nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒11‒28
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. The Modi ed R a Robust Measure of Association for Time Series By Rehman, Atiq-ur-; Malik, Muhammad Irfan
  2. Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach By Bognanni, Mark; Herbst, Edward
  3. Determination of long-run and short-run dynamics in EC-VARMA models via canonical correlations By George Athanasopoulos; D.S. Poskitt; Farshid Vahid; Wenying Yao
  4. Realized wavelet-based estimation of integrated variance and jumps in the presence of noise By Baruník, Jozef; Vácha, Lukáš
  5. Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections By Bańbura, Marta; Giannone, Domenico; Lenza, Michele

  1. By: Rehman, Atiq-ur-; Malik, Muhammad Irfan
    Abstract: Since times of Yule (1926), it is known that correlation between two time series can produce spurious results. Granger and Newbold (1974) see the roots of spurious correlation in non-stationarity of the time series. However the study of Granger, Hyung and Jeon (2001) prove that spurious correlation also exists in stationary time series. These facts make the correlation coefficient an unreliable measure of association. This paper proposes ‘Modified R’ as an alternate measure of association for the time series. The Modified R is robust to the type of stationarity and type of deterministic part in the time series. The performance Modified R is illustrated via extensive Monte Carlo Experiments.
    Keywords: Correlation Coefficient; Spurious Regression; Stationary Series
    JEL: C01 C15 C52 C63
    Date: 2014–04–24
  2. By: Bognanni, Mark (Federal Reserve Bank of Cleveland); Herbst, Edward (Federal Reserve Board of Governors)
    Abstract: Vector autoregressions with Markov-switching parameters (MS-VARs) offer dramatically better data fit than their constant-parameter predecessors. However, computational complications, as well as negative results about the importance of switching in parameters other than shock variances, have caused MS-VARs to see only sparse usage. For our first contribution, we document the effectiveness of Sequential Monte Carlo (SMC) algorithms at estimating MSVAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of being simpler to implement, readily parallelizable, and unconstrained by reliance on convenient relationships between prior and likelihood. For our second contribution, we exploit SMC’s flexibility to demonstrate that the use of priors with superior data fit alters inference about the presence of time variation in macroeconomic dynamics. Using the same data as Sims, Waggoner, and Zha (2008, we provide evidence of recurrent episodes characterized by a flat Phillips Curve.
    Keywords: Vector Autoregressions; Sequential Monte Carlo; Regime-Switching Models; Bayesian Analysis
    JEL: C11 C15 C32 C52 E3 E4 E5
    Date: 2014–11–12
  3. By: George Athanasopoulos; D.S. Poskitt; Farshid Vahid; Wenying Yao
    Abstract: This article studies a simple, coherent approach for identifying and estimating error correcting vector autoregressive moving average (EC-VARMA) models. Canonical correlation analysis is implemented for both determining the cointegrating rank, using a strongly consistent method, and identifying the short-run VARMA dynamics, using the scalar component methodology. Finite sample performances are evaluated via Monte-Carlo simulations and the approach is applied to model and forecast US interest rates. The results reveal that EC-VARMA models generate significantly more accurate out-of-sample forecasts than vector error correction models (VECMs), especially for short horizons.
    Keywords: Cointegration, Error correction, Scalar Component Model, Multivariate Time Series.
    Date: 2014
  4. By: Baruník, Jozef; Vácha, Lukáš
    Abstract: We introduce wavelet-based methodology for estimation of realized variance allowing its measurement in the time-frequency domain. Using smooth wavelets and Maximum Overlap Discrete Wavelet Transform, we allow for the decomposition of the realized variance into several investment horizons and jumps. Basing our estimator in the two-scale realized variance framework, we are able to utilize all available data and get feasible estimator in the presence of microstructure noise as well. The estimator is tested in a large numerical study of the finite sample performance and is compared to other popular realized variation estimators. We use different simulation settings with changing noise as well as jump level in different price processes including long memory fractional stochastic volatility model. The results reveal that our wavelet-based estimator is able to estimate and forecast the realized measures with the greatest precision. Our timefrequency estimators not only produce feasible estimates, but also decompose the realized variation into arbitrarily chosen investment horizons. We apply it to study the volatility of forex futures during the recent crisis at several investment horizons and obtain the results which provide us with better understanding of the volatility dynamics.
    Keywords: quadratic variation,realized variance,jumps,market microstructure noise,wavelets
    Date: 2014
  5. By: Bańbura, Marta; Giannone, Domenico; Lenza, Michele
    Abstract: This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large models that can be cast in a linear state space representation. We build large vector autoregressions (VARs) and a large dynamic factor model (DFM) for a quarterly data set of 26 euro area macroeconomic and financial indicators. Both approaches deliver similar forecasts and scenario assessments. In addition, conditional forecasts shed light on the stability of the dynamic relationships in the euro area during the recent episodes of financial turmoil and indicate that only a small number of sources drive the bulk of the fluctuations in the euro area economy. JEL Classification: C11, C13, C33, C53
    Keywords: Bayesian shrinkage, conditional forecast, dynamic factor model, large cross-sections, vector autoregression
    Date: 2014–09

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