scholarly journals The assessment of government incentives on savings, Hungary 2006–2019

2021 ◽  
Vol 71 (4) ◽  
pp. 569-585
Keyword(s):  
Time Series ◽  
Life Insurance ◽  
Household Savings ◽  
Short Term ◽  
Government Bonds ◽  
Time Period ◽  
Government Incentives ◽  
The Government ◽  
Policy Incentives

Abstract This study analyses the effectiveness of government incentives on household savings in Hungary prior to the Covid pandemic and the ensuing economic turmoil. Time series pertaining to life insurance, voluntary pension savings, and long-term and short-term government bonds are tested in relation to government incentives. The novelty of this study is the test on complex mix of policy incentives and saving funds. The analysis applies the multiple breakpoint test and OLS regression, based on the behavioural life cycle hypothesis. The conclusion is that in the analysed time period the government incentives had a significant effect and promoted savings behaviour, with the exception of short-term government bonds.

scholarly journals GLOBALISASI EKONOMI DAN PENGANGGURAN: STUDI KASUS INDONESIA

2021 ◽  
Vol 10 (3) ◽  
pp. 263
Author(s):  
Ari Setyawan ◽  
I Wayan Suparta ◽  
Neli Aida
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Unemployment Rate ◽  
Time Series Data ◽  
Economic Globalization ◽  
Series Data ◽  
Real Wages ◽  
Short Term ◽  
The Government

ABSTRACTThis study aims to examine the effect of economic globalization on the unemployment rate in Indonesia and the relationship of other macroeconomic variables such as economic growth, inflation rate, and real wage with unemployment. The data used is in the form of annual time series data from 1986 to 2018, whose research results are analyzed using the ARDL method. This study concludes that economic globalization can reduce the unemployment rate in Indonesia in the short term, although in the long term, it increases the unemployment rate. Economic growth and inflation in the short and long term have not been able to reduce the current unemployment rate, while the increase in real wages has reduced the unemployment rate in the short term, although not in the long term. By looking at these results, we need to be wary of economic globalization because economic globalization has a destructive impact in the long term. So that concrete and consistent efforts are needed from the government, the private sector, and other stakeholders so that Indonesia gets the maximum benefit from economic globalization, especially in job creation and reducing unemployment.JEL : B22, E22.Keywords : unemployment, economic globalization, economic growth, inflation, real wages. ABSTRAKPenelitian ini bertujuan melihat pengaruh tingkat globalisasi ekonomi terhadap tingkat pengangguran di Indonesia serta hubungan variabel makroekonomi lain seperti tingkat pertumbuhan ekonomi, tingkat inflasi dan tingkat upah riil dengan tingkat pengangguran. Data yang dipergunakan berupa data time series tahunan dari periode 1986 hingga 2018 yang hasil penelitiannya dianalisis menggunakan metode ARDL. Kesimpulan penelitian ini yaitu globalisasi ekonomi mampu mengurangi tingkat pengangguran di Indonesia dalam jangka pendek meskipun dalam jangka panjang malah meningkatkan tingkat pengangguran. Pertumbuhan ekonomi dan inflasi baik dalam jangka pendek dan jangka panjangnya belum mampu menurunkan tingkat pengangguran yang ada sedangkan naiknya upah riil mampu menurunkan tingkat pengangguran dalam jangka pendek meskipun tidak dalam jangka panjang. Dengan melihat hasil ini, kita perlu waspada terhadap globalisasi ekonomi karena globalisasi ekonomi ini memiliki dampak buruk dalam jangka panjang sehingga dibutuhkan upaya kongkrit dan konsisten baik dari pemerintah, swasta maupun para stakeholder lain agar Indonesia memperoleh manfaat yang sebesar-besarnya dari globalisasi ekonomi khusunya dalam upaya penciptaan lapangan kerja dan mengurangi pengangguran.

SEIR and Regression Model-based COVID-19 outbreak predictions in India (Preprint)

2020 ◽  
Author(s):  
Gaurav Pandey
Keyword(s):  
Regression Model ◽  
Short Term ◽  
Seir Model ◽  
Major Threat ◽  
Global Pandemic ◽  
Time Period ◽  
The Government ◽  
Major Attention ◽  
Respiratory Droplets

UNSTRUCTURED COVID-19 pandemic has become a major threat to the country. To date, well-tested medication or antidote is not available to cure this disease. According to WHO reports, COVID-19 is a severe acute respiratory syndrome which is transmitted through respiratory droplets and contact routes. Analysis of this disease requires major attention by the Government to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed for India till 30th March 2020 and predictions have been made for the number of cases for the next 2 weeks. SEIR model and Regression model have been used for predictions based on the data collected from John Hopkins University repository in the time period of 30th January 2020 to 30th March 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for the SEIR model and 1.75 for the regression model. The RMSLE error rate between the SEIR model and a Regression model was found to be 2.01. Also, the value of R0 which is the spread of the disease was calculated to be 2.02. Expected cases may rise between 5000-6000 in the next two weeks of time. This study will help the Government and doctors in preparing their plans for the next two weeks. Based on the predictions for short-term intervals, these models can be tuned for forecasting in long-term intervals.

scholarly journals DETERMINANTS OF GOVERNMENT SECURITIES YIELD USING VECM

2021 ◽  
Vol 22 (2) ◽  
pp. 127-146
Author(s):  
Batara Maju Simatupang
Keyword(s):  
Exchange Rate ◽  
Country Risk ◽  
Short Term ◽  
Government Bonds ◽  
Vector Error Correction ◽  
Indonesian Government ◽  
Vector Error ◽  
Study Methodology ◽  
The Government

This study aims to uncover the determinants’ effect on the return rate of government securities (GS). This study's data uses the government bonds that can be traded with the ten-year tenor, and the time-horizon of the collected data spans from 2009:M1 to 2018:M6. The study methodology utilizes the vector error correction model (VECM) model to determine the short-term backward behavior, which refers to the situation where the short-term balances are corrected for the long-term balances. Additionally, it is also to reveal the relationship between the variables within the model. Thus, this study is to see whether GS’s reciprocal level has been at the value of efficient return or not. The results show that the cointegrated determinants of the Bank Indonesia (BI) rate / seven days repo, outstanding tradable government bonds, Fitch Rating, exchange rate, sovereign country risk, and regional bond index positively affects the GS yield. In contrast, the determinants of Fitch Rating, exchange rate, sovereign country risk, and outstanding tradable government bond negatively influence GS yields. The implication of this research is that the Indonesian government securities are interdependent with the identified determinants; thus, the Indonesian government should maintain the movement of those determinants to ensure that its GS stays positive.

scholarly journals SEIR and Regression Model based COVID-19 outbreak predictions in India

2020 ◽  
Author(s):  
Rajan Gupta ◽  
Gaurav Pandey ◽  
Poonam Chaudhary ◽  
Saibal Pal
Keyword(s):  
Regression Model ◽  
Short Term ◽  
Seir Model ◽  
Major Threat ◽  
Global Pandemic ◽  
Time Period ◽  
The Government ◽  
Major Attention ◽  
Respiratory Droplets

AbstractCOVID-19 pandemic has become a major threat to the country. Till date, well tested medication or antidote is not available to cure this disease. According to WHO reports, COVID-19 is a severe acute respiratory syndrome which is transmitted through respiratory droplets and contact routes. Analysis of this disease requires major attention by the Government to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analyzed for India till 30th March 2020 and predictions have been made for the number of cases for the next 2 weeks. SEIR model and Regression model have been used for predictions based on the data collected from John Hopkins University repository in the time period of 30th January 2020 to 30th March 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0 which is the spread of the disease was calculated to be 2.02. Expected cases may rise between 5000-6000 in the next two weeks of time. This study will help the Government and doctors in preparing their plans for the next two weeks. Based on the predictions for short-term interval, these models can be tuned for forecasting in long-term intervals.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

A Note Concerning the Dynamics of Government Bond Yields

2021 ◽  
pp. 056943452098827
Author(s):  
Tanweer Akram
Keyword(s):  
Interest Rate ◽  
Financial Markets ◽  
Yield Curve ◽  
Empirical Studies ◽  
State Theory ◽  
Government Bond ◽  
Bond Yields ◽  
Short Term ◽  
Government Bonds

Keynes argued that the central bank can influence the long-term interest rate on government bonds and the shape of the yield curve mainly through the short-term interest rate. Several recent empirical studies that examine the dynamics of government bond yields not only substantiate Keynes’s view that the long-term interest rate responds markedly to the short-term interest rate but also have relevance for macroeconomic theory and policy. This article relates Keynes’s discussions of money, the state theory of money, financial markets, investors’ expectations, uncertainty, and liquidity preference to the dynamics of government bond yields for countries with monetary sovereignty. Investors’ psychology, herding behavior in financial markets, and uncertainty about the future reinforce the effects of the short-term interest rate and the central bank’s monetary policy actions on the long-term interest rate. JEL classifications: E12; E40; E43; E50; E58; E60; F30; G10; G12; H62; H63

scholarly journals An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Katerina G. Tsakiri ◽  
Antonios E. Marsellos ◽  
Igor G. Zurbenko
Keyword(s):  
New York ◽  
Time Series ◽  
Water Discharge ◽  
Winter Period ◽  
Statistical Methodology ◽  
Summer Period ◽  
Short Term ◽  
Efficient Prediction ◽  
Climatic Time Series

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

Italy's politics without policy: Balancing Atlanticism and Europeanism in the Middle East

Modern Italy ◽  
2008 ◽  
Vol 13 (2) ◽  
pp. 135-153 ◽  
Author(s):  
Raffaella A. Del Sarto ◽  
Nathalie Tocci
Keyword(s):  
Foreign Policy ◽  
Middle East ◽  
The United States ◽  
The European Union ◽  
Short Term ◽  
The Government ◽  
Specific Interests ◽  
Policy Shifts ◽  
The Eu

Focusing on Italy's Middle East policies under the second Berlusconi (2001–2006) and the second Prodi (2006–2008) governments, this article assesses the manner and extent to which the observed foreign policy shifts between the two governments can be explained in terms of the rebalancing between a ‘Europeanist’ and a transatlantic orientation. Arguing that Rome's policy towards the Middle East hinges less on Italy's specific interests and objectives in the region and more on whether the preference of the government in power is to foster closer ties to the United States or concentrate on the European Union, the analysis highlights how these swings of the pendulum along the EU–US axis are inextricably linked to a number of underlying structural weaknesses of Rome's foreign policy. In particular, the oscillations can be explained by the prevalence of short-term political (and domestic) considerations and the absence of long-term, substantive political strategies, or, in short, by the phenomenon of ‘politics without policy’ that often characterises Italy's foreign policy.

ON THE CONSUMPTION/DISTRIBUTION THEOREM UNDER THE LONG-RUN GROWTH CRITERION SUBJECT TO A DRAWDOWN CONSTRAINT

2010 ◽  
Vol 13 (06) ◽  
pp. 931-957 ◽  
Author(s):  
MICHAEL J. KLASS ◽  
KRZYSZTOF NOWICKI
Keyword(s):  
Growth Rate ◽  
Short Term ◽  
Long Run ◽  
Time Period ◽  
Total Capital ◽  
Growth Criterion ◽  
Drawdown Constraint ◽  
Riskless Asset ◽  
Distribution Theorem

Consider any discrete time sequence of investment fortunes Fn which has a finite long-run growth rate [Formula: see text] when subject to the present value capital drawdown constraint Fne-rn ≥ λ* max 0≤k≤nFke-rk, where 0 ≤ λ* < 1, in the presence of a riskless asset affording a return of er dollars per time period per dollar invested. We show that money can be withdrawn for consumption from the invested capital without either reducing the long-run growth rate of such capital or violating the drawdown constraint for our capital sequence, while simultaneously increasing the amount of capital withdrawn for consumption at the identical long-term rate of V(r, λ*). We extend this result to an exponentially increasing number of consumption categories and discuss how additional yearly contributions can temporarily augment the total capital under management. In addition, we assess the short-term practicality of creating such an endowment/consumption/distribution program.

scholarly journals A time series analysis of determinants of FDI in Afghanistan: Which one matters the most, security or economic growth?

2018 ◽  
Vol 6 (2) ◽  
pp. 19
Author(s):  
Abdul Fareed Delawari
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Foreign Direct Investment ◽  
Time Series Analysis ◽  
Direct Investment ◽  
Economic Factors ◽  
Market Economic ◽  
The Government ◽  
The Relationship

Afghanistan has been practicing market economic system since 2002. Since then, the government has been initiating different policies and announced various incentives to attract foreign direct investment (FDI) to the country. However, the outcome has not been satisfactory due to several political and economic factors. This paper explores the relationship between security, economic growth and FDI in Afghanistan, using ARDL model. The paper covers a period from 2002 to 2016. The empirical results of this study show that there is a negative long-term relationship between security and FDI. Hence,  the author concludes that, to attract FDI to the country, insuring security should be the top priority of the government of Afghanistan.

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