scholarly journals The Debt-growth Nexus: A Dynamic Panel Data Estimation

2005 ◽  
Author(s):  
Andrea Filippo Presbitero
Keyword(s):  
Panel Data ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Panel Data Estimation ◽  
Data Estimation ◽  
Dynamic Panel Data Estimation

scholarly journals DETERMINANTS OF BANK COST EFFICIENCY: EMPIRICAL EVIDENCE FROM BANGLADESH

2020 ◽  
Author(s):  
Israth Sultana ◽  
Mohammad Morshedur Rahman
Keyword(s):  
Panel Data ◽  
Cost Efficiency ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Capital Adequacy ◽  
Operating Costs ◽  
Dynamic Panel ◽  
Panel Data Estimation ◽  
Data Estimation ◽  
Dynamic Panel Data Estimation

Due to the trust of depositors, banks should be responsible for efficient utilization of resources to achieve cost efficiency (CE) which in turn contributes to raising income. Previous studies found that the average CE of banks in Bangladesh was around 80%. This study aims to find the determinants of CE in Bangladesh from a sample of 33 banks during a period from 2009 to 2016. Stochastic Frontier Approach (SFA) was used to measure CE in the first stage. In the second stage, different types of regression estimations were used like pooled ordinary least square, fixed effect or random effect panel regression, System-Dynamic Panel Data Estimation and Arellano-Bond Dynamic Panel Data Estimation for comparison. The results showed that Generalized Method of Moments (GMM) specifically the Arellano-Bond Dynamic Panel Data Estimation was best suited for problems of endogeneity, serial correlation, heteroskedasticity and cross sectional dependence in data The results revealed that regulatory capital, risk measured by non-performing loan ratio, liquidity measured by total loans to total deposits, and level of operating costs had a significant negative impact on CE. In contrast, lagged cost efficiency, profitability, years of operation, net interest income had a significant positive impact on CE. To attain competitive advantage by performing with higher CE, policymakers should focus on capital regulation measured by capital adequacy ratio, risk level, profit earning capacity, aggressiveness of banks, bank size, years of operation and level of operating costs.  

scholarly journals Dynamic panel data estimation of an integrated Grossman and Becker–Murphy model of health and addiction

2018 ◽  
Vol 56 (2) ◽  
pp. 703-733 ◽  
Author(s):  
Andrew M. Jones ◽  
Audrey Laporte ◽  
Nigel Rice ◽  
Eugenio Zucchelli
Keyword(s):  
Panel Data ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Panel Data Estimation ◽  
Data Estimation ◽  
Dynamic Panel Data Estimation

scholarly journals Linear Dynamic Panel-data Estimation Using Maximum Likelihood and Structural Equation Modeling

2018 ◽  
Vol 18 (2) ◽  
pp. 293-326 ◽  
Author(s):  
Richard Williams ◽  
Paul D. Allison ◽  
Enrique Moral-Benito
Keyword(s):  
Maximum Likelihood ◽  
Panel Data ◽  
Structural Equation ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Equation Modeling ◽  
Dynamic Panel ◽  
Panel Data Estimation ◽  
Data Estimation ◽  
Dynamic Panel Data Estimation

Panel data make it possible both to control for unobserved confounders and to include lagged, endogenous regressors. However, trying to do both simultaneously leads to serious estimation difficulties. In the econometric literature, these problems have been addressed by using lagged instrumental variables together with the generalized method of moments, while in sociology the same problems have been dealt with using maximum likelihood estimation and structural equation modeling. While both approaches have merit, we show that the maximum likelihood–structural equation models method is substantially more efficient than the generalized method of moments method when the normality assumption is met and that the former also suffers less from finite sample biases. We introduce the command xtdpdml, which has syntax similar to other Stata commands for linear dynamic panel-data estimation. xtdpdml greatly simplifies the structural equation model specification process; makes it possible to test and relax many of the constraints that are typically embodied in dynamic panel models; allows one to include time-invariant variables in the model, unlike most related methods; and takes advantage of Stata's ability to use full-information maximum likelihood for dealing with missing data. The strengths and advantages of xtdpdml are illustrated via examples from both economics and sociology.

Comparative analysis of profitability of real estate, industrial construction and infrastructure firms: evidence from India

2020 ◽  
Vol 25 (2) ◽  
pp. 273-291
Author(s):  
Edison Jolly Cyril ◽  
Harish Kumar Singla
Keyword(s):  
Real Estate ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Industrial Construction ◽  
Content Type ◽  
The Real Estate ◽  
Construction Firms ◽  
Panel Data Estimation ◽  
Data Estimation ◽  
Dynamic Panel Data Estimation

Purpose This study aims to identify the most profitable segment of construction firms amongst real estate, industrial construction and infrastructure. This paper also examines the determinants of profitability of real estate, industrial construction and infrastructure firms. Design/methodology/approach The data of 67 firms (20 real estate, 21 industrial construction and 26 infrastructure) is collected for a 15-year period (2003–2017). Two models are created using total return on assets (ROA) and return on invested capital (ROIC) as dependent variables.. Leverage, liquidity, age, growth, size and efficiency of the firm are identified as firm-specific independent variables. Two economic variables, i.e. growth in GDP and inflation, are also used as independent variables. Initially, the models are tested for stationarity, multicollinearity and heteroscedasticity, and finally, the coefficients are estimated using Arellano–Bond dynamic panel data estimation to account for heteroscedasticity and endogeneity. Findings The results suggest that industrial construction is the most profitable segment of construction, followed by real estate and infrastructure. Their profitability is positively driven by liquidity, efficiency and leverage. The real estate firms are somewhat less profitable compared to industrial construction firms, and their profitability is positively driven by liquidity. The infrastructure firms have low ROA and ROIC. Originality/value The real estate, infrastructure and industrial construction drastically differ from each other. The challenges involved in real estate, infrastructure and industrial construction are altogether different. Therefore, authors present a comparative analysis of the profitability of real estate, infrastructure and industrial construction segments of the construction and compare their determinants of profitability. The results provided in the study are robust and reliable because of the use of a superior econometric model, i.e. Arellano–Bond dynamic panel data estimation with robust estimates, which accounts for heteroscedasticity and endogeneity in the model.

scholarly journals The Determinants of Services Sector Growth: A Comparative Analysis of Selected Developed and Developing Economies

2018 ◽  
Vol 57 (1) ◽  
pp. 27-44
Author(s):  
Muhammad Salam ◽  
Javed Iqbal ◽  
Anwar Hussain ◽  
Hamid Iqbal
Keyword(s):  
Panel Data ◽  
Developing Economies ◽  
Panel Data Analysis ◽  
Trade Openness ◽  
Dynamic Panel Data ◽  
Services Sector ◽  
Panel Data Estimation ◽  
The Common ◽  
Data Estimation ◽  
Dynamic Panel Data Estimation

This study empirically examines the possible factors that determine the services sector growth, both in selected developed and developing economies. For estimation purpose, the study employs the static as well as the dynamic panel data estimation technique with panel data over the period 1990-2014. The results suggest that GDP per capita, FDI net inflow, trade openness and innovations are the common factors that significantly affect the services sector growth both in developed and in developing economies. However, the productivity gap is the only factor that does not have any significant impact on services sector growth, both in developed and developing economies, which indicates that the Baumol's cost disease has been cured. Keywords: Services Sector Growth, Panel Data Analysis, Innovations

scholarly journals Mall Flicks - The Mall Boom in Turkey with an Unexpected Byproduct: The Movie Sector Expansion

2017 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Can Erbil ◽  
Emin Koksal ◽  
Caglar Yurtseven
Keyword(s):  
Estimation Method ◽  
Dynamic Panel Data ◽  
Shopping Mall ◽  
Panel Data Estimation ◽  
Metropolitan Development ◽  
Cultural Life ◽  
Urbanization Economies ◽  
First Time ◽  
Data Estimation ◽  
Dynamic Panel Data Estimation

Turkey has been experiencing a mall boom since the end of the 1990s. This boom’s impacts are obvious on the construction, real estate and shopping sectors. However, surprisingly, this boom encouraged significant enlargement of the movie sector as well. This enlargement affects the cultural life of people, in addition to creating urbanization economies. In this paper, for the first time in the literature, effects of shopping mall expansion on the movie sector are studied. We construct a theoretical model and support our theoretical findings empirically, by employing a dynamic panel data estimation method and utilizing a unique dataset from Turkey. We show that without the mall boom, the recent fast expansion in the movie sector in Turkey would not be possible. Our findings may apply to similar metropolitan development booms in other emerging markets.

scholarly journals An assessment about the relationship between educational inequality and economic growth in Brazilian Northeast region

2020 ◽  
Vol 5 (1) ◽  
pp. 1-12
Author(s):  
Frederico Uchoa
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Educational Inequality ◽  
Dynamic Panel Data ◽  
Economic Policies ◽  
Dynamic Panel ◽  
Unequal Distribution ◽  
Education Inequality ◽  
Panel Data Estimation ◽  
The Impact

In this paper we analyze the impact of education inequality on the income of formal workers in Northeast Brazil. For this study, we analyzed the data collected from censuses data and estimate a dynamic panel data model. Statistical analyses were performed by using the quasi-maximum likelihood linear dynamic panel data estimation, an approach that produce consistent estimates with large n and small T. We found a negative and statistically significant impact of education inequality on economic growth, which is convergent with the literature that advocates that an unequal distribution of education reduces growth. Our results suggest that economic policies should be targeted not only more at education but also more equal access to education.

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