nep-ets New Economics Papers
on Econometric Time Series
Issue of 2008‒09‒20
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Volatility transmission and volatility impulse response functions in European electricity forward markets By Yannick LE PEN; Benoît SEVI
  2. Forecasting Macroeconomic Variables in a Small Open Economy: A Comparison between Small- and Large-Scale Models By Rangan Gupta; Alain Kabundi
  3. Realisations of Finite-Sample Frequency-Selective Filters By D.S.G. Pollock
  4. Testing for seasonal unit roots in heterogeneous panels using monthly data in the presence of cross sectional dependence By Otero, Jesús; Smith, Jeremy; Giulietti, Monica
  5. A New Procedure to Test for H Self-Similarity By Les Oxley; Chris Price; William Rea; Marco Reale
  6. Volatility forecasting: the jumps do matter By Fulvio Corsi; Davide Pirino; Roberto Renò
  7. Do we need time series econometrics By Rao, B. Bhaskara; Singh, Rup; Kumar, Saten
  8. Phillips Curve Inflation Forecasts By James H. Stock; Mark W. Watson

  1. By: Yannick LE PEN; Benoît SEVI
    Abstract: Using daily data from March 2001 to June 2005, we estimate a VAR-BEKK model and find evidence of return and volatility spillovers between the German, the Dutch and the British forward electricity markets. We apply Hafner and Herwartz [2006, Journal of International Money and Finance 25, 719-740] Volatility Impulse Response Function(VIRF) to quantify the impact of shock on expected conditional volatility. We observe that a shock has a high positive impact only if its size is large compared to the current level of volatility. The impact of shocks are usually not persistent, which may be an indication of market efficiency. Finally, we estimate the density of the VIRF at different forecast horizon. These fitted distributions are asymmetric and show that extreme events are possible even if their probability is low. These results have interesting implications for market participants whose risk management policy is based on option prices which themselves depend on the volatility level.
    Keywords: volatility impulse response function, GARCH, non Gaussian distributions, electricity market, forward markets
    JEL: C3 G1 Q43
    Date: 2008
  2. By: Rangan Gupta (Department of Economics, University of Pretoria); Alain Kabundi (Department of Economics and Econometrics, University of Johannesburg)
    Abstract: This paper compares the forecasting ability of five alternative models in predicting four key macroeconomic variables, namely, per capita growth rate, the Consumer Price Index (CPI) inflation, the money market rate, and the growth rate of the nominal effective exchange rate for the South African economy. Unlike the theoretical Small Open Economy New Keynesian Dynamic Stochastic General Equilibrium (SOENKDSGE), the unrestricted VAR, and the small-scale Bayesian Vector Autoregressive (BVAR) models, which are estimated based on four variables, the Dynamic Factor Model (DFM) and the large-scale BVAR models use information from a data-rich environment containing 266 macroeconomic time series observed over the period of 1983:01 to 2002:04. The results, based on Root Mean Square Errors (RMSEs), for one- to four-quarters-ahead out-of-sample forecasts over the horizon of 2003:01 to 2006:04, show that, except for the one-quarter-ahead forecast of the growth rate of the of nominal effective exchange rate, large-scale BVARs outperform the other four models consistently and, generally, significantly.
    Keywords: Small Open Economy New Keynesian Dynamic Stochastic Model, Dynamic Factor Model, VAR, BVAR, Forecast Accuracy
    JEL: C11 C13 C33 C53
    Date: 2008–09
  3. By: D.S.G. Pollock
    Abstract: A filtered data sequence can be obtained by multiplying the Fourier ordinates of the data by the ordinates of the frequency response of the filter and by applying the inverse Fourier transform to carry the product back to the time domain. Using this technique, it is possible, within the constraints of a finite sample, to design an ideal frequency-selective filter that will preserve all elements within a specified range of frequencies and that will remove all elements outside it. Approximations to ideal filters that are implemented in the time domain are commonly based on truncated versions of the infinite sequences of coefficients derived from the Fourier transforms of rectangular frequency response functions. An alternative to truncating an infinite sequence of coefficients is to wrap it around a circle of a circumference equal in length to the data sequence and to add the overlying coefficients. The coefficients of the wrapped filter can also be obtained by applying a discrete Fourier transform to a set of ordinates sampled from the frequency response function. Applying the coefficients to the data via circular convolution produces results that are identical to those obtained by a multiplication in the frequency domain, which constitutes a more efficient approach.
    Keywords: Signal extraction; Linear filtering; Frequency-domain analysis
    Date: 2008–09
  4. By: Otero, Jesús (Facultad de Economía, Universidad del Rosario); Smith, Jeremy (Department of Economics,University of Warwick); Giulietti, Monica (Aston Business School, University of Aston)
    Abstract: This paper generalises the monthly seasonal unit root tests of Franses (1991) for a heterogeneous panel following the work of Im, Pesaran, and Shin (2003), which we refer to as the F-IPS tests. The paper presents the mean and variance necessary to yield a standard normal distribution for the tests, for different number of time observations, T, and lag lengths. However, these tests are only applicable in the absence of cross-sectional dependence. Two alternative methods for modifying these F-IPS tests in the presence of cross-sectional dependency are presented : the first is the cross-sectionally augmented test,denoted CF-IPS, following Pesaran (2007), the other is a bootstap method, denoted BF-IPS. In general, the BF-IPS tests have greater power than the CF-IPS tests, although for large T and high degree of cross-sectional dependency the CF-IPS test dominates the BF-IPS test.
    Keywords: Panel unit root tests ; seasonal unit roots ; monthly data ; cross sectional dependence ; Monte Carlo
    JEL: C12 C15 C22 C23
    Date: 2008
  5. By: Les Oxley (University of Canterbury); Chris Price; William Rea; Marco Reale
    Abstract: It is now recognized that long memory and structural change can be confused because the statistical properties of times series of lengths typical of many nancial and economic series are similar for both mod- els. We propose a new test aimed at distinguishing between unifractal long memory and structural change. The approach, which utilizes the computationally ecient methods based upon Atheoretical Regression Trees (ART), establishes through simulation the bivariate distribution of the number of breaks reported by ART with the CUSUM range for simulated fractionally integrated series. This bivariate distribution is then used to empirically construct a test. We apply these methods to the realized volatility series of 16 stocks in the Dow Jones Industrial Average. We show the realised volatility series are statistically sig- nicantly dierent from fractionally integrated series with the same estimated d value. We present evidence that these series have struc- tural breaks. For comparison purposes we present the results of tests by Zhang and Ohanissian, Russell, and Tsay for these series.
    Keywords: Long-range dependence; strong dependence; global dependence
    JEL: C13 C22
    Date: 2008–09–12
  6. By: Fulvio Corsi; Davide Pirino; Roberto Renò
    Abstract: This study reconsiders the role of jumps for volatility forecasting by showing that jumps have positive and mostly significant impact on future volatility. This result becomes apparent once volatility is correctly separated into its continuous and discontinuous component. To this purpose, we introduce the concept of threshold multipower variation (TMPV), which is based on the joint use of bipower variation and threshold estimation. With respect to alternative methods, our TMPV estimator provides less biased and robust estimates of the continuous quadratic variation and jumps. This technique also provides a new test for jump detection which has substantially more power than traditional tests. We use this separation to forecast volatility by employing an heterogeneous autoregressive (HAR) model which is suitable to parsimoniously model long memory in realized volatility time series. Empirical analysis shows that the proposed techniques improve significantly the accuracy of volatility forecasts for the S&P500 index, single stocks and US bond yields, especially in periods following the occurrence of a jump
    Keywords: volatility forecasting, jumps, bipower variation, threshold estimation, stock, bond
    JEL: G1 C1 C22 C53
    Date: 2008–06
  7. By: Rao, B. Bhaskara; Singh, Rup; Kumar, Saten
    Abstract: Whether or not there is a need for the unit roots and cointegration based time series econometric methods is a methodological issue. An alternative is the econometrics of the London School of Economics (LSE) and Hendry approach based on the simpler classical methods of estimation. This is known as the general to specific method (GETS). Like all other methodological issues it is difficult to resolve which approach is better. However, we think that GETS is conceptually simpler and very useful in applied work.
    Keywords: GETS, Cointegration, Box-Jenkins’s Equations, Hendry, Granger.
    JEL: B49 C22 B41
    Date: 2008–01–16
  8. By: James H. Stock; Mark W. Watson
    Abstract: This paper surveys the literature since 1993 on pseudo out-of-sample evaluation of inflation forecasts in the United States and conducts an extensive empirical analysis that recapitulates and clarifies this literature using a consistent data set and methodology. The literature review and empirical results are gloomy and indicate that Phillips curve forecasts (broadly interpreted as forecasts using an activity variable) are better than other multivariate forecasts, but their performance is episodic, sometimes better than and sometimes worse than a good (not naïve) univariate benchmark. We provide some preliminary evidence characterizing successful forecasting episodes.
    JEL: C53 E37
    Date: 2008–09

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