Literatura académica sobre el tema "Employment forecasting – Hawaii – Mathematical models"

Новизна исследования заключается в разработке качественно нового подхода к оценке кадрового потенциала, основанного на методах оценки и прогнозирования персонала. Целью исследования - формирование аналитики в разрезе различных видов предприятий/отраслей/регионов, что позволяет разрабатывать качественные рекомендации для развития профессиональных кадров в том или ином направлении, для развития социальной и сервисной инфраструктуры в регионе для удержания профессиональных кадров. Актуальностью работы является структуризация вопроса кадровой подготовки в учебных заведениях и возможности контролирования (прогнозирования) трудоустройства выпускников на предприятиях, как важный элемент человеческого капитала. Актуальность данного вопроса очевидна, в связи с возможностью выявлением количественных показателей и выявление его зависимости от основных факторов влияния. В работе представлены две математические модели прогнозирования системы занятости. В ней выполнен математический анализ трудоустройства выпускников учебных заведений любого уровня (колледжей, высших учебных заведений, курсов повышения квалификации и прочее), проведено прогнозирование востребованности этих выпускников предприятиями региона/отрасли/страны. Результатом работы является разработка математического аппарата необходимого для выполнения полноценного анализа и дальнейшее прогнозирования (востребованности) специальности (программы/курса) на рынке труда, как в определённом регионе, так и в стране в целом и построение демографических моделей. Использование математического аппарата позволит потенциальным пользователям (учебным заведением, работодателям и другим заинтересованным лицам) получить инструмент и количественные показатели для дальнейшего планирования работы при подготовке специалистов и разработке образовательных программ. The novelty of the research lies in the development of a qualitatively new approach to the assessment of human resources potential, based on the methods of personnel assessment and forecasting. The purpose of the study is to form analytics in the context of various types of enterprises/industries/regions, which allows us to develop high-quality recommendations for the development of professional personnel in a particular direction, for the development of social and service infrastructure in the region to retain professional personnel. The relevance of the work is the structuring of the issue of personnel training in educational institutions and the possibility of monitoring (forecasting) the employment of graduates in enterprises, as an important element of human capital. The relevance of this issue is obvious, due to the possibility of identifying quantitative indicators and identifying its dependence on the main factors of influence. The paper presents two mathematical models for predicting the employment system, it performs a mathematical analysis

The article is devoted to the assessment and forecasting of demographic indicators, gross regional product, employment, labor force and unemployment by industry in the Ryazan region until 2025-2050. The article analyzes the trends in the demographic development of the Ryazan region, including the dynamics of fertility, mortality and migration. The consequences of population aging and the peculiarities of changes in the age and sex structure of the region's population are also considered. To solve the problem of modeling and forecasting, economic and mathematical models have been developed that include the parameters of socio-economic development. The social component is based on a systematic approach to forecasting employment, depending on the anthropogenic load index, which takes into account life expectancy and standard of living, literacy of the population, crime rate, ecological state and other indicators of socio-economic development of the region. The economic component uses econometric analysis by types of economic activities in the Ryazan region, as well as time series analysis to predict employment in both the medium and short term. In terms of the labor market, the labor force is forecasted taking into account the socio-economic effect of hidden unemployment. In conclusion, forecasts are made about the dynamics of unemployment in the Ryazan region and the influence of demographic factors on the formation of the labor force.

Background: To carry out mathematical modeling of key indicators of the spread of the coronavirus epidemic and, with their help, evaluate the forecast of the dynamics of its completion time. Aim: Due to a substantial request for the practice of making informed decisions to isolate the population in the face of the uncertainty of the increased risks of infection. Methods: The regression analysis was used as a method that uses the best parameter estimation of mathematical models, providing high quality dynamics of key indicators of the spread of the epidemic. To build the models, statistical data were used, which are generated by monitoring by coordinating councils to combat the spread of COVID-19 in the regions of the Russian Federation. Results: The proposed methodological apparatus allowed, based on the monitoring data of the coordinating council to combat the spread of St. Petersburg coronavirus, to carry out modeling and prediction of the course of the disease in the region. Conclusion: The proposed approach makes it possible to justifiably recommend management decisions to the administration and health authorities to create normal economic and social living conditions for residents of Russian regions, their employment, including training, during the spread of coronavirus. Recommendations: Continue to improve the apparatus for modeling and forecasting key distribution indicators of COVID-19.

It has become vital to effectively characterize the self-similar and regular patterns in time series marked by short-term and long-term memory in various fields in the ever-changing and complex global landscape. Within this framework, attempting to find solutions with adaptive mathematical models emerges as a major endeavor in economics whose complex systems and structures are generally volatile, vulnerable and vague. Thus, analysis of the dynamics of occurrence of time section accurately, efficiently and timely is at the forefront to perform forecasting of volatile states of an economic environment which is a complex system in itself since it includes interrelated elements interacting with one another. To manage data selection effectively and attain robust prediction, characterizing complexity and self-similarity is critical in financial decision-making. Our study aims to obtain analyzes based on two main approaches proposed related to seven recognized indexes belonging to prominent countries (DJI, FCHI, GDAXI, GSPC, GSTPE, N225 and Bitcoin index). The first approach includes the employment of Hurst exponent (HE) as calculated by Rescaled Range ([Formula: see text]) fractal analysis and Wavelet Entropy (WE) in order to enhance the prediction accuracy in the long-term trend in the financial markets. The second approach includes Artificial Neural Network (ANN) algorithms application Feed forward back propagation (FFBP), Cascade Forward Back Propagation (CFBP) and Learning Vector Quantization (LVQ) algorithm for forecasting purposes. The following steps have been administered for the two aforementioned approaches: (i) HE and WE were applied. Consequently, new indicators were calculated for each index. By obtaining the indicators, the new dataset was formed and normalized by min-max normalization method’ (ii) to form the forecasting model, ANN algorithms were applied on the datasets. Based on the experimental results, it has been demonstrated that the new dataset comprised of the HE and WE indicators had a critical and determining direction with a more accurate level of forecasting modeling by the ANN algorithms. Consequently, the proposed novel method with multifarious methodology illustrates a new frontier, which could be employed in the broad field of various applied sciences to analyze pressing real-world problems and propose optimal solutions for critical decision-making processes in nonlinear, complex and dynamic environments.

AbstractThe pandemic caused by the coronavirus disease 2019 (COVID-19) has produced a global health calamity that has a profound impact on the way of perceiving the world and everyday lives. This has appeared as the greatest threat of the time for the entire world in terms of its impact on human mortality rate and many other societal fronts or driving forces whose estimations are yet to be known. Therefore, this study focuses on the most crucial sectors that are severely impacted due to the COVID-19 pandemic, in particular reference to India. Considered based on their direct link to a country’s overall economy, these sectors include economic and financial, educational, healthcare, industrial, power and energy, oil market, employment, and environment. Based on available data about the pandemic and the above-mentioned sectors, as well as forecasted data about COVID-19 spreading, four inclusive mathematical models, namely—exponential smoothing, linear regression, Holt, and Winters, are used to analyse the gravity of the impacts due to this COVID-19 outbreak which is also graphically visualized. All the models are tested using data such as COVID-19 infection rate, number of daily cases and deaths, GDP of India, and unemployment. Comparing the obtained results, the best prediction model is presented. This study aims to evaluate the impact of this pandemic on country-driven sectors and recommends some strategies to lessen these impacts on a country’s economy.

For any country one of the most important social and economic problems is an unemployment. In today's conditions this problem is one of the main economic problems in Ukraine and poses a real threat to the state and social well-being. The main problem of unemployment is caused by the fact that the unemployed face the loss of qualification, social status and the lowering of the standard of living. Due to the decline in income of the population, through the loss of work, the demand for goods and services on the domestic market is decreasing, tax revenues to the state budget are decreasing, social pressure and criminality are growing. The level of unemployment is an indicator of social processes of the state, a characteristic of stability and confidence in the future of the country. The creation of an adequate predict of the level of unemployment taking into account the accidental nature of the problem provides the choice of management strategy in the employment sphere, taking into account the peculiarities of the economic situation, priorities of social development, makes it possible to assess its current state, trends and changes, as well as to take appropriate management decisions in the employment sphere. To combat this economic phenomenon, state support for business, such as subsidies for retaining employees at their workplaces, can be implemented. In general, at the level of the state it is necessary to develop new measures of a strategic nature in order not to fight against unemployment, but to prevent it. The work examines the main statistical methods of predict, which are based on the data of one time series. The peculiarities of using trend models for predicts are examined. It is noted that in the current conditions of computer software usage, the choice of trend formations for predicting is essentially simplified: different trend forms can be produced for the same time series and the one which best describes the output series by mathematical criteria can be selected. This work is devoted to the study of the problem of predict the level of unemployment in Ukraine. It is suggested to make predicts on the basis of autoregressive models of time series. In the work the models of autoregression, autoregression with a coveted average and autoregression with a trend are examined in detail. As a result, the information and analytical system for modeling and forecasting of financial processes was created. The method of prediction on the basis of auto-regressive time series model that we reviewed consists in creating a model for predicting the future events (predicting the level of unemployment) ґrunning on the known events of the past, and predicting the future data before they will be measured. The found average absolute volumetric forecast error (MAPE) and Tale coefficient allowed us to conclude that the proposed model is appropriate for making short-term forecasts of unemployment.

The thesis, entitled "Essays on macroeconometrics and short-term forecasting",

is composed of three chapters. The first two chapters are on nowcasting,

a topic that has received an increasing attention both among practitioners and

the academics especially in conjunction and in the aftermath of the 2008-2009

economic crisis. At the heart of the two chapters is the idea of exploiting the

information from data published at a higher frequency for obtaining early estimates

of the macroeconomic variable of interest. The models used to compute

the nowcasts are dynamic models conceived for handling in an efficient way

the characteristics of the data used in a real-time context, like the fact that due to the different frequencies and the non-synchronicity of the releases

the time series have in general missing data at the end of the sample. While

the first chapter uses a small model like a VAR for nowcasting Italian GDP,

the second one makes use of a dynamic factor model, more suitable to handle

medium-large data sets, for providing early estimates of the employment in

the euro area. The third chapter develops a topic only marginally touched

by the second chapter, i.e. the estimation of dynamic factor models on data characterized by block-structures.

The firrst chapter assesses the accuracy of the Italian GDP nowcasts based

on a small information set consisting of GDP itself, the industrial production

index and the Economic Sentiment Indicator. The task is carried out by using

real-time vintages of data in an out-of-sample exercise over rolling windows

of data. Beside using real-time data, the real-time setting of the exercise is

also guaranteed by updating the nowcasts according to the historical release calendar. The model used to compute the nowcasts is a mixed-frequency Vector

Autoregressive (VAR) model, cast in state-space form and estimated by

maximum likelihood. The results show that the model can provide quite accurate

early estimates of the Italian GDP growth rates not only with respect

to a naive benchmark but also with respect to a bridge model based on the

same information set and a mixed-frequency VAR with only GDP and the industrial production index.

The chapter also analyzes with some attention the role of the Economic Sentiment

Indicator, and of soft information in general. The comparison of our

mixed-frequency VAR with one with only GDP and the industrial production

index clearly shows that using soft information helps obtaining more accurate

early estimates. Evidence is also found that the advantage from using soft

information goes beyond its timeliness.

In the second chapter we focus on nowcasting the quarterly national account

employment of the euro area making use of both country-specific and

area wide information. The relevance of anticipating Eurostat estimates of

employment rests on the fact that, despite it represents an important macroeconomic

variable, euro area employment is measured at a relatively low frequency

(quarterly) and published with a considerable delay (approximately

two months and a half). Obtaining an early estimate of this variable is possible

thanks to the fact that several Member States publish employment data and

employment-related statistics in advance with respect to the Eurostat release

of the euro area employment. Data availability represents, nevertheless, a

major limit as country-level time series are in general non homogeneous, have

different starting periods and, in some cases, are very short. We construct a

data set of monthly and quarterly time series consisting of both aggregate and

country-level data on Quarterly National Account employment, employment

expectations from business surveys and Labour Force Survey employment and

unemployment. In order to perform a real time out-of-sample exercise simulating

the (pseudo) real-time availability of the data, we construct an artificial

calendar of data releases based on the effective calendar observed during the first quarter of 2012. The model used to compute the nowcasts is a dynamic

factor model allowing for mixed-frequency data, missing data at the beginning

of the sample and ragged edges typical of non synchronous data releases. Our

results show that using country-specific information as soon as it is available

allows to obtain reasonably accurate estimates of the employment of the euro

area about fifteen days before the end of the quarter.

We also look at the nowcasts of employment of the four largest Member

States. We find that (with the exception of France) augmenting the dynamic

factor model with country-specific factors provides better results than those

obtained with the model without country-specific factors.

The third chapter of the thesis deals with dynamic factor models on data

characterized by local cross-correlation due to the presence of block-structures.

The latter is modeled by introducing block-specific factors, i.e. factors that

are specific to blocks of time series. We propose an algorithm to estimate the model by (quasi) maximum likelihood and use it to run Monte Carlo

simulations to evaluate the effects of modeling or not the block-structure on

the estimates of common factors. We find two main results: first, that in finite samples modeling the block-structure, beside being interesting per se, can help

reducing the model miss-specification and getting more accurate estimates

of the common factors; second, that imposing a wrong block-structure or

imposing a block-structure when it is not present does not have negative

effects on the estimates of the common factors. These two results allow us

to conclude that it is always recommendable to model the block-structure

especially if the characteristics of the data suggest that there is one.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished