Simple State-space Model Research Articles

A Simple State-Space Model of Human Driver Applicable to Windy Conditions

The paper is concerned with the design, verification and evaluation of a car-driving test scenario for human driver assessment. The scenario implemented in Unreal Engine adds four different wind characteristics which disturb the motion of a simulated vehicle. Besides, the driver is instructed to change the driving lane at defined intervals. These forcing functions enable the identification of the human-machine loop using state-space models. The parameters characterising the human dynamics are extracted from the model of the whole loop. As opposed to rather obsolete McRuer models, this approach follows the recent trends in the modelling of human-machine systems as multiloop systems or quadratically optimal controllers. Our results suggest that the model relying on a single transfer function with 4 parameters loses prediction capabilities during more realistic scenarios, in which random disturbances, such as wind gusts, affect the vehicle. In such cases, the multiloop model with the same number of parameters is able to capture human behaviour more accurately than McRuer model.

On the persistence of near-surface temperature dynamics in a warming world.

We consider issues related to the effect of climate change on the persistence of (trend-corrected) temperatures using global gridded data for both land and oceans. We first discuss how the presence of trends and additive noise affects inference about persistence. Ignoring a trend induces an upward bias, while not accounting for noise induces a downward bias. We show that the increase in persistence in the commonly used Warm Spell Duration Index is simply an artifact of increasing temperatures. To purge the impact of both trends and noise, we adopt a simple state-space model. Of separate interest, we document a much larger noise component for land than for oceans. The estimates of the persistence are much larger for oceans than for land. Inspection of the estimates across various subsamples and the application of tests for structural changes suggest the same pattern of persistence for both land and oceans across time, with few minor exceptions. Hence, our results show that surface temperature persistence has remained constant during the observed period.

Energy-optimal control of intelligent track inspection trains: design and experiment

This study proposes and evaluates the effectiveness of a new energy-efficient control strategy for Intelligent Track Inspection Trains (ITITs). First, a sufficiently simple state-space model for a real ITIT is introduced. This model is employed to formulate and solve a constrained optimization problem to determine the vehicle's optimal speed profile using the pseudo-spectral method to achieve the highest energy savings. Then, we take advantage of the model predictive control (MPC) scheme to create a controller to follow the resulting optimal speed trajectory as closely as possible. The results obtained from co-simulation between a high-fidelity model of the ITIT built within AMESim and the MPC controller developed in the Matlab/Simulink environment show that the optimal speed trajectory is tracked very well when the MPC controller is applied to the vehicle's high-fidelity model. During the co-simulations, the energy consumption in terms of the battery's state of charge (SOC) changes for the MPC-based optimal speed trajectory following was around 6% less than that quantity for a conventional non-optimal cruise controller. Moreover, in the experiment with the real ITIT, the energy consumption in terms of the SOC changes for the non-optimal cruise controller was 5% more than that value for the MPC-based optimal speed trajectory following.

A framework for pre-processing individual location telemetry data for freshwater fish in a river section

Animal movement study often relies on individual tracking. The data scale (in time and space) varies according to the species, the environment where individuals live, or the exogenous processes that drive movement. To explore freshwater fish movement in rivers, fine-scale data are needed. Also, in rivers, recorded telemetry frequently shows missing data and location errors. The irregular time-steps, huge amount of data, environmental complexity (river section) and how fish move in such anisotropic environments undermine the use of statistical frameworks such as state-space models. To deal with these specificities, data pre-treatment can be required. We propose a generic method of telemetry data pre-processing, which can be transposed to other datasets. This framework includes interpolation to handle trajectories at fine time scales and performs data analysis within a state-space model.We combined analyses on observed and simulated data at various interpolation time-steps to choose the one that best preserves the general movement while reducing the total amount of data required. First, we directly compared raw and interpolated data, and the results of parameter inference of a simple state-space model using the interpolated data. The state-space model infers behavioural state based on speed and turning angle between successive locations in animal trajectories. We also included two additional variables computed from raw data: a quantitative indicator of the correspondence between the interpolated trajectory and the raw data, and the variance of turning angles of raw data within the interpolation time-step. We were finally able to determine the most appropriate time-step to obtain locations that were regularly spaced in time and to reduce the amount of data while maintaining the precision of the raw data. Computational time was reduced 12-fold by using a 30-second time-step to interpolate data simulated at 3-second intervals. The inclusion of the two variables derived from raw data compensated for the loss of information in interpolated trajectories and allowed more efficient discrimination between behaviours.

Improving inference for nonlinear state-space models of animal population dynamics given biased sequential life stage data.

State‐space models (SSMs) are a popular tool for modeling animal abundances. Inference difficulties for simple linear SSMs are well known, particularly in relation to simultaneous estimation of process and observation variances. Several remedies to overcome estimation problems have been studied for relatively simple SSMs, but whether these challenges and proposed remedies apply for nonlinear stage‐structured SSMs, an important class of ecological models, is less well understood. Here we identify improvements for inference about nonlinear stage‐structured SSMs fit with biased sequential life stage data. Theoretical analyses indicate parameter identifiability requires covariates in the state processes. Simulation studies show that plugging in externally estimated observation variances, as opposed to jointly estimating them with other parameters, reduces bias and standard error of estimates. In contrast to previous results for simple linear SSMs, strong confounding between jointly estimated process and observation variance parameters was not found in the models explored here. However, when observation variance was also estimated in the motivating case study, the resulting process variance estimates were implausibly low (near‐zero). As SSMs are used in increasingly complex ways, understanding when inference can be expected to be successful, and what aids it, becomes more important. Our study illustrates (a) the need for relevant process covariates and (b) the benefits of using externally estimated observation variances for inference about nonlinear stage‐structured SSMs.

On the Effect of Bank of Japan’s Outright Purchase on the JGB Yield Curve

This paper examines an impact of Bank of Japan (BOJ)’s outright purchase on the Japanese government bond (JGB) yield curve. Particularly, we develop a simple state space model, which incorporates new factors regarding the BOJ’s announcement for its outright purchase and the current market outstanding with standard level and spread factors. Based on the model with a filtering method, we also implement an empirical analysis with time series of the BOJ’s announcement records during 2014/10/22–2017/8/3 in the quantitative–qualitative easing period to estimate the sensitivities of interest rates against the changes in the market expectation for the net supply with each sector of JGB. We expect the current work provides a basis for considering quantitative effects on the term structure by BOJ’s policy changes such as termination or significant reduction of the BOJ’s outright purchase. For instance, our scenario analysis shows substantial increase in the 30 year yield with widening of 20–30 year spread.

On the Effect of Bank of Japan's Outright Purchase on the JGB Yield Curve

This paper examines an impact of Bank of Japan (BOJ)'s outright purchase on the JGB (Japanese government bond) yield curve. Particularly, we develop a simple state space model, which incorporates new factors regarding the BOJ's announcement for its outright purchase and the current market outstanding with standard level and spread factors. Based on the model with a filtering method, we also implement an empirical analysis with time series of the BOJ's announcement records during 2014/10/22-2017/8/3 in the quantitative-qualitative easing(QQE) period to estimate the sensitivities of interest rates against the changes in the market expectation for the net supply with each sector of JGB. We expect the current work provides a basis for considering quantitative effects on the term structure by BOJ's policy changes such as termination or significant reduction of the BOJ's outright purchase. For instance, our scenario analysis shows substantial increase in the 30 year yield with steepening of 20-30 year spread.

A Bayesian Analysis of Unobserved Component Models Using Ox

This article details a Bayesian analysis of the Nile river flow data, using a simple state space model. This allows the article to concentrate on implementation issues surrounding this model. For this data set, Metropolis-Hastings and Gibbs sampling algorithms are implemented in the programming language Ox. The Markov chain Monte Carlo methods only provide output conditioned upon the full data set. For filtered output, conditioning only on past observations, the particle filter is introduced. The sampling methods are flexible, and this advantage is used to extend the model to incorporate a stochastic volatility process. The volatility changes both in the Nile data, and for comparison also in daily S&P 500 return data, are investigated. The posterior density of parameters and states is found to provide information on which elements of the model are easily identifiable, and which elements are estimated with less precision.

Hong Kong's growth synchronization with China and the US: A trend and cycle analysis

This paper investigates the synchronization of Hong Kong's economic growth with mainland China and the US. We identify trends of economic growth based on the permanent income hypothesis. Specifically, we first confirm whether real consumption in Hong Kong and mainland China satisfies the permanent income hypothesis, at least in a weak form. We then identify the permanent and transitory components of income of each economy using a simple state-space model. We use structural vector autoregression models to analyze how permanent and transitory shocks originating from mainland China and the US affect the Hong Kong economy, and how such influences evolve over time. Our main findings suggest that transitory shocks from the US remain a major driving force behind Hong Kong's business cycle fluctuations. On the other hand, permanent shocks from mainland China have a larger impact on Hong Kong's trend growth.

Hong Kong’s Growth Synchronization with China and the U.S.: A Trend and Cycle Analysis

This paper investigates the synchronization of Hong Kong SAR’s economic growth with mainland China and the United States. This paper identifies trends of economic growth based on the permanent income hypothesis. Specifically, the paper confirms whether real consumption in Hong Kong SAR and mainland China satisfy the permanent income hypothesis, at least in a weak form. It then identifies the permanent and transitory components of income of each economy using a simple state-space model. It uses structural vector autoregression models to analyze how permanent and transitory shocks originating from mainland China and the United States affect the Hong Kong economy, and how such influences evolve over time. The paper’s main findings suggest that transitory shocks from the United States remain a major driving force behind Hong Kong SAR’s business cycle fluctuations. On the other hand, permanent shocks from mainland China have a larger impact on Hong Kong SAR’s trend growth.

Hong Kong's Growth Synchronisation with China and the U.S.: A Trend and Cycle Analysis

This paper investigates the synchronisation of Hong Kong's economic growth with mainland China and the US. We identify trends of economic growth based on the permanent income hypothesis. Specifically, we first confirm whether real consumption in Hong Kong and mainland China satisfies the permanent income hypothesis, at least in a weak form. We then identify the permanent and transitory components of income of each economy using a simple state-space model. We use structural vector autoregression models to analyse how permanent and transitory shocks originating from mainland China and the US affect the Hong Kong economy, and how such influences evolve over time. Our main findings suggest that transitory shocks from the US remain a major driving force behind Hong Kong’s business cycle fluctuations. On the other hand, permanent shocks from mainland China have a larger impact on Hong Kong’s trend growth.

Estimation of time-varying selectivity in stock assessments using state-space models

Time-varying selectivity is one of the main challenges in single species age-based assessment models. In classical deterministic VPA-type models the fishing mortality rates are unfiltered representations of the observed catches. As a consequence the selectivity becomes time-varying, but this representation is too fluctuating, because it includes the observation noise. In parametric statistical catch at age models a common assumption is that the selectivity is constant in all years, although time-varying selectivity can be introduced by splitting the data period in blocks with different selectivities, or by using smoothing splines and penalized time-deviances. However, these methods require subjective choices w.r.t. the degree of time-varying allowed. A simple state-space assessment model is presented as an alternative, which among other benefits offers an objective way of estimating time-varying selectivity pattern. The fishing mortality rates are considered (possibly correlated) stochastic processes, and the corresponding process variances are estimated within the model. The model is applied to North Sea cod and it is verified from simulations that time-varying selectivity can be estimated.

A Bayesian Approach to Adapting Forecasts to Structural Changes in a Simple State-Space Model

Most forecasting models often fail to produce appropriate forecasts because they are built on the assumption that data is being generated from only one stochastic process. However, in many real world problems, the time series data are generated from one stochastic process initially and then abruptly undergo certain structural changes. In this paper, we assume that the basic underlying process is the simple state-space model with random level and deterministic drift, but is interrupted by three types of exogenous shocks; level shift, drift change, and outlier. A Bayesian procedure to detect, estimate, and adapt to the structural changes is developed and compared to simple, double, and adaptive exponential smoothing using simulated data and the U.S. leading composite index.

How healthy are the rhinoceros populations in the Hluhluwe-iMfolosi Park?

Arising from a study conducted in the Hluhluwe-iMfolosi Park by the author in the late nineties, a method is proposed for improving the estimate of the size of a wildlife population by combining data from current and past surveys. The method is based on a simple state space model which takes into account the (unknown) birth rate in the population and all known losses (mortalities and relocations) and gains (introductions) in the population between successive surveys, as well as the errors in the survey estimates. The method is applied to the White- and Black Rhinoceros populations in the Hluhluwe-iMfolosi Park and tentative conclusions are drawn on the health of these two populations.

Simultaneous observation of the wheels’ torques and the vehicle dynamic state

The goal of the present study is to show how it is possible to estimate online the individual torques applied at each wheel of an automotive vehicle by using an unknown input observer together with a simple nonlinear state-space model. The necessary measurements are the steering angle, the usual rotation speed of each wheel plus the vertical load at the centre of each wheel. By doing this, this study anticipates the affordability of a new generation of wheel bearing with embedded measurements of transmitted forces. Successful simulated experimentations using a realistic simulator are shown. Validations are done using classical case studies of longitudinal, lateral and coupled dynamics. The present limits and some possible improvements of the proposed method are also discussed.

Using Markov switching models to infer dry and rainy periods from telecommunication microwave link signals

Abstract. A Markov switching algorithm is introduced to classify attenuation measurements from telecommunication microwave links into dry and rainy periods. It is based on a simple state-space model and has the advantage of not relying on empirically estimated threshold parameters. The algorithm is applied to data collected using a new and original experimental set-up in the vicinity of Zürich, Switzerland. The false dry and false rain detection rates of the algorithm are evaluated and compared to 3 other algorithms from the literature. The results show that, on average, the Markov switching model outperforms the other algorithms. It is also shown that the classification performance can be further improved if redundant information from multiple channels is used.

Mind the gaps: a state-space model for analysing the dynamics of North Sea herring spawning components

Abstract Payne, M. R. 2010. Mind the gaps: a state-space model for analysing the dynamics of North Sea herring spawning components. – ICES Journal of Marine Science, 67: 1939–1947. The North Sea autumn-spawning herring (Clupea harengus) stock consists of a set of different spawning components. The dynamics of the entire stock have been well characterized, but although time-series of larval abundance indices are available for the individual components, study of the dynamics at the component level has historically been hampered by missing observations and high sampling noise. A simple state-space statistical model is developed that is robust to these problems, gives a good fit to the data, and proves capable of both handling and predicting missing observations well. Furthermore, the sum of the fitted abundance indices across all components proves an excellent proxy for the biomass of the total stock, even though the model utilizes information at the individual-component level. The Orkney–Shetland component appears to have recovered faster from historic depletion events than the other components, whereas the Downs component has been the slowest. These differences give rise to changes in stock composition, which are shown to vary widely within a relatively short time. The modelling framework provides a valuable tool for studying and monitoring the dynamics of the individual components of the North Sea herring stock.

On the causes of volatility effects of conglomerate breakups

We describe four channels through which breakups can potentially increase idiosyncratic volatility for parent firms. These are: loss of diversification (portfolio effect), change in growth opportunities, change in operational efficiency, and the flow and assimilation of information (information effect). The relevance of each channel depends on the mode of a breakup. We explain conceptually and show empirically, using a sample of 530 breakups (259 spinoffs and 271 equity carveouts), that the portfolio effect is dominant for spinoff parents, while the information effect gains importance for carveout parents. Our novel insight is that the magnitude of the information effect depends on the pre-announcement information set held by investors; we provide a simple state-space model and empirical evidence to support this intuition. We also find a relation between the change in operational efficiency and the change in idiosyncratic volatility for spinoff parents.

Quantitative estimation of Ca2+ concentrations with simple state space model of calcium imaging signals

Quantitative estimation of Ca2+ concentrations with simple state space model of calcium imaging signals

On the Causes of Volatility Effects of Conglomerate Breakups

We describe four channels through which breakups can potentially increase idiosyncratic volatility for parent firms. These are: loss of diversification (portfolio effect), change in growth opportunities, change in operational efficiency, and the flow and assimilation of information (information effect). The relevance of each channel depends on the mode of a breakup. We explain conceptually and show empirically, using a sample of 530 breakups (259 spinoffs and 271 equity carveouts), that the portfolio effect is dominant for spinoff parents, while the information effect gains importance for carveout parents. Our novel insight is that the magnitude of the information effect depends on the pre-announcement information set held by investors; we provide a simple state-space model and empirical evidence to support this intuition. We also find a relation between the change in operational efficiency and the change in idiosyncratic volatility for spinoff parents.