Search

Deep learning technology has been successfully applied in more and more fields. In this paper, the application of deep neural networks in higher-order nonparametric spatial autoregressive models is studied. For spatial model, we propose the higher-order nonparametric spatial autoregressive neural network (HNSARNN) to fit the model. This method offers both good interpretability and prediction performance, and solves the black box problem in deep learning models to some degree. In various scenarios of spatial data distribution, the proposed method demonstrates superior performance compared to traditional approaches for handling nonparametric functions (such as the B-spline method). Simulation results show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The vector autoregressive (VAR) model has been popularly employed in operational practice to study multivariate time series. Despite its usefulness in providing associated metrics such as the impulse response function (IRF) and forecast error variance decomposition (FEVD), the traditional VAR model estimated via the usual ordinary least squares is vulnerable to outliers. To handle potential outliers in multivariate time series, this paper investigates two robust estimation methods of the VAR model, the reweighted multivariate least trimmed squares and the multivariate MM-estimation. The robust information criteria are also proposed to select the appropriate number of temporal lags. Via extensive simulation studies, we show that the robust VAR models lead to much more accurate estimates than the original VAR in the presence of outliers. Our empirical results include logged daily realized volatilities of six common safe haven assets: futures of gold, silver, Brent oil and West Texas Intermediate (WTI) oil and currencies of Swiss Francs and Japanese Yen. Our sample covers July 2017–June 2020, which includes the history-writing price drop of WTI on April 20, 2020. Our baseline results suggest that the traditional VAR model may significantly overestimate some parameters, as well as IRF and FEVD metrics. In contrast, robust VAR models provide more reliable results, the validity of which is verified via various approaches. Empirical implications based on robust estimates are further illustrated. [ABSTRACT FROM AUTHOR]

Many scholars have empirically tested the influence of geopolitical risk on economic activities and financial indicators. This paper attracts a new research strand by investigating the geopolitical risk determinants. More precisely, we examine if the international geopolitical risk of a selected group of countries spills over to Russia. Alternatively, it inspects if the geopolitical tension among nations is cointegrated. This group of countries includes the United States, Germany, China, and Ukraine. The current paper designed and computed an empirical model using the Autoregressive Distributed Lag model (ARDL) during the period 1993:01–2022:05. The results reveal that the international geopolitical risk of Russia is cointegrated with the other four nations. In the short run, the international geopolitical risk of the four nations spills over to Russia by increasing its international geopolitical risk. While in the long run, the same impacts persist. But the effect of the United States' geopolitical risk becomes statistically insignificant. The results also show that Germany has the largest effect on Russia's geopolitical risk in the long run. Moreover, the increase in oil prices overflows Russia by decreasing its international geopolitical risk. Thus, rival nations should reach a settlement to reduce the geopolitical tension. Otherwise, the world economic performance will deteriorate. [ABSTRACT FROM AUTHOR]

This paper examines the macroeconomic effects of oil demand and supply shocks on an emerging market economy using a Bayesian vector autoregressive model combining zero and sign restrictions. The empirical analysis relies on a rich set of macroeconomic indicators mirroring the economic performance in Brazil. The effects on the Brazilian output of oil price changes driven by global demand shocks are much larger and more persistent than those driven by oil production. Oil price changes that are not related to changes in oil supply or global economic activity have a small impact on the Brazilian variables. The main policy implication of this study is that emerging market economies should identify correctly the source of oil price fluctuation for implementing the best economic policy. [ABSTRACT FROM AUTHOR]

Diagnostic checking is an important predefined step before using autoregressive models. Although many portmanteau tests were proposed for diagnostic checking, they still struggle with the issue of significant size distortion. In this paper, we develop new diagnostic checking methods based on jackknife empirical likelihood. It is demonstrated that the suggested testing statistics asymptotically have a typical chi-squared distribution. To verify the performance of the finite sample, some simulations are constructed. Additionally, a real example of five agricultural futures is provided to illustrate the merits of our diagnostic checking procedure. [ABSTRACT FROM AUTHOR]

The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies' future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe. [ABSTRACT FROM AUTHOR]

The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets. This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil markets pre-COVID-19 and during COVID-19. Through the application of various statistical techniques, including cointegration tests, vector autoregressive models, vector error correction models, autoregressive distributed lag models, and Granger causality analyses, we explore the relationship between these markets and assess the safe-haven properties of gold and crude oil for cryptocurrencies. Our findings reveal that during the COVID-19 pandemic, gold is a strong safe-haven for Bitcoin, Litecoin, and Monero while demonstrating a weaker safe-haven potential for Bitcoin Cash, EOS, Chainlink, and Cardano. In contrast, gold only exhibits a strong safe-haven characteristic before the pandemic for Litecoin and Monero. Additionally, Brent crude oil emerges as a strong safe-haven for Bitcoin during COVID-19, while West Texas Intermediate and Brent crude oils demonstrate weaker safe-haven properties for Ether, Bitcoin Cash, EOS, and Monero. Furthermore, the Granger causality analysis indicates that before the COVID-19 pandemic, the causal relationship predominantly flowed from gold and crude oil toward the cryptocurrency markets; however, during the COVID-19 period, the direction of causality shifted, with cryptocurrencies exerting influence on the gold and crude oil markets. These findings provide subtle implications for policymakers, hedge fund managers, and individual or institutional cryptocurrency investors. Our results highlight the need to adapt risk exposure strategies during financial turmoil, such as the crisis precipitated by the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The current paper empirically investigates the effect of environmental stress on economic growth in the Gulf Cooperation Council countries during 1995–2016. A panel cointegration analysis, specifically an autoregressive distributed lag model, is used to achieve the paper's goal. The present work is motivated by the high carbon dioxide emissions per capita and environmental stress in these countries relative to other countries, and it assumes that the income per capita is a function of the natural resource's rents and environmental stress. The findings show that environmental stress enhances economic growth, mainly in the long run. At the same time, the natural resources' rents improve it in the short run and impede it in the long run. These results are significant because they tell that the Gulf Cooperation Council countries' environmental stress did not reach critical levels that produce vast negative influences on the economy, and the resource curse hypothesis is valid in the long run. The current study's policy implication states that economic policymakers should monitor and evaluate future environmental stress outcomes in these countries. There is no guarantee that the positive influence prevails. Therefore, the Gulf Cooperation Council countries should adopt genuine procedures to diversify their economies. Besides, it should continue its initial steps to expand renewable energy resources, i.e., nuclear, wind, and solar. [ABSTRACT FROM AUTHOR]

This paper provides new insights into the growth-maximizing size of government in Turkey. Unlike previous studies that traditionally use the share of government spending in GDP as a proxy variable of government size, in this paper we consider a fairly large number of proxy variables ranging from the share of tax revenues in GDP to the share of public investment in total investment. After reviewing the potential non-linear relationship between government size and growth, we estimate various thresholds for government size that maximize growth. To this end, we use the threshold autoregressive model proposed by Hansen (1996: 413-430; 2000: 575-603)) and apply it to Turkey's annual time-series data for the period from 1974 to 2019. Overall, we arrive at the following main result: Government size is non-linearly related to growth, confirming the existence of a growth-maximizing threshold for any measure of government size, beyond which growth tends to slow down as government size continues to increase. More precisely, the results show that the estimated thresholds for government size lie within the range of 4.28–15.19%, depending on the definition or proxy variable representing government size. [ABSTRACT FROM AUTHOR]

Clouds are important factors when projecting future climate. Unfortunately, future cloud fractional cover (the portion of the sky covered by clouds) is associated with significant uncertainty, making climate projections difficult. In this paper, we present the European Cloud Cover dataset, which can be used to learn statistical relations between cloud cover and other environmental variables, to potentially improve future climate projections. The dataset was created using a novel technique called Area Weighting Regridding Scheme to map satellite observations to cloud fractional cover on the same grid as the other variables in the dataset. Baseline experiments using autoregressive models document that it is possible to use the dataset to predict cloud fractional cover. [ABSTRACT FROM AUTHOR]

In this paper, a new robust adaptive beamforming method based on the autoregressive (AR) power spectrum is proposed. To improve the robustness of the Capon spectrum, the AR model is applied to realize the desired signal power and interference power estimations, which are used to reconstruct the covariance matrix. Besides, the eigenvalue decomposition is used to remove the redundancy of the reconstructed interference-plus-noise covariance matrix, where the number of interferences is confirmed by the maximum ratio index of the adjacent eigenvalues. Numerical simulations highlight that the proposed method is more robust against some common errors compared with several beamformers. [ABSTRACT FROM AUTHOR]

The paper aims to forecast the Philippine storm frequencies using nonlinear Poisson autoregressive model with exogenous variables. The nonlinearity is defined by its kernel which is an artificial neural network (ANN) with one hidden layer and two output neurons, and is trained to simultaneously forecast two semesters ahead for a given input. Furthermore, the covariates studied were the Average Sea Surface Temperatures in the NINO3.4 region (5∘− 5∘S,170∘− 120∘W) and the Average Sea Surface Temperatures in the eastern pole (0∘− 10∘S,90∘− 110∘E) of the Dipole Mode Index. The data, taken from the Japan Meteorological Agency's Regional Specialized Meteorological Center with time points running from 1950 to October 2021, is modeled at a semester-level granularity. The estimation is done using maximum likelihood estimation by minimizing the negative log-likelihood function. Furthermore, Bayesian hyper-parameter optimization was used to tune the model across different activation functions, number of hidden neurons, training optimizers, and train and validation splits. Lastly, the best model from the hyper-parameter optimization was then compared to univariate Poisson autoregressive model (and its Bayesian counterpart) and Negative Binomial Autoregressive model. The proposed model captures well the characteristics of the data both in terms of the point forecast and the associated uncertainties. [ABSTRACT FROM AUTHOR]

Automatic paraphrase generation is an essential task of natural language processing. However, due to the scarcity of paraphrase corpus in many languages, Chinese, for example, generating high-quality paraphrases in these languages is still challenging. Especially in domain paraphrasing, it is even more difficult to obtain in-domain paraphrase sentence pairs. In this paper, we propose a novel approach for domain-specific paraphrase generation in a zero-shot fashion. Our approach is based on a sequence-to-sequence architecture. The encoder uses a pre-trained multilingual autoencoder model, and the decoder uses a pre-trained monolingual autoregressive model. Because these two models are pre-trained separately, they have different representations for the same token. Thus, we call them unaligned pre-trained language models. We train the sequence-to-sequence model with an English-to-Chinese machine translation corpus. Then, by inputting a Chinese sentence into this model, it could surprisingly generate fluent and diverse Chinese paraphrases. Since the unaligned pre-trained language models have inconsistent understandings of the Chinese language, we believe that the Chinese paraphrasing is actually performed in a Chinese-to-Chinese translation manner. In addition, we collect a small-scale English-to-Chinese machine translation corpus in the domain of computer science. By fine-tuning with this domain-specific corpus, our model shows an excellent capability of domain-paraphrasing. Experiment results show that our approach significantly outperforms previous baselines regarding Relevance, Fluency, and Diversity. [ABSTRACT FROM AUTHOR]

The study of multivariate time series forecasting (MTSF) problems has high significance in many areas, such as industrial forecasting and traffic flow forecasting. Traditional forecasting models pay more attention to the temporal features of variables and lack depth in extracting spatial and spatiotemporal features between variables. In this paper, a novel model based on the Transformer, convolutional neural network (CNN), and long short-term memory (LSTM) network is proposed to address the issues. The model first extracts the spatial feature vectors through the proposed Multi-kernel CNN. Then it fully extracts the temporal information by the Encoder layer that consists of the Transformer encoder layer and the LSTM network, which can also obtain the potential spatiotemporal correlation. To extract more feature information, we stack multiple Encoder layers. Finally, the output is decoded by the Decoder layer composed of the ReLU activation function and the Linear layer. To further improve the model's robustness, we also integrate an autoregressive model. In model evaluation, the proposed model achieves significant performance improvements over the current benchmark methods for MTSF tasks on four datasets. Further experiments demonstrate that the model can be used for long-horizon forecasting and achieve satisfactory results on the yield forecasting of test items (our private dataset, TIOB). [ABSTRACT FROM AUTHOR]

This paper revisits the panel autoregressive model, with a primary emphasis on the unit-root case. We study a class of misspecified Random effects Maximum Likelihood (mRML) estimators when T is either fixed or large, and N tends to infinity. We show that in the unit-root case, for any fixed value of T, the log-likelihood function of the mRML estimator has a single mode at unity as N → ∞ . Furthermore, the Hessian matrix of the corresponding log-likelihood function is non-singular, unless the scaled variance of the initial condition is exactly zero. As a result, mRML is consistent and asymptotically normally distributed as N tends to infinity. In the large-T setup, it is shown that mRML is asymptotically equivalent to the bias-corrected FE estimator of Hahn and Kuersteiner (Econometrica 70(4):1639–1657, 2002). Moreover, under certain conditions, its Hessian matrix remains non-singular. [ABSTRACT FROM AUTHOR]

Series compensation grids connected with type-3 wind turbine generator (WTG)-based wind farms have suffered numerous subsynchronous oscillation (SSO) events worldwide. For early alerting of SSO and effective development of protection and control strategies, it is critical to monitor and identify SSO accurately and quickly. Ambient data is continuously available, which is useful for online monitoring. This paper proposes an ambient data-driven SSO online monitoring method based on the Kalman filter (KF) combined with the multi-model partitioning filter (MMPF). The KF is utilized to fit the measured ambient data with an auto regressive (AR) model. Then, the damping factor (or damping ratio) and frequency in the SSO mode can be acquired by solving the roots of the characteristic polynomial corresponding to the AR model. Moreover, the MMPF is an effective model order selection method applied to the KF for better identification. The performance of the MMPF-KF method is demonstrated by simulations and real-time experiments. The results of case studies validate the effectiveness of the proposed method under various conditions. [ABSTRACT FROM AUTHOR]

The energy–environment–growth nexus has been examined for the Association of Southeast Asian Nations (ASEAN) region, mainly using time series data. However, the important role of renewable energy and population has largely been ignored in previous studies. As such, this study is conducted to investigate a causal link between renewable energy usage, population, carbon dioxide emissions, and economic growth. In addition, a relatively new and advanced panel vector autoregressive model and the Granger non-causality test for heterogeneous panels are utilized with a sample of seven ASEAN countries for almost three decades since 1990. Key findings from this paper are as follows. First, renewable energy usage responds to population growth and leads to carbon dioxide (CO2) emissions. Second, economic growth and renewable energy usage explain a substantial proportion of the change in energy consumption. Third, a bidirectional Granger causality does exist in each pair among energy consumption, economic growth and CO2 emissions. We argue that moderating population growth and extending renewable energy usage are vital to achieving sustainable economic growth in the ASEAN region. [ABSTRACT FROM AUTHOR]

In this paper we address the issue of finding multirate filter bank matched to a given signal. The salient contributions, of this paper can be summarized as follows: (i) a precise mathematical equivalence, between M-channel circular lattice filter and M-band multirate filter bank, is established; (ii) the concept of signal matched analysis filter bank is defined. Also a precise mathematical formulation is presented which makes it possible to regard the signal matched analysis filter bank as single input multiple output prediction error system or as a whitening filter bank; (iii) it is observed that the problem of finding signal matched analysis bank is mathematically same as vector AR modeling and whitening problem; (iv) with the observation (iii) and the equivalence result of (i) at our disposal, we address the problem of finding signal matched analysis side multirate filter bank. (v) The concept of signal matched synthesis bank is defined. Also a precise mathematical formulation is presented for finding the parameters of the filter bank. We also present simulation results to validate the theory. [ABSTRACT FROM AUTHOR]

Synthetic electronic health records (EHRs) that are both realistic and privacy-preserving offer alternatives to real EHRs for machine learning (ML) and statistical analysis. However, generating high-fidelity EHR data in its original, high-dimensional form poses challenges for existing methods. We propose Hierarchical Autoregressive Language mOdel (HALO) for generating longitudinal, high-dimensional EHR, which preserve the statistical properties of real EHRs and can train accurate ML models without privacy concerns. HALO generates a probability density function over medical codes, clinical visits, and patient records, allowing for generating realistic EHR data without requiring variable selection or aggregation. Extensive experiments demonstrated that HALO can generate high-fidelity data with high-dimensional disease code probabilities closely mirroring (above 0.9 R2 correlation) real EHR data. HALO also enhances the accuracy of predictive modeling and enables downstream ML models to attain similar accuracy as models trained on genuine data. The paper presents HALO, a Hierarchical Autoregressive Language Model, for generating high-fidelity, longitudinal electronic health records (EHR) data. HALO maintains statistical property, supports machine learning modeling without privacy concerns. [ABSTRACT FROM AUTHOR]

The fund allocation in agricultural sector in India is heavily tilted toward input subsidies provision; however, researchers seem to favor investment expenditure instead. The present paper seeks to compare the usefulness of input subsidies as compared to investment with regard to agricultural productivity so that policy makers hit the right tool and avoid less productive state expenditure. We investigated a total of four regression models using autoregressive and distributed lag cointegration in a time series framework covering period from 1983 to 2019. The first model considers all input subsidies in aggregate form, and the rest three models take input subsidies in disaggregate forms, namely fertilizer subsidy, irrigation subsidy and power subsidy, respectively. It is observed from the results that input subsidies still contribute more than what public investment does to agricultural productivity. It is also found that power subsidy is the most effective component of input subsidies followed by fertilizer subsidy. Hence, government expenditure on input subsidies is justified on the ground that it ensures all farmers to have access to affordable agricultural inputs. Targeted subsidies combined with adequate investment in agricultural infrastructure could deliver long-term agricultural development in India. [ABSTRACT FROM AUTHOR]

Estimation and diagnostic procedures for partially linear models with first-order autoregressive [AR(1)] skew-normal errors are proposed in this paper. An EM iterative process with analytic expressions for the M and E-steps, which combines back-fitting and Newton–Raphson algorithms, is developed for the parameter estimation. A linear smoother for the estimation of the effective degrees of freedom concerning the nonparametric component is derived from the iterative process. Local influence analysis is developed based on the conditional expectation of the complete-data log-likelihood function, used in the EM algorithm. A simulation study is also conducted to evaluate the efficiency of the EM algorithm. Finally, the methodology developed through the paper is illustrated with a real data set on daily ozone concentration. [ABSTRACT FROM AUTHOR]

This paper provides evidence on the use of Random Regression Forests (RRF) for optimal lag selection. Using an extended sample of 144 data series, of various data types with different frequencies and sample sizes, we perform optimal lag selection using RRF and compare the results with seven "traditional" information criteria as well as with three other machine learning approaches. We show that the different information criteria produce differing outcomes in terms of optimal lag selection. To quantify performance, we compare the forecast errors on autoregressive models using the optimal lags selected by the criteria and demonstrate that RRF outperforms other approaches. We provide suggestions to researchers as to which approach to use, under different combinations of data type/data frequency and data type/sample size. [ABSTRACT FROM AUTHOR]

Uncertain time series models constitute a pivotal tool for analyzing phenomena evolving over time. However, most of the prevailing research centers on parameter models, which exhibit limitations in addressing intricate temporal dynamics. To address this gap, this paper introduces a non-parametric uncertain time series model. Primarily, we propose the definition of the non-parametric uncertain time series models. Subsequently, tailored to the non-parametric uncertain additive autoregressive model, we propose a non-parametric estimation method based on linear polynomial splines. Following this, the effectiveness of these estimations is verified through residual analysis and uncertain hypothesis tests. Finally, we apply the introduced non-parametric estimation approach to the Brent crude oil spot price. [ABSTRACT FROM AUTHOR]

This paper continues the work of Kang and Larsson Theoretical and Applied Climatology 116:537–548 (2014) What is the link between temperature and carbon dioxide levels? A Granger causality analysis based on ice core data. Theoretical and Applied Climatology 116:537-548, by adding methane to the study of causality between temperature and carbon dioxide. The data used goes 800,000 years back in time which is possible due to it being extracted from ice cores located at Dome C in Antarctica. First we use linear interpolation to make the three data sets equidistant to be able to employ statistical methods. We adjust for a deterministic trend and find the best model fit to be an autoregressive model with lag two and a piecewise quadratic trend. By employing multivariate Granger causality tests we find strong evidence that temperature, carbon dioxide and methane all Granger cause each other in both directions i.e. carbon dioxide Granger causes temperature and temperature Granger causes carbon dioxide, etc. This is in accordance with the findings of Kang and Larsson, with the extension that we can add methane to establish a trivariate feedback system between temperature, carbon dioxide and methane. [ABSTRACT FROM AUTHOR]

This paper proposes an extension of the Bayesian instrumental variables regression which allows spatial and temporal correlation among observations. For that, we introduce a double separable covariance matrix, adopting a Conditional Autoregressive structure for the spatial component, and a first-order autoregressive process for the temporal component. We also introduce a Bayesian multiple imputation to handle missing data considering uncertainty. The inference procedure is described joint with a step by step Monte Carlo Markov Chain algorithm for parameters estimation. We illustrate our methodology through a simulation study and a real application that investigates how broadband affects the Gross Domestic Product of municipalities in the state of Mato Grosso do Sul from 2010 to 2017. [ABSTRACT FROM AUTHOR]

In this paper, the problem of suboptimal stabilization of an object with discrete time, output and control uncertainties, and bounded external perturbation is considered. The autoregressive nominal model coefficients, uncertainty amplification coefficients, norm and external disturbance offset are assumed to be unknown. The quality indicator is the worst-case asymptotic upper bound of the output modulus of the object. The solution of the problem in conditions of non-identifiability of all unknown parameters is based on the method of recurrent target inequalities and optimal online estimation, in which the quality index of the control problem serves as an identification criterion. A non-linear replacement of the unknown parameter perturbations that reduces the optimal online estimation problem to a fractional linear programming problem is proposed. The performance of adaptive suboptimal control is illustrated by numerical simulation results. [ABSTRACT FROM AUTHOR]

This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape (CARS) model with beta density to predict the direction of stock returns. The CARS model is continuously valued, which makes it different from binary classification models. An empirical study is performed on the US stock market, and the results show that the predicting power of the CARS model is not only statistically significant but also economically valuable. We also compare the CARS model with the probit model, and the results demonstrate that the proposed CARS model outperforms the probit model for return direction forecasting. The CARS model provides a new framework for return direction forecasting. [ABSTRACT FROM AUTHOR]

It is widely accepted that the highly volatile Panamax market has many peculiarities; for example, Panamax vessels transport the major and the minor dry bulk cargoes worldwide. In contrast, the variety of cargoes and the flexibility in various trade routes, which the Panamax vessels follow, create a broad market with a relatively open structure. The importance of the Panamax market has also been highlighted by a recently upgraded contribution of the Baltic Panamax Index (BPI) to the Baltic Dry Index (BDI), affecting the progress of the BDI significantly. This paper investigates the behaviour of the Panamax market focusing on expectations and time lags. Expectations play a critical role in the freight market both for short-term and long-term decision making. In particular, we investigate the relationship between time lags and time-charter, trip and spot market rates, and the average earnings of the Panamax vessels of various ages. Time series analysis is used to reach our conclusions. The Hannan–Quinn criterion has been selected to identify the Panamax freight market's significant lags for 1989–2020. An autoregressive model (AR) has been constructed to perform the statistical analysis. The findings indicate a strong correlation between time lags and the Panamax freight market, forecasting the behaviour of the market indeed. A better understanding of the Panamax market's behaviour can improve shipowners and charterers' planning decisions practically. [ABSTRACT FROM AUTHOR]

Active fires emit aerosols and greenhouse gases in the atmosphere. In this paper, the behavior of active fires over a period of 1 year in Nepal, Bhutan, and Sri Lanka is studied using spatial statistics. In these countries, these fires are mainly forest and vegetation fires; they wreak havoc to the environment by damaging flora and fauna and emitting toxic gases. This study is based on data acquired through remote sensing of data acquisition platform, NASA's MODIS. Spatial statistics is used here to study the incidence of such fires with respect to geographical location. The behaviors of parameters of various autoregressive models like Spatial Durban Model, Spatial Lag Model, Spatial Error Model, Manski Model, and Kelegian Prucha Model are minutely analyzed. The best model with the highest pseudo R2 is selected. The spatial behavior of the fire radiative power (FRP) for the three countries is also predicted using spatial interpolation and kriging. The burning potential of vegetations in unsampled areas is envisaged by thus predicting FRP. This study gives a country-wise perspective to the behavior of fire; this is with reference to South Asia. It holds a great significance for countries of the developing world which lack a strong backbone of good-quality official records. Through the statistical analyses of data collected by such platforms, important information on impact of forest fires can be indirectly assessed. [ABSTRACT FROM AUTHOR]

India is home to some of the most polluted cities on the planet. The worsening air quality in most of the cities has gone to an extent of causing severe impact on human health and life expectancy. An early warning system where people are alerted well before an adverse air quality episode can go a long way in preventing exposure to harmful air conditions. Having such system can also help the government to take better mitigation and preventive measures. Forecasting systems based on machine learning are gaining importance due to their cost-effectiveness and applicability to small towns and villages, where most complex models are not feasible due to resource constraints and limited data availability. This paper presents a study of air quality forecasting by application of statistical models. Three statistical models based on autoregression (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) models were applied to the datasets of PM2.5 concentrations of Delhi and Bengaluru, and forecasting was done for 1-day-ahead and 7-day-ahead time frames. All three models forecasted the PM2.5 reasonably well for Bengaluru, but the model performance deteriorated for the Delhi region. The AR, MA, and ARIMA models achieved mean absolute percentage error (MAPE) of 10.82%, 7.94%, and 8.17% respectively for forecast of 7 days and MAPE of 7.35%, 5.62%, and 5.87% for 1-day-ahead forecasts for Bengaluru. For the Delhi region, the model gave an MAPE of 27.82%, 24.62%, and 27.32% for the AR, MA, and ARIMA models respectively in the 7-day-ahead forecast, and 24.48%, 23.53%, and 23.72% respectively for 1-day-ahead forecast. The analysis showed that ARIMA model performs better in comparison to the other models but performance varies with varying concentration regimes. Study indicates that other topographical and meteorological parameters need to be incorporated to develop better models and account for the effects of these parameters in the study. [ABSTRACT FROM AUTHOR]

The local government reform carried out in Turkey in 2014 intended to strengthen fiscal decentralization. From this perspective, this study is the first to examine the impact of local government reform on the fiscal capacity and fiscal effort of Turkish local governments in 81 provinces over the period of 2007–2019. To measure the local fiscal capacities of the provinces where local governments are located, we utilize spatial analysis considering the disparities. We implement Spatial Durbin and Spatial Autoregressive Models to investigate the spillover effects of the local fiscal capacity and fiscal effort for the local governments. The main results of this study are as follows; (1) The aforementioned reform, contrary to its goals, negatively affected the fiscal capacity of local governments. (2) The increase in the shares obtained by the local governments from the general budget made these local governments more dependent on the central sources, which reduced fiscal effort. (3) As expected, fiscal decentralization has a positive effect on local fiscal capacity. (4) The fiscal capacities of municipalities are spatially dependent on each other. This paper fills a gap in the literature in terms of local fiscal capacity in Turkey and provides policymakers with a different perspective on what needs to be done to increase the local fiscal capacity. [ABSTRACT FROM AUTHOR]

The partially linear varying coefficient spatial autoregressive model is a semi-parametric spatial autoregressive model in which the coefficients of some explanatory variables are variable, while the coefficients of the remaining explanatory variables are constant. For the nonparametric part, a local linear smoothing method is used to estimate the vector of coefficient functions in the model, and, to investigate its variable selection problem, this paper proposes a penalized robust regression estimation based on exponential squared loss, which can estimate the parameters while selecting important explanatory variables. A unique solution algorithm is composed using the block coordinate descent (BCD) algorithm and the concave-convex process (CCCP). Robustness of the proposed variable selection method is demonstrated by numerical simulations and illustrated by some housing data from Airbnb. [ABSTRACT FROM AUTHOR]

End-to-end text-to-speech synthesis systems achieved immense success in recent times, with improved naturalness and intelligibility. However, the end-to-end models, which primarily depend on the attention-based alignment, do not offer an explicit provision to modify/incorporate the desired prosody while synthesizing the speech. Moreover, the state-of-the-art end-to-end systems use autoregressive models for synthesis, making the prediction sequential. Hence, the inference time and the computational complexity are quite high. This paper proposes Prosody-TTS, a data-efficient end-to-end speech synthesis model that combines the advantages of statistical parametric models and end-to-end neural network models. It also has a provision to modify or incorporate the desired prosody at the finer level by controlling the fundamental frequency ( f 0 ) and the phone duration. Generating speech utterances with appropriate prosody and rhythm helps in improving the naturalness of the synthesized speech. We explicitly model the duration of the phoneme and the f 0 to have a finer level control over them during the synthesis. The model is trained in an end-to-end fashion to directly generate the speech waveform from the input text, which in turn depends on the auxiliary subtasks of predicting the phoneme duration, f 0 , and Mel spectrogram. Experiments on the Telugu language data of the IndicTTS database show that the proposed Prosody-TTS model achieves state-of-the-art performance with a mean opinion score of 4.08, with a very low inference time using just 4 hours of training data. [ABSTRACT FROM AUTHOR]

Corrosion can initiate cracking that leads to structural integrity reduction. Quantitative corrosion assessment is challenging, and the modeling of corrosion-induced crack initiation is essential for model-based corrosion reliability analysis of various structures. This paper proposes a probabilistic computational analysis framework for corrosion-to-crack transitions by integrating a phase-field model with machine learning and uncertainty quantification. An electro-chemo-mechanical phase-field model is modified to predict pitting corrosion evolution, in which stress is properly coupled into the electrode chemical potential. A crack initiation criterion based on morphology is proposed to quantify the pit-to-cracking transition. A spatiotemporal surrogate modeling method is developed to facilitate this, consisting of a Convolution Neural Network (CNN) to map corrosion morphology to latent spaces, and a Gaussian Process regression model with a nonlinear autoregressive exogenous model (NARX) architecture for prediction of corrosion dynamics in the latent space over time. It enables the real-time prediction of corrosion morphology and crack initiation behaviors (whether, when, and where the corrosion damage triggers the crack initiation), and thus makes it possible for probabilistic analysis, with uncertainty quantified. Examples at various stress and corrosion conditions are presented to demonstrate the proposed computational framework. [ABSTRACT FROM AUTHOR]

In this paper, we are particularly interested in the skew Ornstein-Uhlenbeck (OU) process. The skew OU process is a natural Markov process defined by a diffusion process with symmetric local time. Motivated by its widespread applications, we study its parameter estimation. Specifically, we first transform the skew OU process into a tractable piecewise diffusion process to eliminate local time. Then, we discretize the continuous transformed diffusion by using the straightforward Euler scheme and, finally, obtain a more familiar threshold autoregressive model. The developed Bayesian estimation methods in the autoregressive model inspire us to modify a Gibbs sampling algorithm based on properties of the transformed skew OU process. In this way, all parameters including the pair of skew parameters (p, a) can be estimated simultaneously without involving complex integration. Our approach is examined via simulation experiments and empirical analysis of the Hong Kong Interbank Offered Rate (HIBOR) and the CBOE volatility index (VIX), and all of our applications show that our method performs well. [ABSTRACT FROM AUTHOR]

For parameters in a threshold autoregressive process, the paper proposes a sequential modification of the least squares estimates with a specific stopping rule for collecting the data for each parameter. In the case of normal residuals, these estimates are exactly normally distributed in a wide range of unknown parameters. On the base of these estimates, a fixed-size confidence ellipsoid covering true values of parameters with prescribed probability is constructed. In the i.i.d. case with unspecified error distributions, the sequential estimates are asymptotically normally distributed uniformly in parameters belonging to any compact set in the ergodicity parametric region. Small-sample behavior of the estimates is studied via simulation data. [ABSTRACT FROM AUTHOR]

Contrary to the number of unemployed or vacancies, the number of employees subject to social security contributions (SSC) for Germany is published after a time lag of 2 months. Furthermore, there is a waiting period of 6 months until the values are not revised any more. This paper uses monthly data on the number of people subject to compulsory health insurance (CHI) as auxiliary variable to better nowcast SSC. Statistical evaluation tests using real-time data show that CHI significantly improves nowcast accuracy compared to purely autoregressive benchmark models. The mean squared prediction error for nowcasts of SSC can be reduced by approximately 20%. In addition, CHI outperforms alternative candidate variables such as unemployment, vacancies and industrial production. [ABSTRACT FROM AUTHOR]

To analyze the dynamic and asymmetric contagion reactions of financial markets during the last subprime crisis, this paper proposes a contagion reaction equation combined with the generalized auto regressive conditional heteroskedasticity process to develop a dynamic asymmetric contagion model, and then provides the Markov chain Monte Carlo estimation method of this new model. This paper then constructs an empirical study of two metals futures in China during the last subprime crisis period, applying the model to measure the impact of the contagion reactions as well as assess the model's effectiveness. Our results show: (1) the financial contagion phenomenon is the reason why some financial markets experienced almost corresponding reactions during the subprime crisis; (2) financial contagion reactions behave conspicuously in three particular phases during the subprime crisis; (3) financial contagion reactions have predictive functions for financial market changes and can provide indicators for risk management during crisis periods. [ABSTRACT FROM AUTHOR]

In this paper, we propose an effective Bayesian subset selection method for the double-threshold-variable autoregressive moving-average (DT-ARMA) models. The usual complexity of estimation is increased mainly by capturing the correlation between two threshold variables and including moving-average terms in the model. By adopting the stochastic search variable selection method, combined with the Gibbs sampler and Metropolis-Hastings algorithm, we can simultaneously estimate the unknown parameters and select the best subset model from a large number of possible models. The simulation experiments illustrate that the proposed approach performs well. In applications, two real data sets are analyzed by the proposed method, and the fitted DT-ARMA model is better than the double-threshold autoregressive (DT-AR) model from the view of parsimony. [ABSTRACT FROM AUTHOR]

This paper is concerned with the estimation problem of a periodic autoregressive model with closed skew-normal innovations. The closed skew-normal (CSN) distribution has some useful properties similar to those of the Gaussian distribution. Maximum likelihood (ML), Maximum a posteriori (MAP) and Bayesian approaches are proposed and compared in order to estimate the model parameters. For the Bayesian approach, the Gibbs sampling algorithm and for computing the ML and MAP estimations, the expectation–maximization algorithms are performed. The simulation studies are then conducted to compare the frequentist average losses of competing estimators and to study the asymptotic properties of the given estimators. The proposed model and methods developed in this paper are also applied to a real time series. The accuracy of the CSN and Gaussian models is compared by cross validation criterion. [ABSTRACT FROM AUTHOR]

This paper focuses on the parameter estimation problem of multivariate output-error autoregressive systems. Based on the data filtering technique and the auxiliary model identification idea, we derive a filtering-based auxiliary model recursive generalized least squares algorithm. The key is to filter the input-output data and to derive two identification models, one of which includes the system parameters and the other contains the noise parameters. Compared with the auxiliary model-based recursive generalized least squares algorithm, the proposed algorithm requires less computational burden and can generate more accurate parameter estimates. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Electricity consumption in China has attracted increasing attention by the government in monitoring the economy. The purpose of the study is test whether electricity consumption is an appropriate indicator. To do that, this paper proposes an alternative bootstrap Granger causality test, which can capture the contemporaneous correlation of the term error in the Vector Autoregressive Model, based on a seemingly unrelated regression estimator. Using a quarterly data set containing more dynamic changes, this study reinvestigates the relationship between electricity consumption and economic growth. The results show that there exists a long-run relationship between the two variables. Electricity consumption can be treated as an indicator of the functioning of the economy. A strong unidirectional Granger causality is found running from gross domestic product to electricity consumption. However, the causality relationship from electricity consumption to gross domestic product is relatively weak. Thus, electricity consumption is a useful indicator to check the reliability of GDP data, however, caution is required when using electricity consumption to predict future economic activities in China. [ABSTRACT FROM AUTHOR]

Pressure transient data from downhole gauges are one of the key parameters in characterizing reservoir properties and forecasting future reservoir performance. Reservoir pressure is usually measured under dynamic changes. The collected data usually contain different levels of noise, particularly due to imperfections in measuring instruments and imperfect calibrations. The latter is due to changes between the laboratory environment and reservoir conditions. To have accurate descriptions of reservoir, it is essential to smooth the pressure data. Most related studies have employed the wavelet transform to reduce noise. However, there appears to be little research addressing the use of other smoothing techniques for pressure transient data. This paper, therefore, evaluates and compares the performance of three types of smoothing and noise removal methods, namely wavelet transform as a widely used filtering technique, regression-based smoothers, and autoregressive smoothing methods to reduce artificial noise added to simulated dual-porosity pressure data. Particularly, noise is more pronounced in pressure derivative, and so denoising of pressure derivative requires more effective tools. The effectiveness of the noise removing methods was compared using mean square error. The results show that the regression-based methods lead to the same or even better reduction in the noise level as compared to the wavelet domain filter, while the employed autoregressive method results in a moderate performance. We also test the performance of various combinations of the different smoothing methods to filter the same noisy data. It is shown that the combined locally weighted scatterplot smooth (LOESS) and autoregressive moving average (ARMA) gives the best smoothing performance for pressure derivative data. Application of the combined LOESS-ARMA to real field data shows promising results. [ABSTRACT FROM AUTHOR]

Feature normalization is a key objective in speech related applications. In this paper, we study the effects of the Mean subtraction, Variance normalization, and Autoregressive Moving Average (ARMA) filtering (MVA) normalization method on the ETSI Advanced Front-End (AFE) features. A series of experiments, on the Aurora-2 task, was conducted to show the impact of MVA normalization for different subsets of AFE feature components. Compared to the AFE baseline system, recognition results show performance improvement when only the logarithmic energy coefficient is normalized. However, the performance is degraded through the normalization of the rest of AFE coefficients. To investigate this degradation, other experiments were performed by eliminating the AFE implemented blind equalization post-processing block. It has shown that one part of this degradation can plausibly be interpreted as the effect of over-normalization caused by the MVA post-processing to the AFE original features. Furthermore, by analyzing the statistical distributions of AFE features we found that the effectiveness of MVA could also be affected by the high intra-frame variability of AFE features. [ABSTRACT FROM AUTHOR]

Maximum likelihood (ML) estimation of spatial autoregressive models for large spatial data sets is well established by making use of the commonly sparse nature of the contiguity matrix on which spatial dependence is built. Adding a measurement error that naturally separates the spatial process from the measurement error process are not well established in the literature, however, and ML estimation of such models to large data sets is challenging. Recently a reduced rank approach was suggested which re-expresses and approximates such a model as a spatial random effects model (SRE) in order to achieve fast fitting of large data sets by fitting the corresponding SRE. In this paper we propose a fast and exact method to accomplish ML estimation and restricted ML estimation of complexity of O(n3/2) operations when the contiguity matrix is based on a local neighbourhood. The methods are illustrated using the well known data set on house prices in Lucas County in Ohio. [ABSTRACT FROM AUTHOR]

We analyze how regional economic structures affect the impact of monetary policy on rates of inflation across 34 Indonesian provinces. The paper first applies structural factor augmented vector autoregressive model (SFAVAR) to all the 34 provinces based on monthly provincial data in order to measure the length and magnitude of responses of regional inflation to monetary policy shock, derived from the consequential impulse response functions of 34 provinces. In the second step, we analyze the impact of economic structures on the length and magnitude of regional inflationary responses of 34 provinces. We find that the impacts of monetary policy across regions are significantly influenced by economic structural variables such as manufacturing sector share to GDP, mining sector share to GDP, bank lending share to GDP and export share to GDP. In addition, we found the spatial lag, rate of inflation of neighboring provinces, is also statistically significant. In a similar fashion, economic structural variables such as manufacturing sector share to GDP, construction sector share to GDP and investment share to GDP are found statistically significant in explaining regional differences of monetary policy efficiency. Our findings imply economic structures of provinces have to be incorporated to designing monetary policy in Indonesia. [ABSTRACT FROM AUTHOR]

Metaheuristics are a class of approximate methods, which are designed to attack hard combinatorial optimization problems. In metaheuristics, a neighborhood is defined by the specified move operation for a solution. The neighborhood plays an essential role in the performance of its algorithms. It is important to capture the statistical properties of neighborhoods. In this paper, we present a theoretical analysis of neighborhoods for a wide class of combinatorial optimization problems, instead of just for restricted instances. First, we give a probabilistic model which allows us to compute statistics for various types of neighborhoods. Here we introduce an approach in which the solution space (the landscape) for a wide class of combinatorial optimization problems can be approximated to AR(1), which can be used to capture the statistics of the solution space. The theoretical results obtained from our proposed model closely match empirically observed behavior. Second, we present an application in which we use our probabilistic model of neighborhoods. [ABSTRACT FROM AUTHOR]

In this paper additive models with p-order autoregressive conditional symmetric errors based on penalized regression splines are proposed for modeling trend and seasonality in time series. The aim with this kind of approach is try to model the autocorrelation and seasonality properly to assess the existence of a significant trend. A backfitting iterative process jointly with a quasi-Newton algorithm are developed for estimating the additive components, the dispersion parameter and the autocorrelation coefficients. The effective degrees of freedom concerning the fitting are derived from an appropriate smoother. Inferential results and selection model procedures are proposed as well as some diagnostic methods, such as residual analysis based on the conditional quantile residual and sensitivity studies based on the local influence approach. Simulations studies are performed to assess the large sample behavior of the maximum penalized likelihood estimators. Finally, the methodology is applied for modeling the daily average temperature of San Francisco city from January 1995 to April 2020. [ABSTRACT FROM AUTHOR]

An estimation of the impact of climatic conditions—measured with an index that combines temperature and humidity, the IPTCC—on the hospitalizations and deaths attributed to SARS-CoV-2 is proposed. The present paper uses weekly data from 54 French administrative regions between March 23, 2020 and January 10, 2021. Firstly, a Granger causal analysis is developed and reveals that past values of the IPTCC contain information that allow for a better prediction of hospitalizations or deaths than that obtained without the IPTCC. Finally, a vector autoregressive model is estimated to evaluate the dynamic response of hospitalizations and deaths after an increase in the IPTCC. It is estimated that a 10-point increase in the IPTCC causes hospitalizations to rise by 2.9% (90% CI 0.7–5.0) one week after the increase, and by 4.1% (90% CI 2.1–6.4) and 4.4% (90% CI 2.5–6.3) in the two following weeks. Over ten weeks, the cumulative effect is estimated to reach 20.1%. Two weeks after the increase in the IPTCC, deaths are estimated to rise by 3.7% (90% CI 1.6–5.8). The cumulative effect from the second to the tenth weeks reaches 15.8%. The results are robust to the inclusion of air pollution indicators. [ABSTRACT FROM AUTHOR]

This paper focuses on nonparametric regression modeling of time-series observations with data irregularities, such as censoring due to a cutoff value. In general, researchers do not prefer to put up with censored cases in time-series analyses because their results are generally biased. In this paper, we present an imputation algorithm for handling auto-correlated censored data based on a class of autoregressive nonparametric time-series model. The algorithm provides an estimation of the parameters by imputing the censored values with the values from a truncated normal distribution, and it enables unobservable values of the response variable. In this sense, the censored time-series observations are analyzed by nonparametric smoothing techniques instead of the usual parametric methods to reduce modelling bias. Typically, the smoothing methods are updated for estimating the censored time-series observations. We use Monte Carlo simulations based on right-censored data to compare the performances and accuracy of the estimates from the smoothing methods. Finally, the smoothing methods are illustrated using a meteorological time- series and unemployment datasets, where the observations are subject to the detection limit of the recording tool. [ABSTRACT FROM AUTHOR]