Positive Serial Dependence Research Articles

Linear Summation of Repulsive and Attractive Serial Dependencies: Orientation and Motion Dependencies Sum in Motion Perception.

Recent work from several groups has shown that perception of various visual attributes in human observers at a given moment is biased toward what was recently seen. This positive serial dependency is a kind of temporal averaging that exploits short-term correlations in visual scenes to reduce noise and stabilize perception. To date, this stabilizing "continuity field" has been demonstrated on stable visual attributes such as orientation and face identity, yet it would be counterproductive to apply it to dynamic attributes in which change sensitivity is needed. Here, we tested this using motion direction discrimination and predict a negative perceptual dependency: a contrastive relationship that enhances sensitivity to change. Surprisingly, our data showed a cubic-like pattern of dependencies with positive and negative components. By interleaving various stimulus combinations, we separated the components and isolated a positive perceptual dependency for motion and a negative dependency for orientation. A weighted linear sum of the separate dependencies described the original cubic pattern well. The positive dependency for motion shows an integrative perceptual effect and was unexpected, although it is consistent with work on motion priming. These findings suggest that a perception-stabilizing continuity field occurs pervasively, occurring even when it obscures sensitivity to dynamic stimuli.SIGNIFICANCE STATEMENT Recent studies show that visual perception at a given moment is not entirely veridical, but rather biased toward recently seen stimuli: a positive serial dependency. This temporal smoothing process helps perceptual continuity by preserving stable aspects of the visual scene over time, yet, for dynamic stimuli, temporal smoothing would blur dynamics and reduce sensitivity to change. We tested whether this process is selective for stable attributes by examining dependencies in motion perception. We found a clear positive dependency for motion, suggesting that positive perceptual dependencies are pervasive. We also found a concurrent negative (contrastive) dependency for orientation. Both dependencies combined linearly to determine perception, showing that the brain can calculate contrastive and integrative dependencies simultaneously from recent stimulus history when making perceptual decisions.

Empirical measures of signed serial dependence in categorical time series

The categorical time series analysis has become an area of emerging interest both in research and professional practice. In this article, we propose empirical measures of signed serial dependence, which are particularly important for identifying and fitting an appropriate model to a given categorical time series. We derive asymptotic properties of these measures and a rule for identifying significant dependence. We investigate the finite-sample performance of the proposed measures in a simulation study and show that they are sensitive to both positive and negative serial dependence even for rather short time series.

The Forward Discount Puzzle: Identification of Economic Assumptions

The forward discount puzzle refers to the robust empirical finding that foreign excess returns are predictable. We investigate if expectations errors are the main cause of this predictability using the serial dependence pattern of excess returns implied by economic models as identification device. This approach also allows us to explain why strong predictability of excess returns only occurs during 1980s. Using USD bilateral spot and forward rates from 1975-2009, we show that both the statistically significant positive serial dependence of excess returns in the entire sample and the very weak (mostly insignificant) positive serial dependence in the subsample excluding observations in 1980-87 are consistent with the predictions of the expectations errors explanation. We provide several pieces of new empirical evidence which support the link between the strong predictability in the 1980s and changes in forecasting techniques by foreign exchange market agents.

Predicting the Direction of the Fed's Target Rate

ABSTRACTPredicting the direction of central banks' target interest rates is important for various market participants. This paper advances procedures for predicting the direction of the federal funds target rate using a dynamic extension of the multinomial logit model. I find that the 6‐month Treasury bill spread relative to the federal funds rate, the unemployment rate and the real GDP growth rate have superior predictive content for the direction of the target a week to several months ahead. When these variables are employed, lagged target changes do not provide additional predictive power. This suggests that the apparent positive serial dependence of the target changes is due to the Fed's systematic response to autocorrelated macroeconomic variables. Copyright © 2010 John Wiley & Sons, Ltd.

Are Foreign Excess Returns Always Predictable? Expectations Errors Revisited

Using USD bilateral exchange rates in 1975-2009, we find that the strong predictability of foreign excess returns documented in the literature is mainly driven by a particular sample period. We first show that both the statistically significant positive serial dependence of excess returns in the entire sample and the very weak (mostly insignificant) positive serial dependence in the subsample excluding observations in 1980-87 are consistent with the prediction of the expectations errors alternative. We link this with the change in forecasting techniques over time and provide evidence that the behavior of the survey expectation is compatible with the market expectation.

Nonlinear dynamics in foreign exchange excess returns: Tests of asymmetry

This paper examines the nonlinear dynamic behaviors in foreign exchange excess returns of eight currencies. We applied the BDS test and two other nonlinear statistical techniques, the Markov chain, and time reversibility tests to characterize the exchange rate returns dynamics. The results from the BDS test provide strong evidence of nonlinear dependence on the British pound, the Singapore dollar, the South African rand, and the Swedish krone. The Markov chain test shows evidence of a non-random walk and positive serial dependence in all currencies except for the British pound, the Canadian dollar, and the Swiss franc. Lastly, evidence of time irreversible and asymmetric dynamic behavior is found in seven currencies with the exception of the Canadian dollar. The results indicate that the asymmetry in the Singapore dollar, the South African rand, and the Swiss franc is due to nonlinearity in the functional form as opposed to non-Gaussian innovations.

The consumption-based determinants of the term structure of discount rates

The rate of return of a zero-coupon bond with maturity T is determined by our expectations about the mean (+), variance (-) and skewness (+) of the growth of aggregate consumption between 0 and T. The shape of the yield curve is thus determined by how these moments vary with T. We first examine growth processes in which a higher past economic growth yields a first-degree dominant shift in the distribution of the future economic growth, as assumed for example by Vasicek (J. Financ. Econ. 5, 177–188, 1977). We show that when the growth process exhibits such a positive serial dependence, then the yield curve is decreasing if the representative agent is prudent (\(u^{\prime \prime \prime } > 0\)), because of the increased risk that it yields for the distant future. A similar definition is proposed for the concept of second-degree stochastic dependence, as observed for example in the Cox–Ingersoll–Ross model, with the opposite comparative static property holding under temperance (\(u^{\prime \prime \prime \prime } < 0\)), because the change in downside risk (or skweness) that it generates. Finally, using these theoretical results, we propose two arguments in favor of using a smaller rate to discount cash-flows with very large maturities, as those associated to global warming or nuclear waste management.

Fractal modeling of human isochronous serial interval production

The Hurst exponent (H) was estimated for series of 256 time intervals produced by human participants, collected in 5 sessions performed on different days. Each series was obtained during the continuation phase following synchronization with 25 isochronous intervals generated by a computer and presented through headphones. Dispersional analysis yielded estimates of H > 0.5. These were sufficiently stable to yield statistically significant differences between participants and between each target interval duration (0.5, 0.8, 1.1, and 1.5 s). The results indicate that variability in isochronous serial interval production (ISIP) can be modeled as fractional Gaussian noise, which corroborates and qualifies previous research indicating positive serial dependency or long memory in ISIP data in terms of drift and 1/f noise characteristics. It is concluded that ISIP is a more complex process than is assumed by influential timing models and theories, and that realistic modeling of human timing must account for nonlinear variability patterns.

Estimating Pearson's Correlation Coefficient with Bootstrap Confidence Interval from Serially Dependent Time Series

Pearson's correlation coefficient, rxy, is often used when measuring the influence of one time-dependent variable on another in bivariate climate time series. Thereby, positive serial dependence (persistence) and unknown data distributions impose a challenge for obtaining accurate confidence intervals for rxy. This is met by the presented approach, employing the nonparametric stationary bootstrap with an average block length proportional to the maximum estimated persistence time of the data. A Monte Carlo experiment reveals that this method can produce accurate (in terms of coverage) confidence intervals (of type bias-corrected and accelerated). However, since persistence reduces the number of independent observations, substantially more data points are required for achieving an accuracy comparable to a situation without persistence. The experiment further shows that neglecting serial dependence may lead to serious coverage errors. The presented method proves robust with respect to distributional shape (lognormal/normal) and time spacing (uneven/even). The method is used to confirm that a previous finding of a correlation between solar activity and Indian Ocean monsoon strength in early Holocene is valid. A further result is that the correlation between sunspot number and cosmogenic 10Be concentration vanishes after approximately 1870.

Factor‐Related and Specific Returns of Common Stocks: Serial Correlation and Market Inefficiency

One of the most basic tests of market efficiency is the test for serial correlation of returns. If the return on a typical stock in period t is correlated with its return in period t 1, then the best (unbiased minimum mean square error) prediction for the return in period t equals the prior return multiplied by the correlation coefficient. An investor concerned only about the mean and variance of portfolio return would use the prediction to improve the mean/variance trade-off of portfolio return by shading the portfolio toward stocks predicted to have high returns and away from stocks predicted to have low returns. Hence, using knowledge about the previous month's returns, the investor produces a portfolio with superior (mean/variance) performance. This violates the weak form of market-efficiency (Fama [1]), in the sense that the expected return for each stock, based upon knowledge of the previous month's return, is different from the expected return based only upon the current stock price. The same argument may be applied to any component of returns, as distinct from total return. For example, in a linear multiple-factor-plus-specific-return model of security returns, total excess return is decomposed into factor-related return and specific return. The empirical results in this paper find positive serial dependence in the factor-related component and negative serial dependence in the specific component which nearly offset one another, resulting in zero correlation in total excess returns. Formally,