11,881 research outputs found

    Testing for Neglected Nonlinearity in Cointegrating Relationships

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    This paper proposes pure significance tests for the absence of nonlinearity in cointegrating relationships. No assumption of the functional form of the nonlinearity is made. It is envisaged that the application of such tests could form the first step towards specifying a nonlinear cointegrating relationship for empirical modelling. The asymptotic and small sample properties of our tests are investigated, where special attention is paid to the role of nuisance parameters and a potential resolution using the bootstrap.Cointegration, Nonlinearity, Neural networks, Bootstrap

    Market depth and order size: an analysis of permanent price effects of DAX futures' trades

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    In this paper we empirically analyze the permanent price impact of trades by investigating the relation between unexpected net order flow and price changes. We use intraday data on German index futures. Our analysis based on a neural network model suggests that the assumption of a linear impact of orders on prices (which is often used in theoretical papers) is highly questionable. Therefore, empirical studies, comparing the depth of different markets, should be based on the whole price impact function instead of a simple ratio. To allow the market depth to depend on trade volume could open promising avenues for further theoretical research. This could lead to quite different trading strategies as in traditional models. --

    Testing for ARCH in the Presence of Nonlinearity of Unknown Form in the Conditional Mean

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    Tests of ARCH are a routine diagnostic in empirical econometric and financial analysis. However, it is well known that misspecification of the conditional mean may lead to spurious rejections of the null hypothesis of no ARCH. Nonlinearity is a prime example of this phenomenon. There is little work on the extent of the effect of neglected nonlinearity on the properties of ARCH tests. This paper provides some such evidence and also new ARCH testing procedures that are robust to the presence of neglected nonlinearity. Monte Carlo evidence shows that the problem is serious and that the new methods alleviate this problem to a very large extent.Nonlinearity, ARCH, Neural networks

    Forecasting inflation with thick models and neural networks

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    This paper applies linear and neural network-based ā€œthickā€ models for forecasting inflation based on Phillipsā€“curve formulations in the USA, Japan and the euro area. Thick models represent ā€œtrimmed meanā€ forecasts from several neural network models. They outperform the best performing linear models for ā€œreal-timeā€ and ā€œbootstrapā€ forecasts for service indices for the euro area, and do well, sometimes better, for the more general consumer and producer price indices across a variety of countries. JEL Classification: C12, E31bootstrap, Neural Networks, Phillips Curves, real-time forecasting, Thick Models

    Consistent and Non-Degenerate Model Specification Tests Against Smooth Transition Alternatives

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    In this paper we develop a test of functional form that is consistent against any deviation from the null specification, and directs power towards a class of nonlinear "smooth transition" functional forms. Of separate interest, we provide new details regarding when and whether consistent parametric tests of functional form are asymptotically degenerate. A test of linear autoregression against smooth transition alternatives is never degenerate. Moreover, a test of Exponential STAR has surprising power and non-degeneracy attributes entirely associated with the choice of threshold. In a simulation experiment in which all parameters are randomly selected the proposed test has power nearly identical to the most powerful tests for true STAR, neural network and SETAR processes, and dominates popular tests. We apply the test to monthly U.S. output, money supply, prices and interest rates.smooth transition, consistent test, nondegenerate test, nonlinear, neural networks

    CMOS design of chaotic oscillators using state variables: a monolithic Chua's circuit

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    This paper presents design considerations for monolithic implementation of piecewise-linear (PWL) dynamic systems in CMOS technology. Starting from a review of available CMOS circuit primitives and their respective merits and drawbacks, the paper proposes a synthesis approach for PWL dynamic systems, based on state-variable methods, and identifies the associated analog operators. The GmC approach, combining quasi-linear VCCS's, PWL VCCS's, and capacitors is then explored regarding the implementation of these operators. CMOS basic building blocks for the realization of the quasi-linear VCCS's and PWL VCCS's are presented and applied to design a Chua's circuit IC. The influence of GmC parasitics on the performance of dynamic PWL systems is illustrated through this example. Measured chaotic attractors from a Chua's circuit prototype are given. The prototype has been fabricated in a 2.4- mu m double-poly n-well CMOS technology, and occupies 0.35 mm/sup 2/, with a power consumption of 1.6 mW for a +or-2.5-V symmetric supply. Measurements show bifurcation toward a double-scroll Chua's attractor by changing a bias current

    A novel approach to error function minimization for feedforward neural networks

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    Feedforward neural networks with error backpropagation (FFBP) are widely applied to pattern recognition. One general problem encountered with this type of neural networks is the uncertainty, whether the minimization procedure has converged to a global minimum of the cost function. To overcome this problem a novel approach to minimize the error function is presented. It allows to monitor the approach to the global minimum and as an outcome several ambiguities related to the choice of free parameters of the minimization procedure are removed.Comment: 11 pages, latex, 3 figures appended as uuencoded fil
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