4,947 research outputs found
A recursive scheme for computing autocorrelation functions of decimated complex wavelet subbands
This paper deals with the problem of the exact computation of the autocorrelation function of a real or complex discrete wavelet subband of a signal, when the autocorrelation function (or Power Spectral Density, PSD) of the signal in the time domain (or spatial domain) is either known or estimated using a separate technique. The solution to this problem allows us to couple time domain noise estimation techniques to wavelet domain denoising algorithms, which is crucial for the development of blind wavelet-based denoising techniques. Specifically, we investigate the Dual-Tree complex wavelet transform (DT-CWT), which has a good directional selectivity in 2-D and 3-D, is approximately shift-invariant, and yields better denoising results than a discrete wavelet transform (DWT). The proposed scheme gives an analytical relationship between the PSD of the input signal/image and the PSD of each individual real/complex wavelet subband which is very useful for future developments. We also show that a more general technique, that relies on Monte-Carlo simulations, requires a large number of input samples for a reliable estimate, while the proposed technique does not suffer from this problem
The iterated auxiliary particle filter
We present an offline, iterated particle filter to facilitate statistical
inference in general state space hidden Markov models. Given a model and a
sequence of observations, the associated marginal likelihood L is central to
likelihood-based inference for unknown statistical parameters. We define a
class of "twisted" models: each member is specified by a sequence of positive
functions psi and has an associated psi-auxiliary particle filter that provides
unbiased estimates of L. We identify a sequence psi* that is optimal in the
sense that the psi*-auxiliary particle filter's estimate of L has zero
variance. In practical applications, psi* is unknown so the psi*-auxiliary
particle filter cannot straightforwardly be implemented. We use an iterative
scheme to approximate psi*, and demonstrate empirically that the resulting
iterated auxiliary particle filter significantly outperforms the bootstrap
particle filter in challenging settings. Applications include parameter
estimation using a particle Markov chain Monte Carlo algorithm
Testing Dependence Among Serially Correlated Multi-category Variables
The contingency table literature on tests for dependence among discrete multi-category variables assume that draws are independent, and there are no tests that account for serial dependencies − a problem that is particularly important in economics and finance. This paper proposes a new test of independence based on the maximum canonical correlation between pairs of discrete variables. We also propose a trace canonical correlation test using dynamically augmented reduced rank regressions or an iterated weighting method in order to account for serial dependence. Such tests are useful, for example, when testing for predictability of one sequence of discrete random variables by means of another sequence of discrete random variables as in tests of market timing skills or business cycle analysis. The proposed tests allow for an arbitrary number of categories, are robust in the presence of serial dependencies and are simple to implement using multivariate regression methods
The U.S. Excess Money Growth and Inflation Relation in the Long-Run: A Nonlinear Analysis
This paper specifies, estimates, and evaluates the relation between inflation rate and excess money growth, defined as the difference between money supply growth and real GDP growth, using a smooth transition regression model and U.S. data. The results indicate that the relation is a nonlinear one as supported by the linearity tests. Although deterministic extrapolation exercises indicate that both the linear and nonlinear models are stable, the nonlinear model is favored by several misspecification tests. Deterministic extrapolation exercises also indicate that an increase in excess money supply has positive effect on the long-run inflation rate but the effect is not one-to-one even in high-inflation regime.Inflation; Excess Money Growth; Smooth Transition Regression
Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes
We evaluate the asymptotic and finite-sample properties of direct multi-step estimation (DMS) for forecasting at several horizons. For forecast accuracy gains from DMS in finite samples, mis-specification and non-stationarity of the DGP are necessary, but when a model is well-specified, iterating the one-step ahead forecasts may not be asymptotically preferable. If a model is mis-specified for a non-stationary DGP, omitting either negative residual serial correlation or regime shifts, DMS can forecast more accurately. Monte Carlo simulations clarify the non-linear dependence of the estimation and forecast biases on the parameters of the DGP, and explain existing results.Adaptive estimation, multi-step estimation, dynamic forecasts, model mis-specification.
On the impact of fundamentals, liquidity and coordination on market stability
We develop a coordination game to model interactions between fundamentals and liquidity during unstable periods in financial markets. We then propose a flexible econometric framework for estimation of the model and analysis of its quantitative implications. The specific empirical application is carry trades in the yen–dollar market, including the turmoil of 1998. We find a generally very deep market, with low information disparities amongst agents. We observe occasionally episodes of market fragility, or turmoil with up by the escalator, down by the elevator patterns in prices. The key role of strategic behavior in the econometric model is also confirmed.global games, efficient method of moments, carry trades, tail risk, strategic behavior, financial crises
Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases
In many applications, such as physiology and finance, large time series data
bases are to be analyzed requiring the computation of linear, nonlinear and
other measures. Such measures have been developed and implemented in commercial
and freeware softwares rather selectively and independently. The Measures of
Analysis of Time Series ({\tt MATS}) {\tt MATLAB} toolkit is designed to handle
an arbitrary large set of scalar time series and compute a large variety of
measures on them, allowing for the specification of varying measure parameters
as well. The variety of options with added facilities for visualization of the
results support different settings of time series analysis, such as the
detection of dynamics changes in long data records, resampling (surrogate or
bootstrap) tests for independence and linearity with various test statistics,
and discrimination power of different measures and for different combinations
of their parameters. The basic features of {\tt MATS} are presented and the
implemented measures are briefly described. The usefulness of {\tt MATS} is
illustrated on some empirical examples along with screenshots.Comment: 25 pages, 9 figures, two tables, the software can be downloaded at
http://eeganalysis.web.auth.gr/indexen.ht
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