. inflation, and analyze their empirical forecasting performance over 1, 6, 12, and 24 months using in-sample and out-of-sample procedures. For comparison purposes, we also apply two parametric estimators, the naive AR(1) and the ARFIMA(1, d, 1) models. We evaluate the forecasting accuracy of the competing methods using the mean square error (MSE) and mean absolute error (MAE) criteria. We evaluate the statistical significance of forecasting accuracy of competing forecasts using the Diebold-Mariano (1995) test. Overall, our results preforms slightly better than the Lahiani and Scaillet (2009) threshold estimator based on the MSE and MAE criteria. This improvement in performance does not prove significant enough to cause a rejection of the null hypothesis of equality of predictive accuracy. The Boubaker (2017) estimator, on the other hand, significantly outperforms the time-invariant estimators over longer horizons. Over shorter horizons, however, the Boubaker (2017) estimator does not exhibit a significantly better predictive performance than the time-invariant long-memory estimators with the exception of the naive AR(1) model.
    Keywords: long memory, wavelet analysis, time-varying persistence
    JEL: C13 C22 C32 C54 E31
    Date: 2018–11
  6. By: Matteo Barigozzi; Marc Hallin
    Abstract: Volatilities, in high-dimensional panels of economic time series with a dynamic factor structure on the levels or returns, typically also admit a dynamic factor decomposition. A two-stage dynamic factor model method recovering common and idiosyncratic volatility shocks therefore was proposed in Barigozzi and Hallin (2016). By exploiting this two-stage factor approach, we build one-step-ahead conditional prediction intervals for large n×T panels of returns. We provide consistency and consistency rates results for the proposed estimators as both n and T tend to infinity. Finally, we apply our methodology to a panel of asset returns belonging to the S&P100 index in order to compute one-step-ahead conditional prediction intervals for the period 2006-2013. A comparison with the componentwise GARCH (1,1) benchmark (which does not take advantage of cross-sectional information) demonstrates the superiority of our approach, which is genuinely multivariate (and high-dimensional), nonparametric, and model-free.
    Keywords: Volatility, Dynamic Factor Models, Prediction intervals, GARCH
    Date: 2018–11
  7. By: Crespo Cuaresma, Jesus (WU Wirtschaftsuniversität Wien); Doppelhofer, Gernot (Norwegian School of Economics); Feldkircher, Martin (Oesterreichische Nationalbank (Austrian Central Bank)); Huber, Florian (University of Salzburg)
    Abstract: This paper develops a global vector autoregressive (GVAR) model with time-varying parameters and stochastic volatility to analyze whether international spillovers of US monetary policy have changed over time. The proposed model allows assessing whether coefficients evolve gradually over time or are better characterized by infrequent, but large breaks. Our findings point towards pronounced changes in the international transmission of US monetary policy throughout the sample period, especially so for the reaction of international output, equity prices, and exchange rates against the US dollar. In general, the strength of spillovers has weakened in the aftermath of the global financial crisis. Using simple panel regressions, we link the variation in international responses to measures of trade and financial globalization. We find that a broad trade base and a high degree of financial integration with the world economy tend to cushion risks stemming from a foreign shock such as a US monetary policy tightening, whereas a reduction in trade barriers and/or a liberalization of the capital account increase these risks.
    Keywords: Spillovers; zero lower bound; globalization; mixture innovation models
    JEL: C30 E52 F41
    Date: 2018–11–16

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