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Summary We present three non-parametric trispectrum tests that can establish whether the spectral decomposition of kurtosis of high frequency financial asset price time series is consistent with the assumptions of Gaussianity, linearity and time reversiblility. The detection of non-linear and time irreversible probabilistic structure has important implications for the choice and implementation of a range of models of the evolution of asset prices, including Black–Scholes–Merton option pricing model, auto-regressive conditional heteroscedastic or generalized auto-regressive conditional heteroscedastic and stochastic volatility models. We apply the tests to a selection of high frequency Australian stocks.

Following spatial choice theory and MAP methodology, we employ the data drawn from recent nationwide public opinion surveys to probe the latent political choice space in American political competition. Our analyses demonstrate that, in addition to the traditional left–right ideology continuum, there is a second distinct dimension in American political choice space. More importantly, the results from our regression analyses suggest that the second dimension seems to be driven by a cleavage among different reform prospects, ranging from low-politics reformism, to politics-as-usual approach, to high-politics style of change.

EEG and field potential rhythms established in the cortex and thalamus may accommodate the propagation of seizures. This article describes the interaction between thalamus and cortex during pentylenetetrazol (PTZ) seizures in rats with and without prior treatment with ethosuximide (ESM), a well-known antiepileptic drug (AED) that raises the threshold for seizures, was given before PTZ. The AED was given before PTZ convulsant administration. We track this thalamo-cortical association with a novel measure we have called the cross-bicoherence gain, or BISCOH. This quantity allows us to measure the spectral coherence in a purely higher order spectralmethodology. BISCOH is able to track the formation of nonlinearities at specific frequencies in the recorded EEG. BISCOH showed a strong increase in low alpha wave harmonic generationat 10 and 12.5 Hz after ESM treatment (p < 0.02 and p < 0.007, respectively). Conventional coherence failed to show distinctive and significant changes in thalamo-cortical coupling after ESM treatment at those frequencies and instead showed changes at 5 Hz. This rise in cortical rhythms is evidence of harmonic generation or new frequency formation in the thalamo-cortical system withAED therapy. BISCOH could become a powerful tool in unraveling changes in coherence due to neuroelectric modulation resulting from drug treatment or electrical stimulation.

We perform non-linearity tests using daily data for leading currencies that include the Australian dollar, British pound, Brazilian real, Canadian dollar, euro, Japanese yen, Mexican peso, and the Swiss franc to resolve the issue of whether these currencies are driven by fundamentals or exogenous shocks to the global economy. In particular, we use a new method of testing for linear and nonlinear lead/lag relationships between time series, introduced by Brooks and Hinich (J Empir Finance 20:385–404, 1999), based on the concepts of cross-correlation and cross-bicorrelation. Our evidence points to a relatively rare episodic nonlinearity within and across foreign exchange rates. We also test the validity of specifying ARCH-type error structures for foreign exchange rates. In doing so, we estimate Bollerslev’s (J Econom 31:307–327, 1986) generalized ARCH (GARCH) model and Nelson’s (1988) exponential GARCH (EGARCH) model, using a variety of error densities [including the normal, the Student-t distribution, and the Generalized Error Distribution (GED)] and a comprehensive set of diagnostic checks. We apply the Brooks and Hinich (1999) nonlinearity test to the standardized residuals of the optimal GARCH/EGARCH model for each exchange rate series and show that the nonlinearity in the exchange rates is not due to ARCH-type effects. This result has important implications for the interpretation of the recent voluminous literature which attempts to model financial asset returns using this family of models.

This paper investigates the duration dependence of the US stock market cycles. A new classification method for bull and bear market regimes based on the crossing of the market index and its moving average is proposed. We show evidence of duration dependence in whole cycles. The half cycles, however, are found to be duration independent. More importantly, we find that the degree of duration dependence of the US stock market cycles has dropped after the launch of the NASDAQ index.

Using a modification of the Hinich, J Time Ser Anal 3(3):169–176, (1982) bispectrum test for nonlinearity and Gaussianity, the residuals of the Tiao and Box, J Am Stat Assoc 76:802–816, (1981) constrained and unconstrained VAR models for the gas furnace data reject the assumption of Gaussianity and linearity over a grid of bandwidths for estimating the bispectrum. These findings call into question the specification of the linear VAR and VARMA models assumed by Tiao and Box, J Am Stat Assoc 76:802–816, (1981). Utilizing the alternative Hinich J Nonparametr Stat 6:205–221, (1996) nonlinearity test, the residuals of the VAR model were shown to exhibit episodic nonlinearity. The sensitivity of the findings to outliers is investigated by estimating and testing the residuals of L1 and MINIMAX models from 1–6 lags. Building on the linear dynamic specification, a multivariate adaptive regression splines (MARS) model is estimated, using two software implementations, and shown to remove the nonlinearity in the residuals. Leverage plots were used to illustrate the “cost” of imposing a linearity assumption. Out-of-sample forecasting tests from 1–6 periods ahead found that using the sum-of-squared errors criteria, the MARS model out performed ACE, GAM and projection pursuit models.

[I]f you believe it's time to challenge the Washington politics ... to restore a sense of mission to our politics and a sense of possibility to America ... I ask you to believe in yourself I ask you to believe again in the dream that we call America. --Barack Obama (1) In New Orleans, McCain said the word "change" or a variation of it more than 30 times in his speech--a sign that he knows what voters are looking for. --Holly Bailey (2) We have all been told that the 2008 presidential election was historic--not merely a consequential election, but a monumental moment in American history. This may, in fact, prove to be true. But in many ways, the 2008 presidential election was both predictable and quite comparable to a handful of past races. Consider that the 2008 election featured an incumbent party seeking a third consecutive presidential victory, but burdened by a costly war, a sagging economy, and an unpopular president. The elections of 1952 and 1968--both of which produced a change in party control of the White House--were surely relevant touchstones. Both Dwight D. Eisenhower and Richard M. Nixon pounded the incumbent administrations for their incompetence and malfeasance during their election campaigns, while promising to bring needed change and outside-the-Beltway sensibilities to Washington. Like those candidates, Barack Obama had a huge advantage and a clear path to the goal line in 2008. John McCain, on the other hand, needed to convince voters that a COP victory in 2008 would not constitute a "third term" for President George W. Bush. Because of these circumstances, both presidential campaigns attempted to capture the mantel of "reform." Their core strategic assumption was abundantly clear: a candidate who could tap into voters' frustration with the policies and politics of the last few years could expect a substantial electoral bonanza. The most obvious manifestation of this belief is that much of the rhetoric of the 2008 presidential campaign involved words such as "change," "reform," "outsider," and "maverick." But the truth is that we do not know very much about the electoral implications of being a "change" candidate in the United States. There is almost no empirical research on whether voters consider "outsider" candidates as better than "insider" candidates, or whether this particular distinction is even relevant to their political calculus. More specifically, there has been no systematic study of how the notion of "reform" politics played out in the 2008 presidential election. This study takes aim at this gap. We proceed in a straightforward manner. Initially, we propose using insights and assumptions from spatial theory to consider the nature of competition in U.S. presidential elections. Second, we examine data from the National Election Study (NES) from 1992 to 2004 to determine the number and character of dimensions structuring voters' perceptions of presidential candidates in those years. Third, we apply the same approach with data from the 2008 Cooperative Congressional Election Study (CCES) to consider the possibility of a reform--establishment dimension in the Obama-McCain contest. Fourth, we use postelection survey data from the University of Texas at Austin's Government Department polls to more precisely estimate (1) the distribution of voters along a reform-establishment dimension, (2) their perceptions of where the candidates and parties fell along this dimension, and (3) the impact of these perceptions on presidential vote choice. Fifth and finally, we speculate about the pervasiveness and components of reform sentiments in future U.S. elections and policy debates. The Dimensions of Political Competition In studying the potential role of reformist sentiment and candidates in 2008, we assume that voters and candidates are rational actors who attempt to maximize their utility when deciding for whom to vote. …

In this article, we show how tests of nonlinear serial dependence can be applied to high-frequency time series data that exhibit high volatility, strong mean reversion, and leptokurtotis. Portmanteau correlation, bicorrelation, and tricorrelation tests are used to detect nonlinear serial dependence in the data. Trimming is used to control for the presence of outliers in the data. The data that are employed are 161,786 half-hourly spot electricity price observations recorded over nearly a decade in the wholesale electricity market in New South Wales, Australia. Strong evidence of nonlinear serial dependence is found and its implications for time series modeling are discussed.

The purpose of this note is to analyze the capability of bandpass filters to extract a known periodicity. The specific bandpass filters considered are a conventional discrete Fourier transform (DFT) filter and the filter recently proposed by Iacobucci and Noullez. We employ simulation methods to investigate cycle extraction properties. We also examine the implications arising from the Gibbs effect in practical settings that typically confront applied macroeconomists

Motivated by the shortcomings of earlier Chinese efficiency studies, the present paper re-examines the weak-form efficiency of Shanghai and Shenzhen Stock Exchanges. Specifically, our adopted methodologies mitigate the confounding effect of thin trading on return autocorrelation, detect both linear and nonlinear serial dependencies in the adjusted returns series, and capture the persistence of dependency structures over time. The result shows that the adjusted returns series from both markets follow a random walk for long periods of time, only to be interspersed with brief periods of strong linear and/or nonlinear dependency structures. This suggests that there are certain time periods when new information is not fully reflected into stock prices. Another interesting finding is that the existence of serial dependencies in both the Shanghai and Shenzhen Stock Exchanges follows one another closely after October 1997. It indicates that both markets respond in a similar way to influences from political, economic, social and institutional changes.

This article shows that the Hinich (1982) bispectrum test for gaussianity and the Hinich and Rothman (1998) test for time reversibility can be used to falsify the null hypothesis that an autoregressive conditionally heteroskedastic model (ARCH) or its generalization (GARCH) generates nonlinear behavior in the variance of an observed time series. The term “falsify” means that the null hypothesis can be rejected with a given size using a nonparametric test based on the bispectrum where the data is trimmed to control the sizes. Rejecting the null hypothesis implies that the ARCH or GARCH model that is estimated from the data is not a complete statistical description of the dependence structure in the variance of the process.

SUMMARY Objective: Anterior thalamus (AN) has been shown to mediate seizures in both focal and generalized models. Specific regional increase in AN serotonergic activity was observed following AN-DBS in our pentylenetetrazol (PTZ) rodent model of acute seizures, and this increase may inhibit seizures and contribute to the mechanism of anticonvulsant DBS. Methods: Anesthetized rats with AN-directed dialysis cannula with scalp/depth EEG were infused with PTZ at 5.5 mg/(kg min) until an EEG seizure occurred. Eight experimental groups of AN-dialysis infusion were evaluated: controls (dialysate-only), 10 and 100 mM serotonin 5-HT7 agonist 5-carboxamidotryptamine (5-CT), 1, 10 and 100 mM serotonin antagonist methysergide (METH), AN-DBS, and 100 mM METH + AN-DBS. Results: Latency for seizures in control animals was 3120 770 s (S.D.); AN-DBS delayed onset to 5018 1100 (p < 0.01). AN-directed 5-CT increased latency in dose-dependent fashion: 3890 430 and 4247 528 (p < 0.05). Methysergide had an unexpected protective effect at low-dose (3908 550, p < 0.05) but not at 100 mM (2687 1079). The anticonvulsant action of AN-DBS was blocked by prior dialysis using 100 mM METH. Surface EEG burst count and nonlinear analysis (H-Statistic) noted significant (p < 0.05) increased pre-ictal epileptiform bursts in 5-CT, methysergide, but not DBS group compared to control. Conclusion: Increased serotonergic activity in AN raised PTZ seizure threshold, similar to DBS, but without preventing cortical bursting. 5-Carboxamidotryptamine, a 5-HT7 agonist, demonstrated dosedependent seizure inhibition. Methysergide proved to have an inverse, dose-dependent agonist property, antagonizing the action of AN-DBS at the highest dose. Anticonvulsant AN-DBS may in part act to selectively alter serotonin neurotransmission to raise seizure threshold.

The waveform of a simple sustained tone emitted from musical instruments such as a flute played by even the best musician is never exactly periodic. There is always some variation over time in the waveform of a single pitch that is characteristic of the instrument itself. In this paper, we employ the signal-coherence function introduced by Hinich [A statistical theory of signal coherence, J. Oceanic Eng. 25(2) (2000) 256-261] to study the subtle variation of tones from several instruments played by accomplished musicians. This measure characterizes the amount of variation in each Fourier component as a random amplitude-modulation component added to a coherent narrowband sinusoid. The signal-coherence function is computed from several digitized acoustic signals of several musical instruments. The signal-coherence functions show that there are important differences between the same notes produced from different instruments. The signal coherence of a vibrato, a deliberate modulation of a tone, is analyzed for the first time using the signal-coherence function. We show that for most practical playing conditions it has a small effect for lower frequencies. This allows characterization of modulation variations in sustained portions of sound with and without vibrato. The signal-coherence processing method applied to musical acoustics could lead to more realistic music synthesizers.

The objective of this paper is to re-examine the weak-form efficiency of 10 Asian emerging stock markets. Using a battery of nonlinearity tests, the statistical results reveal that all the returns series still contain predictable nonlinearities even after removing linear serial correlation from the data. The next stage of sub-sample analysis using the Hinich [Hinich, M., 1996. Testing for dependence in the input to a linear time series model. Journal of Nonparametric Statistics 6, 205–221] bicorrelation test shows that the 10 Asian series follow a pure noise process for long periods of time, only to be interspersed with brief periods of strong nonlinear dependence. The exploratory investigation found that the cross-country differences in nonlinear departure from market efficiency can be explained by market size and trading activity, while the transient burst of nonlinear periods in each individual market can be attributed largely to the occurrence of economic and political events.

The fractional integrated process has been widely studied since the seminal work of Granger and Joyeux (1980). However, most of the tests for fractional integration are time-domain tests. This article develops a Cramer–von Mises type frequency-domain test for fractional integration. Our test is shown to have high power against a wide variety of alternatives. The test is applied to U.S. real disposable income and the quarterly real GDP data of the G7 countries. It is found that most of the series are fractionally integrated at the 5% level. The result is in line with Hinich and Chong (2007).

Many topics might be discussed in the course of any election, but problems that are in fact discussed, and which affect the electorate's choice, are located in the issue space of a relatively small dimension. Two factors contribute to this phenomenon: (a) party platforms are usually presented to the electorate as packages of issues, and (b) candidates tend to emphasize only a few particular issues in the campaign. We model a dynamic process of changing the issue space by candidates as a matter of their campaign strategy and study factors causing changes in the dimensionality or/and in the structure of the set of issues shaping the political conflict in the election. We show how particular features of an added new issue can change voter perceptions of the candidates or the structure of the political conflict in the election when the new issue is such that (1) voters care about it, (2) a majority of voters are interested changing the status quo of anything associated with this issue, and (3) the existing ideological differences among the candidates have clear reflections in voters' minds.

This paper extends the work in Serletis and Shintani [Serletis A, Shintani M. No evidence of chaos but some evidence of dependence in the US stock market. Chaos, Solitons & Fractals 2003;17:449–454], Elder and Serletis [Elder J, Serletis A. On fractional integrating dynamics in the US stock market. Chaos, Solitons & Fractals [forthcoming, 2007]], and Koustas et al. [Koustas Z, Lamarche J-F, Serletis A. Threshold random walks in the US stock market. Chaos, Solitons & Fractals [forthcoming, 2007]] by examining the empirical evidence for random walk type behavior in the US stock market. In doing so, it uses the FORTRAN 95 program developed by Hinich [Hinich MJ. A statistical theory of signal coherence. IEEE J Oceanic Eng 2000;25:256–261] and detects a statistically significant randomly modulated periodic signal.

This article checks for the adequacy of using GARCH models in exchange rate series. Using the Hinich portmanteau bicorrelation test, we find that a GARCH formulation or any of its variants fails to capture the data generating process of the main Latin American exchange rates. Our results highlight the potential of having misleading public policy when estimates are based in GARCH types of models. This article also complements recent similar findings encountered in European and Asian economies.

This study searches for economic and political events that may explain the episodic nonlinearities detected in the returns series of the Chilean stock market index. This methodology is a reverse form of event study. After applying the Hinich portmanteau bicorrelation test to detect episodes of nonlinear behaviour of the index, we investigate what might be the explanation of this behaviour. Our findings may help to explain the difficulty to forecast asset returns. We also shed some light into the major political and economic events that contribute to the numerous short bursts of nonlinear dependence in the Chilean stock market.

This article uses daily observations for the Canadian dollar–U.S. dollar nominal exchange rate over the recent flexible exchange rate period and a new statistical technique, recently developed by Hinich, to detect major political and economic events that have affected the exchange rate.

This paper uses a new method of testing for linear and nonlinear lead/lag relationships between time series, introduced by Brooks and Hinich [Brooks, C. and Hinich, M. J. Cross-Correlations and Cross-Bicorrelations in Sterling Exchange Rates. Journal of Empirical Finance 20 (1999), 385–404.], on Alberta's natural gas and power markets. The test, based on the concepts of cross-correlation and cross-bicorrelation, is used after pre-whitening of the data to test for the existence of residual nonlinearity as well as the episodic nature of the nonlinearity. Our evidence points to a relatively rare episodic nonlinearity within and across the two series, having important implications for forecasting these series.

We demonstrate that the process of generating smooth transitions can be viewed as a natural result of the filtering operations implied in the generation of discrete-time series observations from the sampling of data from an underlying continuous time process that has undergone a process of structural change. In order to focus discussion, we utilize the problem of estimating the location of abrupt shifts in some simple time series models. This approach will permit us to address salient issues relating to distortions induced by the inherent aggregation associated with discrete-time sampling of continuous time processes experiencing structural change. We also address the issue of how time irreversible structures may be generated within the smooth transition processes.

This letter applies the Hinich portmanteau bicorrelation test jointly with the windowed testing procedure to detect nonlinear behaviour in the rate of returns series for seven Latin American stock market indices. Our results suggest that the nonlinear serial dependencies are episodic in nature. All the stock returns series are characterized by few brief periods of highly significant nonlinearity, followed by long time periods in which the returns follow a pure noise process. Our findings help explain why there are difficulties in forecasting asset returns.

This study employs the Hinich portmanteau bicorrelation test (Hinich 1996; Hinich and Patterson 1995) as a diagnostic tool to determine the adequacy of Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models for eight Asian stock markets. The bicorrelation test results demonstrate that this type of model cannot provide an adequate characterisation for the underlying process of all the selected Asian stock markets. Further investigation using the windowed test procedure reveals that the violation of the covariance stationarity assumption as required by the GARCH process is due to the presence of transient epochs of dependencies in the data. The inadequacy of GARCH models has strong implications for the pricing of stock index options, portfolios selection, development of optimal hedging techniques and risk management.

Consider the problem of estimating the positions of a set of targets in a multidimensional Euclidean space from distances reported by a number of observers when the observers do not know their own positions in the space. Each observer reports the distance from the observer to each target plus a random error. This statistical problem is the basic model for the various forms of what is called multidimensional unfolding in the psychometric literature. Multidimensional unfolding methodology as developed in the field of cognitive psychology is basically a statistical estimation problem where the data structure is a set of measures that are monotonic functions of Euclidean distances between a number of observers and targets in a multidimensional space. The new method presented in this article deals with estimating the target locations and the observer positions when the observations are functions of the squared distances between observers and targets observed with an additive random error in a two-dime...

All signals that appear to be periodic have some sort of variability from period to period regardless of how stable they appear to be in a data plot. A true sinusoidal time series is a deterministic function of time that never changes and thus has zero bandwidth around the sinusoid's frequency. A zero bandwidth is impossible in nature since all signals have some intrinsic variability over time. Deterministic sinusoids are used to model cycles as a mathematical convenience. Hinich [IEEE J. Oceanic Eng. 25 (2) (2000) 256-261] introduced a parametric statistical model, called the randomly modulated periodicity (RMP) that allows one to capture the intrinsic variability of a cycle. As with a deterministic periodic signal the RMP can have a number of harmonics. The likelihood ratio test for this model when the amplitudes and phases are known is given in [M.J. Hinich, Signal Processing 83 (2003) 1349-1352]. A method for detecting a RMP whose amplitudes and phases are unknown random process plus a stationary noise process is addressed in this paper. The only assumption on the additive noise is that it has finite dependence and finite moments. Using simulations based on a simple RMP model we show a case where the new method can detect the signal when the signal is not detectable in a standard waterfall spectrogram display.

This study utilizes the Hinich portmanteau bicorrelation test in conjunction with the windowed testing procedure to examine the cross-temporal universality of non-linear dependencies in the returns series for Asian stock market indices. As a whole, the detected non-linear dependencies do not appear to be persistent or stable across time for all the stock markets. In particular, the underlying process is of a switching type, with the pure noise process from time to time switches to a non-linear dependent stochastic process for some unknown length of time, and then switches back to pure-noise. This provides a plausible explanation for the disappointing forecasting performance of many non-linear models, as these existing models do not take note of the episodic transient nature of the non-linear dependency structures.

This paper advocates a reverse from of event studies that is data-dependent to determine endogeneously the events that trigger non-linear market behavior. Using the Malaysian stock market as our case study, coupled with the ‘windowing' approach proposed by Hinich and Patterson (1995), the present study is able to identify major political and economic events that contributed to the short bursts of non-linear behavior. The present framework can be extended to individual firm to examine the adjustment of its stock price to firm-specific events, which will provide deeper insight into issues on corporate finance.

All signals that are normally called "periodic" have some amplitude and phase variation from period to period. For example an active sonar system transmits a periodic pulse train to detect targets. The received pulses are not perfectly periodic due to random modulation of the pulses from scattering and attenuation. Target scattering and propagation distortion of the transmitted and received signals produce seemingly random variation from pulse to pulse. A stochastic nonparametric model of period-to-period variation is presented in this paper. This model is used to derive an asymptotic likelihood-ratio test for wide bandwidth pulses that undergo a random modulation from pulse to pulse. It is shown that the test statistic is a linear combination of the matched filter using the expected value of the received signal as the matching vector and the signal's periodogram.

In optimizing information flows in networks, it would be useful to predict aspects of the network traffic. Yet, the notion of predicting network traffic does not appear in the relevant literature reporting analysis of network traffic. This literature is both well developed and skeptical about the value of traditional time series analysis on network data. It has consistently reported three "traffic invariants" in the analysis of network and Internet traffic. This study uses such time series analysis on a day's worth of Internet log data and finds poor support for one of the invariants. In the preliminary analysis, evidence of nonlinearity was discovered in these data and the analysis presented here examines this question further. This study posits that nonlinear events may be a traffic invariant although this hypothesis would have to be investigated further. The appearance of nonlinear structures is important to the question of predicting network traffic because there are currently no methods to predict time series with nonlinear structures. The discovery of non-linear structures, then, may mean that developing a predictive model is impossible with current techniques. On the other hand, these nonlinearities may result from interactions from other OSI Layers than the one studied.

In the literature, the identification of non-linear systems using functional series has been approached in many different ways. Since the earlier work by Wiener [1] great attention has been devoted to find algorithms for estimating the coefficients of the so-called Volterra models [2]. These models are a direct result of the work of Frechet [3] on the theory of functionals. Examples of recent development on the field can be found in references [4, 5] and references therein. A method is presented in this paper for estimating the linear and quadratic complex transfer function of a weakly non-linear system. The input to the system that is excited by a signal especially constructed for the purpose. The excitation signal is a sum of sinusoids with the same amplitude and pseudo-randomly jittered phases that are selected for the experiment and recorded. To motivate the non-linear frequency-domain linear plus quadratic model presented in section 2, consider the following simple example. Assume that a non-linear system has only one output and the system response satisfies the homogenous non-linearly perturbed differential equation

A novel method for modeling and forecasting seasonal time series is proposed. Unlike the traditional approach that depends solely on dynamic models, the proposed method combines stochastic dynamic modeling with an analysis filter bank designed to reduce dimensionality and to extract persistent components for reliable long-term forecasting. The filter bank decomposes the time series of interest into seasonal components, and only those components that are highly coherent across the periods are selected for subsequent modeling and forecasting. Experiments show that under suitable conditions, the use of highly coherent components not only reduces the modeling complexity and the required amount of training data but also limits the impact of noise and occasional corruption in the training data and thus provides robust forecasts with reduced variability. Fourier and wavelet filter banks are discussed in detail. Simulated and real-data examples are used to illustrate the method.

An international team of researchers reports a metric multidimensional spatial analysis of the 1999 Ukrainian presidential election. Using data from a nationwide pre-election poll (N=1521), they estimate the spatial positions of the eight leading contenders for that office along with respondent ideal points in that same space. Voters' perceptions in the western and eastern halves of the country are compared. Results are compared with those of the 1998 parliamentary contests, reported earlier. Alternative models of voting criteria—contrasting issue-centered with personality-centered voting—are presented and applied.

This paper proposes and implements a new methodology for forecasting time series, based on bicorrelations and cross-bicorrelations. It is shown that the forecasting technique arises as a natural extension of, and as a complement to, existing univariate and multivariate non-linearity tests. The formulations are essentially modified autoregressive or vector autoregressive models respectively, which can be estimated using ordinary least squares. The techniques are applied to a set of high-frequency exchange rate returns, and their out-of-sample forecasting performance is compared to that of other time series models

A periodic signal can be perfectly predicted far into the future since it perfectly repeats every period. There is always some variation in the waveform over time for signals which are labeled as periodic but which are not truly deterministic. A formal definition is presented in this paper for such a varying periodic signal and the properties of such a class of signals are exploited. A measure called a signal coherence function of the amount of random variation in each Fourier component of the signal is defined and its statistical properties are developed. This signal coherence function is very different from the coherence function between two stationary signals. The method is applied to a digitized record of an acoustic signal generated by a boat in a bag in the Baltic Sea south of Stockholm, Sweden.

Linear dynamical systems are widely used in many different fields from engineering to economics. One simple but important class of such systems is called the single-input transfer function model . Suppose that all variables of the system are sampled for a period using a fixed sample rate. The central issue of this paper is the determination of the smallest sampling rate that will yield a sample that will allow the investigator to identify the discrete-time representation of the system. A critical sampling rate exists that will identify the model. This rate, called the Nyquist rate, is twice the highest frequency component of the system. Sampling at a lower rate will result in an identification problem that is serious. The standard assumptions made about the model and the unobserved innovation errors in the model protect the investigators from the identification problem and resulting biases of undersampling. The critical assumption that is needed to identify an undersampled system is that at least one of the exogenous time series be white noise.

In the absence of market frictions, the cost-of-carry model of stock index futures pricing predicts that returns on the underlying stock index and the associated stock index futures contract will be perfectly contemporaneously correlated. Evidence suggests, however, that this prediction is violated with clear evidence that the stock index futures market leads the stock market. It is argued that traditional tests, which assume that the underlying data generating process is constant, might be prone to overstate the lead-lag relationship. Using a new test for lead-lag relationships based on cross correlations and cross bicorrelations it is found that, contrary to results from using the traditional methodology, periods where the futures market leads the cash market are few and far between and when any lead-lag relationship is detected, it does not last long. Overall, the results are consistent with the prediction of the standard cost-of-carry model and market efficiency.

Two American political scientists and a Ukrainian sociologist analyze the results of Ukraine's 1998 parliamentary elections. The purpose is to test propositions about the cognitive organization of voters' attitudes about issues and evaluations of the political parties that seek to mobilize their vote. The article then examines differences on these scores between the East and West of the country. A test is provided of the spatial model of voting that has been found to be a successful predictor of belief organization in other countries and milieux.

This paper revisits the income convergence hypothesis by using the nonlinear unit root test of Kapetanios et al. [Kapetanios, G., Shin, Y. and A. Snell, 2003. Testing for a unit root in the nonlinear STAR framework. Journal of Econometrics 112, 359-379.]. Out of the 12 OECD income gaps in which nonlinearity has been detected, two cases of long-run converging and four cases of catching up are found. © 2008 Elsevier B.V. All rights reserved.

We introduce a frequency-domain test of time reversibility, the REVERSE test. It is based on the bispectrum. We analytically establish the asymptotic distribution of the test and also explore its finite-sample properties through Monte-Carlo simulation. Following other researchers who demonstrated that the problem of business-cycle asymmetry can be stated as whether macroeconomic fluctuations are time irreversible, we use the REVERSE test as a frequency-domain test of business-cycle asymmetry. Our empirical results show that time irreversibility is the rule rather than the exception for a representative set of macroeconomic time series for five OECD countries.

This paper presents a spatial analysis of political competition in Taiwan in an effort to explore the role of conflict displacement in the process of democratic transition. In recent elections, a new cleavage on socioeconomic justice has emerged as a salient political issue in Taiwan, crosscutting the traditional cleavage on national identity. The authors first trace the historical trajectory of regime transition in order to provide a structural explanation of such a displacement of conflicts. Using data from the 1992 General Survey on Social Changes designed primarily by the authors for the Institute of Ethnology of Academia Sinica, they then present the results of a spatial analysis. The empirical findings confirm that socioeconomic justice together with national identity are the defining dimensions of the latent ideological space in which political competition takes place. The authors argue that, because of the availability of the new issue, political elites in Taiwan are undertaking a partisan realignment in both electoral and legislative politics, a process the authors consider conducive to both the transition to democracy and the consolidation of the new regime.

This paper presents a simple test for dependence in the residuals of a linear parametric time series model fitted to non gaussian data. The test statistic is a third order extension of the standard correlation test for whiteness. but the number of lags used in this test is a function of the sample size. The power of this test goes to one as the sample size goes to infinity for any alternative which has non zero bicovariances c e3(r,s)= E[e(t)e(t + r)e(t + s)] for a zero mean stationary random time series. The asymptotic properties of the test statistic are rigorously determined. This test is important for the validation of the sampling properties of the parameter estimates for standard finite parameter linear models when the unobserved input (innovations) process is white but not gaussian. The sizes and power derived from the asymptotic results are checked using artificial data for a number of sample sizes. Theoretical and simulation results presented in this paper support the proposition that the test wi...

Interest has been growing in testing for nonlinearity and chaos in economic data, but much controversy has arisen about the available results. This paper explores the reasons for these empirical difficulties. We apply five tests for nonlinearity or chaos to various monetary aggregate data series. We find that the inferences vary across tests for the same data, and within tests for varying sample sizes and various methods of aggregation of the data. Robustness of inferences in this area of research seems to be low and may account for the controversies surrounding empirical claims of nonlinearity and chaos in economics.