nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒03‒23
nine papers chosen by
Yong Yin
SUNY at Buffalo

  1. The Identification of Thresholds and Time Delay in Self-Exciting Threshold a Model by Wavelet By Song-Yon Kim; Mun-Chol Kim
  2. Geometric and Long Run Aspects of Granger Causality By Majid Al-Sadoon
  3. Testing for common cycles in non-stationary VARs with varied frecquency data By Hecq A.W.; Urbain J.R.Y.J.; Götz T.B.
  4. Evaluating the accuracy of forecasts from vector autoregressions By Todd E. Clark; Michael W. McCracken
  5. Conditiona l Forecast Selection from Many Forecasts: An Application to the Yen/Dollar Exchange Rate By Kei Kawakami
  6. Nonlinear Dynamics and Recurrence Plots for Detecting Financial Crisis. By Peter Martey Addo; Monica Billio; Dominique Guegan
  7. Structural-break models under mis-specification: implications for forecasting By Boonsoo Koo; Myung Hwan Seo
  8. A New Asymmetric GARCH Model: Testing, Estimation and Application By Hatemi-J, Abdulnasser
  9. The Exponential Model for the Spectrum of a Time Series: Extensions and Applications By Proietti, Tommaso; Luati, Alessandra

  1. By: Song-Yon Kim; Mun-Chol Kim
    Abstract: In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here we composed an empirical wavelet from the SETAR (Self-Exciting Threshold Autoregressive) model and identified the thresholds and time delay in the model using it.
    Date: 2013–03
  2. By: Majid Al-Sadoon
    Abstract: This paper extends multivariate Granger causality to take into account the subspaces along which Granger causality occurs as well as long run Granger causality. The properties of these new notions of Granger causality, along with the requisite restrictions, are derived and extensively studied for a wide variety of time series processes including linear invertible process and VARMA. Using the proposed extensions, the paper demonstrates that: (i) mean reversion in L2 is an instance of long run Granger non-causality, (ii) cointegration is a special case of long run Granger non-causality along a subspace, (iii) controllability is a special case of Granger causality, and finally (iv) linear rational expectations entail (possibly testable) Granger causality restriction along subspaces.
    Keywords: Granger causality, long run Granger causality, L2-mean-reversion, rho-mixing, cointegration, VARMA, controllability, Kalman Decomposition, linear rational expectations
    JEL: C10 C32 C51
    Date: 2013–01
  3. By: Hecq A.W.; Urbain J.R.Y.J.; Götz T.B. (GSBE)
    Abstract: This paper proposes a new way for detecting the presence of common cyclical features when several time series are observed/sampled at different frequencies, hence generalizing the common-frequency approach introduced by Engle and Kozicki (1993) and Vahid and Engle (1993). We start with the mixed-frequency VAR representation investigated in Ghysels (2012) for stationary time series. For non-stationary time series in levels, we show that one has to account for the presence of two sets of long-run relationships. The First set is implied by identities stemming from the fact that the differences of the high-frequency I(1) regressors are stationary. The second set comes from possible additional long-run relationships between one of the high-frequency series and the low-frequency variables. Our transformed VECM representations extend the results of Ghysels (2012) and are very important for determining the correct set of variables to be used in a subsequent common cycle investigation. This has some empirical implications both for the behavior of the test statistics as well as for forecasting. Empirical analyses with the quarterly real GNP and monthly industrial production indices for, respectively, the U.S. and Germany illustrate our new approach. This is also investigated in a Monte Carlo study, where we compare our proposed mixed-frequency models with models stemming from classical temporal aggregation methods.
    Keywords: Regional and Urban History: General;
    Date: 2013
  4. By: Todd E. Clark; Michael W. McCracken
    Abstract: This paper surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by Vector Autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multi-step forecasts and those parts that are applicable to iterated multi-step forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The paper then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.
    Keywords: Economic forecasting ; Vector autoregression
    Date: 2013
  5. By: Kei Kawakami
    Abstract: This paper proposes a new method for forecast selection from a pool of many forecasts. The method uses conditional information as proposed by Giacomini and White (2006). It also extends their pairwise switching method to a situation with many forecasts. I apply the method to the monthly yen/dollar exchange rate and show empirically that my method of switching forecasting models reduces forecast errors compared with a single model.
    Keywords: Conditional predictive ability; Exchange rate; Forecasting; Forecast combinations; Model selection
    JEL: C52 C53 F31 F37
    Date: 2013
  6. By: Peter Martey Addo (Centre d'Economie de la Sorbonne et Università di Venezia - Dipartimento di Economia); Monica Billio (Università di Venezia - Dipartimento di Economia); Dominique Guegan (Centre d'Economie de la Sorbonne)
    Abstract: Identification of financial bubbles and crisis is a topic of major concern since it is important to prevent collapses that can severely impact nations and economies. Our analysis deals with the use of the recently proposed "delay vector variance" (DVV) method, which examines local predictability of a signal in the phase space to detect the presence of determinism and nonlinearity in a time series. Optimal embedding parameters used in the DVV analysis are obtained via a differential entropy based method using wavelet-based surrogates. We exploit the concept of recurrence plots to study the stock market to locate hidden patterns, non-stationarity, and to examine the nature of these plots in events of financial crisis. In particular, the recurrence plots are employed to detect and characterize financial cycles. A comprehensive analysis of the feasibility of this approach is provided. We show that our methodology is useful in the diagnosis and detection of financial bubbles, which have significantly impacted economic upheavals in the past few decades.
    Keywords: Nonlinearity analysis, surrogates, Delay vector variance (DVV) method, wavelets, financial bubbles, embedding parameters, recurrence plots.
    JEL: C14 C40 E32 G01
    Date: 2013–03
  7. By: Boonsoo Koo; Myung Hwan Seo
    Abstract: This paper shows that in the presence of model mis-specification, the conventional inference procedures for structural-break models are invalid. In doing so, we establish new distribution theory for structural break models under the relaxed assumption that our structural break model is the best linear approximation of the true but unknown data generating process. Our distribution theory involves cube-root asymptotics and it is used to shed light on forecasting practice. We show that the conventional forecasting methods do not necessarily produce the best forecasts in our setting. We also propose a new forecasting strategy, which incorporates our new distribution theory, and apply our forecasting method to numerous macroeconomic data. The performance of various contemporary forecasting methods is compared to ours.
    Keywords: structural breaks, forecasting, mis-specification, cube-root asymptotics, bagging
    Date: 2013
  8. By: Hatemi-J, Abdulnasser
    Abstract: Since the seminal work by Engle (1982), the autoregressive conditional heteroscedasticity (ARCH) model has been an important tool for estimating the time-varying volatility as a measure of risk. Numerous extensions of this model have been put forward in the literature. The current paper offers an alternative approach for dealing with asymmetry in the underlying volatility model. Unlike previous papers that have dealt with asymmetry, this paper suggests to explicitly separate the positive shocks from the negative ones in the ARCH modeling approach. A test statistic is suggested for testing the null hypothesis of no asymmetric ARCH effects. In case the null hypothesis is rejected, the model can be estimated by using the maximum likelihood method. The suggested asymmetric volatility approach is applied to modeling separately the potential time-varying volatility in markets that are rising or falling by using the changes in the world market stock price index.
    Keywords: GARCH; Asymmetry; Modelling volatility; Hypothesis testing, World stock price index.
    JEL: C12 C32 G10
    Date: 2013–03–17
  9. By: Proietti, Tommaso; Luati, Alessandra
    Abstract: The exponential model for the spectrum of a time series and its fractional extensions are based on the Fourier series expansion of the logarithm of the spectral density. The coefficients of the expansion form the cepstrum of the time series. After deriving the cepstrum of important classes of time series processes, also featuring long memory, we discuss likelihood inferences based on the periodogram, for which the estimation of the cepstrum yields a generalized linear model for exponential data with logarithmic link, focusing on the issue of separating the contribution of the long memory component to the log-spectrum. We then propose two extensions. The first deals with replacing the logarithmic link with a more general Box-Cox link, which encompasses also the identity and the inverse links: this enables nesting alternative spectral estimation methods (autoregressive, exponential, etc.) under the same likelihood-based framework. Secondly, we propose a gradient boosting algorithm for the estimation of the log-spectrum and illustrate its potential for distilling the long memory component of the log-spectrum.
    Keywords: Frequency Domain Methods; Generalized linear models; Long Memory; Boosting.
    JEL: C22 C52
    Date: 2013–03–19

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