On a family of finite moving‐average trend filters for the ends of series

A family of finite end filters is constructed using a minimum revisions criterion and based on a local dynamic model operating within the span of a given finite central filter. These end filters are equivalent to evaluating the central filter with unavailable future observations replaced by constrained optimal linear predictions. Two prediction methods are considered: best linear unbiased prediction and best linear biased prediction where the bias is time invariant. The properties of these end filters are determined. In particular, they are compared to X‐11 end filters and to the case where the central filter is evaluated with unavailable future observations predicted by global ARIMA models as in X‐11‐ARIMA or X‐12‐ARIMA. Copyright © 2002 John Wiley & Sons, Ltd.     

A nonparametric method for asymmetrically extending signal extraction filters

Two important problems in the X‐11 seasonal adjustment methodology are the construction of standard errors and the handling of the boundaries. We adapt the ‘implied model approach’ of Kaiser and Maravall to achieve both objectives in a nonparametric fashion. The frequency response function of an X‐11 linear filter is used, together with the periodogram of the differenced data, to define spectral density estimates for signal and noise. These spectra are then used to define a matrix smoother, which in turn generates an estimate of the signal that is linear in the data. Estimates of the signal are provided at all time points in the sample, and the associated time‐varying signal extraction mean squared errors are a by‐product of the matrix smoother theory. After explaining our method, it is applied to popular nonparametric filters such as the Hodrick–Prescott (HP), the Henderson trend, and ideal low‐pass and band‐pass filters, as well as X‐11 seasonal adjustment, trend, and irregular filters. Finally, we illustrate the method on several time series and provide comparisons with X‐12‐ARIMA seasonal adjustments. Copyright © 2010 John Wiley & Sons, Ltd.     

Predicting cyclical turning points with leading index in a markov switching model

We have evaluated the Commerce Department's Composite Index of Leading Indicators as a predictor of business cycle turning points using the two-state Markov switching model as the filter. Contrary to some recent studies, we found that the predictive performance of CLI is quite good and, with an exception of the 1973:11 peak, it made very little difference to the prediction of turning points whether real-time data are used instead of the revised series. We found, however, that imposing any degree of autoregression in the errors on the simple regime-shift model caused the filter to signal turning points inappropriately. Also, we found no evidence of duration dependence in post-war U.S. business cycles.     

State space trend-cycle decomposition of the ARIMA(1,1,1) process

It has been common practice to decompose an integrated time series into a random walk trend and a stationary cycle using the state space model. Application of state space trend-cycle decomposition, however, often results in a misleading interpretation of the model, especially when the observability of the state space model and the redundant relationships among the model parameters are not properly considered. In this study, it is shown that spurious trend-cycle decomposition, discussed by Nelson (1988), results from an unobservable state space model, and the usual assumption of independent noise processes in the model results in parameter redundancy. Equivalence relationships for the ARIMA(1,1,1) process and the state space model consisting of a random walk trend and an AR(1) cycle, where the noise processes of the trend and of the cycle are generally correlated, are also derived. © 1997 John Wiley & Sons, Ltd.     

Forecasting Latent Volatility through a Markov Chain Approximation Filter

We propose a new methodology for filtering and forecasting the latent variance in a two‐factor diffusion process with jumps from a continuous‐time perspective. For this purpose we use a continuous‐time Markov chain approximation with a finite state space. Essentially, we extend Markov chain filters to processes of higher dimensions. We assess forecastability of the models under consideration by measuring forecast error of model expected realized variance, trading in variance swap contracts, producing value‐at‐risk estimates as well as examining sign forecastability. We provide empirical evidence using two sources, the S&P 500 index values and its corresponding cumulative risk‐neutral expected variance (namely the VIX index). Joint estimation reveals the market prices of equity and variance risk implicit by the two probability measures. A further simulation study shows that the proposed methodology can filter the variance of virtually any type of diffusion process (coupled with a jump process) with a non‐analytical density function. Copyright © 2015 John Wiley & Sons, Ltd.     

Detecting and Predicting Economic Accelerations,Recessions, and Normal Growth Periods in Real‐Time

The dichotomous characterization of the business cycle in recessions and expansions has been central in the literature over the last 50 years. However, there are various reasons to question the adequacy of this dichotomous, recession/expansion approach for our understanding of the business cycle dynamics, as well as for the prediction of future business cycle developments. In this context, the contribution of this paper to the literature is twofold. First, since a positive rate of growth at the level of economic activity can be considered as the normal scenario in modern economies due to both population and technological growth, it proposes a new non‐parametric algorithm for the detection and dating of economic acceleration periods, trend or normal growth periods, and economic recessions. Second, it uses an ordered probit framework for the estimation and forecasting of these three business cycle phases, applying an automatized model selection approach using monthly macroeconomic and financial data on the German economy. The empirical results show that this approach has superior out‐of‐sample properties under real‐time conditions compared to alternative probit models specified individually for the prediction of recessions and/or economic accelerations. Copyright © 2016 John Wiley & Sons, Ltd.     

Inbreeding considerations in a REM sleep model for rat swimming activity

Over the past few years, our laboratory group has elaborated a repeated measures rat swimming test. It provides an animal base for showing that the REM sleep mechanism is important to both emotional responsiveness and environmental adaptations. All of that work has been done with Sprague-Dawley rats obtained from a local supplier. Work done with two European rat stocks (by researchers in France and The Netherlands) shows general agreement with our own. In this presentation, we directly compare rats derived from an English vendor's Sprague-Dawley stock with the U.S. based Sprague-Dawley stock which we have been using. We also make strain comparisons via the F344 and the Long Evans strains. Although the literature has numerous examples of swimming test differences between inbred and wild rat stocks, strain difference effects have not been reported. We report that there are significant differences attributable to inbred strain but not to vendor on this measure.     

Bayesian Analysis of a Threshold Stochastic Volatility Model

This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of the mean equation follows a threshold distribution in which the mean innovation switches between two regimes. In our model, the threshold is treated as an unknown parameter. We show that the proposed threshold SV model can not only capture the time‐varying volatility of returns, but can also accommodate the asymmetric shape of conditional distribution of the returns. Parameter estimation is carried out by using Markov chain Monte Carlo methods. For model selection and volatility forecast, an auxiliary particle filter technique is employed to approximate the filter and prediction distributions of the returns. Several experiments are conducted to assess the robustness of the proposed model and estimation methods. In the empirical study, we apply our threshold SV model to three return time series. The empirical analysis results show that the threshold parameter has a non‐zero value and the mean innovations belong to two separately distinct regimes. We also find that the model with an unknown threshold parameter value consistently outperforms the model with a known threshold parameter value. Copyright © 2016 John Wiley & Sons, Ltd.     

Inbreeding considerations in a REM sleep model for rat swimming activity

Summary Over the past few years, our laboratory group has elaborated a repeated measures rat swimming test. It provides an animal base for showing that the REM sleep mechanism is important to both emotional responsiveness and environmental adaptations. All of that work has been done with Sprague-Dawley rats obtained from a local supplier. Work done with two European rat stocks (by researchers in France and The Netherlands) shows general agreement with our own. In this presentation, we directly compare rats derived from an English vendor's Sprague-Dawley stock with the U.S. based Sprague-Dawley stock which we have been using. We also make strain comparisons via the F344 and the Long Evans strains. Although the literature has numerous examples of swimming test differences between inbred and wild rat stocks, strain difference effects have not been reported. We report that there are significant differences attributable to inbred strain but not to vendor on this measure.Supported by NIH/MBRS Grant 08192. Student coworkers who received program support were: D. Gillham, E. Hannas, M. Ochoa, and J. Lopez. N. Phillips made the figure.     

Linear signal extraction with intervention techniques in non-linear time series

Seasonal adjustment is performed in some data-producing agencies according to the ARIMA-model-based signal extraction theory. A stochastic linear process parametrized in terms of an ARIMA model is first fitted to the series, and from this model the models for the trend, cycle, seasonal, and irregular component can be derived. A spectrum is associated to every component model and is used to compute the optimal Wiener–Kolmogorov filter. Since the modelling is linear, prior linearization of the series with intervention techniques is performed. This paper discusses the performance of linear signal extraction with intervention techniques in non-linear processes. In particular, the following issues are discussed: (1) the ability of intervention techniques to linearize time series which present non-linearities; (2) the stability of the linear projection giving the components estimators under non-linear misspecifications; (3) the capacity of the WK filter to preserve the linearity in some components and the non-linearities in others. Copyright © 1998 John Wiley & Sons, Ltd.     

Unemployment variation over the business cycles: a comparison of forecasting models

Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non‐linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non‐linearity in the unemployment series. Only recently have there been some developments in applying non‐linear models to estimate and forecast unemployment rates. A major concern of non‐linear modelling is the model specification problem; it is very hard to test all possible non‐linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non‐linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back‐propagation model and a generalized regression neural network model to estimate and forecast post‐war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out‐of‐sample forecast results obtained by the ANN models with those obtained by several linear and non‐linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd.     

Identifying US business cycle regimes using dynamic factors and neural network models

We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in-sample and out-of-sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.     

Sigma-delta ADC中数字滤波器设计

本文主要论述了用于音频系统ADC的过采样抽取滤波器的设计。系统讨论了滤波器的多级实现。采用了求平均值的方法去除1bit sigma-delta引入的直流电平。通过滤波器的多级化,左右声道共享组合电路,滤波采取时分复用思想,通过合理安排数字滤波器滤波执行时序以共享乘累加单元,有效地缩小了电路面积。     

Stock index prediction based on wavelet transform and FCD-MLGRU

With the development of artificial intelligence, deep learning is widely used in the field of nonlinear time series forecasting. It is proved in practice that deep learning models have higher forecasting accuracy compared with traditional linear econometric models and machine learning models. With the purpose of further improving forecasting accuracy of financial time series, we propose the WT-FCD-MLGRU model, which is the combination of wavelet transform, filter cycle decomposition and multilag neural networks. Four major stock indices are chosen to test the forecasting performance among traditional econometric model, machine learning model and deep learning models. According to the result of empirical analysis, deep learning models perform better than traditional econometric model such as autoregressive integrated moving average and improved machine learning model SVR. Besides, our proposed model has the minimum forecasting error in stock index prediction.     

Operational filtering of traffic data

An operational filter of traffic state variables is presented for use in designing computer-aided traffic surveillance and control systems. A total of 166 data sets from three traffic surveillance systems were used in the filter development. All the data sets were best represented by an ARIMA (0,1,3) filter. This filter has the following advantages: (1) it yields minimum mean-square-error forecasts if stationarity of the observations can be obtained; (2) it provides much better results than the existing ad hoc filters; (3) it is computationally tractable; and (4) it requires modest computer storage of data. Suggestions and implications for the use of this filter are given.     

Trichomes as models for studying plant cell differentiation

Trichomes, originating from epidermal cells, are present on nearly all terrestrial plants. They exist in diverse forms, are readily accessible, and serve as an excellent model system for analyzing the molecular mechanisms in plant cell differentiation, including cell fate choices, cell cycle control, and cell morphogenesis. In Arabidopsis, two regulatory models have been identified that function in parallel in trichome formation; the activator–inhibitor model and the activator–depletion model. Cotton fiber, a similar unicellular structure, is controlled by some functional homologues of Arabidopsis trichome-patterning genes. Multicellular trichomes, as in tobacco and tomato, may form through a distinct pathway from unicellular trichomes. Recent research has shown that cell cycle control participates in trichome formation. In this review, we summarize the molecular mechanisms involved in the formation of unicellular and multicellular trichomes, and discuss the integration of the cell cycle in its initiation and morphogenesis.     

A Bayesian analysis of periodic integration

Recent empirical research into the seasonal and trend properties of macroeconomic time series using periodic models has resulted in strong evidence in favour of periodic integration (PI). PI implies that the differencing filter necessary to remove a stochastic trend varies across seasons and, hence, that seasonal fluctuations are related to the stochastic trend. Previous studies finding evidence of PI have used classical econometric techniques. In this paper, we investigate the possible sensitivity of this empirical result by using Bayesian techniques. An application of posterior odds analysis and highest posterior density interval tests to several quarterly UK macroeconomic series suggests strong evidence for PI, even when we allow for structural breaks in the deterministic seasonals. A predictive exercise indicates that PI usually outperforms other competing models in terms of out-of-sample forecasting. © 1997 John Wiley & Sons, Ltd.     

Why do regime‐switching models forecast so badly?

Most non‐linear techniques give good in‐sample fits to exchange rate data but are usually outperformed by random walks or random walks with drift when used for out‐of‐sample forecasting. In the case of regime‐switching models it is possible to understand why forecasts based on the true model can have higher mean squared error than those of a random walk or random walk with drift. In this paper we provide some analytical results for the case of a simple switching model, the segmented trend model. It requires only a small misclassification, when forecasting which regime the world will be in, to lose any advantage from knowing the correct model specification. To illustrate this we discuss some results for the DM/dollar exchange rate. We conjecture that the forecasting result is more general and describes limitations to the use of switching models for forecasting. This result has two implications. First, it questions the leading role of the random walk hypothesis for the spot exchange rate. Second, it suggests that the mean square error is not an appropriate way to evaluate forecast performance for non‐linear models. Copyright © 1999 John Wiley & Sons, Ltd.     

Expansion of amino acid homo-sequences in proteins: Insights into the role of amino acid homo-polymers and of the protein context in aggregation

Expansion of amino acid homo-sequences, such as polyglutamines or polyalanines, in proteins has been directly implicated in various degenerative diseases through a mechanism of protein misfolding and aggregation. However, it is still unclear how the nature of the expansion and the protein context influence the tendency of a protein to aggregate. Here, we have addressed these questions using spinocerebellar ataxia type-3 (ATX3) protein, the best characterised of the polyglutamine proteins, chosen as a model system. Using a transfected mammalian cell line, we demonstrate that ATX3 aggregation is noticeably reduced by deletion or replacement of regions other than the polyglutamine tract. The nature of the amino acid homo-sequences also has a strong influence on aggregation. From our studies, we draw general conclusions on the effect of the protein architecture and of the amino acid homo-sequence on pathology. Received 3 March 2006; received after revision 19 April 2006; accepted 22 May 2006     

The variance ratio and trend stationary model as extensions of a constrained autoregressive model

This paper shows that a constrained autoregressive model that assigns linearly decreasing weights to past observations of a stationary time series has important links to the variance ratio methodology and trend stationary model. It is demonstrated that the proposed autoregressive model is asymptotically related to the variance ratio through the weighting schedules that these two tools use. It is also demonstrated that under a trend stationary time series process the proposed autoregressive model approaches a trend stationary model when the memory of the autoregressive model is increased. These links create a theoretical foundation for tests that confront the random walk model simultaneously against a trend stationary and a variety of short‐ and long‐memory autoregressive alternatives. Copyright © 2009 John Wiley & Sons, Ltd.