nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒03‒30
eleven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Volatility Inference in the Presence of Both Endogenous Time and Microstructure Noise By Yingying Li; Zhiyuan Zhang; Xinghua Zheng
  2. Priors about Observables in Vector Autoregressions By Marek Jarocinski; Albert Marcet
  3. Ten Things You Should Know About DCC By Massimiliano Caporin; Michael McAleer
  4. Asymptotically UMP Panel Unit Root Tests By Becheri, I.G.; Drost, F.C.; Akker, R. van den
  5. A Noncausal Autoregressive Model with Time-Varying Parameters: An Application to U.S. Inflation By Markku Lanne; Jani Luoto
  6. Noncausality and Inflation Persistence By Markku Lanne
  7. Factor Models in High-Dimensional Time Series: A Time-Domain Approach By Marc Hallin; Marco Lippi
  8. Long Memory Analysis: An Empirical Investigation By Nazarian, Rafik; Naderi, Esmaeil; Gandali Alikhani, Nadiya; Amiri, Ashkan
  9. Financial Time Series Forecasting by Developing a Hybrid Intelligent System By Abounoori, Abbas Ali; Naderi, Esmaeil; Gandali Alikhani, Nadiya; Amiri, Ashkan
  10. Bias in the Mean Reversion Estimator in Continuous-Time Gaussian and Lévy Processes By Yong Bao; Aman Ullah; Yun Wang; Jun Yu
  11. Co-dependence of Extreme Events in High Frequency FX Returns By Arnold Polanski; Evarist Stoja

  1. By: Yingying Li; Zhiyuan Zhang; Xinghua Zheng
    Abstract: In this article we consider the volatility inference in the presence of both market microstructure noise and endogenous time. Estimators of the integrated volatility in such a setting are proposed, and their asymptotic properties are studied. Our proposed estimator is compared with the existing popular volatility estimators via numerical studies. The results show that our estimator can have substantially better performance when time endogeneity exists.
    Date: 2013–03
  2. By: Marek Jarocinski; Albert Marcet
    Abstract: Standard practice in Bayesian VARs is to formulate priors on the autoregressive parameters, but economists and policy makers actually have priors about the behavior of observable variables. We show how this kind of prior can be used in a VAR under strict probability theory principles. We state the inverse problem to be solved and we propose a numerical algorithm that works well in practical situations with a very large number of parameters. We prove various convergence theorems for the algorithm. As an application, we first show that the results in Christiano et al. (1999) are very sensitive to the introduction of various priors that are widely used. These priors turn out to be associated with undesirable priors on observables. But an empirical prior on observables helps clarify the relevance of these estimates: we find much higher persistence of output responses to monetary policy shocks than the one reported in Christiano et al. (1999) and a significantly larger total effect.
    Keywords: vector autoregression, Bayesian estimation, prior about observables, inverse problem, monetary policy shocks
    JEL: C11 C22 C32
    Date: 2013–03
  3. By: Massimiliano Caporin; Michael McAleer (University of Canterbury)
    Abstract: The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of GARCC, which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal BEKK in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.
    Keywords: DCC; BEKK; GARCC; Stated representation; Derived model; Conditional covariances; Conditional correlations; Regularity conditions; Moments; Two step estimators; Assumed properties; Asymptotic properties; Filter; Diagnostic check
    JEL: C18 C32 C58 G17
    Date: 2013–03–19
  4. By: Becheri, I.G.; Drost, F.C.; Akker, R. van den (Tilburg University, Center for Economic Research)
    Abstract: Abstract This paper considers optimal unit root tests for a Gaussian cross-sectionally independent heterogeneous panel with incidental intercepts and heterogeneous alternatives generated by random perturbations. We derive the (asymptotic and local) power envelope for two models: an auxiliary model where both the panel units and the random perturbations are observed, and the second one, the model of main interest, for which only the panel units are observed. We show that both models are Locally Asymptotically Normal (LAN). It turns out that there is an information loss: the power envelope for the auxiliary model is above the envelope for the model of main interest. Equality only holds if the alternatives are homogeneous. Our results exactly identify in which setting the unit root test of Moon, Perron, and Phillips (2007) is asymptotically UMP and, in fact, they show it is not possible to exploit possible heterogeneity in the alternatives, confirming a conjecture of Breitung and Pesaran (2008). Moreover, we propose a new asymptotically optimal test and we extend the results to a model allowing for cross-sectional dependence.
    Keywords: panel unit root test;Local Asymptotic Normality;limit experiment;asymptotic power envelope;information loss
    JEL: C22 C23
    Date: 2013
  5. By: Markku Lanne; Jani Luoto
    Abstract: We propose a noncausal autoregressive model with time-varying parameters, and apply it to U.S. postwar inflation. The model .fits the data well, and the results suggest that inflation persistence follows from future expectations. Persistence has declined in the early 1980.s and slightly increased again in the late 1990.s. Estimates of the new Keynesian Phillips curve indicate that current inflation also depends on past inflation although future expectations dominate. The implied trend inflation estimate evolves smoothly and is well aligned with survey expectations. There is evidence in favor of the variation of trend inflation following from the underlying marginal cost that drives inflation.
    JEL: C22 C51 C53 E31
    Date: 2013
  6. By: Markku Lanne
    Abstract: We use noncausal autoregressions to examine the persistence properties of quarterly U.S. consumer price inflation from 1970:1.2012:2. These nonlinear models capture the autocorrelation structure of the inflation series as accurately as their conventional causal counterparts, but they allow for persistence to depend on the size and sign of shocks to inflation as well as the inflation rate. Inflation persistence has decreased since the early 1980.s, after which persistence is also greater following small and negative shocks than large and positive ones. At high levels of inflation, shocks are absorbed more slowly before the early 1980.s and faster thereafter compared to low levels of inflation.
    JEL: C22 C51 E31
    Date: 2013
  7. By: Marc Hallin; Marco Lippi
    Abstract: High-dimensional time series may well be the most common type of dataset in the socalled“big data” revolution, and have entered current practice in many areas, includingmeteorology, genomics, chemometrics, connectomics, complex physics simulations, biologicaland environmental research, finance and econometrics. The analysis of such datasetsposes significant challenges, both from a statistical as from a numerical point of view. Themost successful procedures so far have been based on dimension reduction techniques and,more particularly, on high-dimensional factor models. Those models have been developed,essentially, within time series econometrics, and deserve being better known in other areas.In this paper, we provide an original time-domain presentation of the methodologicalfoundations of those models (dynamic factor models usually are described via a spectralapproach), contrasting such concepts as commonality and idiosyncrasy, factors and commonshocks, dynamic and static principal components. That time-domain approach emphasizesthe fact that, contrary to the static factor models favored by practitioners, the so-called generaldynamic factor model essentially does not impose any constraints on the data-generatingprocess, but follows from a general representation result.
    Date: 2013–03
  8. By: Nazarian, Rafik; Naderi, Esmaeil; Gandali Alikhani, Nadiya; Amiri, Ashkan
    Abstract: This study is an attempt to review the theory and applications of autoregressive fractionally integrated moving average (ARFIMA) and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models, mainly for the purpose of the description of the observed persistence in the mean and volatility of a time series. The long memory feature in FIGARCH models makes them a better candidate than other conditional heteroskedasticity models for modeling volatility in financial series. ARFIMA model also has a considerable capacity for modeling the return behavior of these time series. The daily data related to Tehran Stock Exchange (TSE) index was used for the purpose of this study. Considering the fact that the existence of conditional heteroskedasticity effects were confirmed in the stock return series, robust regression technique was used for estimation of different ARFIMA models. Furthermore, different GARCH-type models were also compared. The results of ARFIMA model are indicative of the absence of long memory in return series of the TSE index and the results from FIGARCH model show evidence of long memory in conditional variance of this series.
    Keywords: Stock Market, Long Memory, ARFIMA, FIGARCH
    JEL: C22 C58 G14 G17
    Date: 2013–01–30
  9. By: Abounoori, Abbas Ali; Naderi, Esmaeil; Gandali Alikhani, Nadiya; Amiri, Ashkan
    Abstract: The design of models for time series forecasting has found a solid foundation on statistics and mathematics. On this basis, in recent years, using intelligence-based techniques for forecasting has proved to be extremely successful and also is an appropriate choice as approximators to model and forecast time series, but designing a neural network model which provides a desirable forecasting is the main concern of researchers. For this purpose, the present study tries to examine the capabilities of two sets of models, i.e., those based on artificial intelligence and regressive models. In addition, fractal markets hypothesis investigates in daily data of the Tehran Stock Exchange (TSE) index. Finally, in order to introduce a complete design of a neural network for modeling and forecasting of stock return series, the long memory feature and dynamic neural network model were combined. Our results showed that fractal markets hypothesis was confirmed in TSE; therefore, it can be concluded that the fractal structure exists in the return of the TSE series. The results further indicate that although dynamic artificial neural network model have a stronger performance compared to ARFIMA model, taking into consideration the inherent features of a market and combining it with neural network models can yield much better results.
    Keywords: Stock Return, Long Memory, NNAR, ARFIMA, Hybrid Models
    JEL: C22 C45 C53 G10
    Date: 2013–01–17
  10. By: Yong Bao (Department of Economics, Purdue University); Aman Ullah (Department of Economics, University of California,); Yun Wang (School of International Trade and Economics, University of International Business and Economics); Jun Yu (Sim Kee Boon Institute for Financial Economics, School of Economics and Lee Kong Chian School of Business, Singapore Management University)
    Abstract: This paper develops the approximate finite-sample bias of the ordinary least squares or quasi max- imum likelihood estimator of the mean reversion parameter in continuous-time Levy processes. For the special case of Gaussian processes, our results reduce to those of Tang and Chen (2009) (when the long-run mean is unknown) and Yu (2012) (when the long-run mean is known). Simulations show that in general the approximate bias works well in capturing the true bias of the mean reversion estimator under difference scenarios. However, when the time span is small and the mean reversion parameter is approaching its lower bound, we nd it more difficult to approximate well the finite-sample bias.
    JEL: C10 C22
    Date: 2013–03
  11. By: Arnold Polanski (University of East Anglia); Evarist Stoja (University of Bristol)
    Abstract: In this paper, we investigate extreme events in high frequency, multivariate FX returns within a purposely built framework. We generalize univariate tests and concepts to multidimensional settings and employ these novel techniques for parametric and nonparametric analysis. In particular, we investigate and quantify the co-dependence of cross-sectional and intertemporal extreme events. We find evidence of the cubic law of extreme returns, their increasing and asymmetric dependence and of the scaling property of extreme risk in joint symmetric tails.
    Date: 2013–03

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