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The U.S. shale revolution, using new technologies to extract crude oil, has led to new dynamics in the supply side of the global oil market. We ask whether the shale revolution has dampened the role of geopolitical risk in oil price volatility. We extend a reduced form Structural Break Threshold Vector Autoregressive (SBT-VAR) model to a structural SBT-VAR model and identify the structural innovations by allowing for conditional heteroskedasticity. Compared with the conventional reduced form VAR and TVAR models, a SBT-VAR with a constant threshold and a break in April 2014 are supported by the data. We then analyse the conditional (co)variance impulse response with respect to two distinct shock scenarios, one with only a geopolitical risk shock, the other with a simultaneous shale production shock and a geopolitical risk shock. The volatility responses are due to the identified contemporaneous relationships amongst geopolitical risk, shale production and oil prices, and are conditional on volatilities at the points in time. With the extra unit shale production shock, we find that the volatility response of oil prices to a geopolitical risk shock is higher, but the response is less correlated with the geopolitical risk factor.

The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon it in favor of the simpler but more restrictive Vector AutoRegressive (VAR) model. We narrow this gap with a new optimization-based approach to VARMA identification built upon the principle of parsimony. Among all equivalent data-generating models, we use convex optimization to seek the parameterization that is "simplest" in a certain sense. A user-specified strongly convex penalty is used to measure model simplicity, and that same penalty is then used to define an estimator that can be efficiently computed. We establish consistency of our estimators in a double-asymptotic regime. Our non-asymptotic error bound analysis accommodates both model specification and parameter estimation steps, a feature that is crucial for studying large-scale VARMA algorithms. Our analysis also provides new results on penalized estimation of infinite-order VAR, and elastic net regression under a singular covariance structure of regressors, which may be of independent interest. We illustrate the advantage of our method over VAR alternatives on three real data examples.

Video stabilization is the process of improving the video quality by removing annoying fluctuant motion caused by camera jittering. A key issue of a successful solution is the temporal adaptability to motion and the overall robustness with respect to different motion types. However, most previous methods usually produce non-motion adaptive stabilized videos. In other words, under-smoothing in slow motion segments and over-smoothing in rapid motion segments will be produced for complex shaky videos. To overcome these drawbacks, we propose a novel video stabilization approach using a motion morphological component (MMC decomposition. Specifically, the observed motion is decomposed into three MMCs: low-frequency smoothed (LFS motion, high-frequency compensatory (HFC motion, and shaky motion. LFS motion helps to largely stabilize videos, and HFC motion helps to recover missing motion to deal with over-smoothing. Subsequently, we present an MMC-based model to retrieve the desired smoothed motion, in which weighted nuclear norm and autoregression priors are used for LFS motion, while a sparsity prior is adopted for HFC motion. In addition, we design an adaptive weight setting scheme to detect rapid motions and to calculate the optimal weights. Finally, we develop a stabilization algorithm under the Alternating Direction Method of Multipliers (ADMM framework. Experimental results demonstrate that our method can achieve high-quality results compared with that of other state-of-the-art stabilization methods in terms of robustness and efficiency, both quantitatively and qualitatively.

Newton-step approximations to pseudo maximum likelihood estimates of spatial autoregressive models with a large number of parameters are examined, in the sense that the parameter space grows slowly as a function of sample size. These have the same asymptotic efficiency properties as maximum likelihood under Gaussianity but are of closed form. Hence they are computationally simple and free from compactness assumptions, thereby avoiding two notorious pitfalls of implicitly defined estimates of large spatial autoregressions. For an initial least squares estimate, the Newton step can also lead to weaker regularity conditions for a central limit theorem than those extant in the literature. A simulation study demonstrates excellent finite sample gains from Newton iterations, especially in large multiparameter models for which grid search is costly. A small empirical illustration shows improvements in estimation precision with real data., 36 pages

We consider a new class of spatio-temporal models with sparse autoregressive coefficient matrices and exogenous variable. To estimate the model, we first profile the exogenous variable out of the response. This leads to a profiled model structure. Next, to overcome endogeneity issue, we propose a class of generalized methods of moment (GMM) estimators to estimate the autoregressive coefficient matrices. A novel bagging-based estimator is further developed to conquer the over-determined issue which also occurs in Chang et al. (2015) and Dou et al. (2016). An adaptive forward–backward greedy algorithm is proposed to learn the sparse structure of the autoregressive coefficient matrices. A new BIC-type selection criteria is further developed to conduct variable selection for GMM estimators. Asymptotic properties are further studied. The proposed methodology is illustrated with extensive simulation studies. A social network dataset is analyzed for illustration purpose.

This paper develops parameter instability and structural change tests within predictive regressions for economic systems governed by persistent vector autoregressive dynamics. Specifically, in a setting where all – or a subset – of the variables may be fractionally integrated and the predictive relation may feature cointegration, we provide sup-Wald break tests that are constructed using the Local speCtruM (LCM) approach. The new tests cover both parameter variation and multiple structural changes with unknown break dates, and the number of breaks being known or unknown. We establish asymptotic limit theory for the tests, showing that it coincides with standard testing procedures. As a consequence, existing critical values for tied-down Bessel processes may be applied, without modification. We implement the new structural change tests to explore the stability of the fractionally cointegrating relation between implied- and realized volatility (IV and RV). Moreover, we assess the relative efficiency of IV forecasts against a challenging time-series benchmark constructed from high-frequency data. Unlike existing studies, we find evidence that the IV–RV cointegrating relation is unstable, and that carefully constructed time-series forecasts are more efficient than IV in capturing low-frequency movements in RV.

Monitoring economic conditions in real time, or nowcasting, and Big Data analytics share some challenges, sometimes called the three “Vs”. Indeed, nowcasting is characterized by the use of a large number of time series (Volume), the complexity of the data covering various sectors of the economy, with different frequencies and precision and asynchronous release dates (Variety), and the need to incorporate new information continuously and in a timely manner (Velocity). In this paper, we explore three alternative routes to nowcasting with Bayesian Vector Autoregressive (BVAR) models and find that they can effectively handle the three Vs by producing, in real time, accurate probabilistic predictions of US economic activity and a meaningful narrative by means of scenario analysis.

This work introduces a methodology to estimate coffee prices based on the use of Extreme Learning Machines. The process is initiated by identifying the presence of nonstationary components, like seasonality and trend. These components are withdrawn if they are found. Next, the temporal lags are selected based on the response of the Partial Autocorrelation Function filter. As predictors, we address the following models: Exponential Smoothing (ES), Autoregressive (AR) and Autoregressive Integrated and Moving Average (ARIMA) models, Multilayer Perceptron (MLP) and Extreme Learning Machines (ELMs) neural networks. The computational results based on three error metrics and two coffee types (Arabica and Robusta) showed that the neural networks, especially the ELM, can reach higher performance levels than the other models. The methodology, which presents preprocessing stages, lag selection, and use of ELM, is a novelty that contributes to the coffee prices forecasting field.

The output gap, the difference between potential and actual output, has a direct impact on policy decisions, e.g., monetary policy. Estimating this gap and its further analysis remain the subject of controversial debates due to methodological problems. We propose a local polynomial regression combined with a Self-Exciting Threshold AutoRegressive (SETAR) model and its forecasting extension for a systematic output gap estimation. Furthermore, local polynomial regression is proposed for the (multivariate) OECD production function approach and its reliability is demonstrated in forecasting output growth. A comparison of the proposed gap to the Hodrick–Prescott filter as well as to estimations by experts from the FED and OECD shows a higher correlation of our output gap with those from those economic institutions. Furthermore, sometimes gaps with different magnitude and different positions above or below the trend are observed between different methods. This may cause competing policy implications which can be improved with our results.

One of the major challenges in developing powered lower limb prostheses is emulating the behavior of an intact lower limb with different walking speeds over diverse terrains. Numerous studies have been conducted on control algorithms in the field of rehabilitation robotics to achieve this overarching goal. Recent studies on powered prostheses have frequently used a hierarchical control scheme consisting of three control levels. Most control structures have at least one element of discrete transition properties that requires numerous sensors to improve classification accuracy, consequently increasing computational load and costs. In this study, we proposed a user-independent and free-mode method for eliminating the need to switch among different controllers. We constructed a database by using four OPAL wearable devices (Mobility Lab, APDM Inc., USA) for seven able-bodied subjects. We recorded the gait of each subject at three ambulation speeds during ground-level walking to train a nonlinear autoregressive network with an exogenous input recurrent neural network (NARX RNN) to estimate foot orientation (angular position) in the sagittal plane using shank angular velocity as external input. The trained NARX RNN estimated the foot orientation of all the subjects at different walking speeds over flat terrain with an average root-mean-square error (RMSE) of 2.1° ± 1.7°. The minimum correlation between the estimated and measured values was 86%. Moreover, a t-test showed that the error was normally distributed with a high certainty level (0.88 minimum p -value).

A computationally simple bias correction for linear dynamic panel data models is proposed and its asymptotic properties are studied when the number of time periods is fixed or tends to infinity with the number of panel units. The approach can accommodate both fixed-effects and random-effects assumptions, heteroskedastic errors, as well as higher-order autoregressive models. Panel-corrected standard errors are proposed that allow for robust inference in dynamic models with cross-sectionally correlated errors. Monte Carlo experiments suggest that under the assumption of strictly exogenous regressors the bias-corrected method of moment estimator outperforms popular GMM estimators in terms of efficiency and correctly sized tests.

Recent research has demonstrated the adaptiveness of variability in emotion regulation (ER) by showing that variability between and, when controlled for depression, within ER strategies as assessed via the standard deviation was associated with less negative affect. We first replicated associations with negative affect by using the relative standard deviation, which is less confounded with the mean. Second, following research on affect dynamics, we extended this line of research by examining five additional ER dynamic measures covering ER instability, inertia, predictability, differentiation, and diversity. Reanalyzing data from five ambulatory assessment data sets (N = 717), we found that (a) the eight ER dynamic measures loaded on five factors that explained unique variance, (b) most ER dynamic measures had good reliabilities, and (c) between-strategy mean endorsement was positively, whereas between-strategy variability and ER predictability were negatively associated with negative affect. These results suggest that the variable but predictable use of emotion regulation strategies in daily life is beneficial for individuals' affective well-being in daily life. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

This paper proposes a Bayesian multifactor spatio-temporal model with a dynamic spatial autoregressive coefficient to estimate time-varying network interdependence with time-series cross-sectional data. To correct biases caused by unobserved time-varying homophily and common shocks with heterogeneous impact, we adopt the multifactor method to control for latent confounders and tackle the classic ``reflection problem." We use Bayesian shrinkage techniques to select latent factors. Monte Carlo studies and two empirical examples demonstrate that the multifactor specification reduces biases and improves the identification of network interdependence, and that falsely assuming a time-invariant network effect not only misses the variation of interdependence but also mis-estimates its central tendency. The proposed model is robust to misspecifcation and can accommodate various types of social networks.

This study examines the bullwhip effect in a manufacturing/remanufacturing supply chain consisting of one retailer, one manufacturer, one supplier, and one third-party collector. Products returned from the end users are converted to ‘as-good-as-new’ products through remanufacturing. Both manufactured and remanufactured products are used to meet the stochastic demand of the retailer. We incorporate the price of the product in the demand through an autoregressive moving average (ARMA) pricing process, realizing a market demand that evolves according to a non-standard ARMA(1,1) process. For this demand model, we employ the order-up-to inventory policy with a minimum mean square error forecasting scheme and measure the bullwhip effect at each echelon of the supply chain. We investigate the behaviour of the bullwhip effect analytically, and a numerical evaluation shows that the bullwhip effect or the anti-bullwhip effect may occur at each echelon depending on the values of the autoregressive and moving average parameters of the underlying demand process. We also investigate the effects of the remanufacturing yield, the variance of the market shock on price, and the lead times on the bullwhip effect.

In recent years, model averaging, by which estimates are obtained based on not one single model but a weighted ensemble of models, has received growing attention as an alternative to model selection. To-date, methods for model averaging have been developed almost exclusively for point-valued data, despite the fact that interval-valued data are commonplace in many applications and the substantial body of literature on estimation and inference methods for interval-valued data. This paper focuses on the special case of interval time series data, and assumes that the mid-point and log-range of the interval values are modelled by a two-equation vector autoregressive with exogenous covariates (VARX) model. We develop a methodology for combining models of varying lag orders based on a weight choice criterion that minimises an unbiased estimator of the squared error risk of the model average estimator. We prove that this method yields predictors of mid-points and ranges with an optimal asymptotic property. In addition, we develop a method for correcting the range forecasts, taking into account the forecast error variance. An extensive simulation experiment examines the performance of the proposed model averaging method in finite samples. We apply the method to an interval-valued data series on crude oil future prices.

Specific features of using the Geman-McClure function have been analyzed that are based on its properties in predicting telecommunication processes with anomalies or deviations by using an autoregressive model AR(p). The proposed modification of model AR(p) involves the presenting of prediction error in Geman-McClure metric that is based on this function and subsequent determining of coefficients of AR(p) model in this metric by employing equations presented in this paper that are similar to the Yule-Walker equations in presenting the prediction error of AR(p) model in L2 metric. Based on the comparative analysis and simulation, it has been established that AR(p) model in the Geman-McClure metric as compared with the classic AR(p) model in L2 metric makes it possible to enhance the prediction accuracy in telecommunication processes with anomalies or deviations by the factor of up to 1.5, while the efficiency of its use increases with the rise of model order p and the degree of process correlation that is subject to prediction. It has been shown that the practical use in telecommunications of the proposed modification of AR(p) model in the Geman-McClure metric is the most effective and expedient for the long-term prediction (large values of model order p) of strongly correlated processes that can be characterized by the presence of anomalies or deviations at relatively large values of internal parameter of this metric that ensures the speed of calculations in predicting the processes without a significant deterioration of its accuracy.

In this study, we consider cross-sectional interaction models including spatial autoregressive models and peer effects models as special cases. Our model allows the endogenous effect – the effect of others’ outcomes on one’s own outcome – to be nonlinear and nonparametric. For the model estimation, we propose a sieve instrumental variable estimator and establish both its consistency and asymptotic normality. Furthermore, we propose a nonparametric specification test for the linearity of the endogenous effect. Under the null hypothesis of linearity, we show that the test statistic is asymptotically distributed as normal. As an empirical illustration, we focus on the data on regional economic performance investigated by Gennaioli et al. (2013). This empirical analysis highlights the usefulness of the proposed model and method.

In this paper, we study the asymptotic behavior of the quadratic variation for the class of AR(1) processes driven by white noise in the second Wiener chaos. Using tools from the analysis on Wiener space, we give an upper bound for the total-variation speed of convergence to the normal law, which we apply to study the estimation of the model’s mean-reversion. Simulations are performed to illustrate the theoretical results.

This paper considers a problem of estimating brain effective connectivity from EEG signals using a Granger causality (GC) concept characterized on state-space models. We propose a state-space model for explaining coupled dynamics of the source and EEG signals where EEG is a linear combination of sources according to the characteristics of volume conduction. Our formulation has a sparsity prior on the source output matrix that can further classify active and inactive sources. The scheme is comprised of two main steps: model estimation and model inference to estimate brain connectivity. The model estimation consists of performing a subspace identification and the active source selection based on a group-norm regularized least-squares. The model inference relies on the concept of state-space GC that requires solving a discrete-time Riccati equation for the covariance of estimation error. We verify the performance on simulated data sets that represent realistic human brain activities under several conditions including percentages of active sources, a number of EEG electrodes and the location of active sources. The performance of estimating brain networks is compared with a two-stage approach using source reconstruction algorithms and VAR-based Granger analysis. Our method achieved better performances than the two-stage approach under the assumptions that the true source dynamics are sparse and generated from state-space models. The method is applied to a real EEG SSVEP data set and we found that the temporal lobe played a role of a mediator of connections between temporal and occipital areas, which agreed with findings in previous studies.

In this paper, we investigate brain directed connectivity (BDC) networks for emotion recognition using electroencephalogram (EEG) source signals that were estimated from high-density sensor EEG signals, for the first time. Currently, a variety of features extracted from sensor EEG signals are used for emotion recognition. However, they cannot unambiguously describe the location of emotions associated with neural activities and information propagation or the interaction between brain regions. In addition, most current studies use low-density sensor EEG signals. Moreover, source signals estimated from high-density sensor EEG signal have not been employed for emotion recognition to date. We designed a BDC network-based framework using EEG source signals to investigate emotion recognition. The global cortex factor-based multivariate autoregressive (GCF-MVAR) method was utilized to extract emotion-related BDC features. Our study revealed that the combined BDC and DE features facilitated a recognition accuracy of up to 89.58 %, which is higher than the rate obtained from BDC features and DE features alone. The sensor features derived from high-density EEG signals also exhibited higher recognition accuracy compared to low-density EEG signals. These findings suggest that BDC features derived from EEG source signals can better characterize human emotional states and are meaningful for emotion recognition.

Anuran richness patterns are strongly influenced by environmental factors. However, investigations on this issue have focused on the influence of abiotic factors without considering the joint effect of many existing variables, including the data sampling methodology and human demography. In this study we investigated the relationship between 21 environmental variables and anuran richness in brazilian biomes. Environmental variables represent a combination of human demographics, topographic, climatic and vegetation characteristics, and data sampling methodologies. We used principal component factorization and regressive and autoregressive models to select the most relevant variables for explaining anuran richness. Richness was correlated with demographic density, vegetation, accumulated rainfall, accumulated rainfall in the third and fourth quarter of the year, and accumulated rainfall in the first and second half of the year. However, the regressive and autoregressive models showed that human demographic density, sampling time, and sampling methodology were the best predictors of anuran richness. Our results highlight the importance of considering the effects of the human footprint and the methodology used for data collection on anuran species richness.

Long horizon state of health (SOH) monitoring and remaining useful life (RUL) prediction are of industrial value in prognostic and health management (PHM) of lithium-ion batteries (LIBs) to ensure their reliable functionality by early detection. Machine Learning, as a data-driven health diagnostic technique, has been widely utilized in solitary and hybrid structures. However, an accurate SOH estimation and RUL prediction method with less computational burden are highly desirable for the online state prediction in an electric vehicle application. This paper evaluates nonlinear autoregressive with external input (NARX) recurrent neural network (RNN) and time delay neural network (TDNN) in their prediction precision using the NASA dataset. The superior method, NARXRNN, is employed for two different datasets to estimate the battery's SOH and predict its RUL on a broad horizon. The results reveal the outstanding performance by presenting the root mean square error within 3% and mean absolute error within 2% for unseen data. Therefore, this method is capable to accurately predict the SOH of LIBS from historical data at low computational complexity. It is a promising model for long horizon SOH and RUL prediction and practical for online applications.

This article develops a robust global strategy for identifying the linear parameter varying (LPV) errors-in-variables (EIVs) systems subjected to randomly missing observations and outliers. The parameter interpolated LPV autoregressive exogenous model with an uncertain/noisy input is investigated and a nonlinear state-space model is considered for the input generation model (IGM). The parameters estimation of the LPV EIV systems with nonideal observations is realized using the expectation-maximization algorithm which is particular effective for the incomplete data issue. To ensure the robustness in the identification, the Student's t-distribution which is characterized by its adjustable degree of freedom, is used to handle the measurement non-normality. Since the posterior distributions of the latent states in the IGM are also involved in the identification process and they are difficult to calculate directly, the particle filter is introduced to recursively approximate them instead. Finally, the verification examples are given to demonstrate the effectiveness of the developed strategy.

Gas field production forecast is an important basis for decision-making in the gas industry, how to accurately predict the dynamic production during gas field development is an important content of reservoir engineering research. Reservoir numerical simulation is the most common method for predicting oil and gas production. However, it requires a lot of data to build an accurate geological model and the models are tedious and time-consuming. At present, many scholars have used machine learning and data mining methods to predict oil and gas production, but they have not considered whether the use of increasing production measures will affect the predicted results. Thus, ARIMA-RTS optimal smooth algorithm is the first applied to establish prediction model of gas well production. According to the historical production data, the model is processed, the production differential autoregressive integral moving average (ARIMA) model in time series is established, then ARIMA model is combined with RTS (Rauch Tung Striebel) smoothing, the production prediction model is constructed. RTS smoothing algorithm is an enhanced version of Kalman filter, the measurements are the first processed by the forward filter, and then, a separate backward smoothing pass is used for obtaining the smoothing solution. The correctness of ARIMA-RTS model was verified with the actual production data. The results show that prediction based on ARIMA-RTS model can accurately reflect the production performance of gas well. This method can effectively reduce the error caused by stimulation when prediction. Use the ARIMA-RTS model and the ARIMA-Kalman model to predict production of the same gas well, and the prediction accuracy of ARIMA-RTS model is higher than that of ARIMA-Kalman model in production wells with stimulation, compared with the ARIMA-Kalman model, the mean relative error fitted by the ARIMA-RTS model is reduced by 46.3%, and the relative mean square error is reduced by 56.48%. ARIMA-RTS optimal smooth algorithm improves the prediction accuracy of gas well that use stimulation, we therefore conclude that the ARIMA-RTS optimal smooth algorithm can service us better forecasting gas well production used stimulation, as well as other fuels output.

We propose a likelihood-based Bayesian method that exploits up-to-date sequential Monte Carlo methods to efficiently estimate long-run risk models in which the conditional variance of consumption growth follows either an autoregressive (AR) process or an autoregressive gamma (ARG) process. We use the U.S. quarterly consumption and asset returns data from the postwar period to implement estimation. Our findings are: (1) informative priors on the preference parameters can help to improve model performance; (2) expected consumption growth has a very persistent component, whereas consumption volatility is less persistent; (3) while the ARG-based model performs better than the AR-based one statistically, the latter could fit asset returns better; and (4) the solution method matters more for estimation in the AR-based model than in the ARG-based model.

Achieving sustainable development requires an increasing share of green technologies. World energy demand is expected to rise significantly especially in developing economies. The increasing energy demands will be entertained with conventional energy sources at the cost of higher emissions unless eco-friendly technologies are used. This study examines the asymmetric effects of nuclear energy on carbon emissions for Pakistan from 1974 to 2019. Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) unit root tests suggest that variables are integrated of order one and bound test of Autoregressive Distributed Lag (ARDL) and nonlinear ARDL confirm a long-run relationship among selected variables. The ARDL, Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS) results show that the coefficient of nuclear energy has a negative and significant impact on emissions in both short and long run. Further, the NARDL finding shows that there exists an asymmetric long-run association between nuclear energy and CO2 emissions. The vector error correction method (VECM) results indicate that there exists a bidirectional causal relationship between nuclear energy and carbon emissions in both the short and long run. Additionally, the impact of nuclear energy on ecological footprint has been examined and our findings remain robust.

A portmanteau-type statistic based on sample covariance is proposed to test for the existence of the unit-root of an autoregressive model under a heavy-tailed linear noise, where the noise ɛ t = ∑ j = 0 ∞ d j η t − j and { η t } is a sequence of i.i.d. innovations belonging to the domain of attraction of an α -stable distribution for some α ∈ ( 0 , 2 ) . The proposed procedure is nonparametric and easy to implement. Under certain regular assumptions, the limit distribution of the statistics is shown to be a functional of a standard stable distribution. Further, the good finite sample studies show that the new test outperforms PP, DF and ADF tests in size and power. Applications to the daily world crude oil price and 3-month AA financial commercial paper rate are also given to illustrate the performance of the new test.

The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generated the signal rather than in the specific realization of the waveform. Therefore, one problem faced by the brain is to segment time series based on underlying dynamics. We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model-based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error, and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on datasets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https://github.com/ttesileanu/bio-time-series., Comment: v2.1; 34 pages, 14 figures

Over the last few years, economic uncertainty has become a global concern. Not only has its impact on economic activities, but there are pieces of evidence that show uncertainty can be the reason for CO2 emissions. It is also expected that the economic policy uncertainty may decrease or delay economic production, which may lead to a reduction in carbon emissions. Furthermore, uncertainty may decrease friendly environment policies and budgets, which cause increase in carbon emissions. Thus, there may be an asymmetric relationship between economic uncertainty and the amount of CO2 emissions. This study investigates the effects of economic policy uncertainty and economic activity on carbon emission applying a Nonlinear Autoregressive Distributive Lag (NARDL) cointegration approach in Iran between 1971 and 2018. Findings show that both policy uncertainty and economic growth contribute to CO2 emissions. The negative and positive shocks of GDP and uncertainty index on CO2 emissions in both the short-run and long-run are significant. It can be concluded that there is an asymmetric effect of economic production on CO2 emissions in Iran. The results of analyzing asymmetric effects of economic uncertainty show a symmetric relationship between uncertainty index and CO2 emissions. In a way that a shock in uncertainty index lowers carbon emission. To sum up, since uncertainty may affect the analysis of carbon emissions incorrectly, some environmental policies such as allocating a budget for R&D on clean energy, and environmental taxes must be implemented.

This paper aims to explore a modified method of high-dimensional dynamic variance–covariance matrix estimation via risk factor mapping, which can yield a dependence estimation of asset returns within a large portfolio with high computational efficiency. The essence of our methodology is to express the time-varying dependence of high-dimensional return variables using the co-movement concept of returns with respect to risk factors. A novelty of the proposed methodology is to allow mapping matrices, which govern the co-movement of returns, to be time-varying. We also consider the flexible modeling of risk factors by a copula multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model. Through the proposed risk factor mapping model, the number of parameters and the time complexity are functions of a small number of risk factors instead of the number of stocks in the portfolio, making our proposed methodology highly scalable. We adopt Bayesian methods to estimate unknown parameters and various risk measures in the proposed model. The proposed risk mapping method and financial applications are demonstrated by an empirical study of the Hong Kong stock market. The assessment of the effectiveness of the mapping via risk measure estimation is also discussed.

Introduction Multivariate time series prediction of infectious diseases is significant to public health, and the deep learning method has attracted increasing attention in this research field. Material and methods An adaptively temporal graph convolution (ATGCN) model, which learns the contact patterns of multiple age groups in a graph-based approach, was proposed for COVID- 19 and influenza prediction. We compared ATGCN with autoregressive models, deep sequence learning models, and experience- based ATGCN models in short-term and long-term prediction tasks. Results Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term (12.5% and 10% improvements on RMSE) and long-term (12.4% and 5% improvements on RMSE) prediction tasks. And the RMSE of ATGCN predictions fluctuated least in different age groups of COVID- 19 (0.029 ± 0.003) and influenza (0.059±0.008). Compared with the Ones-ATGCN model or the Pre-ATGCN model, the ATGCN model was more robust in performance, with RMSE of 0.0293 and 0.06 on two datasets when horizon is one. Discussion Our research indicates a broad application prospect of deep learning in the field of infectious disease prediction. Transmission characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature selection. Conclusions The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups, indicating its great potentials for exploring the implicit interactions of multivariate variables.

This paper explores the least absolute deviation (LAD) estimator of the autoregressive model with heavy-tailed G-GARCH(1, 1) noise. When the tail index α ∈ ( 1 , 2 ] , it is shown that the LAD estimator asymptotically converges to a linear function of a series of α -stable random vectors with a rate of convergence n 1 − 1 ∕ α . The result is significantly different from that of the corresponding least square estimator which is not consistent, and partially solves the problem on the asymptoticity of the LAD estimator when the tail index is less than 2. A simulation study is carried out to assess the performance of the LAD estimator and a real example is given to illustrate this approach.

Stationarity is fundamental for time-series modeling and prediction. In this article, we focus on the radial basis function network-based autoregressive (RBF-AR) models which have been widely used in practical applications. Compared to previous work, we give a less-restrictive sufficient condition for the asymptotic stationarity of the RBF-AR model. The parameter estimation of the RBF-AR model is converted to the optimization of a variable projection functional with constraints of stationarity to always derive a stationary model. The constrained evolutionary algorithm is used to solve the optimization problem. Numerical results demonstrate the effectiveness of the proposed method.

We analyze an unbalanced panel monthly predictions of nonfarm payroll (NFP) changes between January 2008 and December 2020 sourced from Bloomberg. Unsurprisingly, we find that prediction quality varies across economists and we reject the hypothesis of equal predictive ability. In an error decomposition, we find evidence of significantly biased forecasts. Participation rate in the survey is affecting this bias. We find that survey participants under-predict job losses in times of market turmoil while also under-predicting the recovery thereafter, especially during the COVID19 labor shock. For prediction of NFP changes, autoregressive models are outperformed by a deep learning long short-term memory network. However, the consensus forecast yields better forecasts than model-based approaches and are further improved by combining the forecasts of the best performing economists. The COVID19 labor shock is shown to have adverse effects on the prediction performance of economists. However, not all economists are affected equally.

The paper presents a prediction of non-linear exogenous signal by optimized intelligent auto-regressive neural network model (ARNN). A signal comprises of two sets of data called deterministic and error. The former type of data represents the degradation index of a signal, while the error is the uncertainties associated with the signal. To understand and predict signals, a intelligent approach is taken through the use of ARNN model. In this approach, the rst step is to diagnose whether a time series signal is normally distributed or not by utilizing the Jarque-Bera test. The high and low volatility data ele- ments can be separated via kurtosis hypothesis. The deterministic component of the signal is also predicted by developing a neural network based non-linear autoregressive model (NN-NARX) and the error component by using a linear model. The nal forecast is formed by combining the results determined from each of the models and evaluated using the mean square error results. Vali- dation of the prediction is obtained through a comparison of the results with other models such as ARNN, traditional ARMX, and NARX models. The re- sults show that the proposed model provides improved predictions, minimize high dependence on design parameters with low computational cost.

The nexus between foreign direct investment and economic growth has long been among the most debated issues in macroeconomics. Some studies find a positive link between the two factors, but others find no evidence. This current research fills the gap by analysing the causal nexus between foreign direct investment and economic growth in Indonesia for the period 1970-2018. Indonesia as a developing country is one of the largest recipients of FDI flow; hence the study on the impact of FDI on the economic growth is very much important. This current research employs a contemporary time-series procedure, involving several unit-root tests namely Augmented-Dickey-Fuller (ADF), Phillips-Perron (PP), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Lee-Strazicich (LS), an Auto-Regressive-Distributed-Lag (ARDL) bounds-testing method for cointegration, and Granger causality test. The findings provide evidence of long-run and short-run causal direction from GDP to FDI. In contrast, FDI generates only a short-run relationship on GDP. The Granger causality test confirms the finding in ARDL that there is a unidirectional causality running from GDP to FDI.

This article proposes a speed sensor fault diagnosis methodology based on a learning-based data-driven principle in induction motor drive systems. The proposed method is derived from signal estimation and residual evaluation. First, a speed estimator is designed with a nonlinear autoregressive exogenous (NARX) learning model and a randomized learning technique called random vector functional link (RVFL) network. A data preprocessing method by discrete wavelet transform (DWT) is applied to better trace the signal trends, in order to further improve the speed estimation accuracy. After the estimation, the residual between the measured and estimated signals can be obtained, and a decision-making mechanism is developed for fault diagnosis based on an outlier test. The offline test results show that the proposed method can accurately estimate the speed signal with a 1.554e -4 root-mean-square error (RMSE) and outperforms the state-of-the-art methods. Moreover, real-time tests are also carried out to verify the feasibility and stability during the online stage. Moreover, the proposed approach does not require any motor parameters and other additional hardware, which makes it quite suitable for online practical applications. This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 51907163 and Grant U1934204.

This paper proposes a Bayesian-averaging heterogeneous vector autoregressive portfolio choice strategy with many big models that outperforms existing methods out-of-sample on numerous daily, weekly, and monthly datasets. The strategy assumes that excess returns are approximately determined by a time-varying regression with a large number of explanatory variables that are the sample means of past returns. Investors consider the possibility that every period there is a regime change by keeping track of many models, but doubt that any specification is able to perfectly predict the distribution of future returns, and compute portfolio choices that are robust to model misspecification. This paper was accepted by Tyler Shumway, finance.

Partially observable Markov decision processes have been widely adopted in the automatic planning literature since it elegantly captures both execution and observation uncertainties. In our previous paper, we proposed a model called vector autoregressive partially observable Markov decision process (VAR-POMDP) which extends the traditional POMDP by considering the temporal correlation among continuous observations. However, it is a non-trivial problem to develop a tractable planning algorithm for the VAR-POMDP model with performance guarantees as most existing algorithms need to explicitly enumerate all possible observation histories, which is in an unbounded continuous space. In this letter, we extend the famous point-based value iteration algorithm to a double point-based value iteration and show that the VAR-POMDP model can be solved by dynamic programming through approximating the exact value function by a class of piece-wise linear functions. Meanwhile, we prove that the approximation error is bounded. The effectiveness of the proposed planning algorithm is illustrated by an example.

With the recent expansion of social media in the form of social networks, online portals, and microblogs, users have generated a vast number of opinions, reviews, ratings, and feedback. Businesses, governments, and individuals benefit greatly from this information. While this information is intended to be informative, a large portion of it necessitates the use of text mining and sentiment analysis models. It is a matter of concern that reviews on social media lack text context semantics. A model for sentiment classification for customer reviews based on manifold dimensions and manifold modeling is presented to fully exploit the sentiment data provided in reviews and handle the issue of the absence of text context semantics. This paper uses a deep learning framework to model review texts using two dimensions of language texts and ideogrammatic icons and three levels of documents, sentences, and words for a text context semantic analysis review that enhances the precision of the sentiment categorization process. Observations from the experiments show that the proposed model outperforms the current sentiment categorization techniques by more than 8.86%, with an average accuracy rate of 97.30%.

Motivated by the extended Poisson INAR(1), which allows innovations to be serially dependent, we develop a new family of binomial-mixed Poisson INAR(1) (BMP INAR(1)) processes by adding a mixed Poisson component to the innovations of the classical Poisson INAR(1) process. Due to the flexibility of the mixed Poisson component, the model includes a large class of INAR(1) processes with different transition probabilities. Moreover, it can capture some overdispersion features coming from the data while keeping the innovations serially dependent. We discuss its statistical properties, stationarity conditions and transition probabilities for different mixing densities (Exponential, Lindley). Then, we derive the maximum likelihood estimation method and its asymptotic properties for this model. Finally, we demonstrate our approach using a real data example of iceberg count data from a financial system.

In a time series context, the study of the partial autocorrelation function (PACF) is helpful for model identification. Especially in the case of autoregressive (AR) models, it is widely used for order selection. During the last decades, the use of AR-type count processes, i.e., which also fulfil the Yule–Walker equations and thus provide the same PACF characterization as AR models, increased a lot. This motivates the use of the PACF test also for such count processes. By computing the sample PACF based on the raw data or the Pearson residuals, respectively, findings are usually evaluated based on well-known asymptotic results. However, the conditions for these asymptotics are generally not fulfilled for AR-type count processes, which deteriorates the performance of the PACF test in such cases. Thus, we present different implementations of the PACF test for AR-type count processes, which rely on several bootstrap schemes for count times series. We compare them in simulations with the asymptotic results, and we illustrate them with an application to a real-world data example.

Tensor-valued data are becoming increasingly available in economics and this calls for suitable econometric tools. We propose a new dynamic linear model for tensor-valued response variables and covariates that encompasses some well-known econometric models as special cases. Our contribution is manifold. First, we define a tensor autoregressive process (ART), study its properties and derive the associated impulse response function. Second, we exploit the PARAFAC low-rank decomposition for providing a parsimonious parametrization and to incorporate sparsity effects. We also contribute to inference methods for tensors by developing a Bayesian framework which allows for including extra-sample information and for introducing shrinking effects. We apply the ART model to time-varying multilayer networks of international trade and capital stock and study the propagation of shocks across countries, over time and between layers.

There is a pool of techniques and methods in addressing dynamics behaviors in higher frequency data, prominent among them is the ARCH/GARCH techniques. In this paper, the various types and assumptions of the ARCH/GARCH models were tried in examining the dynamism of exchange rate and international crude oil prices in Nigeria. And it was observed that the Nigerian foreign exchange rates behaviors did not conform with the assumptions of the ARCH/GARCH models, hence this paper adopted Lag Variables Autoregressive (LVAR) techniques originally developed by Agung and Heij multiplier to examine the dynamic response of the Nigerian foreign exchange rates to crude oil prices. The Heij coefficient was used to calculate the dynamic multipliers while the Engel & Granger two-step technique was used for cointegration analysis. The results revealed an insignificant dynamic long-term response of the exchange rate to crude oil prices within the periods under review. The coefficient of dynamism was insignificantly in most cases of the sub-periods. The paper equally revealed that the significance of the dynamic multipliers depends greatly on external information about both market indicators which are two-way interactions. Thus, the paper recommends periodic intervention in the foreign exchange market by the monetary authorities to stabilize the market against any shocks in the international crude oil market, since crude oil is the main source of foreign exchange in Nigeria.

Recently, many efforts have been devoted to speeding up neural machine translation models. Among them, the non-autoregressive translation (NAT) model is promising because it removes the sequential dependence on the previously generated tokens and parallelizes the generation process of the entire sequence. On the other hand, the autoregressive translation (AT) model in general achieves a higher translation accuracy than the NAT counterpart. Therefore, a natural idea is to fuse the AT and NAT models to seek a trade-off between inference speed and translation quality. This paper proposes an ARF-NAT model (NAT with auxiliary representation fusion) to introduce the merit of a shallow AT model to an NAT model. Three functions are designed to fuse the auxiliary representation into the decoder of the NAT model. Experimental results show that ARF-NAT outperforms the NAT baseline by 5.26 BLEU scores on the WMT’14 German-English task with a significant speedup (7.58 times) over several strong AT baselines.

Uncertain time series analysis has been developed for studying the imprecise observations. In this paper, we propose a nonlinear model called uncertain max-autoregressive (UMAR) model. The unknown parameters in model are estimated by the least squares estimation. Then the residual analysis is presented. In many cases, there are some outliers in the time series due to short-term change in the underlying process. The UMAR model offers an alternative for detecting outliers in the imprecise observations. Based on the previous theoretical results, the UMAR model is used to forecast the future. Finally, an example suggests that the new proposed time series model works well compared to the uncertain autoregressive (UAR) model.

The general purpose of a dynamic factor model (DFM) is to summarize a large number of time series into a few common factors. Here we explore a number of DFM specifications applied to 80 granular, non-overlapping indexes of commercial property prices in the US, quarterly from 2001 to 2017. We examine the nature and the structure of the factors and the index forecasts that can be produced using the DFMs. We consider specifications of 1, 2, 3 and 4 common factor trends. As a major motivation for the use of DFMs is their ability to improve out-of-sample forecasting of systems of numerous related series, we apply the DFM estimated factor returns in an Autoregressive Distributed Lag (ARDL) model to forecast the individual real estate price series. We compare the forecasted residuals to a conventional Autoregressive (AR) forecast model as a "benchmark" for two markets: Boston apartments and Dallas commercial. The results show that the ARDL model predicts the crisis and subsequent recovery really well, whereas the "benchmark" model typically follows the previous price trend. We find that the DFM forecasts are most precise with only one or two factors. The two prominent factors may reflect general economic conditions and the rental housing market, respectively.

Background: Adaptive model predictive control (MPC) algorithms that recursively update the glucose prediction model are shown to be promising in the development of fully automated multivariable artificial pancreas systems. However, the recursively updated glycemic prediction models do not explicitly consider prior knowledge in the identification of the model parameters. Prior information of the glycemic effects of meals and physical activity can improve model accuracy and yield better glycemic control algorithms. Methods: A glucose prediction model based on regularized partial least squares (rPLS) method where the prior information is encoded as the regularization term is developed to provide accurate predictions of the future glucose concentrations. An adaptive MPC is developed that incorporates dynamic trajectories for the glucose setpoint and insulin dosing constraints based on the estimated plasma insulin concentration (PIC). The proposed adaptive MPC algorithm is robust to disturbances caused by unannounced meals and physical activities even in cases with missing glucose measurements. The effectiveness of the proposed adaptive MPC based on rPLS is investigated with in silico subjects of the multivariable glucose-insulin-physiological variables simulator (mGIPsim). Results: The efficacy of the proposed adaptive MPC strategy in regulating the blood glucose concentration (BGC) of people with T1DM is assessed using the average percent time in range (TIR) for glucose, defined as 70 to 180 mg/dL inclusive, and the average percent time in hypoglycemia (54 mg/dL) and level 2 hypoglycemia (≤54 mg/dL). The TIR for a cohort of 20 virtual subjects of mGIPsim is 81.9% ± 7.4% (with no hypoglycemia or severe hypoglycemia) for the proposed MPC compared with 73.9% ± 7.6% (0.2% ± 0.1% in hypoglycemia and 0.1% ± 0.1% in level 2 hypoglycemia) for an MPC based on a recursive autoregressive exogenous (ARX) model. Conclusions: The adaptive MPC algorithm that incorporates prior knowledge in the recursive updating of the glucose prediction model can contribute to the development of fully automated artificial pancreas systems that can mitigate meal and physical activity disturbances.