scholarly journals The Effect of COVID-19 Pandemic on Corporate Dividend Policy in Indonesia: The Static and Dynamic Panel Data Approaches

Economies ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Georgina Maria Tinungki ◽  
Robiyanto Robiyanto ◽  
Powell Gian Hartono
Keyword(s):  
Panel Data ◽  
Dynamic Model ◽  
Method Of Moments ◽  
Capital Market ◽  
Dividend Policy ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Positive Signal ◽  
Dynamic Panel ◽  
The Capital Market

This research examines the effect of the crisis due to the COVID-19 pandemic on dividend policy in Indonesia. The purposive sampling method was used to collect data from corporates listed on the IDX from 2014 to 2020 and analyzed using static and dynamic panel data approaches. The fixed-effect models (FEM) were selected for the static panel data regression. Meanwhile, the first difference-generalized method of moments (FD-GMM) and system-generalized method of moments (SYS-GMM) were used for determine the robustness of the estimated dynamic panel data. The results showed that the crisis due to the pandemic led to higher dividend distribution on SYS-GMM. Furthermore, companies maintained the dividend level as a positive signal for investors which lifted the sluggish trade condition in the capital market. Profitability and previous year dividends positively affect dividend policy robustly. Furthermore, the results showed that age affects dividend policy on FD-GMM. Financial leverage has a robust effect, and firm size has an effect on FD-GMM in different directions, while investment opportunity does not affect dividend policy. Statistically, the FEM selected that violates the best linear unbiased estimation was proven to form parameters that were not much different from the estimates produced by the dynamic model, both from the coefficient of influence direction and significance, and the omitted variable bias occurs as evidenced in the robust test with dynamic model was solved. This research is also used as a reference for considering investors’ investment decisions in the new normal condition. Therefore, dividend policy can be considered as a positive signal to investors with the ability to stock trading activities in the capital market.

scholarly journals NONPARAMETRIC ESTIMATION OF VARYING COEFFICIENT DYNAMIC PANEL DATA MODELS

2008 ◽  
Vol 24 (5) ◽  
pp. 1321-1342 ◽  
Author(s):  
Zongwu Cai ◽  
Qi Li
Keyword(s):  
Panel Data ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Discrete Variables ◽  
Dynamic Panel ◽  
Dynamic Panel Data Models ◽  
Individual Variations

We suggest using a class of semiparametric dynamic panel data models to capture individual variations in panel data. The model assumes linearity in some continuous/discrete variables that can be exogenous/endogenous and allows for nonlinearity in other weakly exogenous variables. We propose a nonparametric generalized method of moments (NPGMM) procedure to estimate the functional coefficients, and we establish the consistency and asymptotic normality of the resulting estimators.

scholarly journals القياس الاقتصادي لأثر الشمول المالي على الاستقرار المصرفي في العراق

2020 ◽  
Vol 26 (119) ◽  
pp. 323-344
Author(s):  
ثريا عبد الرحيم علي الخزرجي ◽  
صبيان طارق سعيد الأعرجي
Keyword(s):  
Panel Data ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Dynamic Panel

يُشير الشمول المالي الى وصول الخدمات المالية بكلفة منخفضة وجودة عالية من القطاع المالي الرسمي الى كافة فئات المجتمع خاصة الفئات المهمشة ومن ثم استخدامها والاستفادة منها. كما يرتبط الشمول المالي مع الاستقرار المصرفي فضلاً عن ارتباطه مع النزاهة المالية والحماية المالية للمستهلك، لهذا فأنه يُحقق جملة من الأهداف ومن أهمها دعم وتعزيز الاستقرار المصرفي. وهذا ما جعله يُلفت أنظار الكثير من الدول والبنوك المركزية في الآونة الأخيرة.    تهدف الدراسة الى بيان أثر مؤشرات الشمول المالي على الاستقرار المصرفي في عينة شملت 32 مصرف من المصارف الاهلية في القطاع المصرفي العراقي للمدَّة من النصف الأول لعام 2016 الى النصف الثاني من العام 2018 على وفق فرضية الدراسة التي ذهبت الى وجود تأثير إيجابي لمؤشرات الشمول المالي على مؤشر الاستقرار المصرفي، اذ استندت الدراسة على بيانات دائرة المدفوعات في البنك المركزي العراقي فضلاً عن التقارير الدورية لمصارف العينة. وباستخدام أساليب البيانات المزدوجة الديناميكية (Dynamic Panel Data) تحديداً طريقة العزوم المعممة (Generalized method of moments -GMM) لم تتوصل النتائج الى قبول الفرضية بشكل تام وان بعض مؤشرات الشمول المالي كعدد فروع المصارف وحجم الودائع تؤثر عكساُ على الاستقرار المصرفي فضلاً عن عدم ثبوت تأثير الائتمان النقدي وعدد الحسابات المصرفية للشركات على مؤشر الاستقرار، باستثناء مؤشر عدد الحسابات المصرفية للأفراد فقد أثبتت نتائج التقدير معنوية تأثيره الإيجابي.

scholarly journals Bias correction methods for dynamic panel data models with fixed effects

2017 ◽  
Vol 6 (2) ◽  
pp. 58
Author(s):  
Mohamed Abonazel
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Estimation Methods ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Cross Sectional Dimension ◽  
Ls Estimator

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

Do energy efficiency building codes help minimize the efficiency gap in the U.S.? A dynamic panel data approach

2020 ◽  
pp. 0958305X2094388
Author(s):  
Bishwa S Koirala ◽  
Alok K Bohara
Keyword(s):  
Energy Efficiency ◽  
Panel Data ◽  
Data Model ◽  
Unobserved Heterogeneity ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Residential Sector ◽  
Energy Efficiency Policy ◽  
Efficiency Gap

This study estimates the effects of energy efficiency policy in the residential sector using panel data of 48 contiguous states starting from 1970 to 2017. To avoid any unobserved heterogeneity and facilitate efficiency in estimation, this study employs a Dynamic Panel Data model with a two-step Generalized Method of Moments technique. The results suggest that energy efficiency policy for the residential sector has saved about 8.6 percent in energy consumption, which is about 22 percent of the total stated saving, leaving an energy efficiency gap of 1.5771 quadrillion Btu. Consistent with previous estimations, this study finds that theoretical saving amounts overestimate energy efficiency output and overinflate the increase in potential energy efficiency by about 32 percent. Since energy efficiency policy has failed to achieve the stated amount of saving in the residential sector, households have no incentive to adopt the energy efficiency policy, which has created an unusual gap in energy efficiency.

Estimating Dynamic Panel Data Models in Political Science

2002 ◽  
Vol 10 (1) ◽  
pp. 25-48 ◽  
Author(s):  
Gregory Wawro
Keyword(s):  
Panel Data ◽  
Political Science ◽  
Dynamic Models ◽  
Party Identification ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Panel Data Models ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Moments Estimators

Panel data are a very valuable resource for finding empirical solutions to political science puzzles. Yet numerous published studies in political science that use panel data to estimate models with dynamics have failed to take into account important estimation issues, which calls into question the inferences we can make from these analyses. The failure to account explicitly for unobserved individual effects in dynamic panel data induces bias and inconsistency in cross-sectional estimators. The purpose of this paper is to review dynamic panel data estimators that eliminate these problems. I first show how the problems with cross-sectional estimators arise in dynamic models for panel data. I then show how to correct for these problems using generalized method of moments estimators. Finally, I demonstrate the usefulness of these methods with replications of analyses in the debate over the dynamics of party identification.

scholarly journals GMM ESTIMATION AND INFERENCE IN DYNAMIC PANEL DATA MODELS WITH PERSISTENT DATA

2009 ◽  
Vol 25 (5) ◽  
pp. 1348-1391 ◽  
Author(s):  
Hugo Kruiniger
Keyword(s):  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Panel Data Models ◽  
Finite Sample ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Weak Instruments ◽  
Distributional Properties ◽  
Persistent Data

In this paper we consider generalized method of moments–based (GMM-based) estimation and inference for the panel AR(1) model when the data are persistent and the time dimension of the panel is fixed. We find that the nature of the weak instruments problem of the Arellano–Bond (Arellano and Bond, 1991,Review of Economic Studies58, 277–297) estimator depends on the distributional properties of the initial observations. Subsequently, we derive local asymptotic approximations to the finite-sample distributions of the Arellano–Bond estimator and the System estimator, respectively, under a variety of distributional assumptions about the initial observations and discuss the implications of the results we obtain for doing inference. We also propose two Lagrange multiplier–type (LM-type) panel unit root tests.

INTANGIBLE DETERMINANTS OF MARKET VALUE IN THE NEW ECONOMY: A DYNAMIC PANEL DATA ANALYSIS OF THE INDIAN SOFTWARE INDUSTRY

2009 ◽  
Vol 54 (03) ◽  
pp. 379-398 ◽  
Author(s):  
SUPRIYO DE
Keyword(s):  
Panel Data ◽  
Intangible Assets ◽  
Software Industry ◽  
Generalized Method Of Moments ◽  
Market Value ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Market Values ◽  
Software Firms ◽  
The Impact

Intangible assets like human capital and organization capital have driven the success of India's software industry. This article analyzes the impact of intangible assets on the market value of Indian software firms using a dynamic panel data model. Measures of tangible and intangible assets are constructed using firm-level panel data. The estimation technique uses system generalized method of moments (GMM) and minimum distance estimation (MDE). This methodology accounts for unobserved firm heterogeneity, endogenous explanatory variables and persistent variables. The results conclusively show that intangible assets have a significant impact on market values of Indian software firms.

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