Econometrics Models - Panel Data

This study investigates the relationship between exports, foreign direct investments (FDI) and economic growth in five Eurozone countries (Greece, Portugal, Ireland, Spain, and Italy) using panel data for the period 1970 to 2011. The panel data causality results revealed that there is bidirectional causality between exports and economic development, while there is no causality between economic growth and FDI nor between FDI and exports.

Recent research argues that robots could replace workers in dangerous work environments to reduce occupational injuries. However, robot development and usage in most developing countries remain at an infant stage, robots may increase the likelihood of occupational injuries due to conditions such as limited human capital and defects. By using a city-level dataset based on the Guangdong Province in China, we find that robot application is associated with an increase in the rate of occupational injuries in the first two years, and then exhibits nonsignificant and even negative effects afterwards. We also find that local governments can reduce or even eliminate the effect of robot application on occupational injuries by strengthening safety regulations. In addition, although local governments are keen on pushing robot application and industrial intelligence, the wide application of robots may impose a burden on the public health expenditure of local governments due to occupational injuries. This study extends our knowledge by challenging the traditional view that robot application can reduce the rate of occupational injuries, and also provides suggestions on the safety regulations of developing countries at the early stage of industrial intelligence.

The purpose of this study was to apply new methods of econometric models to the Life
Cycle Assessment (LCA) of physical assets, by integrating investments such as maintenance, technology,
sustainability, and technological upgrades, and to propose a means to evaluate the Life Cycle
Investment (LCI), with emphasis on sustainability. Sustainability is a recurrent theme of existing
studies and will be a concern in coming decades. As a result, equipment with a smaller environmental
footprint is being continually developed. This paper presents a method to evaluate asset depreciation
with an emphasis on the maintenance investment, technology depreciation, sustainability depreciation,
and technological upgrade investment. To demonstrate the value added of the proposed
model, it was compared with existing models that do not take the previously mentioned aspects into
consideration. The econometric model is consistent with asset life cycle plans as part of the Strategic
Asset Management Plan of the Asset Management System. It is clearly demonstrated that the proposed
approach is new and the results are conclusive, as demonstrated by the presented models and
their results. This research aims to introduce new methods that integrate the factors of technology
upgrades and sustainability for the evaluation of assets’ LCA and replacement time. Despite the
increase in investment in technology upgrades and sustainability, the results of the Integrated Life
Cycle Assessment First Method (ILCAM1), which represents an improved approach for the analyzed
data, show that the asset life is extended, thus increasing sustainability and promoting the circular
economy. By comparison, the Integrated Life Cycle Investment Assessment Method (ILCIAM) shows
improved results due to the investment in technology upgrades and sustainability. Therefore, this
study presents an integrated approach that may offer a valid tool for decision makers.

The aim of the study is the relationship between foreign direct investments, exports and economic growth. The analysis of this study included E7 (Emerging 7) the so-called developing countries are Turkey, Mexico, China, India, Brazil, Russia and Indonesia in the analysis in this context. This study covered the period of 1992-2018. Pesaran (2007) panel unit root test was used for the analysis of the series stationary. Panel ARDL approach, which allows short and long term relationship, is used for series with different levels of stationary. According to the results of the error correction model established for all panel, any shock in the gross domestic product equation was adjusted by approximately 0.86 % within the first year. Error correction models were estimated for all units, and error correction terms for all units expect India were obtained as negative and statistically significant. As a result of the panel causality test, one-way causality findings were found between economic growth and exports. Additionally, there was a causality relationship with foreign direct investments and exports.

Even though several papers about the impacts of minimum wage increases on employment have been published in the last decades, the debate is still ongoing among economists, policymakers, and the general public. Many recent papers have focused on the potential disemployment effects of the minimum wage regarding teenagers and restaurant workers since both groups present high rates of current minimum wage employment and job offerings. In this paper, I use Monte Carlo simulations with a broad set of data-generating processes and estimation strategies to assess the validity of various methodological strategies and assumptions often encountered in the corresponding literature. Simulation results show that the distributed lag specification with unit-specific time trends and leaded values of the minimum wage, in first-differences, produced less biased estimates for most of the generating processes. This preferred specification is then used to estimate the impacts of minimum wage amendments on youth employment in Canadian provinces between 1990 and 2018. Estimation results demonstrate that the long-run impacts of minimum wage hikes on employment in Canada are likely to be small and statistically insignificant, even for low-skilled young workers. Moreover, my results demonstrate that the standard fixed effects estimator generally used in former studies is likely to identify spurious relationships between minimum wage and employment levels. Results from the preferred specification are robust to both potential reverse causality biases and yearly "strong" real increases in minimum wage levels.

Growth of Agriculture sector in India in general and in the state of Rajasthan in particular has been phenomenal in recent years in spite of the fact that about 60% of area in the State is under desert. Nevertheless in the State most of the major crops are grown in all the districts albeit with varying proportion. We have chosen for the analysis two important crops one each from desert and non desert regions namely; pearl millet from among desert crops and wheat  among non-desert crops which together contribute  more than 50% in cereal consumption in the State. Using panel data approach this paper analyzes factors affecting production of pearl millet and wheat vulnerability over time from 1981 to 2007 covering all districts. Also in this paper we include effect of infrastructure and other factors which deem affecting the financial position of the farmers. Gross Cropped Area and energized well are significant in pearl millet production while rainfall, fertilizer consumption, cropping intensity, gross irrigated area and energized well, play statistically significant role in production of wheat. Inclusion of a time invariant factor, i.e., desert dummy shows the effects of production in desert and non-desert areas. Desert affected the production of pearl millet positively while that of wheat negatively.

This study investigates the determinants of FDI inflows in sixteen Central and Eastern European (CEEC) by incorporating the traditional factors (GDP size and trade openness) with economic freedoms over the period 1996-2009. The study uses the composite economic freedoms index of the Heritage Foundation, which has ten subcomponents. The study also identifies whether and how the impact of the ten subcomponents of the economic freedoms index differs. The estimation results verify the positive and economically significant role of GDP size, openness, and economic freedoms on FDI inflows in the CEEC. The results also reveal the existence of notable differences in the impact of the subcomponents of the economic freedoms on FDI inflows. According the findings, trade and fiscal freedoms have remarkably higher impact than the other subcomponents of the economic freedoms. Such a finding would help policy makers in the decision making process of which components of the economic freedoms should ...

Tourism is one of the most significant contributors to the Indonesia economy of growth and trade, with share of national tourism to GDP is 13.9 per cent (BPS RI, 2012), through foreign exchange earnings as revenue from tourist consumption. As an economic activity, tourism in Indonesia influenced by various factors economic and non-economic domestically and internationally that can influence the development of tourism (supply-demand side). The study purposed to analyze determinants of investment, demand and supply of Indonesian tourism sector. Based on the 2 SLS method on simultaneous model, the results of the analysis are: (1) tourism arrivals, tourism expenditure, investment, CPI, consumption, government spending, export and import tourism affected the national tourism demand, (2) investment, total consumption on previous year, and travel warning have positive impact to national tourism supply, (3) GDP, Indonesia tourism price, and competitor price as competitors of Indonesia tourism was the most influenced variable, (4) International tourism investment in Indonesia has significant impact to the development of tourism, and (5) tourism supply and demand in Indonesia are impacted by international tourism trade (goods/services).

Empirical research of international trade with the use of gravity model is often estimated with the FE estimator. Indeed, this method is appropriate in the face of heterogeneity, that is typical of pairs of countries, which influence the effect of the determinants of bilateral trade. However, the disadvantage of the FE approach is that it assumes all the slopes of the variables of interest are common across all trading pairs in the sample. The use of mixed effects model in this study allows the coefficients of national incomes’ product and the common internetization rate of trading countries to vary across the pairs. In order to capture unspecified heterogeneity by allowing the intercept and slopes to have a stochastic component in their variation, the 2-level and 3-level hierarchical linear models are estimated based on the data from the period 1999-2011. The results indicate that not only typical gravity model factors, but also globalization factors as internetization rate, researchers rate, share of high-technology products’ export, energy use, foreign languages proficiency and monetary union influence the bilateral trade between EU-members.

This research is concerned with a statistical method that has recently become widespread
in the international literature; although, it is still limited in Hungarian research. The method
is geographically weighted regression (GWR), which is demonstrated through an example
of its application. GWR is a local model that is founded on the basis of regression,
prominently taking into consideration the geographical distance. Since it does not calculate
the global relations of the whole data, but concentrates on the relationship of the dependent
and independent variables locally within a determined search area, it allows consideration
of the spatially varying processes. Simply, GWR is a developed version of the global
regression model, since, through its use, it is possible to take into account the local features
that are hidden by the global approach.

Abstract
In This study we aim to measure the Relationship between Remittances and economic growth
through a group of North African countries, Egypt, Morocco, Tunisia and Algeria, using two models
for 1980-2015 using time series data in the first model and approach or method Estimating the
Auto regrisive distributed lag of the allocated time slots ARDL for each country by using a set of
traditional growth determinants and worker's transformations as Regressors on economic growth
in a linear fonction as follows: Gross capital formation That explaine Privat investment within
economic countries in the sample, The economic openness index and the individual Remittances of
worker’s a􀅶d i􀅵􀅵igra􀅶ts are reduced i􀅶 a fu􀅶ctio􀅶 of the li􀅶ear figure o􀅶 the growth rate of four
Independent models according to the ARDL methodology, and through the second model The data
were used according to the PANEL-ARDL /PMG approach for the same sample and the same
variables to compare the results obtained, as well as between the time series results and the data
of panel to answer the study hypotheses.
Keywords: Economic Growth, Migrant Remittances, ARDL models, Panel models.

A lot of research nowadays, is conducted using a mixed methods approach. This translates to massive and volumes of datasets. Making sense of the data without the right research tools is like looking for a needle in a haystack. SPSS (Statistical Package for the Social Sciences) is a powerful program which provides many ways to rapidly examine data and test scientific hunches. SPSS can produce basic descriptive statistics, such as averages and frequencies, as well as advanced tests such as time-series analysis and multivariate analysis. The program is also capable of producing high-quality graphs and tables. Knowing how to make the program work for you now, will make future work in independent research projects and beyond much easier and more sophisticated.

This course aims to provide participants with the strategic understanding and applied skills in planning, conducting, analyzing, and reporting findings from quantitative research. At the end of the course, participants will be able to plan, conduct, and critically assess the quality of the quantitative data gathered during their data collection process. Specifically, participants will learn how to use SPSS in Quantitative Data Analysis.

This study investigates the determinants of unemployment in selected ASEAN states: Indonesia, Malaysia, Philippines, Singapore, and Thailand; employing the methods of panel data analysis. Specifically, this study aimed to address the following:
(1) identify and compare the determinants of unemployment rate; (2) investigate if there is evidence of a short run tradeoff between inflation and unemployment commonly known as the Phillips Curve; (3) provide analysis on the determinants of unemployment rate in each country; (4) present trends of relevant macroeconomic indicators; and (5) provide policy recommendations that will address the unemployment problems in the 5 ASEAN states. This study made use of secondary time series data of the 9 macroeconomic variables of the 5 ASEAN states from 1980-2011. In this study, the economic model employed is that unemployment rate is a function of real GDP, inflation rate, population, real interest rate, foreign direct investment, real wage, and literacy rate.

Results of the least squares dummy variable (LSDV) regression shows that real
GDP, inflation rate, real interest rate, and real wage are negatively associated with the
unemployment rate. Population shows a positive relationship to unemployment. Furthermore, results suggest that Phillips Curve is relevant in the 5 ASEAN states indicating that the higher the inflation rates, the lower the unemployment in the short-run.

Trends of the macroeconomic indicators used in the study is presented giving emphasis on the real GDP and unemployment rate to see the disparities between each ASEAN member state and see the changes of the variables over time.

This study also found the determinants of unemployment rate in each of the selected ASEAN member states through employing the ordinary least squares (OLS) regression. More importantly, this study aims to provide possible policy recommendations that will help in minimizing unemployment incidence in the selected ASEAN states in preparation for the forthcoming changes to be brought about by the ASEAN Economic Community in 2015.

Economic uncertainty is a driver of the business cycle. Its leading properties make it a key advanced indicator to assess the impact of socioeconomic factors on suicide for prevention purposes. This paper evaluates the effect of economic uncertainty on suicide rates worldwide. Uncertainty is gauged by a global economic policy uncertainty index. Suicide rates from 183 countries between 2000 and 2019 are matched to annual economic uncertainty, controlling for unemployment and economic growth in a fixed-effects panel model. Overall, the analysis suggests that increases in lagged economic uncertainty, as well as in unemployment and economic growth, may lead to an increased risk of suicide. When replicating the experiment for different regions of the world, the greatest impact of an increase in economic uncertainty can be found in Africa and the Middle East. Given the anticipatory nature of economic uncertainty regarding the evolution of economies, and its relationship with suicide rates, the results highlight the usefulness of uncertainty indicators as tools for the early detection of periods of increased suicide risk and the design of suicide prevention strategies.

The aim of this paper is to provide empirical evidence on the relationship between financial development and economic growth and to examine whether there are differences in the size of the impact of financial development on economic growth in countries with different income levels. Furthermore, the impact of additional determinants of economic growth is analyzed. Research problem is lack of empirical evidence on the relationship between financial development and
economic growth when financial development is measured by proxy variables on banks and stock markets; low treatment of potential endogeneity problem as well as lack of empirical evidence on
the differences in the size of the impact of financial development on economic growth in countries with different income levels. Balanced panel data for 94 countries during the period between 1992 and 2011 are used in this analysis. Five-year averaged data are used to reduce the
impact of business cycles and measurement errors. Six models are estimated by using panel fixed and random effects models. However, the analysis indicates that distribution of error terms deviates from normal; the assumption of homoscedasticity is rejected and the presence of
endogenous regressor is reported. Therefore, in order to overcome these issues, the dynamic one step system GMM estimator is used to estimate models. The obtained results indicate that banking sector and stock market development have statistically significant positive impact on
economic growth. It has been shown that the impact of banking sector development strongly contributes to economic growth compared to stock market development. Besides, the results show that education has no statistically significant impact on economic growth while inflation,
government spending and trade openness have a negative impact. The sixth model indicates that on average financial development strongly impacts economic growth in high and middle-income
countries compared to low-income countries, and that the strongest impact is obtained for high income countries. The results of this paper may motivate policy makers to foster the financial development since it contributes to economic growth. Since financial development contributes to economic growth in countries with different income levels, there is a need to foster its development in low-income countries as well.

R is an open source language and environment for statistical computing, data mining, modeling, and data graphics. It provides a wide variety of statistical and graphical techniques such as linear and non-linear modeling, statistical tests, time series analysis, classification, and clustering. R is one of the most used business analytics tools. For example, Facebook uses R for behavior analysis related to status updates and profile pictures. Google uses it to analyze advertising effectiveness and economic forecasting. Twitter leverages R for data visualization and semantic clustering.

This course aims to facilitate the participant's first steps in R and equip them with the tools and understanding to expand their technical know-how according to the needs of their specific research. It is neither a computer science nor a methodological course, but aimed at the practical needs of empirically working researchers. Participants' will learn how to programme in R and how to use R for effective data analysis and visualization. The course begins with developing a basic understanding of the R working environment. Then participants are introduced to the necessary arithmetic and logical operators, salient functions for manipulating data, and getting help using R. Next, the common data structures, variables, and data types used in R, are demonstrated and applied. Participants also learn to write R scripts and build R markdown documents to share their code with others. Further, participants utilize the various packages available in R for visualization, reporting, data manipulation, and statistical analysis.

Gikatilla was a prolific writer; he wrote his first work
(Ginnat Egoz, אגוז גנת) when only twenty-six. It is a kabbalistic
treatise in three parts (Hanau, 1615).
• The title (from Cant. vi.11). It literally means “garden
of nuts,”. Kabalisitically, “Ginnat” consisting of
the initials of "Gemaṭria,” “Noṭarḳon,” “Temurah”,
the three main elements of Kabbala, while “Egoz”
(the nut) is the emblem of mysticism.
Portae Lucis, Latin translation of Gikatilla’s work Shaarei Ora -
Gates of Light
• The first part, in five chapters, treats of the various
names of God occurring in the Bible. According
to Gikatilla, “YHVH” is the only name which represents
the substance of God; the other names are
merely predicates of the divine attributes. “YHVH”
stands for God as He is, while “Elohim” denotes God
as the creative power. The name "ẓeba'ot” (hosts),
he says, applies to all the beings of the three natures,
earthly, heavenly (or spheres), and spirits (or forms).
The interpretation of "ẓeba'ot” as (“host of letters”)
leads him over to the second part.
• The second part treats of the letters of the alphabet.
He declares that the number ten emanated from
YHVH, the primitive cause, and is the source of all
being; he attempts to prove his statement by different
combinations based on religion, philosophy,
physics, and mysticism. He shows that the Talmudic
view that space is filled with spirits agrees with the
belief of the philosophers that there is no vacuum.
He also treats here of the revolutions of the sun and
moon, giving the relative sizes of the planets.
1
2 3 JEWISH ENCYCLOPEDIA BIBLIOGRAPHY
• The third part is a treatise, in four chapters, on
the vowels. The three primitive vowels, "ḥolem,”
“shuruḳ,” and "ḥiriḳ,” represent the upper, middle,
and lower worlds; the three compound ones,
"ẓere,” “segol,” and “shewa,” represent the composition
or the construction of the worlds; the “pataḥ"
and "ḳameẓ" represent their movements.
Gikatilla at times criticizes the Sefer Yeẓirah and the Pirḳe
Hekalot. The seven heavens (Ḥag. 12a) are identified by
him with the seven planets. He holds Maimonidesin great
esteem even when he opposes him, and quotes him very
often. Other authorities quoted by him are Ibn Gabirol,
Samuel ibn Naghrela, and Abraham ibn Ezra. Isaac ben
Samuel of Acre in his Me'irat 'Enayyim severely criticizes
Gikatilla for too free usage of the Holy Name.

Abstract: The paper examined the issues in internal audit reliability. It specifically provided conceptual and practical insights into some of the consequential issues worth pondering about when assessing the reliability of the internal audit functions. Particularly, the paper identified and discussed four (4) of such issues namely: competency of internal auditors, level of management support/influence, independence and objectivity of internal auditors; and regulatory issues. The paper took a conceptual approach and drew its basis from the Institutional and Protection Motivation theories. Based on literature and other documentary evidences, the paper took the position that the four identified factors would most likely influence the internal audit reliability. Among other policy implications, the paper, therefore, proposed a conceptual research framework to be empirically examined by future researchers.

This work aims to identify the most relevant economic and financial indicators for predicting the insolvency of the Brazilian electricity distribution utilities. The work was carried out based
on 18 indicators from accounting statements published on the website of the Brazilian National Electric Energy Agency (ANEEL) for 62 distribution utilities in the period 2011-2018. The identification of the most relevant indicators was carried out through an exhaustive search, in
which logistic regression models for panel data classify the utilities between solvents and insolvents, but with different indicators in the explanatory variables set. The analyzes identified five important indicators, namely the net profit, the financial dependence/independence, the general liquidity and the non-technical losses.

La informalidad laboral se ha convertido en un problema latente en casi todos los países del mundo, no solo por sus efectos en la productividad y la captación fiscal, sino por sus consecuencias atroces en el bienestar colectivo. En virtud de ello, la presente investigación examina la informalidad laboral en México con fundamentos en el enfoque teórico estructuralista; se hace uso de un modelo econométrico de panel de datos, señalando al desempleo, la pobreza y el empleo en el sector terciario como las principales causas de este fenómeno; se destaca la influencia de factores geográficos y regionales en el comportamiento de la informalidad y se brindan algunas recomendaciones en materia de política pública para hacer frente a este problema.

Society is currently struggling with the near extinction of fossil fuel, a non-renewable source of energy supply, resulting in the overburdening of electrical grid systems. This has contributed towards environmental pollution and consequently global warming. For this reason, there is call for a reliable and environmentally friendly source of energy supply, typically known as the green energy. Many options are available such as the wind vans, hydropower and the solar panel power systems. However, solar panel stands above the rest with respect to reliability, efficiency and its positive impact on the environment. Our research aims to improve the efficiency of solar panels systems by implementing a tracker that automatically corrects the misalignment between solar panel and the sun by keeping the solar panel perpendicular to the sun. As more sunlight strikes the panel, a lot of light is absorbed and thus increasing the energy output tremendously. The solar tracker is based on a proposed light sensor algorithm which uses four light dependent resistors (LDRs) which have been positioned in a compartment close to the solar panel. All the LDRs are exposed to sunlight and the one with highest light intensity determines the direction in which servo motors rotate the solar panel. The proposed system resulted in the ability to track the sun on both axes, the daily and seasonal movement of the sun and ensures an increase in solar panel efficiency and electricity generated as compared to the existing systems.

The purpose of this study is to examine the relationship between financial development and human development from the health and welfare dimensions of developing countries. This study aims to determine whether the financial developments of the countries have an effect on the basic human development of the individuals and whether human development indicators have an impact on financial development. In this study, the relationship between financial development and human development were revealed by using data obtained from developing countries. The financial development levels of countries was measured using the financial development index. The index is calculated by using M3 / GDP, private sector loans / GDP and loans to banks from private sector / GDP ratios. The human development index is calculated by considering various health indicators and GNP per capita. The data includes annual data for the period 1970-2016. Pedroni and Kao cointegration analysis and Dumitrescu and Hurlin panel causality analysis were performed in the study. The results show that there is long-term cointegration relationship and two-way causality between financial and human development in developing countiries.

The purpose of this research was to investigate the nature of the relationship between board diversity and financial performance of deposit money Banks quoted on the Nigerian stock exchange. Based on extensive review of the literature, three board diversity variables were identified namely gender (measured by the proportion of women in the boardroom), non-executive directors (measured by the proportion of non-executive directors that make up the boardroom) and board size. Financial performance was measured using Return on Assets (ROA) and Return on Equity (ROE). The Fixed effects Panel data regression model was used to test the nature of relationship between the board diversity variables and the financial performance variables, using secondary data from the Banks annual financial statements covering the period from 2006-2017. The result of the analysis showed that gender diversity has a statistically significant positive impact on banks financial performance. On the other hand, the study also indicated that non-executive directors and board size do not have a significant impact on banks performance. Based on the findings from this study, it was therefore recommended that quoted deposit money banks in Nigeria should raise female proportions in their boardroom so as to improve financial performance.

This article aims to test the effect of human capital investment on economic growth in 20 countries in the MENA region through by using panel study on the period from 1999 to 2007.
The results show that the investment in human capital has a positive and significant effect on economic growth of most countries in the sample. However, the results confirm the theoretical contributions emphasizing that the effect of human capital investment has a different impact on economic growth depending on the level of development of each country.

Bu çalışmada, firma performansını etkileyen faktörler incelenmek istenmiştir. Firma performansının ölçülmesinde hisse senedinin yılsonu kapanış fiyatı ve hisse getiri oranı kullanılmıştır. Hisse senedinin yılsonu kapanış fiyatı ve hisse getiri oranı ile nakit kar payı dağıtım oranı, aktif karlılığı, fiyat/ kazanç oranı, hisse başına kar, net kar büyümesi, piyasa değeri / defter değeri, piyasa değeri ve özsermaye karlılığı değişkenleri arasındaki ilişki incelenmiştir. Çalışmada, İMKB 30 Endeksine dâhil olan 16 şirket 1998-2010 yılları için panel regresyon modeli ile incelenmiştir. Çalışmada iki model oluşturulmuş ve buna göre, Model 1’de hisse kapanış fiyatını, Temettü Ödeme Oranındaki (DPR) ve Hisse Başına Kardaki (EPS) değişimi artırırken, Aktif Karlılığındaki (ROA) değişimin azalttığı ve Piyasa Değeri Defter Değeri (M_B) ve Piyasa Değeri Artışı (MVI) ise Hisse Kapanış Fiyatını (CLS) etkilemediği sonucuna ulaşılmıştır. Model 2’de ise Hisse Getiri Oranını (RSR), Piyasa Değeri Artışı (MVI) ve Hisse Başına Kar (EPS) artırırken, Aktif Karlılığı (ROA) ise etkilemediği gözlenmiştir.

ABSTRACT
In this study, there has been intended to analyze the factors affecting the performance of firms. Year end closing price of stocks and rate of stock return were used to measure performance of the firm. There has been analyzed the relationships between the variables of year-end closing price of the stock, rate of stock return and cash dividend payout ratio, return on assets, price/ earnings ratio, earnings per share, net profit growth, market to book value ratio, market value and return on equity. In the study, there have been analyzed 16 companies included in ISE 30 Index for 1998-2010 years using the panel regression model. There were created two models in the research and according to this, whereas Closing Price of the Stock (CLS) was increased by the change in Dividend Payout Ratio (DPR) and Earnings Per Share (EPS) in Model 1, it increased the change in Return on Assets (ROA) and it did not affect the Market to Book value (M_B) and Market Value Increase (MVI) and Closing Price of Stock (CLS). In Model 2, there has been observed that whereas Market Value Increase (MVI) and Earnings Per Share (EPS) have increased the Rate of Stock Return (RSR), it has not affected Return on Assets (ROA).

La presente investigación evalúa el impacto del canon y sobrecanon petrolero en el desarrollo económico y social de la región Piura durante el período 1984-2014, postulándose como hipótesis central de investigación que el canon y sobrecanon petrolero de la región Piura no ha impactado positiva ni significativamente en su desarrollo económico y social. Utilizando un análisis econométrico y un análisis de género, los resultados obtenidos en la investigación muestran evidencia a favor de la hipótesis central de investigación. De modo específico, el análisis econométrico, permite concluir que en términos de desarrollo económico que el canon y sobrecanon petrolero de la región Piura no ha tenido un impacto estadísticamente significativo en el Índice de Actividad Económica Regional. Lo mismo ocurre en términos sociales, donde dichos recursos tampoco han tenido un impacto positivo ni estadísticamente significativo sobre el Índice de Desarrollo Humano. Finalmente, respecto al análisis de género realizado, se concluye que existiría un escaso alineamiento de los recursos del canon y sobrecanon petrolero con los ejes estratégicos así como objetivos generales (centrales) y específicos del Plan Estratégico de Desarrollo Regional Concertado.

Indian insurance markets have changed radically and deeply in the last countable years. Deregulation, globalization of insurance institutions, intensified competition, electronic commerce, bancassurance, and the emergence of new risks are among the challenges confronted by insurance markets. These trends pose both global and local challenges for insurance firms as key advancement in insurance and financial services markets influence insurance markets on a global scale. The paper endeavoured to link insurance investment decisions with underwriting activities of insurance companies.Although, underwriting and investment are two important and related business activities of insurance companies, impact of underwriting activities on investment for life insurers has not been rigorously examined in the literature. Using a sample of public life insurer, this article conducts an empirical investigation of how underwriting impact investment in the period of 2001–2014. The result of study suggests that premium and claim is significantly influenced the investment of insurance sector. In the aftermath of expanding liberalization in the insurance industry together with the worldwide financial crisis has posed a great deal of challenges for insurance regulatory authorities in monitoring investment of insurance companies.Researcher believes the current paper provides some helpful bits of knowledge in this vein.

This paper develops estimators for simultaneous equations with spatial autoregressive or spatial moving average error components. We derive a limited information estimator and a full information estimator. We give the generalized method of moments to get each coefficient of the spatial dependence of each equation in spatial autoregressive case as well as spatial moving average case. The results of our Monte Carlo suggest that our estimators are consistent. When we estimate the coefficient of spatial dependence it seems better to use instrumental variables estimator that takes into account simultaneity. We also apply these set of estimators on real data.

This research initiated in this paper may be improved in two directions. First, refine the measure of the human capital available in a country to take account of its different dimensions such its stock, its use, its quality, its structure, and its return. Second, compare the results obtained using the augmented Solow model with those obtained by estimating specifications derived from endogenous growth models.

This study deals with convergence analysis. Based on economic growth theories,
the differences between regions were examined. With this study convergence process has
been examined and analyzed between 12 regions which are placed on NUTS 1 in Turkey.
In the empirical part of the study, it was focused on beta and sigma convergence analysis
between NUTS 1 regions buy considering 2004-2017 years in Turkey. Beta convergence
was analyzed using panel data models formulated in two variations based on the
mathematical concept of convergence. These models have investigated interregional
effect whether there is absolute and conditional convergence between the regions or not
in Turkey. In order to test conditional convergence, public investment indicator is
included in the model to reveal interregional structural differences. As a result of the
analysis, it was concluded that there was no absolute convergence between the regions of
NUTS 1, but there was conditional convergence between 2004-2017. After the
convergence analysis, assumptions that economic relations have changed from region to
region, the effect of public investments and research and development expenditures on
per capita income were estimated by using random coefficient panel data model. In line
with these results, it is concluded that R & D expenditures and public investments have a
positive effect on per capita income in each region.