nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒05‒14
nine papers chosen by
Yong Yin
SUNY at Buffalo

  1. The Local Fractional Bootstrap By Mikkel Bennedsen; Ulrich Hounyo; Asger Lunde; Mikko S. Pakkanen
  2. Bootstrapping high-frequency jump tests By Prosper Dovonon; Sílvia Gonçalves; Ulrich Hounyo; Nour Meddahi
  3. Priors for the Long Run By Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E
  4. Are experts’ probabilistic forecasts similar to the NBP projections? By Halina Kowalczyk; Ewa Stanisławska
  5. Forecasting Inflation using Functional Time Series Analysis By Zafar, Raja Fawad; Qayyum, Abdul; Ghouri, Saghir Pervaiz
  6. Improving Markov switching models using realized variance By Liu, Jia; Maheu, John M
  7. The Variance-Frequency Decomposition as an Instrument for VAR Identification: an Application to Technology Shocks By Lovcha, Yuliya; Pérez Laborda, Àlex
  8. On the invertibility of seasonally adjusted series By Gil-Alana, Luis; Lovcha, Yuliya; Pérez Laborda, Àlex
  9. A data-driven selection of an appropriate seasonal adjustment approach By Webel, Karsten

  1. By: Mikkel Bennedsen; Ulrich Hounyo; Asger Lunde; Mikko S. Pakkanen
    Abstract: We introduce a bootstrap procedure for high-frequency statistics of Brownian semistationary processes. More specifically, we focus on a hypothesis test on the roughness of sample paths of Brownian semistationary processes, which uses an estimator based on a ratio of realized power variations. Our new resampling method, the local fractional bootstrap, relies on simulating an auxiliary fractional Brownian motion that mimics the fine properties of high frequency differences of the Brownian semistationary process under the null hypothesis. We prove the first order validity of the bootstrap method and in simulations we observe that the bootstrap-based hypothesis test provides considerable finite-sample improvements over an existing test that is based on a central limit theorem. This is important when studying the roughness properties of time series data; we illustrate this by applying the bootstrap method to two empirical data sets: we assess the roughness of a time series of high-frequency asset prices and we test the validity of Kolmogorov's scaling law in atmospheric turbulence data.
    Date: 2016–05
  2. By: Prosper Dovonon; Sílvia Gonçalves; Ulrich Hounyo; Nour Meddahi
    Keywords: jumps,bootstrap,block multipower variation,
    Date: 2016–05–09
  3. By: Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E
    Abstract: We propose a class of prior distributions that discipline the long-run behavior of Vector Autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run are conjugate, and can thus be easily implemented using dummy observations and combined with other popular priors. In VARs with standard macroeconomic variables, a prior based on the long-run predictions of a wide class of dynamic stochastic general equilibrium models yields substantial improvements in the forecasting performance.
    Date: 2016–05
  4. By: Halina Kowalczyk; Ewa Stanisławska
    Abstract: We assess similarity of the Polish central bank’s forecasts published in Inflation Reports and economic experts’ forecasts (from NBP Survey of Professional Forecasters), an important issue in monetary policy. Contrary to other studies which use point forecasts, we are interested is comparing whole forecasts’ distributions. We are especially interested whether the SPF experts mirror the NBP projections. For this purpose, we propose employing methods based on distance between distributions. Unfortunately, substantial heterogeneity of forecasts, as well as short and atypical period analyzed, limit drawing firm conclusions with this respect.
    Keywords: survey data, fan charts, probabilistic forecasts, inflation forecasts, GDP growth forecasts, distribution similarity
    JEL: D83 D84 E37
    Date: 2016
  5. By: Zafar, Raja Fawad; Qayyum, Abdul; Ghouri, Saghir Pervaiz
    Abstract: In present study we model the data using Functional Time series Analysis (FTSA). The method is basically univariate, so to check its efficiency we compared it with seasonal ARIMA models. We have used data sets of monthly frequency from 2002-2011 to forecast Consumer Price Index (CPI) of Pakistan. We withhold some data of last year (i.e. of 2011) and based on remaining year (2002-2010) we fitted model and forecasted the values of monthly CPI. Our study compares the performance of FTSA model and ARIMA model using the test data of 2011. Comparison based on forecast evaluation criteria’s and forecasted value of 2011, indicates that FTSA model using CPI general data outperforms SARIMA models
    Keywords: Forecasting, Inflation, SARIMA, FTSA
    JEL: C22 C53
    Date: 2015–03–20
  6. By: Liu, Jia; Maheu, John M
    Abstract: This paper proposes a class of models that jointly model returns and ex-post variance measures under a Markov switching framework. Both univariate and multivariate return versions of the model are introduced. Bayesian estimation can be conducted under a fixed dimension state space or an infinite one. The proposed models can be seen as nonlinear common factor models subject to Markov switching and are able to exploit the information content in both returns and ex-post volatility measures. Applications to U.S. equity returns and foreign exchange rates compare the proposed models to existing alternatives. The empirical results show that the joint models improve density forecasts for returns and point predictions of return variance. The joint Markov switching models can increase the precision of parameter estimates and sharpen the inference of the latent state variable.
    Keywords: infinite hidden Markov model, realized covariance, density forecast, MCMC
    JEL: C11 C32 C51 C58 G1
    Date: 2015–09–01
  7. By: Lovcha, Yuliya; Pérez Laborda, Àlex
    Abstract: Abstract: This paper proposes a new framework to study identification in structural VAR models. The framework is based on the variance-frequency decomposition and focuses on the contribution of the identified shock to the variance of model variables in a given frequency range. We use the hours-productivity debate as a connecting thread in our discussion since the identification problem has attracted a lot of attention in this literature. To start, we employ the framework to study the business cycle properties of a set of different identification schemes for technology shocks. Grounded on the simulation results, we propose a new model-based procedure which delivers a precise estimate of the response of hours. Finally, we put all the schemes to work with real data, obtaining substantial evidence in favor of plausible RBC parametrizations, especially from identification restrictions that perform better in simulations. This analysis also reveals that the schemes that recover a very strong response of hours (higher than the implied by typical RBC parameterizations) tend to overstate the contribution of the technology shock to the fluctuations of hours worked at business cycle frequencies. Keywords: Business cycle, frequency domain, hours worked, productivity, vector autoregressions. Classification: C1, E3
    Keywords: Cicles econòmics, 33 - Economia,
    Date: 2016
  8. By: Gil-Alana, Luis; Lovcha, Yuliya; Pérez Laborda, Àlex
    Abstract: This paper examines the implications of the seasonal adjustment by an ARIMA model based (AMB) approach in the context of seasonal fractional integration. According to the AMB approach, if the model identified from the data contains seasonal unit roots, the adjusted series will not be invertible that has serious implications for the posterior analysis. We show that even if the ARIMA model identified from the data contains seasonal unit roots, if the true data generating process is stationary seasonally fractionally integrated (as it is often found in economic data), the AMB seasonal adjustment produces dips in the periodogram at seasonal frequencies, but the adjusted series still can be approximated by an invertible process. We also perform a small Monte Carlo study of the log-periodogram regression with tapered data for negative seasonal fractional integration. An empirical application for the Spanish economy that illustrates our results is also carried out at the end of the article. JEL Classification: C15. Keywords: seasonality; invertibility; fractional integration; TRAMO-Seats; tapering
    Keywords: Simulació, Mètodes de, 33 - Economia,
    Date: 2016
  9. By: Webel, Karsten
    Abstract: Recent releases of X-13ARIMA-SEATS and JDemetra+ enable their users to choose between the non-parametric X-11 and the parametric ARIMA model-based approach to seasonal adjustment for any given time series without the necessity of switching between different software packages. To ease the selection process, we develop a decision tree whose branches combine conceptual differences between the two methods with empirical issues. The latter primarily include a thorough inspection of the squared gains of final X-11 and Wiener-Kolmogorov seasonal adjustment filters as well as a comparison of various revision measures. We finally illustrate the decision tree on selected German macroeconomic time series.
    Keywords: ARIMA model-based approach,linear filtering,signal extraction,unobserved components,X-11 approach
    JEL: C13 C14 C22
    Date: 2016

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