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The international financial and economic crisis highlights that central banks should go beyond their traditional emphasis on low inflation to adopt an explicit goal of financial stability. Our paper addresses this highly topical issue of macro-prudential framework with the focus on effectiveness of monetary policy in affecting some financial stability indicators, in the experience of several Central and Eastern European countries during 2003M01-2012M06. Using a Structural Vector Autoregressive model and impulse response function, we analyze the impact of short-term interest rates upon industrial production, loan to deposit ratio for the banking system, stock prices and exchange rate (proxy variables for financial stability). We want to test if the interest rate is conducive to financial stability. Our empirical results show that the effectiveness of the short-term interest rate in affecting selected asset prices depends on monetary policy strategy. In the case of the Czech Republic, Hungary, Poland and Romania, the interest rate instrument used for inflation targeting is conducive to financial stability. Among countries with a fixed exchange rate regime, only in Bulgaria does transmission of the foreign interest rate impulse to domestic variables promote financial stability. Additionally, our results show that in Latvia and Lithuania adjustments to the monetary policy of the European Central Bank (ECB) are not in accordance with country-specific conditions. The paper contributes to a policy debate on the design of macro-prudential polices in the aftermath of the boom-bust cycle experienced by the Central and Eastern European countries in the second half of the last decade. [ABSTRACT FROM AUTHOR]

Overlooking the importance of non-stationarity and normally distributed models in previous studies, this paper aims to fill the gap in literature based on time series between 2000 and 2019 for an Alps Adriatic country on tourism-led employment. Before implementing the Granger Causality Test, the vector autoregressive model is applied. The results confirmed that tourist arrivals significantly impact the hospitality labour market in Slovenia as employment in the lodging industry was driven in bi-causal directions to overall tourist arrivals. The demonstrated validity of substantial tourism-led employment has implications for job creation and economic growth, focusing on domestic tourists. [ABSTRACT FROM AUTHOR]

In this paper, we develop a mixture of autoregressive (MoAR) process model with time varying and freely indexed covariates under the flexible class of two–piece distributions using the scale mixtures of normal (TP-SMN) family. This novel family of time series (TP-SMN-MoAR) models was used to examine flexible and robust clustering of reported cases of Covid-19 across 313 counties in the U.S. The TP-SMN distributions allow for symmetrical/ asymmetrical distributions as well as heavy-tailed distributions providing for flexibility to handle outliers and complex data. Developing a suitable hierarchical representation of the TP-SMN family enabled the construction of a pseudo-likelihood function to derive the maximum pseudo-likelihood estimates via an EM-type algorithm. [ABSTRACT FROM AUTHOR]

Coronavirus (COVID-19) has captured the attention of the globe very rapidly. Therefore, predicting the spread of the disease has become an indispensa- ble process, this is being due to its extremely infectious nature and due to the negative effects that some courses of actions, which were taken to minimize the spread of the disease, have on economy and key sectors (e.g., health, pharmaceu- tical and industrial sectors). Therefore, in this research work, the nonlinear autore- gressive exogenous model (NARX) is developed to predict the spread of COVID-19 in Jordan by mapping the related factors (i.e. sociodemographic characteristics and government actions) to the number of confirmed COVID-19 cases in the twelve governorates in Jordan. It has been shown that the developed NARX model can predict the number of confirmed cases with a root mean square error of approxi- mately 28. The NARX model developed in this paper can therefore lead to an efficient management of the available resources, and help decision-makers in selecting the best course of actions to minimize the spread of COVID-19. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new first-order generalized Poisson integer-valued autoregressive process, for modeling integer-valued time series exhibiting a piecewise structure and overdispersion. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares and conditional maximum likelihood estimators are derived. The asymptotic properties of the estimators are established. Moreover, two special cases of the process are discussed. Finally, some numerical results of the estimates and a real data example are presented. [ABSTRACT FROM AUTHOR]

This paper considers the vector autoregressive model of order p, VAR(p), with multivariate t error distributions, the latter being more prevalent in real life than the usual multivariate normal distribution. It is believed that the maximum-likelihood equations for the multivariate t distribution have convergence problem, hence we develop estimation procedures for VAR(p) model using the normal mean–variance mixture representation of multivariate t distribution. The procedure relies on the computational ease available in Expectation Maximization-based algorithms. The estimators obtained are explicit functions of sample observations and therefore are easy to compute. Extensive simulation experiments show that the estimators have negligible bias and are considerably more efficient than an existing method that uses the least-squares error approach. It is shown that the proposed estimators are robust to plausible deviations from an assumed distribution and hence are more advantageous when compared with the other estimator. One real-life example is given for illustration purposes. [ABSTRACT FROM AUTHOR]

Spatio-temporal disease mapping models give a great worth in epidemiology, especially in describing the pattern of disease incidence across geographical space and time. This paper analyses the spatial and temporal variability of dengue disease rates based on generalized linear mixed models. For spatio-temporal study, the models incorporate spatially correlated random effects as well as temporal effects. In this study, two different spatial random effects are applied and compared. The first model is based on Leroux spatial model, while the second model is based on the stochastic partial differential equation approach. For the temporal effects, both models follow an autoregressive model of first-order model. The models are fitted within a hierarchical Bayesian framework with integrated nested Laplace approximation methodology. The main objective of this study is to compare both spatio-temporal models in terms of their ability in representing the disease phenomenon. The models are applied to weekly dengue fever data in Peninsular Malaysia reported to the Ministry of Health Malaysia in the year 2017 according to the district level. [ABSTRACT FROM AUTHOR]

Time lag effect exists widely in the course of economic operation. Some economic variables are affected not only by various factors in the current period but also by various factors in the past and even their own past values. As a class of dynamical models, autoregressive distributed lag (ARDL) models are frequently used to conduct dynamic regression analysis. In this paper, we are interested in the quantile regression (QR) modeling of the ARDL model in a dynamic framework. By combining the working likelihood of asymmetric Laplace distribution (ALD) with the expectation–maximization (EM) algorithm into the considered ARDL model, the iterative weighted least square estimators (IWLSE) are derived. Some Monte Carlo simulations are implemented to evaluate the performance of the proposed estimation method. A dataset of the consumption of electricity by residential customers is analyzed to illustrate the application. [ABSTRACT FROM AUTHOR]

This paper investigates causal relationships between gross domestic product (GDP) and the number of small and medium sized enterprises (SMEs) controlled for unemployment rates (UNR) within Slovenia and Croatia. Macro-economic time series data on GDP, SMEs and UNR are analysed in a unit root framework and applied regression analysis. These processes are known as the Johansen co-integration test and Granger-Causality-test. The results show that UNR, GDP and SMEs did not have causal relationships in Croatia between January 2008 and December 2013. UNR and SMEs have a bidirectional relationship in Slovenia with a greater number of SMEs per capita than in Croatia. During their economic recovery period between January 2014 and December 2017, Slovenia and Croatia have experienced the causal unidirectional relation from SMEs to GDP as a positive signal under seen to policy makers on usefulness of investment in SMEs during economic prosperity. The results of a vector autoregressive model suggest a 1% change in the number of SMEs in Croatia decreases GDP in the time of crisis by almost 1.8%. For Slovenia, there is no statistically significant cointegration vector pertained to SMEs-led growth. Finally, the unidirectional causality relation from SMEs to UNR is statistically significant for Slovenia. [ABSTRACT FROM AUTHOR]

This paper uses the gross domestic product growth rates of Malaysia, Thailand, Indonesia and China in an empirical examination to determine whether an integrated time series should be differenced before it is used for forecasting. The results reveal that Mallows model combination (M.M.A.) of original and differenced series is a better choice than just differencing the series only if the perturbation instability measure is more than 1.25 for autoregressive (A.R.) model, and 1.105 for moving average (M.A.) model and autoregressive fractional integrated moving average (A.R.F.I.M.A.) model. Furthermore, it is found that M.M.A. performs better in forecasting with better model stability for the case of M.A. and A.R.F.I.M.A. than A.R. However, M.M.A. is very sensitive in financial crisis. [ABSTRACT FROM AUTHOR]

This paper describes a new approach to fill missing data in hydrologic series. Based on a multiple-order autoregressive model, our algorithm represents the random term with an empirical distribution function that includes different parameters for the low, medium and high ranges of the modelled hydrologic variable. The algorithm involves a corrective mechanism that preserves the original statistical distribution of the series that are filled, while also eliminating the possibility of obtaining negative values for low flows. The algorithm requires multiple correlated hydrologic time series with sufficient data to permit accurate calculation of their statistical properties. It ensures that both the original statistical dependence among the data series and the statistical distribution functions will be preserved after the missing data had been filled. The model has been tested using 15 streamflow series in the Upper Bow River watershed in Alberta, Canada. [ABSTRACT FROM AUTHOR]

Autoregressive (AR) models are useful in time series analysis. Inferences under such models are distorted in the presence of measurement error, a common feature in applications. In this article, we establish analytical results for quantifying the biases of the parameter estimation in AR models if the measurement error effects are neglected. We consider two measurement error models to describe different data contamination scenarios. We propose an estimating equation approach to estimate the AR model parameters with measurement error effects accounted for. We further discuss forecasting using the proposed method. Our work is inspired by COVID-19 data, which are error-contaminated due to multiple reasons including those related to asymptomatic cases and varying incubation periods. We implement the proposed method by conducting sensitivity analyses and forecasting the fatality rate of COVID-19 over time for the four most populated provinces in Canada. The results suggest that incorporating or not incorporating measurement error effects may yield rather different results for parameter estimation and forecasting. [ABSTRACT FROM AUTHOR]

The highly non-linear and nonstationary nature of runoff events in changing environments makes accurate and reliable runoff forecasting difficult. We propose a hybrid model by integrating an autoregressive (AR) model, Bayesian inference, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, Bayesian optimization, and support vector regression. Two Bayesian inference methods (the No-U-Turn Sampler (NUTS) and variational inference) were used to calculate the parameters of the AR model to obtain a Bayesian AR (BAR) model. Credible intervals were used to analyse the uncertainty of the parameters and model prediction results. The above model is applied to the daily runoff predictions of hydrological stations in the Yellow River basin of China. The results show that (1) the hybrid model can improve the prediction accuracy and (2) the NUTS algorithm-based model provides a narrower reliable interval and performs better in uncertainty analyses. [ABSTRACT FROM AUTHOR]

The classical vector autoregressive model is a fundamental tool for multivariate time series analysis. However, it involves too many parameters when the number of time series and lag order are even moderately large. This article proposes to rearrange the transition matrices of the model into a tensor form such that the parameter space can be restricted along three directions simultaneously via tensor decomposition. In contrast, the reduced-rank regression method can restrict the parameter space in only one direction. Besides achieving substantial dimension reduction, the proposed model is interpretable from the factor modeling perspective. Moreover, to handle high-dimensional time series, this article considers imposing sparsity on factor matrices to improve the model interpretability and estimation efficiency, which leads to a sparsity-inducing estimator. For the low-dimensional case, we derive asymptotic properties of the proposed least squares estimator and introduce an alternating least squares algorithm. For the high-dimensional case, we establish nonasymptotic properties of the sparsity-inducing estimator and propose an ADMM algorithm for regularized estimation. Simulation experiments and a real data example demonstrate the advantages of the proposed approach over various existing methods. for this article are available online. [ABSTRACT FROM AUTHOR]

In this study, a new one-parameter discrete distribution obtained by compounding the Poisson and xgamma distributions is proposed. Some statistical properties of the new distribution are obtained including moments and probability and moment generating functions. Two methods are used for the estimation of the unknown parameter: the maximum likelihood method and the method of moments. Additionally, the count regression model and integer-valued autoregressive process of the proposed distribution are introduced. Some possible applications of the introduced models are considered and discussed. [ABSTRACT FROM AUTHOR]

To analyse count time series data inflated at the r + 1 values { 0 , 1 , ... , r } , we propose a new first-order integer-valued autoregressive process with r-geometrically inflated Poisson innovations. Some statistical properties together with conditional maximum likelihood estimate are provided. For the purpose of statistical monitoring, we focus on the cumulative sum chart, exponentially weighted moving average chart and combined jumps chart towards the proposed process. Numerical simulations indicate that the conditional maximum likelihood estimator is unbiased. Moreover, the cumulative sum chart is the best choice to monitor our model in practice. Some applications about telephone complaints data are provided to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

In the present study, we provide a motivating example with a financial application under COVID-19 pandemic to investigate autoregressive (AR) modeling and its diagnostics based on asymmetric distributions. The objectives of this work are: (i) to formulate asymmetric AR models and their estimation and diagnostics; (ii) to assess the performance of the parameters estimators and of the local influence technique for these models; and (iii) to provide a tool to show how data following an asymmetric distribution under an AR structure should be analyzed. We take the advantages of the stochastic representation of the skew-normal distribution to estimate the parameters of the corresponding AR model efficiently with the expectation-maximization algorithm. Diagnostic analytics are conducted by using the local influence technique with four perturbation schemes. By employing Monte Carlo simulations, we evaluate the statistical behavior of the corresponding estimators and of the local influence technique. An illustration with financial data updated until 2020, analyzed using the methodology introduced in the present work, is presented as an example of effective applications, from where it is possible to explain atypical cases from the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In this work, we develop and study upper and lower one-sided EWMA control charts for monitoring correlated counts with finite range. Often in practice, data of that kind can be adequately described by a first-order binomial or beta-binomial autoregressive model. Especially, when there is evidence that data demonstrate extra-binomial variation, the latter model is preferable than the former. The proposed charts can be used for detecting upward or downward shifts in process mean level. Practical guidelines concerning the statistical design of the proposed charts are given, while the effect of the extra-binomial variation is investigated as well. Comparisons with existing control charting procedures are also provided. Finally, an illustrative real-data example is also given. [ABSTRACT FROM AUTHOR]

To provide a more flexible model of count data, we extend the first-order integer-valued autoregressive model with serially dependent innovations based on the dependent thinning operator. This model is appropriate for modelling the number of dependent random events affecting each other when the number of new cases depend on the previous count through a linear functional relationship. Several statistical properties of the model are determined, parameters are estimated by some methods and their properties are studied via simulations. This study was carried out to investigate the efficiency of the new model by two real count data sets, the number of contagious diseases and robbery. [ABSTRACT FROM AUTHOR]

The study explores the nonlinear linkage between government expenditure and government revenue using quarterly data from the first quarter of 1965 to the second quarter of 2019. Linear models are first deployed to determine the order of integration of the variables, cointegration, granger causality and variance decomposition within the SVAR model. Nonlinear techniques are employed to spot the asymmetric relation of the univariate and the expenditure and revenue linkage. Asymmetric adjustments are carried out to unknot the dynamic mechanism based on the threshold vector autoregressive model (TVAR) and threshold vector error-correction model (TVECM). Finally, the Markov Switching model is employed to determine the tendency of the variables to remain in a particular region and their transition probabilities. The empirical findings suggest the presence of nonlinear but one-way causal relation between government expenditure and revenue. The results show that adjustment mechanism of government expenditure towards the equilibrium is more persistent than government revenue when the threshold level is attained. [ABSTRACT FROM AUTHOR]

Accurate forecasts of home sales can provide valuable information for not only policymakers, but also financial institutions and real estate professionals. Against this backdrop, the objective of our article is to analyse the role of consumers' home buying attitudes in forecasting quarterly U.S. home sales growth. Our results show that the home sentiment index in standard classical and Minnesota prior-based Bayesian V.A.R.s fail to add to the forecasting accuracy of the growth of home sales derived from standard economic variables already included in the models. However, when shrinkage is achieved by compressing the data using a Bayesian compressed V.A.R. (instead of the parameters as in the B.V.A.R.), growth of U.S. home sales can be forecasted more accurately, with the housing market sentiment improving the accuracy of the forecasts relative to the information contained in economic variables only. [ABSTRACT FROM AUTHOR]

This study investigates whether oil prices have enough predictive information to predict the direction of the movement of exchange rate by examining the type of cointegration relationship between exchange rate and oil prices in India between 1991Q1 and 2013Q1. Our findings suggest the existence of cointegration relationship between exchange rate and oil prices using both Engle–Granger two-step cointegration test and Johansen cointegration test. Using a momentum threshold autoregressive consistent model, we find evidence in favour of asymmetric cointegration between the two variables. Nevertheless we find no evidence to support asymmetric cointegration relationship between the two variables when threshold autoregressive, threshold autoregressive consistent, and momentum threshold autoregressive models are used. Thus, the results suggest that for certain time period, the adjustment process between exchange rate and oil price is constant, which makes it conducive for predicting the direction of exchange rate movement. However, evidence of asymmetric cointegration suggests that the stable relationship is likely to be interrupted with intervals of structural change implying correction in the dynamics of influencing factors. [ABSTRACT FROM AUTHOR]

Vector autoregressive (VAR) models aim to capture linear temporal interdependencies among multiple time series. They have been widely used in macroeconomics and financial econometrics and more recently have found novel applications in functional genomics and neuroscience. These applications have also accentuated the need to investigate the behavior of the VAR model in a high-dimensional regime, which provides novel insights into the role of temporal dependence for regularized estimates of the model's parameters. However, hardly anything is known regarding properties of the posterior distribution for Bayesian VAR models in such regimes. In this work, we consider a VAR model with two prior choices for the autoregressive coefficient matrix: a nonhierarchical matrix-normal prior and a hierarchical prior, which corresponds to an arbitrary scale mixture of normals. We establish posterior consistency for both these priors under standard regularity assumptions, when the dimension p of the VAR model grows with the sample size n (but still remains smaller than n). A special case corresponds to a shrinkage prior that introduces (group) sparsity in the columns of the model coefficient matrices. The performance of the model estimates are illustrated on synthetic and real macroeconomic datasets. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The lower levels of financial inclusion and severe financial inclusion gaps in Africa motivates the investigation of whether mobile phones, economic growth, bank competition and stability matter for financial inclusion. Data from 49 countries for the periods 2004–2016 were collected and analysed using a five-variable panel structural vector autoregressive model. There was evidence to show that financial inclusion responds positively and significantly to shocks in bank competition, economic growth, mobile phones and bank stability. However, the results reveal that all the variables respond to one standard deviation shock in financial inclusion, suggesting that while the variables matter for financial inclusion, they also require financial inclusion for their effective operation. Hence, the conclusion is that financial inclusion plays a central in the effective running of economies. [ABSTRACT FROM AUTHOR]

The daily runoff is a complicated hydrological time-series, its extreme changes will affect not only the safety of the ships underway but also the dispatching of water transportation. Daily runoff forecast is a hot topic in hydrologic analysis and water transport planning. The fractional difference-autoregressive model based on wavelet analysis (WFIAR) is used to forecast the daily runoff. The daily runoff time series from 2002 to 2013 of Hankou Hydrologic Station are decomposed by the orthogonal db4 wavelet function into a series of stationary high-frequency sub-sequences and a non-stationary low frequency subsequence. According to the Hurst exponent of the low frequency subsequence, the appropriate difference order is determined and fractional difference is made. The differenced low-frequency part and high-frequency parts set up autoregressive models separately. These models are combined to forecast the daily runoff time series in 2014 and 2015 separately. The first-order difference autoregression (DIAR) model and the first-order autoregression (WDIAR) model based on wavelet analysis are also established to compare the forecasting accuracy of different models. The results show that the prediction accuracy of WFIAR is higher than the other two models that reveal the advantage of WDIAR model in daily runoff forecast. [ABSTRACT FROM AUTHOR]

This study is the first attempt to scrutinize the causal relationship between economic policy uncertainty (EPU) and innovation in the case of China, using the autoregressive distributed lag (ARDL) approach to co-integration approach of innovation accounting for causality analysis. The empirical findings show that EPU can negatively affect innovation. EPU indicates a significantly negative impact on innovation as well as on the gross domestic product (GDP) growth rate. The combined results based on ARDL, innovation accounting approach (IAA) (variance decompositions and impulse response functions), and fully modified ordinary least square (FMOLS) raise an important point that calls for attention. The point is relating to the causality running from EPU to innovation. The future of China is uncertain, so when the economic uncertainty is higher, it lowers the value of future activities of the economy of China. [ABSTRACT FROM AUTHOR]

Analysis and forecasting of water temperature are important for water ecological management. The objective of this study is to compare models for water temperature during the summer season for an impounded river. In a case study, we consider hydro-climatic and water temperature data for the Fourchue River (St-Alexandre-de-Kamouraska, Quebec, Canada) between 2011 and 2014. Three different models are applied, which are broadly characterized as deterministic (CEQUEAU), stochastic (Auto-regressive Moving Average with eXogenous variables or ARMAX) and nonlinear (Nonlinear Autoregressive with eXogenous variables or NARX). The efficiency of each model is analysed and compared. The results show that the ARMAX is the best performing water temperature model for the Fourchue River and the CEQUEAU model also simulates water temperature adequately without the overfitting issues that seem to plague the autoregressive models. EDITOR M.C. Acreman ASSOCIATE EDITOR R. Hirsch [ABSTRACT FROM AUTHOR]

This article considers the volatility modeling for autoregressive univariate time series. A benchmark approach is the stationary autoregressive conditional heteroscedasticity (ARCH) model of Engle. Motivated by real data evidence, processes with nonconstant unconditional variance and ARCH effects have been recently introduced. We take into account this type of nonstationarity in variance and propose simple testing procedures for ARCH effects. Adaptive McLeod and Li’s portmanteau and ARCH-LM tests for checking the presence of such second-order dynamics are provided. The standard versions of these tests, commonly used by practitioners, suppose constant unconditional variance. The failure of these standard tests with time-varying unconditional variance is highlighted. The theoretical results are illustrated by means of simulated and real data. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

Let observationsy1, …,ynbe generated from a first-order autoregressive (AR) model with positive errors. In both the stationary and unit root cases, we derive moment bounds and limiting distributions of an extreme value estimator,, of the AR coefficient. These results enable us to provide asymptotic expressions for the mean squared error (MSE) ofand the mean squared prediction error (MSPE) of the corresponding predictor,, ofyn + 1. Based on these expressions, we compare the relative performance of() and the least-squares predictor (estimator) from the MSPE (MSE) point of view. Our comparison reveals that the better predictor (estimator) is determined not only by whether a unit root exists, but also by the behavior of the underlying error distribution near the origin, and hence is difficult to identify in practice. To circumvent this difficulty, we suggest choosing the predictor (estimator) with the smaller accumulated prediction error and show that the predictor (estimator) chosen in this way is asymptotically equivalent to the better one. Both real and simulated datasets are used to illustrate the proposed method. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Models of internal migration can be found in the demographic, geographic, and economic literatures. Unlike most of these models, which are based on cross-sectional analysis of migration rates, the approach here is to incorporate longitudinal as well as cross-sectional aspects by using models from the panel-data literature. Empirical modeling using this approach is possible using a data base recently revised and updated by the U.S. Bureau of the Census. The focus of this article is on model-selection aspects, relying heavily on diagnostic and graphical methods. Understanding the variance structure of the data is particularly important for forecasting purposes. An interesting aspect of the variance structure is the presence of rampant cross-sectional heteroscedasticity, moderate contemporaneous correlation, and mild longitudinal autocorrelation of the transformed data. An important conclusion of the model selection and diagnostic verification is that, based on the available data, percentage changes of state-to-state rates are stationary and can be modeled by an autoregressive process of order 1. A corollary of this conclusion is that the recent migration rate takes on an important role in short-term forecasting of internal migration. [ABSTRACT FROM AUTHOR]

For over a decade now, various efforts have been put in place by various governments of the developing economies to promote economic growth, financial development and expand trade with mixed results. The ability of financial development and/or trade openness to influence economic growth in the developing economies has been a subject of hot debate and remains inconclusive. While a number of scholars are of the view that compelling cointegration exists among each of these constructs, another set of substantial authors have documented that economic growth, trade openness and financial development do evolve independent of each other. Drawing from four financial developments–growth nexus theories, this study used the ARDL bound estimation techniques to examine the existence of cointegration among economic growth, financial development and trade openness in Nigeria. We intend to know what policy instruments need to be manipulated so as to achieve economic growth and financial stability. Our results show that a two-way cointegration exists between economic growth and financial development, on the one hand, as well as between economic growth and trade openness, on the other hand. We therefore recommend that in order to achieve economic growth, policy-makers should pursue strong financial development and increase trade openness. [ABSTRACT FROM AUTHOR]