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This study aims to examine the hysteresis hypothesis in unemployment using monthly data from 13 countries in transition.

Stationarity in the unemployment rate of selected transition economies was analyzed using four different group unit root tests, namely, linear, structural breaks, non-linear and structural breaks and non-linear.

The empirical results show that the unemployment hysteresis hypothesis is valid for the majority of transition economies, including Bulgaria, Croatia, the Czech Republic, Estonia, Hungary, the Kyrgyz Republic, Latvia, Lithuania, Poland, Romania and Slovenia. However, the results strongly reject the null hypothesis of unemployment hysteresis for the Kazakhstan and the Slovak Republics.

This study revealed that, for countries in transition, advanced unit root tests exhibit greater validity when compared to standard tests

This study aims to investigate, for the first time in the literature, the stochastic properties of the US aggregate health-care price inflation rate series, using the data on health-care inflation rates for a panel of 17 major US urban areas for the period 1966-2006.

This goal is undertaken by applying the first- and second-generation panel unit root tests and the panel stationary test developed recently by Carrion-i-Silvestre et al. (2005) that allows for endogenously determined multiple structural breaks and is flexible enough to control for the presence of cross-sectional dependence.

The empirical findings indicate that after controlling for the presence of cross-sectional dependence, finite sample bias, and asymptotic normality, the US aggregate health-care price inflation rate series can be characterized as a non-stationary process and not as a regime-wise stationary innovation process.

The research findings apply to understanding of health-care sector price escalation in US urban areas. These findings have timely implications for the understanding of the data structure and, therefore, constructs of economic models of urban health-care price inflation rates. The results confirming the presence of a unit root indicating a high degree of inflationary persistence in the health sector suggests need for further studies on health-care inflation rate persistence using the alternative measures of persistence. This study’s conclusions do not apply to non-urban areas.

The mean and variance of US urban health-care inflation rate are not constant. Therefore, insurers and policy rate setters need good understanding of the interplay of the various factors driving the explosive health-care insurance rates over the large US metropolitan landscape. The study findings have implications for health-care insurance premium rate setting, health-care inflation econometric modeling and forecasting.

Payers (private and public employers) of health-care insurance rates in US urban areas should evaluate the value of benefits received in relation to the skyrocketing rise of health-care insurance premiums.

This is the first empirical research focusing on the shape of urban health-care inflation rates in the USA.

This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.

One of the main theories regarding the relationship between government expenditure and gross domestic product (GDP) is Wagner’s law. This law was developed in the late-19th century by Adolph Wagner (1835–1917), a prominent German economist, and depicts that an increase in government expenditure is a feature often associated with progressive states. This paper aims to examine the validity of Wagner’s law in Egypt for 1960–2018. The relationship between real government expenditure and real GDP is tested using three versions of Wagner’s law.

To test the validity of Wagner in Egypt, law time-series analysis is used. The methodology used in this paper is: unit-root tests for stationarity, Johansen cointegration approach, error-correction model and Granger causality.

The results provide strong evidence of long-term relationship between GDP and government expenditure. Moreover, the causal relationship is found to be bi-directional. Hence, this study provides support for Wagner’s law in the examined context.

It should be noted, however, that there are some limitations to this study. For instance, in this paper, the government’s size was measured through government consumption expenditure rather than government expenditure due to data availability, which does not fully capture the government size. Moreover, the data available was limited and does not fully cover the earliest stages of industrialization and urbanization for Egypt. Furthermore, although time-series analysis provides a more contextualized results and conclusions, the obtained conclusions suffer from their limited generalizability.

This paper aims to specifically make a contribution to the empirical literature for Wagner’s law, by testing the Egyptian data using time-series econometric techniques for the longest time period examined so far, which is 1960–2018.

This study aims to examine the short- and long-term equilibrium relationship between All share price index (ASPI), macroeconomic variables and the economic crisis in Sri Lanka.

Monthly time series data for inflation (CPI), industrial production (IP), an exchange rate (EX), an interest rate (TB), short-term interest rate (CD) and economic crisis were used from 2010 to 2021. The ADF test, the bound testing approach, the CUSUM test and the CUSUMQ test were used in this study.

The findings show a long-run stable relationship between stock price, macroeconomic variables and political crisis (i.e., CPI, IP, ER, TB, CD and economic crisis). The results of the Johansen cointegration test suggest that there is at least one cointegrating equation, indicating that there is a long-run equilibrium relationship between macroeconomic variables and stock prices in Sri Lanka.

The vector error correction estimates show that the coefficient of the error correction term is significant with a negative sign, indicating that a long-run dynamic relationship exists between macroeconomic variables and stock prices. In the short term, economic crisis has had a big effect on stock prices suggesting that Sri Lanka’s domestic financial markets are linked to the stability of the country.

This research establishes the links between stock returns, macroeconomic variables and economic crisis. So far, research has been unable to establish the empirical nature of such links. The authors believe that this paper fills that gap.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

Social spending is at the forefront of the tools used to repair the damage caused by the global epidemic. However, one of the most critical questions in recent days is as follows: what are the effects of social expenditures in eliminating unemployment? The primary purpose of this article is to provide empirical evidence on the impact of social spending on chronic unemployment in the selected organization for economic co-operation and development (OECD) countries.

In this study, the data of 30 selected OECD countries between 1991 and 2018 have been compiled. First, countries have been divided into four categories according to their spending intensity to determine the effects of social spending on the long-term unemployment rate. Then, the auto-regressive distributed lag (ARDL) approach and the error correction models (ECM) examine the variables' short- and long-term interactions.

The author found that the change in the share of social expenditures in GDP affects chronic unemployment similarly. This finding is consistent with the results of studies in the literature dealing with the relationship between public sector size and unemployment. However, the research findings are specifically about the effects of social expenditures on chronic unemployment. In this respect, the results reflect that expenditures with passive characteristics have an expansionary effect on long-term unemployment. In addition, the progressive effect of social expenditures on chronic unemployment is increasing in countries with high expenditure intensity. In countries with relatively low spending intensity, the impact of social spending is limited to the short run and is lower.

Multiple studies have reported that public policies developed in line with the incentives of active employment and public or private sector investments reduce the unemployment rate by positively affecting the output/employment level. This study, unlike other studies, focuses on the effects of social expenditures on chronic unemployment. It also compares the effects of social spending on the long-term unemployment rate for countries with varying spending intensities. Therefore, this article tests the impact of social expenditures used against a concrete socioeconomic problem in the OECD sample. In this respect, the findings contribute to the literature by addressing the relationship between social spending and chronic unemployment.

The purpose of the paper is to test and analyze the equilibrium economic relationships of the East Africa Community (EAC).

To attain the study's purpose the authors applied the Johansen cointegration test, including long-run structural modeling (LRSM), vector-error-correlation-model (VECM) and variance-decomposition (VDC).

At I(1), both Philips‐Peron (PP) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests show that the East Africa member states' economies are cointegrated. The result was further substantiated by the tests based on Johansen cointegration and VECM procedures, showing significant long-run and short-run economic relations. The result further reveals that despite some uncommon issues among member states such as Tanzania and Kenya, however, their economic relationships remain significant though it is negative. Moreover, the finding revealed positive and significant short-run economic relationships between Kenya, Burundi and Rwanda.

The paper applies the cointegration techniques in the context of EAC. The result is likely to be adding value to the policymaker and also to the existing literature on the subject. This may trigger policy implications and open new research direction within the region and out.

The study aims to empirically examine the relationship between monetary policy and economic growth, as well as to explore the long-run and the short-run effect of monetary policy on the economic growth of a developing country (Bangladesh) and a developed country (the United Kingdom).

Depending on data availability, the study employed secondary data covering the period of 1980–2019. The augmented Dickey–Fuller test and the Phillips–Perron test were used for the stationarity test. Further, the F-bounds test was run to justify the long-run relationship between monetary policy and economic growth. Thereafter, long-run coefficients were revealed from the auto-regressive distributed lag (ARDL) model and short-run coefficients from the error correction model. Furthermore, the vector error correction model (VECM) Granger causality approach was employed to demonstrate the causality of studied variables. Lastly, different diagnostics tests ensured the robustness of the models.

F-bounds test outcomes suggest that monetary policy has a long-run relationship with economic growth in both countries. Long-run coefficients revealed that money supply has a positive long-run impact on economic growth in both countries. Unlike the UK, the exchange rate exhibits an adverse effect on the economic growth of Bangladesh. The bank rate seems to promote economic growth for the UK. Findings also depict that increase in lending interest rates hurts the economic growth for both countries. Besides, the short-run coefficients portray random effects at different lags in both cases. Lastly, causality among studied variables is revealed using the VECM Granger causality approach.

The novelty of this study lies in consideration of both developing and developed countries in the same study.

The purpose of this paper is to examine the issue of high current account deficit (CAD) from various perspectives focussing its behaviour, financing pattern and sustainability for India.

To begin with the trends, composition and dynamics of CAD for India are analysed. Next, the influence of capital flows on current account is investigated using Granger non-causality test proposed by Toda and Yamamoto (1995) between current account balance (CAB) to GDP ratio and financial account balance to GDP ratio. Also, the sustainability of India’s current account is examined using different econometrics techniques. In particular, Husted’s (1992), Johansen’s cointegration and vector error correction model (VECM) is applied along with conducting unit root and structural break tests wherever applicable. Further, long-run and short-run determinants of the CAB are estimated using Johansen’s VECM.

The study found that the widening of CAD is due to fall in household financial savings and corporate investments. Also, it was found that a large part of India’s CAD has been financed by FDI and portfolio investments which are partly replaced by short-term volatile flows. The unit root and cointegration tests indicate a sustainable current account for India. Further, econometric analysis reveals that India’s current account is driven by fiscal deficit, terms of trade growth, inflation, real deposit rate, trade openness, relative income growth and the age dependency factor.

Since India’s CAD has widened and is expected to widen primarily due to rise in gold and oil imports, policy makers should focus on achieving phenomenal export growth so that a sustainable current account is maintained. Also, with rising working-age and skilled population, India should focus more on high-value product exports rather than low-value manufactured items. Further, on the structural side it is important to correct fiscal deficit as it is one of the important factors contributing to large CAD.

The paper is an important empirical contribution towards explaining India’s CAD over time using latest and comprehensive data and econometric models.