Monthly Dataset Research Articles

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.

The inter‐decadal change in anomalous summertime water vapour transport modes over the tropical Indian Ocean–western Pacific in the mid‐1980s

Using the monthly reanalysis data sets of National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) for 1961–2015, the inter‐decadal change in inter‐annual water vapour transport over the tropical Indian Ocean–western Pacific during summer and the related mechanisms were analysed in this article. The results show that two major modes of anomalous water vapour transport over the tropical Indian Ocean–western Pacific (60°–140°E, 10°S–30°N) experienced a significant inter‐decadal change in the mid‐1980s. The first mode (EOF1) shows that the anticyclonic water vapour transport over the northwestern Pacific moves significantly southwards after the mid‐1980s. Before the mid‐1980s, the anomalous water vapour originates from the subtropical western Pacific and moves through the South China Sea to eastern China. However, the anomalous water vapour mainly originates from the tropical western Pacific and moves through the South China Sea to eastern China after the mid‐1980s. The second mode (EOF2) reflects the enhanced effect of the anomalous water vapour transport over the tropical Indian Ocean on East Asia after the mid‐1980s. Before the mid‐1980s, the distribution of the water vapour anomalies is uneven over the tropical Indian Ocean and the magnitude of water vapour is relatively small, so it has a weak effect on rainfall over East Asia. After the mid‐1980s, anomalous anticyclonic water vapour transport originates from the tropical Indian Ocean and moves along the Arabia Sea, Indian Peninsula and Indo‐China Peninsula and then forms an anomalous cyclonic water vapour transport over south China. Furthermore, the possible causes of the inter‐decadal transition in anomalous water vapour transport are investigated for the two major modes. The significant inter‐decadal transition in anomalous water vapour transport is associated with the enhanced effect of the sea surface temperature (SST). Before the mid‐1980s, the relationships between the two major modes and the SST of the tropical oceans are relatively weak. After the mid‐1980s, the EOF1 becomes significantly regulated by the El Niño‐Southern Oscillation (ENSO) in the previous winter, and the positive (negative) phase of the principal component of the first mode (PC1) corresponds to significantly positive (negative) SST anomalies over the tropical northern Indian Ocean and Maritime Continent during the same period. For EOF2, the anomalous water vapour transport over the tropical Indian Ocean is related to the enhanced inter‐annual variability of tropical Indian Ocean dipole (TIOD) after the mid‐1980s.

Are BRICS Exchange Rates Chaotic?

In 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.

Economic growth and the development of banking and insurance sector in the post-liberalized India

Purpose The importance of banking and insurance, as an important part of the financial system, has been well accepted in the growth literature. Acting as financial intermediaries they perform important functions that may contribute in economic growth. Addressing this issue, the purpose of this paper is to empirically examine the relationship between banking, insurance and economic growth in India in the post-liberalized era when the private sector was allowed to operate banking and insurance business. Design/methodology/approach In order to find the long-run and short-run relationship between banking, insurance and economic growth, the study uses the VAR-vector error correction model (VECM) along with Granger causality test to explore any causal relationship. Findings The results indicate that there is the long-term relationship between banking, insurance and economic growth and the causality results show a bi-directional relationship between insurance activity and economic growth; however, banking is not granger cause of insurance or economic growth rather it is economic growth that cause banking development. Research limitations/implications The only limitation to the study is the non-availability of monthly figures of GDP. The study therefore, as suggested by RBI, uses monthly data set of Index of Industrial Production to measure economic growth. Practical implications The findings of the study give policy directions to the policymakers to make strategies that are conducive toward boosting development in insurance in order to achieve the targeted economic growth. Originality/value This work is the first attempt to study the conjoint relationship between banking, insurance and economic growth on the Indian economy after the reforms were initiated in the financial sector.

Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States

Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000–2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints.

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Forecasting the number of patient visits to hospitals has aroused an increasingly large interest from both theoretic and application perspectives. To enhance the accuracy of forecasting hospital visits, this paper proposes a hybrid approach by coupling wavelet decomposition (WD) and artificial neural network (ANN) under the framework of “decomposition and ensemble”. In this model, the WD is first employed to decompose the original monthly data of the number of patient visits to hospitals into several components and one residual term. Then, the ANN as a powerful prediction tool is implemented to fit each decomposed component and generate individual prediction results. Finally, all individual prediction values are fused into the final prediction output by simple addition method. For illustration and verification, four sets of monthly series data of the number of patient visits to hospitals are used as the sample data, and the results show that the proposed model can obtain significantly more accurate forecasting results than all considered popular forecasting techniques.

Dynamic fuel price pass-through: Evidence from a new global retail fuel price database

We estimate the dynamic effects of crude oil price shocks on retail fuel prices, the pass-through, using the local projection approach of Jordà (2005). Using a novel monthly dataset of retail fuel prices in 162 countries over the period from 2000:1 to 2014:12, we find that: (i) retail gasoline prices respond positively to crude oil price shocks, but the responses vary across regions and income groups; (ii) there is also some variation across country groups in the persistence of the effects of crude oil price shocks on retail gasoline prices; and (iii) declines in crude oil prices lead to smaller effects on retail gasoline prices than increases in crude oil prices, pointing to an asymmetry in the fuel price pass-through.

The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach

The difficulty in modelling inflation and the significance in discovering the underlying data generating process of inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting the U.S. inflation based on autoregressive and structural models of the term structure. We employ two nonlinear methodologies: the econometric Least Absolute Shrinkage and Selection Operator (LASSO) and the machine learning Support Vector Regression (SVR) method. The SVR has never been used before in inflation forecasting considering the term–spread as a regressor. In doing so, we use a long monthly dataset spanning the period 1871:1–2015:3 that covers the entire history of inflation in the U.S. economy. For comparison reasons we also use OLS regression models as benchmark. In order to evaluate the contribution of the term-spread in inflation forecasting in different time periods, we measure the out-of-sample forecasting performance of all models using rolling window regressions. Considering various forecasting horizons, the empirical evidence suggests that the structural models do not outperform the autoregressive ones, regardless of the model’s method. Thus we conclude that the term-spread models are not more accurate than autoregressive ones in inflation forecasting.

Water quality modelling of the River Ganga using artificial neural network with reference to the various training functions

The River Ganga (2,525 km long) is the largest River basin in India, covering 26.2 percent of India's total geographical area and recently granted living entity status by the court. It is the holiest River and also among the dirtiest in the world. That’s why it is mandatory to maintain its water quality (WQ). Though, monitoring and assessment of WQ of a River is a very challenging task. In this research work, Soft Computing (SC) based popular and commononly used Artificial Neural Network (ANN) technique has been used for modelling the WQ of the Ganga River by developing a prediction model based on six different training functions. Five sampling stations along this River stretch were selected from DEVPRAYAG to ROORKEE in the Uttarakhand state of India. The monthly data sets of five water quality parameters temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD) and total coliform (TC) for the time period from 2001 to 2015 have been taken. The feed forward error back propagation neural network method has been used to develop the WQ-prediction model by conducting various experiments following a neural network structure of 5-10-1, 0.1 as a training goal and various training functions. Using the Mean square error (MSE) statistical method the prediction performance of the developed model was evaluated. The model developed with traincgp (Conjugate Gradient with Polak-Ribiere Restarts) comes out to be the worst one (MSE=0.786) while the other model with trainlm (Levenberg-Marquardt backpropagation) rule proved to be the best one (MSE=0.163) among others. Consequently, it is found that ANNs are capable of predicting WQ of the River Ganga with acceptable results.

Dipole behaviour in maximum significant wave height over the Southern Indian Ocean

ABSTRACTThe study reports on existence of a dipole pattern in maximum significant wave height (MSWH) as well in mean significant wave height over the Southern Indian Ocean (SIO) based on analysis using 24 years of satellite observations. Analysis using quality checked daily altimeter data from nine satellite missions for entire Indian Ocean region revealed the spatio‐temporal variability of observed dipole phenomena. Interestingly, the second empirical orthogonal function (EOF) mode (EOF2) in monthly and monthly anomaly MSWH data clearly shows the prevalence of dipole over the SIO having a period of 6 months for monthly dataset Principal Component (PC) leading to a 3 months oscillation. The EOF of monthly anomaly dataset analysis also exhibited a similar dipole pattern for the EOF2 mode having a period of 2.1 years. In addition, the study also considered seasonal EOF analysis for four austral seasons December–January–February (DJF), March–April–May (MAM), June–July–August (JJA), and September–October–November (SON). The EOF2 for all seasons exhibits a similar dipole pattern in SIO basin, except for the DJF months. Based on seasonal EOF analysis, the DJF and JJA EOF2s have similar dipole pattern that reverses its polarity because of wind variability for the MAM and SON EOF2s justifying 3 months dipole oscillation obtained for monthly datasets. The Radon transform is used to investigate the orientation of this dipole, and for the first time this method is used in the field of climatology to study orientation and the degree of polarity of a dipole pattern. Furthermore, the study investigated possible correlation of dipole with various proven climate indices establishing the fact that Southern Annular Mode Index (SAMI) showed a significant correlation. The other seasonal datasets also correlated well with SAMI, except DJF months that do not show the dipole pattern, and it had a good correlation with Nino3 index representing the El Nino Southern Oscillation Index.

Seasonal changes of the volatile density in the coma and on the surface of comet 67P/Churyumov–Gerasimenko

Starting from several monthly data sets of Rosetta's COmetary Pressure Sensor we reconstruct the gas density in the coma around comet 67P/Churyumov-Gerasimenko. The underlying inverse gas model is constructed by fitting ten thousands of measurements to thousands of potential gas sources distributed across the entire nucleus surface. The ensuing self-consistent solution for the entire coma density and surface activity reproduces the temporal and spatial variations seen in the data for monthly periods with Pearson correlation coefficients of 0.93 and higher. For different seasonal illumination conditions before and after perihelion we observe a systematic shift of gas sources on the nucleus.

The Bank Lending Channel of Conventional and Unconventional Monetary Policy

Using a new monthly dataset on bank-level lending rates, we study the transmission of conventional and unconventional monetary policy in the euro area via shifts in the supply of credit. We find that a bank lending channel is operational for both types of measures, though its functioning differs: for standard operations the transmission is weaker for banks with more capital and a more solid funding structure, in line with an important role of asymmetric information. However, in response to non-standard measures lending supply expands by more at banks with stronger capital and funding positions, suggesting a crucial role for regulatory and economic constraints. We also find that the transmission of unconventional measures is attenuated by their negative effect on future bank’s capital position via the net interest income (reverse bank capital channel). Finally, we find that large sovereign exposures mute the response of lending rates to conventional policy, but amplify the transmission of unconventional measures.

The effect of seasonality and stationarity on specific humidity forecast over Nigeria

This paper investigates the importance of seasonality and stationarity of climatological data to effective forecast of a meteorological parameter – the specific humidity, ‘q’. This was done by computing q using monthly datasets of temperature and relative humidity from twenty carefully selected stations for twenty-six (26) years (1970–1995). The data were retrieved from the archives of the Nigerian Meteorological Agencies. The data were analysed to examine its seasonality as well as stationarity which are considered to be the basic requirements for good and accurate forecasting in climate studies. The stations were grouped into four climatic regions namely: the Sahel, Guinea Savanna, Rainforest and Derived Savanna regions. It was found that stations from the Guinea Savanna and Sahelian regions show more seasonality and stationarity compared to stations in Rainforest and Derived Savanna regions. Forecast was then carried out using three techniques which are: the ordinary Autoregressive model, Harmonic Analysis and Exponential Smoothing modelling techniques. These were analysed and evaluated using series of statistical error analysis methods. Exponential smoothing technique, which incorporates the seasonal components of the data into its model, was found to give the most accurate and reliable results with minimal statistical errors, re-iterating the importance of seasonality and stationarity in effective climate forecast.

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Abstract Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

Data imputation analysis for Cosmic Rays time series

The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II – runs the bootstrap Expectation Maximization algorithm, MICE – runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI – an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.

The Transmission Mechanism of Credit Support Policies in the Euro Area

We use an original monthly dataset of 131 individual euro area banks to examine the effectiveness and transmission mechanism of the Eurosystem?s credit support policies since the start of the crisis. First, we show that these policies have indeed been succesful in stimulating the credit ?ow of banks to the private sector. Second, we ?nd support for the lending view of monetary transmission. Speci?cally, the policies have had a greater impact on loan supply of banks that are more constrained to obtain unsecured external funding, i.e. small banks (size effect), banks with less liquid balance sheets (liquidity effect), banks that depend more on wholesale funding (retail effect) and low-capitalized banks (capital effect). The role of bank capital is, however, ambiguous. Besides the above favorable direct e¤ect on loan supply, lower levels of bank capitalization at the same time mitigate the size, retail and liquidity effects of the policies. The drag on the other channels has even been dominant during tthe sample period, i.e. better capitalized banks have on average responded more to the credit support policies of the Eurosystem as a result of more favourable size, retail and liquidity effects.

Navigating Through Torpedo Attacks and Enemy Raiders: Merchant Shipping and Freight Rates During World War I

During World War I ocean freight rates rose to extraordinary levels. Using a new monthly dataset it is shown that freight rates can be well explained by economic activity, commodity prices, war risk and world tonnage in the period 1912 to 1916. In the first two years of the war part of the British merchant fleet was directly controlled by the Government but neutral shipping was basically free to operate as normal. In this period neutral shipping accounted for about one third of British imports. In the final two years of the war a much stricter regime of freight control was introduced that resulted in the withdrawal of a large proportion of neutral shipowners from British and Allied trade. Together with the mounting losses of tonnage due to the German submarine campaign this created an acute shortage of carrying capacity and reduced imports. It is argued that the policy of freight control may have rested on a misconception of the role of freight rates as a source of the high wartime inflation.

Search for Environmental Causation of the Cladoceran Dynamics in Lake Kinneret, Israel

Ten years of monthly dataset of cladoceran (Diaphanosoma sp., Bosmina spp., Ceriodaphnia spp.) density in Lake Kinneret (spatially scattered lake sampling stations), Israel, was studied with the aim of searching impact factors controlling their dynamics. Statistical regressions indicated that out of several environmental factors (among others, non-pyrrhophyte algal biomass, invertebrate predation) only temperature factor was relevant. Additional speculative assumptions of zooplanktivore fish predation suggest this factor as a significant impact on cladoceran dynamics.