183,279 research outputs found
VARMA versus VAR for Macroeconomic Forecasting
No full textIn this paper, we argue that there is no compelling reason for restricting the class of multivariate models considered for macroeconomic forecasting to VARs given the recent advances in VARMA modelling methodology and improvements in computing power. To support this claim, we use real macroeconomic data and show that VARMA models forecast macroeconomic variables more accurately than VAR models.Forecasting, Identification, Multivariate time series, Scalar components, VARMA models.
A Complete VARMA Modelling Methodology Based on Scalar Components
No full textThis paper proposes an extension to scalar component methodology for the identification and estimation of VARMA models. The complete methodology determines the exact positions of all free parameters in any VARMA model with a predetermined embedded scalar component structure. This leads to an exactly identified system of equations that is estimated using full information maximum likelihood.Identification, Multivariate time series, Scalar components, VARMA models.
Cointegrated VARMA models and forecasting US interest rates
No full textWe bring together some recent advances in the literature on vector autoregressive moving-average models creating a relatively simple specification and estimation strategy for the cointegrated case. We show that in the cointegrated case with fixed initial values there exists a so-called final moving representation which is usually simpler but not as parsimonious than the usual Echelon form. Furthermore, we proof that our specification strategy is consistent also in the case of cointegrated series. In order to show the potential usefulness of the method, we apply it to US interest rates and find that it generates forecasts superior to methods which do not allow for moving-average terms.Cointegration, VARMA models, forecasting
Vector Autoregresive Moving Average Identification for Macroeconomic Modeling: Algorithms and Theory
No full textThis paper develops a new methodology for identifying the structure of VARMA time series models. The analysis proceeds by examining the echelon canonical form and presents a fully automatic data driven approach to model specification using a new technique to determine the Kronecker invariants. A novel feature of the inferential procedures developed here is that they work in terms of a canonical scalar ARMAX representation in which the exogenous regressors are given by predetermined contemporaneous and lagged values of other variables in the VARMA system. This feature facilitates the construction of algorithms which, from the perspective of macroeconomic modeling, are efficacious in that they do not use AR approximations at any stage. Algorithms that are applicable to both asymptotically stationary and unit-root, partially nonstationary (cointegrated) time series models are presented. A sequence of lemmas and theorems show that the algorithms are based on calculations that yield strongly consistent estimates.Keywords: Algorithms, asymptotically stationary and cointegrated time series, echelon
Macro-scale matter wave generation in charged particle dynamics in a magnetic field, a consequence of quantum entanglement
No full textMatter wave interference effects on the macro-scale predicted by the author in charged particle dynamics in a magnetic field [R.K. Varma, Phys. Rev. E 64, 036608 (2001)], and observed subsequently [R.K. Varma, A.M. Punithavelu, S.B. Banerjee, Phys. Rev. E 65, 026503 (2002); R.K. Varma, S.B. Banerjee, Phys. Scr. 75, 19 (2007)] have been shown here to be an interesting consequence of quantum entanglement between the parallel and perpendicular degrees of freedom of the particle. Treating the problem in the framework of the inelastic scattering theory, it is shown that these macro-scale matter waves are generated in the ‘parallel’ degree of freedom as a modulation of the plane wave state of the particle along the field concomitantly with the excitation of Landau levels in the perpendicular degree of freedom in an inelastic scattering episode. We highlight here the role of quantum entanglement leading to the generation of this macro-scale quantum entity which has been shown to exhibit observable consequences. This case also exemplifies a situation exhibiting quantum entanglement on the macro-scale
Conditional Correlations and Volatility Spillovers Between Crude Oil and Stock Index Returns
No full textThis paper investigates the conditional correlations and volatility spillovers between crude oil returns and stock index returns. Daily returns from 2 January 1998 to 4 November 2009 of the crude oil spot, forward and futures prices from the WTI and Brent markets, and the FTSE100, NYSE, Dow Jones and S&P500 index returns, are analysed using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer, Hoti and Chan (2008), and DCC model of Engle (2002). Based on the CCC model, the estimates of conditional correlations for returns across markets are very low, and some are not statistically significant, which means the conditional shocks are correlated only in the same market and not across markets. However, the DCC estimates of the conditional correlations are always significant. This result makes it clear that the assumption of constant conditional correlations is not supported empirically. Surprisingly, the empirical results from the VARMA-GARCH and VARMA-AGARCH models provide little evidence of volatility spillovers between the crude oil and financial markets. The evidence of asymmetric effects of negative and positive shocks of equal magnitude on the conditional variances suggests that VARMA-AGARCH is superior to VARMA-GARCH and CCC.conditional correlations;crude oil prices;multivariate GARCH;forward and futures prices;spot;stock indices;volatility spillovers
Strongly Sublinear Algorithms for Testing Pattern Freeness
No full textFor a permutation , a function contains a -appearance if there exists such that for all , if and only if . The function is -free if it has no -appearances. In this paper, we investigate the problem of testing whether an input function is -free or whether differs on at least values from every -free function. This is a generalization of the well-studied monotonicity testing and was first studied by Newman, Rabinovich, Rajendraprasad and Sohler (Random Structures and Algorithms 2019). We show that for all constants , , and permutation , there is a one-sided error -testing algorithm for -freeness of functions that makes queries. We improve significantly upon the previous best upper bound by Ben-Eliezer and Canonne (SODA 2018). Our algorithm is adaptive, while the earlier best upper bound is known to be tight for nonadaptive algorithms.28 pages, 2 figures; We thank anonymous reviewers for comments that helped us significantly improve the presentatio
Description of a new species of Sitana Cuvier, 1829 from southern India
No full textDeepak, V., Khandekar, Akshay, Varma, Sandeep, Chaitanya, R. (2016): Description of a new species of Sitana Cuvier, 1829 from southern India. Zootaxa 4139 (2): 167-182, DOI: http://doi.org/10.11646/zootaxa.4139.2.
Global Vipassana Pagoda: Main features and history of construction
No full textGlobal Vipassana Pagoda is the world's largest span stone masonry dome. It is located in the North-East of Mumbai, India, and was erected during years 2000-2008. Despite it is a relatively new construction, its importance and the classic way of utilizing domes and masonry to carry gravity loads, makes it a heritage masterpiece to preserve and monitor with the aim of preventing undesired damages and structural deficiencies. This Pagoda exhibits unique features, since it is conceived as a dome over dome construction (one lower segmental dome, an intermediate inverse catenary and an upper conical dome), to achieve the impressive height of 90 meter. This paper recalls the main geometric features of the pagoda and gives a brief insight into the long construction history, that took about 8 years for the erection from the foundation to the pinnacle
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