Testing for Neglected Nonlinearity in Long Memory Models

This paper constructs tests for the presence of nonlinearity of unknown form in addition to a fractionally integrated, long memory component in a time series process. The tests are based on artificial neural network structures and do not restrict the parametric form of the nonlinearity. The tests only require a consistent estimate of the long memory parameter. Some theoretical results for the new tests are obtained and detailed simulation evidence is also presented on the power of the tests. The new methodology is then applied to a wide variety of economic and financial time series.Long memory, Non-linearity, Artificial neural networks, Realized volatility, Absolute returns, Real exchange rates, Unemployment

Nonlinear Models with Strongly Dependent Processes and Applications to Forward Premia and Real Exchange Rates

This paper considers estimation and inference in some general non linear time series models which are embedded in a strongly dependent, long memory process. Some new results are provided on the properties of a time domain MLE for these models. The paper also includes a detailed simulation study which compares the time domain MLE with a two step estimator, where the Local Whittle estimator has been initially employed to filter out the long memory component. The time domain MLE is found to be generally superior to two step estimation. Further, the simulation study documents the difficulty of precisely estimating the parameter associated with the speed of transition. Finally, the fractionally integrated, nonlinear autoregressive- ESTAR model is found to be extremely useful in representing some financial time series such as the forward premium and real exchange rates.Non-linearity, ESTAR models, Strong dependence, Forward premium, Real exchange rates

Nonlinear Correlograms and Partial Autocorrelograms

This paper proposes neural network based measures of predictability in conditional mean, and then uses them to construct nonlinear analogues to autocorrelograms and partial autocorrelograms. In contrast to other measures of nonlinear dependence that rely on nonparametric estimation of densities or multivariate integration, our autocorrelograms are simple to calculate and appear to work well in relatively small samples.Nonlinear autocorrelograms, Nonlinear time series models, Neural networks, Model selection criteria, Nonlinear partial autocorrelograms

Testing the Martingale Difference Hypothesis Using Neural Network Approximations

The martingale difference restriction is an outcome of many theoretical analyses in economics and finance. A large body of econometric literature deals with tests of that restriction. We provide new tests based on radial basis function neural networks. Our work is based on the test design of Blake and Kapetanios (2000, 2003a,b). However, unlike that work we can provide a formal theoretical justification for the validity of these tests using approximation results from Kapetanios and Blake (2007). These results take advantage of the link between the algorithms of Blake and Kapetanios (2000, 2003a,b) and boosting. We carry out a Monte Carlo study of the properties of the new tests and find that they have superior power performance to all existing tests of the martingale difference hypothesis we consider. An empirical application to the S&P500 constituents illustrates the usefulness of our new test.Martingale difference hypothesis, Neural networks, Boosting

Testing Linearity in Cointegrating Relations with an Application to Purchasing Power Parity

This paper develops a linearity test that can be applied to cointegrating relations. We consider the widely used RESET specification test and show that when this test is applied to nonstationary time series its asymptotic distribution involves a mixture of noncentral chi^2 distributions, which leads to severe size distortions in conventional testing based on the central chi^2. Nonstationarity is shown to introduce two bias terms in the limit distribution, which are the source of the size distortion in testing. Appropriate corrections for this asymptotic bias leads to a modified version of the RESET test which has a central chi^2 limit distribution under linearity. The modified test has power not only against nonlinear cointegration but also against the absence of cointegration. Simulation results reveal that the modified test has good size infinite samples and reasonable power against many nonlinear models as well as models with no cointegration, confirming the analytic results. In an empirical illustration, the linear purchasing power parity (PPP) specification is tested using US, Japan, and Canada monthly data after Bretton Woods. While commonly used ADF and PP cointegration tests give mixed results on the presence of linear cointegration in the series, the modified test rejects the null of linear PPP cointegration.Nonlinear cointegration, Specification test, RESET test, Noncentral chi^2 distribution

Choosing Lag Lengths in Nonlinear Dynamic Models

Given that it is quite impractical to use standard model selection criteria in a nonlinear modeling context, the builders of nonlinear models often choose lag length by setting it equal to the lag length chosen for a linear autoregression of the data. This paper studies the performance of this procedure in a variety of circumstances, and then proposes some new and simple model selection procedures, based on linear approximations of the nonlinear forms. The idea here is to apply standard selection criteria to these linear approximations, rather than to autoregressions that make no provision for nonlinear behavior. A simulation study compares the properties of these proposed procedures with the properties of linear selection procedures.Nonlinear time series models, Neural networks, Model selection criteria, Polynomial approximations, Volterra expansions.

Measurement of the absolute wavefront curvature radius in a heterodyne interferometer

We present an analytical derivation of the coupling parameter relating the angle between two interfering beams in a heterodyne interferometer to the differential phase-signals detected by a quadrant photo-diode. This technique, also referred to as Differential Wavefront Sensing (DWS), is commonly used in space-based gravitational wave detectors to determine the attitude of a test-mass in one of the interferometer arms from the quadrant diode signals. Successive approximations to the analytical expression are made to simplify the investigation of parameter dependencies. Motivated by our findings, we propose a new measurement method to accurately determine the absolute wave-front curvature of a single measurement beam. We also investigate the change in coupling parameter when the interferometer "test-mirror" is moved from its nominal position, an effect which mediates the coupling of mirror displacement noise into differential phase-measurements.Comment: double-spaced, 21 pages, 5 figure

Higher-order triplet interaction in energy-level modeling of excited-state absorption for an expanded porphyrin cadmium complex

Recent measurements of transmission versus fluence for a methanol-solvated asymmetric pentaazadentate porphyrin-like (APPC) cadmium complex, [(C6H4-APPC)Cd]Cl, showed the limitations of current energy-level models in predicting the transmission behavior of organic reverse saturable absorbers at fluences greater than 1 J/cm². A new model has been developed that incorporates higher-order triplet processes and accurately fits both nanosecond and picosecond transmission-versus-fluence data. This model has provided the first known determination of a higher triplet excited-state absorption cross section and lifetime for an APPC complex and also described a previously unreported feature in the transmission-versus-fluence data. The intersystem crossing rate and the previously neglected higher triplet excited-state absorption cross section are shown to govern the excited-state population dynamics of methanol-solvated [(C6H4-APPC)Cd]Cl most strongly at more-practical device energies