INDETERMINACY AND VOLATILITY OF EXCHANGE RATES UNDER IMPERFECT CURRENCY SUBSTITUTION

We know that when currencies are perfect substitutes, exchange rates could become indeterminate. We show that even when currencies are less than perfect substitutes exchange rates could display volatility unrelated to economic fundamentals. With increases in currency substitution: (1) the exchange rate becomes more sensitive to changes in economic fundamentals, increasing its volatility; (2) the exchange rate could become indeterminate, and it is more likely to become so if governments pursue similar monetary policies; (3) currencies with high nominal interest rates would decline significantly and the exchange rate becomes more sensitive to changes in the supply of those currencies.     

Score test for testing zero-inflated Poisson regression against zero-inflated generalized Poisson alternatives

In several cases, count data often have excessive number of zero outcomes. This zero-inflated phenomenon is a specific cause of overdispersion, and zero-inflated Poisson regression model (ZIP) has been proposed for accommodating zero-inflated data. However, if the data continue to suggest additional overdispersion, zero-inflated negative binomial (ZINB) and zero-inflated generalized Poisson (ZIGP) regression models have been considered as alternatives. This study proposes the score test for testing ZIP regression model against ZIGP alternatives and proves that it is equal to the score test for testing ZIP regression model against ZINB alternatives. The advantage of using the score test over other alternative tests such as likelihood ratio and Wald is that the score test can be used to determine whether a more complex model is appropriate without fitting the more complex model. Applications of the proposed score test on several datasets are also illustrated.     

Some bounds related to Harris family of distributions

As a lifetime distribution, Harris family of distributions are applied to the lifetime of a series system with random number of components. In this paper, properties of various ageing classes of mixtures of Harris family of distributions, where the tilt parameter of a Harris distribution is taken as a random variable, are studied. We obtain an upper bound for maximum error in evaluating its reliability function. Two bounds are also presented for survival function and expectation of the mixed Harris family. We also provide some interesting bounds for its residual survival function. Our results generalize several previous findings in this connection. Some illustrative examples are also provided.     

Heavy or semi-heavy tail,that is the question

While there has been considerable research on the analysis of extreme values and outliers by using heavy-tailed distributions, little is known about the semi-heavy-tailed behaviors of data when there are a few suspicious outliers. To address the situation where data are skewed possessing semi-heavy tails, we introduce two new skewed distribution families of the hyperbolic secant with exciting properties. We extend the semi-heavy-tailedness property of data to a linear regression model. In particular, we investigate the asymptotic properties of the ML estimators of the regression parameters when the error term has a semi-heavy-tailed distribution. We conduct simulation studies comparing the ML estimators of the regression parameters under various assumptions for the distribution of the error term. We also provide three real examples to show the priority of the semi-heavy-tailedness of the error term comparing to heavy-tailedness. Online supplementary materials for this article are available. All the new proposed models in this work are implemented by the shs R package, which can be found on the GitHub webpage.     

Scientific Periodicals in Iran: A Systems Approach

Scientific periodicals fulfill a significant role in the communication, exchange, and sharing of scientific findings. In recent years, the Iranian Ministry of Science, Research, and Technology (MSRT) has concentrated on non-medical, scientific periodicals. The present paper offers a short history of scientific periodicals and their role in Iran. The authors further examine the current status in the Iranian Ministry of Science, Research, and Technology by using a systems approach. Scientific periodicals in Iran involve four external entities and six major subsystems, including resources, format, content, publication, refereeing, and structure. Investigation of the current failings of the periodicals in the MSRT reveals that the main orientation of the system does not facilitate scientific communication, ease of starting new periodicals, and promotion of natural development of these periodicals. The authors suggest ways to improve the status of scholarly periodicals in Iran.     

Rates of convergence of autocorrelation estimates for periodically correlated autoregressive Hilbertian processes

Autoregressive Hilbertian (ARH) processes are of great importance in the analysis of functional time series data and estimation of the autocorrelation operators attracts the attention of various researchers. In this paper, we study estimators of the autocorrelation operators of periodically correlated autoregressive Hilbertian processes of order one (PCARH(1)), which is an extension of ARH(1) processes. The estimation method is based on the spectral decomposition of the covariance operator and considers two main cases: known and unknown eigenvectors. We show the consistency in the mean integrated quadratic sense of the estimators of the autocorrelation operators and present upper bounds for the corresponding rates.     

Women’s Well-Being in Iran: Territorial Analysis Using a Multidimensional Approach

This paper employs a multidimensional index for assessing women’s well-being in Iranian provinces using demographic and health survey macro data. Besides economic status as a traditional dimension for objective well-being, other factors such as health, education, risk, and technology as well as self-reported life satisfaction and happiness (psychological well-being) have been included as non-material well-being dimensions. We found that the values of women’s well-being are distributed unequally across the provinces. Southern provinces demonstrate the worst performances and central provinces have the best. Generally, Iranian women are in poor condition in terms of economic variables (e.g. income and participation in the job market), psychological well-being (life satisfaction), HIV literacy, use of the internet, and access to mass media. On the other hand, in recent years, they have made significant progress in education (such as the rate of literacy, high school degree), and health (safe pregnancy care and nutritional supplements).     

A non-homogeneous skew-Gaussian Bayesian spatial model

In spatial statistics, models are often constructed based on some common, but possible restrictive assumptions for the underlying spatial process, including Gaussianity as well as stationarity and isotropy. However, these assumptions are frequently violated in applied problems. In order to simultaneously handle skewness and non-homogeneity (i.e., non-stationarity and anisotropy), we develop the fixed rank kriging model through the use of skew-normal distribution for its non-spatial latent variables. Our approach to spatial modeling is easy to implement and also provides a great flexibility in adjusting to skewed and large datasets with heterogeneous correlation structures. We adopt a Bayesian framework for our analysis, and describe a simple MCMC algorithm for sampling from the posterior distribution of the model parameters and performing spatial prediction. Through a simulation study, we demonstrate that the proposed model could detect departures from normality and, for illustration, we analyze a synthetic dataset of CO\(_2\) measurements. Finally, to deal with multivariate spatial data showing some degree of skewness, a multivariate extension of the model is also provided.     

Vector and recurrent singular spectrum analysis: which is better at forecasting?

Singular spectrum analysis (SSA) is an increasingly popular and widely adopted filtering and forecasting technique which is currently exploited in a variety of fields. Given its increasing application and superior performance in comparison to other methods, it is pertinent to study and distinguish between the two forecasting variations of SSA. These are referred to as Vector SSA (SSA-V) and Recurrent SSA (SSA-R). The general notion is that SSA-V is more robust and provides better forecasts than SSA-R. This is especially true when faced with time series which are non-stationary and asymmetric, or affected by unit root problems, outliers or structural breaks. However, currently there exists no empirical evidence for proving the above notions or suggesting that SSA-V is better than SSA-R. In this paper, we evaluate out-of-sample forecasting capabilities of the optimised SSA-V and SSA-R forecasting algorithms via a simulation study and an application to 100 real data sets with varying structures, to provide a statistically reliable answer to the question of which SSA algorithm is best for forecasting at both short and long run horizons based on several important criteria.     

Climate change and non-migration — exploring the role of place relations in rural and coastal Bangladesh

Population and Environment - Of growing research and policy interest are the experiences of people living under conditions of climate change–induced environmental stress, which either are...