scholarly journals Estimation Model And Selection Method Of Panel Data Regression : An Overview Of Common Effect, Fixed Effect, And Random Effect Model

2018 ◽  
Author(s):  
rizka zulfikar
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Unbiased Estimation ◽  
Cross Section Data ◽  
Section Data ◽  
Panel Data Regression ◽  
Data Regression

The estimation in the regression analysis with cross section data is done by estimating the least squares method called Ordinary Least Square (OLS). Regression Method Data Panel will give the result of estimation which is Best Linear Unbiased Estimation (BLUE). Data Panel Regression is a combination of cross section data and time series, where the same unit cross section is measured at different times. So in other words, panel data is data from some of the same individuals observed in a certain period of time. If we have T time periods (t = 1,2, ..., T) and N the number of individuals (i = 1,2, ..., N), then with panel data we will have total observation units of N x T. If sum unit time is the same for each individual, then the data is called balanced panel. If instead, the number of time units is different for each individual, then it is called the unbalanced panel. While other data types, namely:time-series data and cross-section. In time series, one or more variables will be observed on one observation unit within a certain time frame. While data cross-section is the observation of several units of observation in a single point of time.Unlike the usual regression, panel data regression must go through the precise estimation modeling step.SEE ALSO :Zulfikar, R., & Mayvita, P. A. (2017). THE EFFECTS OF POLITICAL EVENTS AGAINST ABNORMAL RETURN AND TOTAL VOLUME SHARIA SHARES ACTIVITY THAT LISTED IN JAKARTA ISLAMIC INDEX (JII). JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen, 14(02), 64-74.Zulfikar, R., & AdeMayvita, P. (2017). Pengujian Metode Fuzzy Time Series Chen dan Hsu Untuk Meramalkan Nilai Indeks Bursa Saham Syariah Di Jakarta Islamic Index (JII). Wiga: Jurnal Penelitian Ilmu Ekonomi, 7(2), 108-124.

scholarly journals ANALISIS REGRESI DATA PANEL TERHADAP INDEKS PEMBANGUNAN MANUSIA (IPM) JAWA TIMUR TAHUN 2006-2015

2018 ◽  
Vol 18 (2) ◽  
pp. 69
Author(s):  
Muhammad Jamil Hidayat ◽  
Alfian Futuhul Hadi ◽  
Dian Anggraeni
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Series Data ◽  
Cross Section Data ◽  
Section Data ◽  
Effect Model ◽  
Per Capita

Panel data is a combination of time series and cross section data. Panel data regression is used because in a time there is time researchers can’t perform analysis only by using time series data and cross section data only. This is because the number of factors used in the analysis phase, so that if the researcher only uses cross section data then the researcher can’t see the influence of factors that affect as well as on the growth of HDI that occurs from time to time in a certain period. Whereas it is quite possible that the conditions between one year and another will be different. Based on the model estimation, it is used with fixed effect model (FEM) approach. Modeling HDI with FEM in 2006-2015 period resulted in R2 value of 94.23%. The results showed that from 2006-2015 the ratio of student-teacher (RST), health facilities (HF), percentage of expenditure per capita by group of food (PPF) and regional per capita expenditure (PPE) have significant effect to HDI. Keywords: HDI, Panel, Fixed Effect Model

scholarly journals ANALISIS DETERMINAN KEMISKINAN DI PROVINSI JAWA TENGAH

JEJAK ◽  
2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Dita Wahyu Puspita
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Total Population ◽  
Poverty Level ◽  
Literacy Rate ◽  
Section Data ◽  
Central Java ◽  
Panel Data Regression ◽  
Data Regression

<p>This study aims to determine factors of poverty in the province of Central Java period 2008 to 2012. Central Java province was chosen because it has the second highest poverty level among 33 provinces in Indonesia. In this study, the factors that infl uence poverty are the numbers of population live in poverty, unemployment, Gross Regional Domestic Product (GDP) and literacy rate. The method used is the panel data regression. Panel data is the data that combines the time series and cross-section data. In this study, it is found that unemployment, GDP and total population have signifi cant aff ect on poverty in the province of Central Java.</p><p>Penelitian ini bertujuan untuk mengetahui determinan kemiskinan di provinsi Jawa Tengah periode 2008 sampai 2012. Dipilihnya Jawa Tengah karena dari 33 provinsi yang ada di Indonesia, Jawa Tengah merupakan provinsi dengan penduduk miskin terbanyak ke dua. Dalam penelitian ini faktor-faktor yang mempengaruhi kemiskinan di antaranya yaitu jumlah penduduk miskin, banyaknya pengangguran, Pendapatan Domestik Regional Bruto (PDRB) dan Angka Melek Huruf dan semua variable tadi dipilih periode 2008 sampai 2012. Metode penelitian yang digunakan yaitu metode regresi data panel. Data panel merupakan data yang menggabungkan antara data time series dan data cross-section. Dalam penelitian ini pula ditemukan bahwa pengaruh pengangguran, PDRB dan jumlah atau populasi penduduk Jawa Tengah signifi kan. Artinya berpengaruh pada kemiskinan di provinsi Jawa Tengah.</p>

scholarly journals Analisis Pengaruh Pertumbuhan Ekonomi, Jumlah Angkatan Kerja dan Upah Minimum Regional Terhadap Pengangguran Terdidik di Jawa Timur

2017 ◽  
Vol 4 (2) ◽  
pp. 187
Author(s):  
R. Achmad Ryan Z ◽  
Nanik Istiyani ◽  
Anifatul Hanim
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Regression Analysis ◽  
Panel Data ◽  
Labor Force ◽  
Cross Section ◽  
Secondary Data ◽  
Minimum Wages ◽  
Panel Data Regression ◽  
Data Regression

This research aimed to determine how much the influence of economic growth, the number of labor force and regional minumum wages on educated unemployment in East Java. The data used in this research is secondary data such as time series and cross section. The analytical method used in this research is panel data regression analysis. The results showed that variable of the economic growth was positifly and not significantly affect to the educated unemployment in East Java, whereas the number of labor force and regional minimum wages was positivly and significantly affect to the educated unemployment in East Java. Keywords: The Economic Growth, the Number of Labor Force and Regional Minimum Wages.

scholarly journals Determinan Pengangguran Terdidik Lulusan Universitas di Pulau Jawa

2021 ◽  
Vol 10 (3) ◽  
pp. 178-187
Author(s):  
Leni Anjarwati ◽  
Whinarko Juliprijanto
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Cross Section ◽  
Quantitative Methods ◽  
Negative Impact ◽  
Positive Impact ◽  
Panel Data Analysis ◽  
Series Data ◽  
Cross Section Data ◽  
Section Data

This study aims to determine the factors that influence educated unemployment in Java. The data used in this study is secondary data using quantitative methods. Data analysis uses panel data analysis which is a combination of time series and cross-section data. The time-series data uses data for the 2015-2019 period and cross-section data from 6 provinces on the island of Java. The results showed that simultaneously all variables had a significant effect on the level of educated unemployment. While partially shows that the variable level of education and PMDN have a significant positive impact on educated unemployment, and the UMR variable has a significant negative impact on educated unemployment.

scholarly journals Pengaruh Rasio Keuangan Terhadap Financial Distress Pada Perusahaan Sektor Industri Dasar Dan Kimia Yang Terdaftar Di Bursa Efek Indonesia

2020 ◽  
Vol 5 (1) ◽  
pp. 11-28
Author(s):  
Andi Runis Makkulau
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Financial Distress ◽  
Time Series Data ◽  
Series Data ◽  
Sales Growth ◽  
Cross Section Data ◽  
Current Ratio ◽  
Section Data ◽  
Equity Ratio

Penelitian ini bertujuan untuk mengetahui dan menganalisis pengaruh Current Ratio, Cash Ratio, Debt To Equity Ratio dan Sales Growth terhadap Financial Distress pada Perusahaan Sektor Industri Dasar dan Kimia yang Terdaftar di Bursa Efek Indonesia. Populasi dalam penelitian ini adalah perusahaan sektor industri dasar dan kimia yang terdaftar di Bursa Efek Indonesia. Berdasarkan metode purposive sampling, sampel yang diperoleh yaitu sebanyak 10 perusahaan. Penelitian ini menggunakan data kuantitatif dan bersumber dari jenis data sekunder yaitu data panel yang merupakan data gabungan dari data runtut waktu (time series data) dan data silang (cross section data).           Hasil penelitian ini menunjukkan bahwa Current Ratio, Cash Ratio, Debt to Equity Ratio dan Sales Growth berpengaruh negatif tetapi tidak signifikan terhadap Financial Distress. Hal ini berarti bahwa Current Ratio, Cash Ratio, Debt to Equity Ratio dan Sales Growth tidak dapat menjelaskan Financial Distress.   Kata Kunci : Current Ratio, Cash Ratio, Debt To Equity Ratio, Sales Growth, Financial Distress  

Consumer Behavior in the Nineteenth Century and Ontario Workers, 1885–1889

1986 ◽  
Vol 46 (2) ◽  
pp. 477-488 ◽  
Author(s):  
Trevor J. O. Dick
Keyword(s):  
Time Series ◽  
Consumer Behavior ◽  
Cross Section ◽  
Time Series Data ◽  
National Income ◽  
Real Income ◽  
Series Data ◽  
Cross Section Data ◽  
Expenditure Patterns ◽  
Section Data

Cross-section data on expenditure patterns and time-series data on nominal national income and the characteristics of the consuming population are combined to yield aggregate expenditure and household budget share estimates for Canadians from 1870 to 1914. Recently developed econometric techniques are used to produce the new time series. Unlike older estimates that give relatively stable budget shares, the new series break at 1900. This finding has significant implications for the debate over Canadian real income changes in the period, a debate prolonged by problems of converting nominal into real income and of estimating real consumption directly.

scholarly journals Determinant of Car Demand in Java Island

2019 ◽  
Vol 8 (1) ◽  
pp. 1-8
Author(s):  
Dyah Candra Kirana ◽  
Prasetyo Ari Bowo
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Cross Section ◽  
Time Series Data ◽  
Panel Data Analysis ◽  
Least Square ◽  
Series Data ◽  
Cross Section Data ◽  
Section Data ◽  
Per Capita

The purpose of this research is to examine factors that affect car demand in Java Island in 2012-2016. The research method used in this research is panel least square The data used in this research is panel data. The panel data consists of time series data (2012-2106) and cross section data (six province in Java Island, those are DKI Jakarta, Jawa Barat, Jawa Tengah, DI Yogyakarta, Jawa Timur, and Banten). Data were obtained from Central Bureau of Statistic Republic of Indonesia (BPS). Data analysis used is panel data analysis. The results showed that income per capita, population, and inflation have simultan effect on car demand in Java Island in 2012-2016. Per capita income has a positive and significant effect on car demand in Java Island in 2012-2016. Population has a positive and significant effect on car demand in Java Island in 2012-2016. Inflation has positive and insignificant effect on car demand in Java Island in 2012-2016.

Pooling of Time Series and Cross Section Data

Econometrica ◽  
1969 ◽  
Vol 37 (3) ◽  
pp. 552
Author(s):  
V. K. Chetty
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Cross Section Data ◽  
Section Data

Time is Not A Theoretical Variable

2010 ◽  
Vol 18 (3) ◽  
pp. 293-294 ◽  
Author(s):  
Nathaniel Beck
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Duration Dependence ◽  
Cross Section Data ◽  
Section Data ◽  
Data Analyst ◽  
Binary Time Series ◽  
Simple Cubic ◽  
Cubic Polynomial

Carter and Signorino (2010) (hereinafter “CS”) add another arrow, a simple cubic polynomial in time, to the quiver of the binary time series—cross-section data analyst; it is always good to have more arrows in one's quiver. Since comments are meant to be brief, I will discuss here only two important issues where I disagree: are cubic duration polynomials the best way to model duration dependence and whether we can substantively interpret duration dependence.

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