scholarly journals ANALISIS INTERVENSI FISKAL TERHADAP KEMISKINAN (STUDI KASUS DANA DESA PULAU KALIMANTAN)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
M. Zaky Mubarak Lubis ◽  
Aprian Subhan Dahraini
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Fixed Effect Model ◽  
Poverty Rate ◽  
Panel Data Model ◽  
Fund Allocation ◽  
Effect Model ◽  
Descriptive Approach ◽  
Quantitative Descriptive ◽  
The Village

ABSTRACTOne of the government's strategies to help the village become independent and autonomous by giving the allocation of village funds. The funds allocated for the rural area, and expected to support the implementation of the people’s empowerment and the rural development, so that funds can improve the people’s prosperity. Furthermore, the aims of this study to look at the influence of Fiscal Intervention which are representated by Village Fund, Village Fund Allocation, and part of Local Earning and Retribution toward Poverty which is representated by Poverty Rate of Residences/Cities in Kalimantan Island during 2015-2017. This research used quantitative descriptive approach with Panel Data Model. The result showed of Fixed Effect Model regression that, Village Fund, Village Fund Allocation, and part of Local Earning and Retribution have have negative significant influence at Poverty Rate for 47 Residences/Cities in Kalimantan Island.

Analisis Pengaruh Dana Desa, Alokasi Dana Desa dan Pendapatan Asli Desa terhadap Jumlah Penduduk Miskin Desa di Kabupaten Tuban

2021 ◽  
Vol 4 (2) ◽  
pp. 238-250
Author(s):  
Tanti Auliya Agustina ◽  
Muchtolifah Muchtolifah ◽  
Sishadiyati Sishadiyati
Keyword(s):  
Panel Data ◽  
Fixed Effect Model ◽  
Coefficient Of Determination ◽  
Poor People ◽  
Analysis Tool ◽  
Rural Poor ◽  
Fund Allocation ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Village

This study aims to analyse the difference in the Number of rural poor people in Tuban Regency between 2019 and 2020 to examine the effect of the Village Fund, Allocation of village funds (ADD) and Village original income on the Number of rural poor people in Tuban Regency in 2019-2020. The analysis tool used is panel data regression which is testing the hypothesis with the F test, t test and Coefficient of Determination (R2) at an error rate of = 5%. The results of panel data regression analysis with the selected model is the Fixed Effect Model showing that there is a difference in the Number of rural poor people in Tuban Regency between 2019 and 2020, the variables used in the model, namely the Village Fund, Allocation of village funds (ADD) and Village original income are able to explain the variation of the variable Number of rural poor by 85% and 15% are influenced by other factors that are not included in the research variables. Keywords: Village Fund, Allocation of village funds (ADD) and Village original income

scholarly journals Reexamination of Okun's law: Empirical analysis from Panel Granger Causality

Industrija ◽  
2020 ◽  
Vol 48 (4) ◽  
pp. 63-80
Author(s):  
Gatot Sasongko ◽  
Niken Artanti ◽  
Andrian Huruta ◽  
Cheng-Wen Lee
Keyword(s):  
Panel Data ◽  
Granger Causality ◽  
Data Model ◽  
Fixed Effect Model ◽  
Causality Analysis ◽  
Okun’S Law ◽  
Okun's Law ◽  
Granger Causality Analysis ◽  
Effect Model ◽  
Panel Granger Causality

This study aims to confirm the existence of Okun's coefficients in Indonesia. Authors conducted panel data of 34 provinces in Indonesia for the period from 2014 to 2019, obtained from the Central Bureau of Statistics. The data were analyzed by using the panel data model and Panel Granger Causality. The Panel Granger causality analysis results show that there was one-way causality between the economic growth and the unemployment. It was caused by several influencing factors, such as education level, population, and employment opportunities. Based on the Fixed Effect Model and Panel Granger Causality results, authors conclude that Okun's Law has not been proven for 34 provinces in Indonesia.

scholarly journals ANALISIS EKONOMI KEBIJAKAN DANA DESA TERHADAP KEMISKINAN DESA DI KABUPATEN TULUNGAGUNG

2017 ◽  
Vol 15 (1) ◽  
pp. 34
Author(s):  
Intan Mala Sari ◽  
Muhammad Faisal Abdullah
Keyword(s):  
Panel Data ◽  
Hypothesis Testing ◽  
Rural Poverty ◽  
Fixed Effect Model ◽  
Coefficient Of Determination ◽  
Error Level ◽  
Effect Model ◽  
Show Difference ◽  
The Difference ◽  
The Village

The purpose of this research is analysis of the difference rural poverty in district tulungagung between on the 2015 and 2016 and analyze influence of fund village and ADD against rural poverty in Tulungagung district. Analysis tools used twin regression with the panel data to hypothesis testing with F test, t test, and  coefficient of determination () at the error level  . The result of analysis regression panel data with the selected model is Fixed Effect Model that show difference rural poverty in district tulungagung between on the 2015 and 2016, variables used in the model are the village fund and ADD take effect on rural poverty with the every score -4,52 for the village fund and -1,52 for ADD. While the coefficient of determination  () is 0,99 or 99%, variable ability show village fund and ADD explain rural poverty in the tulungagung district is 99%. And influence of fund village and ADD against rural poverty is there any difference at the every village in Tulungagung district.

NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Conditional Distribution ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Data Models ◽  
Panel Data Model ◽  
Panel Data Models ◽  
Likelihood Functions ◽  
Correlated Random Effects

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.

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