Historical Time Series Data Research Articles

The Evolving Structure of the American Economy and the Employment Challenge

This paper examines the evolving structure of the American economy, specifically the trends in employment, value added, and value added per employee from 1990 to 2008. Employing historical time series data from the Bureau of Labor Statistics and the Bureau of Economic Analysis, US industries are separated into internationally tradable and nontradable components, allowing for employment and value added trends at both the industry and the aggregate level to be examined. Value added grew across the economy, but almost all of the incremental employment increase of 27.3 million jobs was on the nontradable side, where government and health care are the largest employers and provided the largest increments (an additional 10.4 million jobs) over the past two decades. There are obvious questions about whether those trends can continue; without fast job creation in the nontradable sector during this period, the United States would already have faced a major employment challenge. The nontradable sector also experienced much slower growth in value added per employee; because value added per employee is highly correlated with income, it goes a long way to explain the stagnation of wages across large segments of the workforce. The evolution of the US economy supports the notion of there being a long-term structural challenge with respect to the quantity and quality of employment opportunities in the United States.

Impact of eutrophication on the occurrence of Trichodesmium in the Cochin backwaters, the largest estuary along the west coast of India

Phytoplankton studies in early 1970s have shown the annual dominance of diatoms and a seasonal abundance of Trichodesmium in the lower reaches of the Cochin backwaters (CBW) and adjacent coastal Arabian Sea during the pre-summer monsoon period (February to May). Surprisingly, more recent literature shows a complete absence of Trichodesmium in the CBW after 1975 even though their seasonal occurrence in the adjacent coastal Arabian Sea continued without much change. In order to understand this important ecological feature, we analyzed the long-term water quality data (1965-2005) from the lower reaches of the CBW. The analyses have shown that salinity did not undergo any major change in the lower reaches over the years and values remained >30 throughout the period. In contrast, a tremendous increase was well marked in levels of nitrate (NO(3)) and phosphate (PO(4)) in the CBW after 1975 (av. 15 and 3.5 μM, respectively) compared with the period before (av. 2 and 0.9 μM, respectively). Monthly time series data collected in 2004-2005 period from the lower reaches of the CBW and coastal Arabian Sea have clearly shown that the physical characteristics like salinity, temperature, water column stability, and transparency in both regions are very similar during the pre-summer monsoon period. In contrast, the nutrient level in the CBW is several folds higher (NO(3), 8(;) PO(4), 4; SiO(4), 10; and NH(4), 19 μM) than the adjacent coastal Arabian Sea (NO(3), 0.7; PO(4), 0.5; SiO(4), 0.9; and NH(4), 0.6 μM). The historic and fresh time series data evidences a close coupling between enriched levels of nutrients and the absence of Trichodesmium in the Cochin backwaters.

Quantifying parameter uncertainty and assessing the skill of exponential dispersion rainfall simulation models

AbstractThe exponential dispersion model (EDM) has been demonstrated as an effective tool for quantifying rainfall dynamics across monthly time scales by simultaneously modelling discrete and continuous variables in a single probability density function. Recent applications of the EDM have included development and implementation of statistical software packages for automatically conditioning model parameters on historical time series data. Here, we advance the application of the EDM through an analysis of rainfall records in the North American Laurentian Great Lakes by implementing the EDM in a Bayesian Markov chain Monte Carlo (MCMC) framework which explicitly acknowledges historic rainfall variability and reflects that variability through uncertainty and correlation in model parameters and simulated rainfall metrics. We find, through a novel probabilistic assessment of skill, that the EDM reproduces the magnitude, variability, and occurrence of daily rainfall, but does not fully capture temporal autocorrelation on a daily time scale. These findings have significant implications for the extent to which the EDM can serve as a tool for supporting regional climate assessments, for downscaling regional climate scenarios into local‐scale rainfall time series simulations, and for assessing trends in the historical climate record. Copyright © 2012 Royal Meteorological Society

Analysis of the Productive Performance of Intermediate Industries in Qatar (1986-1999)

Using time series historical data, the current paper analyzes the impacts of production factors on the intermediate industries in Qatar and their effects on productive efficiency. That done by the assessment of the production levels and total value added, in order to evaluate actual growth rates in sector specifics in what is called description of intermediate sector's variables. Results help to predict behaviors of intermediate industries' inputs and outputs in Qatar State. The analysis clarifies that relative importance of value added in the measurements of intermediate industries in Gross Domestic Production of Qatar. Moreover, it is important to verify the positive effects of labor factor in actual growth rates, net production in addition to impacts of capital stock on productivity.

Analysis of the Performance of Intermediate Industries in Iraq (1970-1990)

The paper analyzes historical time series data on the productive performance of the Iraqi intermediate industries pre-invasion. The primary hypothesis here is that Iraq promoted from being classified as developing to developed county in its way to establish modern industries and strong economic sectors in addition to its oil production. However, the primary economic impediment to finance such economic sectors was the financing facilities due to the continuous depletion of its economy because of war with Iran first, the preparations for war with Kuwait and what followed with the international coalition and the United States. Termination of war with Iran helped to generate unprecedented economic development which has been buried with Gulf War of 1990. Invested capital utilization in intermediate industries has deteriorated. That support previously introduced theory that even with continuous economic development efforts, expected resources were extracted from the economic sector and impeded its economic recycling for more development.

Finance and American Inequality in the Last Century

Rising top income shares in the US have received increased attention both within and outside of academia. In this paper, we argue that the size of the top income share in the US has been consistently shaped by the evolution of the financial sector. We test our hypotheses connecting finance and the top income share over time using empirical methods that probe the co-evolution of historical time series data: Granger causality tests, cointegration tests, and error correction model analysis. We find that the growth of the financial sector has had negative impacts on the equal distribution of market incomes in the US during the last century. Specifically, financial developments have increased the portion of national income accruing to the richest 1%, and the richest 0.01% in particular, as well as increasing the level of poverty in the US during the last century.

The use of analogies in forecasting the annual sales of new electronics products

Mathematical models are often used to describe the sales and adoption patterns of products in the years following their launch and one of the most popular of these models is the Bass model. However, using this model to forecast sales time series for new products is problematical because there is no historic time series data with which to estimate the model's parameters. One possible solution is to fit the model to the sales time series of analogous products that have been launched in an earlier time period and to assume that the parameter values identified for the analogy are applicable to the new product. In this paper, we investigate the effectiveness of this approach by applying four forecasting methods based on analogies (and variants of these methods) to the sales of consumer electronics products marketed in the USA. We found that all the methods tended to lead to forecasts with high absolute percentage errors, which is consistent with other studies of new product sales forecasting. The use of the means of published parameter values for analogies led to higher errors than the parameters we estimated from our own data. When using our own data, averaging the parameter values of multiple analogies rather than relying on a single most-similar, product led to improved accuracy. However, there was little to be gained by using more than five or six analogies.

A Research on Interbank Loan Interest Rate Fluctuation Characteristics and the VaR Risk of China’s Commercial Banks

According to the historical time series data of commercial interbank, this paper examines the interest rate fluctuation distribution characteristics, indicating that EGARCH Model can better fit the rate volatility of the interbank market interest. This paper calculates the value at risk (VaR) of five major commercial banks using EGARCH Model with such a conclusion that the difference that major commercial banks face is various. The interest risk of state-owned commercial banks and other financial institutions is more serious than the city commercial banks and foreign banks. The interest risk of rural credit cooperatives is the least serious.

Application of seasonal time series model in the precipitation forecast

Precipitation is an important guiding standard for agricultural production; however, it is highly difficult to forecast due to random sequential and seasonal features. In this research on the historical data of time series, it is found that rainfall has a strong autocorrelation of seasonal characteristics in time series. Utilizing seasonal periodicity with a Seasonal Autoregressive and Moving Average (SARIMA) methodology we analyze the statistical data of precipitation, which is based on Shouguang city in Shandong statistical yearbooks over the 1996:1–2009:12 periods. In this paper the method for time series analysis and forecasting are proposed to study the model. The experimental results could achieve good prediction fitting degree. In this sense, the model is available for actual forecast warning in precipitation. Through the comparison of the model we had found the advantages of forecasting that can make full use of natural rainfall for corresponding areas and save underground water resources.

Marine open cage aquaculture in the eastern Mediterranean Sea: a new trophic resource for bottlenose dolphins

Over the last 2 decades marine open cage aquaculture in the Mediterranean Sea has grown rapidly, leading to increased productivity in the water column near fish farms. Here we investigated the effect of such increase in productivity on the common bottlenose dolphin Tursiops truncatus. We developed an ecosystem model for the inner Ionian Sea Archipelago, western Greece, to: (1) evaluate the trophic interactions between bottlenose dolphins and their surround- ing ecosystem, including cage-associated organisms; (2) investigate simultaneously 3 hypotheses that could explain bottlenose dolphin dynamics: (a) increasing fishing effort and changes in ocean productivity, (b) competition with other species, and (c) increase in number of fish farms; and (3) explore spatial trends in bottlenose dolphins distribution using the increased number of fish farms as an explanatory variable. Comparisons of model predictions with historical time-series data indicate that only the increase in number of cages and thereby in productivity near fish farms contributed to the trends observed in dolphin numbers. Spatial analysis also confirmed an increase in occurrence of these dolphins in the proximity of the farms. These outcomes suggest that high productivity in waters surrounding fish cages—within a coastal area that is markedly oligotrophic—may attract bottlenose dolphins. The present study shows that open cage aquacul- ture has benefited bottlenose dolphins by easing their way of catching prey. Further studies should be conducted in other areas of the Mediterranean Sea to investigate whether the higher occurrence of bottlenose dolphins around fish cages is a common pattern and if it is driven by the trophic status of the ecosystem or by the type of fishes that surround the cages.

A novel model by evolving partially connected neural network for stock price trend forecasting

This paper proposes a novel model by evolving partially connected neural networks (EPCNNs) to predict the stock price trend using technical indicators as inputs. The proposed architecture has provided some new features different from the features of artificial neural networks: (1) connection between neurons is random; (2) there can be more than one hidden layer; (3) evolutionary algorithm is employed to improve the learning algorithm and training weights. In order to improve the expressive ability of neural networks, EPCNN utilizes random connection between neurons and more hidden layers to learn the knowledge stored within the historic time series data. The genetically evolved weights mitigate the well-known limitations of gradient descent algorithm. In addition, the activation function is defined using sin( x) function instead of sigmoid function. Three experiments were conducted which are explained as follows. In the first experiment, we compared the predicted value of the trained EPCNN model with the actual value to evaluate the prediction accuracy of the model. Second experiment studied the over fitting problem which occurred in neural network training by taking different number of neurons and layers. The third experiment compared the performance of the proposed EPCNN model with other models like BPN, TSK fuzzy system, multiple regression analysis and showed that EPCNN can provide a very accurate prediction of the stock price index for most of the data. Therefore, it is a very promising tool in forecasting of the financial time series data.

Empirical modeling of Japan’s markup and inflation, 1976–2000

This paper aims to pursue an empirical model of Japan’s markup and inflation using historical time series data covering the last quarter of the 20th century. A multivariate cointegration analysis of Japan’s macroeconomic data indicates the existence of a long-run economic linkage, which is interpreted as an empirical representation of countercyclical markup. A set of variables in the cointegrated system, apart from markup and inflation, are judged to be weakly exogenous for parameters of interest, thereby allowing us to estimate a partial model given these exogenous variables. The model reduction is then conducted so as to achieve a parsimonious representation of countercyclical markup and inflation dynamics over the sample period of interest.

Effects of multi-annual climate variability on the hydrodynamic evolution (1833 to present) in a shallow aquifer system in northern Belgium

Using a groundwater flow model and long historical meteorological time series data, the evolution of the groundwater flow regime in a multi-layered groundwater flow basin in northern Belgium during the last one and a half centuries (since 1833) is reconstructed. Model output parameters such as piezometric levels, depth to water table, seepage fluxes in the valleys and calculated baseflow to the river system are presented and inter-annual and decadal variations are evaluated against seasonal fluctuations. The main time-varying boundary condition in the model is the aquifer recharge which was estimated using the method of Thornthwaite and Mather based on precipitation and temperature data. The model does not take into account changes in boundary conditions due to changes in land use (deforestation, drainage of cultivated land) or groundwater exploitation. Variations in model output parameters are therefore only due to climatological forcing. Only the natural non-exploited state of the aquifer is considered. Although few historical piezometric measurements are available to verify model output, the results give an indication of the natural hydrodynamic variations on a time scale of decades. Citation Van Camp, M., Coetsiers, M., Martens, K. & Walraevens, K. (2010) Effects of multi-annual climate variability on the hydrodynamic evolution (1833 to present) in a shallow aquifer system in northern Belgium. Hydrol. Sci. J. 55(5), 763–779.

Meteorological variables and malaria in a Chinese temperate city: A twenty-year time-series data analysis

Objectives This study aimed to examine the impact of climate variation on malaria in a temperate region of China. Methods A 20-year historical time-series data analysis was conducted to examine the relationship between meteorological variables, including maximum and minimum temperatures, rainfall, humidity, and cases of malaria in Jinan, a temperate city in northern China. Data were retrieved from 1959 and 1979 and analyzed on a monthly basis. Spearman correlation and cross-correlation analyses were performed to identify time lag values between each meteorological variable and the number of malaria cases. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to quantify the relationship between the meteorological variables and malaria cases. Results The SARIMA models indicate that a 1 °C rise in maximum temperature may be related to a 7.7% to 12.7% increase and a 1 °C rise in minimum temperature may result in approximately 11.8% to 15.8% increase in the number of malaria cases. A clear association between malaria and other selected weather variables, including rainfall and humidity, has not been detected in this study. Conclusions Temperature could play an important role in the transmission of malaria in temperate regions of China.

Applying gray model to predicting trend of textile fashion colors

In this study, a GM(1,1) model is applied to forecast the trend of textile fashion colors. Through the historical time series data of the color suggestion ratios by international forecasting facilities in the past, the GM(1,1) model is applied to forecast the fashion color trend. Besides the GM(1,1) model, a gray neural network model (i.e. GNNM(1,1)) is developed to compare the precision with it as well. The simulated results show that the GM(1,1) model has higher forecast precision than the GNNM(1,1) one. The predicted results of the color trend using the GNNM(1,1) model are less close to the real circumstances than as expected. With the assistance of a gray model-based prediction model, the fashion color trend can be traced more precisely and easily.

Meteorological Variables and Malaria in a Chinese Temperate City: A Twenty-Year Time-Series Data Analysis

ISEE-0022 Background and Objective: Re-emergence of malaria has been observed in China recently. Although the association between climate variation and malaria has been addressed in many countries, little is known about the impact of climate variation on malaria in temperate regions of China. Methods: A 20-year historical time-series data analysis was conducted to examine the relationship between meteorological variables, including maximum and minimum temperatures, rainfall, humidity, air pressure, and cases of malaria in Jinan, a temperate city in northern China. Data were retrieved for the period 1959 to 1979 and analyzed on a monthly basis. Spearman correlation and cross-correlation analyses were performed between each meteorological variable and the number of malaria cases, to identify time lag values. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to quantify the relationship between the meteorological variables and malaria cases. Results: The SARIMA models indicate that a 1°C rise in maximum temperature may be related to a 4.2% to 13.9% increase, and a 1°C rise in minimum temperature, may result in an approximately 6.8% to 14.8% increase in the number of malaria cases. A clear association between malaria and other selected weather variables, including rainfall, humidity and air pressure, has not been detected in this study. Conclusion: Results suggest that temperature could play an important role in the transmission of malaria in temperate regions of China, and climate change may bring about more malaria cases in this region of China if no action is taken.

The Coupling and Decoupling of Business Cycles in Historical Perspective: Scandinavian Evidence from the Early 19th Century

This study considers annual data for 1830-2008 period for Norway, Sweden, the U.K., and the U.S.. Business cycle movements, their links and the factors that may influence these are econometrically investigated. For Norway and Sweden we rely on historical time series data recently published by these countries’ respective central banks. Briefly, I find the following. First, inferences about what drive business cycle movements can be greatly influenced by the availability of a range of macroeconomic time series beyond reliance on just real GDP or CPI data. Second, there is evidence that the relationship between output movements in Norway, Sweden, the U.K., and the U.S. are non-linearly related. Third, it is also apparent that large shocks, such as the Great Depression or global conflicts, do get transmitted and there is seemingly iittle that a chosen monetary policy regime can do to prevent such an outcome. Fourth, there is reasonably clear evidence that 19th and 20th Century business cycles are different and that a plausible explanation for this result is the introduction of counter-cyclical policies in all of the countries in our sample.

Food Calorie Intake under Grain Price Uncertainty in Rural Nepal

This study evaluated the effects of grain output price uncertainty on the farm income of rural households and, consequently, how this uncertainty influenced caloric intake through changes in farm income. Using a national rural household survey data set in Nepal, augmented with output price uncertainty measures calculated from historical time-series data, we found that grain output price uncertainty (especially for rice and wheat) tends to decrease crop production income of rural households. In addition, we found that lower crop income from production decreases calorie intake of rural households. Taken together, these results suggest that output price uncertainty during the production process tends to reduce caloric intake of rural Nepalese households. The price uncertainty seemed to reduce the crop income households need to buy calorie-rich staple foods.

Improved irrigation water demand forecasting using a soft-computing hybrid model

Recently, Computational Neural Networks (CNNs) and fuzzy inference systems have been successfully applied to time series forecasting. In this study the performance of a hybrid methodology combining feed forward CNN, fuzzy logic and genetic algorithm to forecast one-day ahead daily water demands at irrigation districts considering that only flows in previous days are available for the calibration of the models were analysed. Individual forecasting models were developed using historical time series data from the Fuente Palmera irrigation district located in Andalucia, southern Spain. These models included univariate autoregressive CNNs trained with the Levenberg–Marquardt algorithm (LM). The individual models forecasting were then corrected via a fuzzy logic approach whose parameters were adjusted using a genetic algorithm in order to improve the forecasting accuracy. For the purpose of comparison, this hybrid methodology was also applied with univariate autoregressive CNN models trained with the Extended-Delta-Bar-Delta algorithm (EDBD) and calibrated in a previous study in the same irrigation district. A multicriteria evaluation with several statistics and absolute error measures showed that the hybrid model performed significantly better than univariate and multivariate autoregressive CNNs.

The Economic and Demographic Transition, Mortality, and Comparative Development

We propose a unified growth theory to investigate the mechanics generating the economic and demographic transition, and the role of mortality differences for comparative development. The framework can replicate the quantitative patterns in historical time series data and in contemporaneous cross-country panel data, including the bi-modal distribution of the endogenous variables across countries. The results suggest that differences in extrinsic mortality might explain a substantial part of the observed differences in the timing of the take-off across countries and the worldwide density distribution of the main variables of interest.