Monthly Dataset Research Articles

Freezing/thawing index variations over the circum-Arctic from 1901 to 2015 and the permafrost extent

Due to sparse data and discontinuous time observations in the circum-Arctic region, freezing index and thawing index, as useful indicators, are widely used in permafrost distribution, climate changes and cold-region engineering analysis. However, previous researches on freezing/thawing index over this region were estimated based on mean monthly air temperature. In this paper, we analyzed the spatial and temporal variations of the freezing/thawing index over the circum-Arctic from 1901 to 2015 based on the daily datasets, besides monthly datasets. The results showed that freezing index had a downward changing trend and thawing index had an upward trend during 1901–2015. More important, the change trend in freezing/thawing index after 1988 was more significant than before. Furthermore, different freezing/thawing index based on daily datasets and the monthly datasets were assessed and compared according to daily data from 17 meteorological stations, comprehensive relative errors evaluation implied that freezing/thawing based on daily datasets was more accurate generally, although both of other datasets were available in calculating the freezing/thawing index. As the daily datasets are better in calculating annual freezing/thawing index, therefore, the permafrost extent was estimated by a climate-based predictive model combined with snow depth data from Canadian Meteorological Centre (CMC). Finally, considering that the published permafrost map of the circum-Arctic only shows the past permafrost distribution, but it cannot reflect the permafrost distribution after 2000 under the climate warming. Hence, we simulated the current (mean from 2000 to 2015) permafrost area which is 19.96 × 106 km2, and the results showed some discrepancies between published and simulated permafrost extent mainly located in isolated permafrost regions.

High-dimensional macroeconomic forecasting and variable selection via penalized regression

SummaryThis study examines high-dimensional forecasting and variable selection via folded-concave penalized regressions. The penalized regression approach leads to sparse estimates of the regression coefficients and allows the dimensionality of the model to be much larger than the sample size. First, we discuss the theoretical aspects of a penalized regression in a time series setting. Specifically, we show the oracle inequality with ultra-high-dimensional time-dependent regressors. Then we show the validity of the penalized regression using two empirical applications. First, we forecast quarterly US gross domestic product data using a high-dimensional monthly data set and the mixed data sampling (MIDAS) framework with penalization. Second, we examine how well the penalized regression screens a hidden portfolio based on a large New York Stock Exchange stock price data set. Both applications show that a penalized regression provides remarkable results in terms of forecasting performance and variable selection.

A Recent Study Concerning the Climatic Variability over the Kingdom Saudi Arabia for the Period 1948-2018

The present paper provides a recent study of the climatic variability over the Kingdom of Saudi Arabia (KSA) for the period 1948-2018. Moreover, for that period, the relationship of this climatic variability over KSA with the climatic indices is studied. The NCEP/NCAR Reanalysis monthly data sets of the meteorological and climatic parameters over the KSA for the period from the year 1948 to the year 2018 are used. The parameters are (air temperature at levels of 1000 hpa and 500 hpa, mean sea level pressure, geopotential height at 500 hpa level, surface relative humidity, precipitation rate, and outgoing longwave radiation). In addition, the monthly data of climatic indices (North Atlantic Oscillation index (NAO), Arctic Oscillation (AO), Southern Oscillation Index (SOI), Nino 3.4, Global Mean Surface Air Temperature (GMST)) are used. The time series trend, anomaly method, and correlation coefficient techniques are used for data analysis. The results revealed that the annual meteorological and climatic parameters vary dramatically from year to year through the period of study (1948-2018). The trend of this variation is a positive trend for the temperature, pressure and outgoing longwave radiation parameters over KSA. Meanwhile, it is a negative trend of the annual values of surface humidity and precipitation over KSA. The climatic indices influence and control the distribution of annual meteorological and climatic parameters over KSA. The highest significant correlation recorded is between the mean annual of climatic parameters over KSA and the climatic index of global mean surface air temperature (GMST).

A comment on temperature measurement at automatic weather stations in Australia

Two versions of 1-min air-temperature data recorded at Bureau Automatic Weather Stations (AWSs) were compared in three case studies. The aim was to evaluate the difference between 1-min data represented by a measurement at the last second of each minute, compared with an average of four or five 1-s measurements made during the minute. Frequency distributions of the difference between these two values were produced for 44 000 min in three monthly data sets, January and July 2016 and September 2017. Diurnal and seasonal changes in standard deviation of the temperature differences showed that minute-to-minute fluctuations were driven by solar irradiance as the source of turbulent kinetic energy in the planetary boundary layer. Fluctuations in the difference between the two versions of 1-min data were so small overnight in all months that minimum temperature (Tmin) was the same using both methods. In midsummer, any difference between the two values for maximum temperature (Tmax) was greatest at midday. Tmax could be up by 0.1 K higher in the 1-s data compared with Tmax averaged from four measurements in the minute, but less often than 1 min in five. A follow-up test for September 2017 at Mildura when a new Tmax record was set found the difference immaterial, with Tmax the same for the averaged or 1-s values. Thus while the two versions of 1-min air-temperature data showed fluctuating small differences, largest at midday in summer, for the 3 months studied at both sites, fluctuations were too small to cause bias in climatological air-temperature records. This accorded with a numerical experiment confirming the Bureau’s advice that thermal inertia in the AWS measurement systems ensured that its 1-s data represented averages over the prior 40–80 s, providing a 1-min average of air temperature in accord with World Meteorological Organization requirements.

Development and evaluation of irrigation water quality guide using IWQG V.1 software: A case study of Al-Gharraf Canal, Southern Iraq

Abstract In Iraq, rivers represent the main sources of water. Over the past few decades, there has beenwidespread use of water quality indices (WQIs) to assess the quality of water for various uses. In this study, an irrigation water quality guide (IWQG V.1) and its software, designed by Visual Basic 6, was developed based on the specifications of the United Nations Food and Agricultural Organization (FAO) and Meireles et al (2010) irrigation water quality index (IWQI) developed in Brazil. A monthly dataset of Al-Gharraf Canal, in the southern region of Iraq, for three years (2013–2015) containing 612 measurements for 17 water quality parameters was used to evaluate this guide. The guide classified the canal water as (Moderate Restriction) with (65.57) degree score showing its suitability, based on the calculated indices, for use in irrigation of most local crops, assuming proper control of permeability and salinity. The results also showed the good performance of this software as an efficient tool to evaluate, giving the interpretation, advice, and solutions to manage the irrigation water quality and problems. There is a need to establish a long-term management strategic plan to protect Al-Gharraf as an important local water resource.

EVALUATING THE COMPETITION EFFECTS OF UBER’S ENTRY INTO THE BRAZILIAN INCUMBENT CAB-HAILING APP SEGMENT

Abstract This paper assesses the competition effects of Uber’s entry into the incumbent cab-hailing app segment in Brazil. Using a monthly panel data set (from 2014 to 2016) covering the Brazilian municipalities and a difference-in-differences estimator, we show that Uber’s entry into the market resulted in an average reduction of 56.8 percent in the number of rides from cab-hailing apps in the cities where the platform operates. The magnitude of this effect suggests that Uber has had a clearly disruptive effect on the incumbent sector. We also found significant spatial heterogeneity in the competition effects of the platform when comparing the markets of the capitals of the North and Northeast regions (where Uber entered late) with the capitals of the South, Southeast, and Central West regions (where Uber entered early). Only in the second group of municipalities, we found that Uber’s entry caused a reduction in taxi fares (reduction of 12.1 percent). This indicates that the cab-hailing app sector reacted to Uber’s entry by offering discounts in fares after a longer period of exposure to a competitive environment.

Purchasing power parity for the MAVINS

This paper utilizes several tests to examine the PPP theory for MAVINS (Mexico, Australia, Vietnam, Indonesia, Nigeria and South Africa) using recent monthly datasets from January 2003 to May 2016. We test for the stationarity of the variables and found that two of the variables are I(1) while one of the variables is an I(0). Since the variables are a mixture of I(1) and I(0), the paper applied the Mean Group (MG) and the Pooled Mean Group (PMG) ARDL approaches to examine the PPP theory. In choosing between the two estimators, results of the Hausman test indicates that the Mean Group estimator is preferred. Therefore, the results of the Mean Group estimator show that the error correction term which is used to measure the speed of adjustment (from short-run deviation) of the independent variables in converging to long-run equilibrium is negative and significant indicating the presence of cointegration among the variables. This implies that PPP is valid in the long-run in the MAVINS countries. Furthermore, the domestic prices make the nominal exchange rates to appreciate in the long-run and the foreign prices make the nominal exchange rates to appreciate in the short-run. Consequently, appreciation of the nominal exchange rate will cause export to be more expensive, imports cheaper and thereby reducing inflation in the MAVINS based on the data.

Are BRICS exchange rates chaotic?

ABSTRACTIn this paper, we focus on the stochastic (chaotic) attributes of the US dollar-based exchange rates for Brazil, Russia, India, China and South Africa (BRICS) using a long-run monthly dataset covering 1812M01-2017M12, 1814M01-2017M12, 1822M07-2017M12, 1948M08-2017M12, and 1844M01-2017M12, respectively. For our purpose, we consider the Lyapunov exponents, robust to nonlinear and stochastic systems, in both full – samples and in rolling windows. For comparative purposes, we also evaluate a long-run dataset of a developed currency market, namely British pound over the period of 1791M01-2017M12. Our empirical findings detect chaotic behavior only episodically for all countries before the dissolution of the Bretton Woods system, with the exception of the Russian ruble. Overall, our findings suggest that the establishment of the free floating exchange rate system have altered the path of exchange rates removing chaotic dynamics from the phenomenon, and hence, the need for policymakers to intervene in the currency markets for the most important emerging market bloc, should be carefully examined.

Establishment of a long-term lake-surface temperature dataset within the European Alps extending back to 1880

Lake surface temperature (LST) is a key characteristic of lakes, shaping the ecological properties of these inland water bodies and their environment. This study aims at establishing a long-term, high-quality, monthly LST dataset within the European Alps reaching back to 1880, which is provided to the scientific community for further research. Therefore monthly temperature records from Austrian lakes covering a period of about six decades are digitized from hydrological yearbooks. Clustering techniques (rotated empirical orthogonal functions and hierarchical cluster analysis) are used to identify groups of lakes signified by inner similarity and outer separation. These are not only used for an overall quality assessment, but also provide optimal starting conditions for the application of a homogenization procedure, warranting homogeneous LST data from 1950 onwards. LST reconstructions back to 1880 are derived from atmospheric covariates (provided by HISTALP) via sets of transfer-functions. Applied transfer-functions have passed a selection process ensuring mathematical, physical and quality requirements. They are selected from about 160 million candidates according to skill, which is determined through a comprehensive assessment based on validation experiments and several performance measures. Results show overall high skill following a seasonal cycle. From 1880 to 1950 LSTs feature generally slight increases accompanied by a succession of climate states. These are in alignment with outstanding climate periods and sustained, far-reaching repercussions triggered by significant events, which are known from historical documents. LST developments throughout the second half of the twentieth century up to date are characterized by a decline until the mid-1980s, indicating the impact of industrial aerosols. This behaviour is superseded by a steep increase, revealing the gradual unmasking of the anthropogenic greenhouse effect by the continuous reduction of aerosol loads in the atmosphere. The latter substantiates man-made climate change, whose prove is based on atmospheric variables by different data pertaining to the hydrosphere. Potential research hypotheses corresponding to various fields of science that may be investigated by using the here established LST dataset are considered. We hope that these data sets and associated findings will make a contribution to the broader research community.

Predicting climate types for the Continental United States using unsupervised clustering techniques

AbstractThe problem of clustering climate data observation sites and grouping them by their climate types is considered. Machine learning–based clustering algorithms are used in analyzing climate data time series from more than 3,000 climate observation sites in the United States, with the objective of classifying climate type for regions across the United States. Understanding the climate type of a region has applications in public health, environment, actuarial science, insurance, agriculture, and engineering.In this study, daily climate data measurements for temperature and precipitation from the time period 1946–2015 have been used. The daily data observations were grouped into three derived data sets: a monthly data set (daily data aggregated by month), an annual data set (daily data aggregated by year), and a threshold exceeding frequency data set (threshold exceeding frequency provides the frequency of occurrence of certain climate extremes). Three existing clustering algorithms from the literature, namely, k‐means, density‐based spatial clustering of applications with noise, and balanced iterative reducing and clustering using hierarchies, were each applied to cluster each of the data sets, and the resulting clusters were assessed using standardized clustering indices. The results from these unsupervised learning techniques revealed the suitability and applicability of these algorithms in the climate domain. The clusters identified by these techniques were also compared with existing climate classification types such as the Köppen classification system. Additionally, the work also developed an interactive web and map‐based data visualization system that uses efficient big data management techniques to provide clustering solutions in real time and to display the results of the clustering analysis.

Chlorophyll a predictability and relative importance of factors governing lake phytoplankton at different timescales

Assessing the key drivers of eutrophication in lakes and reservoirs has long been a challenge, and many studies have developed empirical models for predicting the relative importance of these drivers. However, the relative roles of various parameters might differ not only spatially (between regions or localities) but also at a temporal scale. In this study, the relative roles of total phosphorus, total nitrogen, ammonia, wind speed and water temperature were selected as potential drivers of phytoplankton biomass by using chlorophyll a as a proxy for biomass. A generalized additive model (GAM) and a random forest model (RF) were developed to assess the predictability of chlorophyll a and the relative importance of various predictors driving algal blooms at different timescales in a freshwater lake. The results showed that the daily datasets yielded better predictability than the monthly datasets. In addition, at a daily scale, water temperature was a more important predictor of chlorophyll a than nutrients, and the importance of phosphorus was comparable to that of nitrogen. In contrast, at a monthly scale, nutrients are more important predictors than water temperature and phosphorus is a better predictor than nitrogen. This study indicates that the drivers of phytoplankton fluctuations vary at different timescales and that timescale has an influence on the relative roles of nitrogen and phosphorus limitation in lakes, which suggests that the temporal scale should be considered when explaining phytoplankton fluctuations. Moreover, this study provides a reference for the monitoring of phytoplankton fluctuations and for understanding the mechanisms underlying phytoplankton fluctuations at different timescales.

Chaos in G7 stock markets using over one century of data: A note

In our study, we tested for chaos in the historical daily and monthly datasets spanning over one century of stock returns for G7 countries. Applying the 0–1 test proposed by Gottwald and Melbourne (2005) and the recent test developed by BenSaïda and Litimi (2013), which is powerful in detecting chaotic dynamics, we found that (a) it is better to denoise the data before testing for chaos and (b), in general, chaos is observed for all countries, using both tests, when we denoised the data.

Intertemporal diversification of sub-sovereign debt

This paper presents a comprehensive empirical analysis of the debt maturity structure of the Swedish municipal sector. The Swedish municipal debt portfolio is characterized by a short maturity structure and an asset-liability mismatch that poses potentially severe roll-over risk. The 2008–2009 financial crisis manifested as a liquidity shock to the sector that highlighted the dangers of short-term funding strategies in conjunction with low levels of intertemporal diversification. In this study we analyze to what extent this led to a change of intertemporal diversification. Based on a unique contract-level monthly data set of municipal loans issued by Kommuninvest of Sweden from January 1997 to June 2016, we construct and estimate a range of dispersion and moment measures to capture the change of various distributional characteristics of the maturity structure. These measures are used as dependent variables in fixed-effects models together with a number of control variables to estimate the effect of the debt-crisis liquidity shock. The main finding is that the crisis did affect the diversification, but not in a persistent way. A possible explanation is that the municipalities found that Kommuninvest through jointly guaranteed lending was able to function as a lender of last resort and thereby mitigates the roll-over risk. It is also found that fiscal and financial properties such as debt-to-tax base ratio, tax base volatility and per capita income are associated with the characteristics of the debt maturity structure of Swedish municipalities, as well as macroeconomic factors such as the term structure of interest rates.

North Atlantic subpolar gyre along predetermined ship tracks since 1993: a monthly data set of surface temperature, salinity, and density

Abstract. We present a binned product of sea surface temperature, sea surface salinity, and sea surface density data in the North Atlantic subpolar gyre from 1993 to 2017 that resolves seasonal variability along specific ship routes (https://doi.org/10.6096/SSS-BIN-NASG). The characteristics of this product are described and validated through comparisons to other monthly products. Data presented in this work were collected in regions crossed by two predetermined ship transects, between Denmark and western Greenland (AX01) and between Iceland, Newfoundland, and the northeastern USA (AX02). The data were binned along a selected usable transect. The analysis and the strong correlation between successive seasons indicate that in large parts of the subpolar gyre, the binning approach is robust and resolves the seasonal timescales, in particular after 1997 and in regions away from the continental shelf. Prior to 2002, there was no winter sampling over the West Greenland Shelf. Variability in sea surface salinity increases towards Newfoundland south of 54∘ N, as well as in the western Iceland Basin along 59∘ N. Variability in sea surface temperature presents less spatial structure with an increase westward and towards Newfoundland. The contribution of temperature variability to density dominates in the eastern part of the gyre, whereas the contribution of salinity variability dominates in the southwestern part along AX02.

Predicting the Tigris River water quality within Baghdad, Iraq by using water quality index and regression analysis

Abstract The monthly water quality data sets from ten stations on the Tigris River within Baghdad for the year 2016 were studied. The water quality index (WQI) was calculated using 11 important parameters according to the assigned weight, and its values were used as the dependent variable in stepwise multiple linear regression (MLR) analysis to develop a water quality model (WQM) for the river. Twenty-three physicochemical water quality variables (2760 values) were included in developing the WQM , they are: Aluminum (Al + 3 ), Fluoride (F−1), Nitrite (NO 2 − 1 ), Nitrate (NO 3 − 1 ), Ammonia (NH3), Temperature (T), Total Alkalinity (TA.), Turbidity (Tur.), Total Hardness (TH), Calcium (Ca + 2 ), Chloride (Cl−1), Magnesium (Mg + 2 ), Potential of Hydrogen (pH), Electrical Conductivity (EC), Sulfate (SO4 − 2 ), Total Dissolved Solids (TDS), Iron (Fe + 2 ), Silica (SiO2), Phosphate (PO 4 − 3 ), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), and Sodium (Na + 1 ). The annual WQI mean value during the study was 266; more than the safe value of 100; consequently, the water quality was considered as unsuitable for drinking. Significant differences in WQI values were detected over the months and across stations with the highest WQI values (poor quality) in winter and spring, while the lowest values (better quality) were in summer and autumn. The WQM, which was developed based on the stepwise MLR analysis, consisted of five parameters: Tur, EC, COD, TH, and pH with significant value (r 0.987, R2 0.974, p WQI = ( − 1 . 597 ) ( T u r ) 0 . 478 (EC) 0 . 409 (COD) 0 . 089 (TH) 0 . 291 (pH) 0 . 095 The study results show that the use of WQI as the dependent variable input improved the prediction of MLR model as a tool to understand, simplify and model the water quality variation. The model developed here can help in rapid low-cost water quality evaluation for best management of the Tigris River.

Dynamic factor model with infinite‐dimensional factor space: Forecasting

SummaryThe paper compares the pseudo real‐time forecasting performance of three dynamic factor models: (i) the standard principal component model introduced by Stock and Watson in 2002; (ii) the model based on generalized principal components, introduced by Forni, Hallin, Lippi, and Reichlin in 2005; (iii) the model recently proposed by Forni, Hallin, Lippi, and Zaffaroni in 2015. We employ a large monthly dataset of macroeconomic and financial time series for the US economy, which includes the Great Moderation, the Great Recession and the subsequent recovery (an update of the so‐called Stock and Watson dataset). Using a rolling window for estimation and prediction, we find that model (iii) significantly outperforms models (i) and (ii) in the Great Moderation period for both industrial production and inflation, and that model (iii) is also the best method for inflation over the full sample. However, model (iii) is outperformed by models (ii) and (i) over the full sample for industrial production.

Historical drought patterns over Canada and their teleconnections with large-scale climate signals

Abstract. Drought is a recurring extreme climate event and among the most costly natural disasters in the world. This is particularly true over Canada, where drought is both a frequent and damaging phenomenon with impacts on regional water resources, agriculture, industry, aquatic ecosystems, and health. However, nationwide drought assessments are currently lacking and impacted by limited ground-based observations. This study provides a comprehensive analysis of historical droughts over the whole of Canada, including the role of large-scale teleconnections. Drought events are characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) over various temporal scales (1, 3, 6, and 12 consecutive months, 6 months from April to September, and 12 months from October to September) applied to different gridded monthly data sets for the period 1950–2013. The Mann–Kendall test, rotated empirical orthogonal function, continuous wavelet transform, and wavelet coherence analyses are used, respectively, to investigate the trend, spatio-temporal patterns, periodicity, and teleconnectivity of drought events. Results indicate that southern (northern) parts of the country experienced significant trends towards drier (wetter) conditions although substantial variability exists. Two spatially well-defined regions with different temporal evolution of droughts were identified – the Canadian Prairies and northern central Canada. The analyses also revealed the presence of a dominant periodicity of between 8 and 32 months in the Prairie region and between 8 and 40 months in the northern central region. These cycles of low-frequency variability are found to be associated principally with the Pacific–North American (PNA) and Multivariate El Niño/Southern Oscillation Index (MEI) relative to other considered large-scale climate indices. This study is the first of its kind to identify dominant periodicities in drought variability over the whole of Canada in terms of when the drought events occur, their duration, and how often they occur.

Asymmetric effects of theWTIcrude oil price on unemployment rates: a comparative study of Canadian provinces and the United States

AbstractSince 2014, Canada has been experiencing a higher unemployment rate due to a slump of the crude oil price. Using a monthly data set of the West Texas Intermediate (WTI) crude oil spot price and unemployment rates of Canadian provinces and theUnited Statesfrom January 1976 to October 2016, we examine the asymmetric effects of oil price on unemployment rates. Specifically, we split the sample into the pre‐technological boom (January 1976 to March 1995) and post‐technological boom periods (April 1995 to October 2016) and analyse whether the asymmetric effects are discernible in these two periods. This is done by applying the ordinary least squares regressions and Granger causality tests. Our findings confirm the asymmetric effects for the full sample in theUnited States. In Canada, the negative and asymmetric relationship is conspicuous particularly in the post‐tech period, and this relationship is more prominent in three oil‐producing Canadian provinces namely Alberta, Saskatchewan and Newfoundland & Labrador. It is noticeable that both in Canada and in the three provinces, the falling oil price affects unemployment adversely only in the post‐tech period. Granger causality tests support the short‐run causal relationships between changes in oil price and unemployment rate in the post‐tech period.

Определяющие факторы закрытия банков: Что важнее – уровни или изменения CAMEL-переменных?

This study examines the role of levels and changes in bank balance sheet variables in explaining bank closure. Using a unique set of monthly bank-level panel data from Russia, we estimate determinants of bank license withdrawals during 2013M7-2017M7. We make two key findings. First, changes in CAMEL indicators are always significantly correlated with probability of bank closure, and the magnitude of parameter estimates decreases with the lag length. Second, while the one-month lagged levels of capital, earnings, and liquidity are significantly associated with the probability of bank closure in the subsequent month, the level of liquidity is the only significant indicator for longer lags. Our key contribution that changes in CAMEL variables matter more than levels is robust to various robustness checks.

The Price Impact of Extreme Weather in Developing Countries

We examine the impact of extreme weather on consumer prices in developing countries by constructing a monthly data set of potential hurricane and flood destruction indices and linking these with consumer price data for 15 Caribbean islands. Our econometric model shows that the price impact of extreme weather events can be large. To illustrate potential welfare losses due to these price effects, we combine our estimates with price elasticities obtained from a demand system and with event probabilities for Jamaica. Our results show that while expected monthly losses are small, rare events can cause large falls in monthly welfare due to price increases.