Exports and Economic Growth in Developing Countries: Evidence from Time-Series and Cross-Section Data

1987 ◽  
Vol 36 (1) ◽  
pp. 51-72 ◽  
Author(s):  
Rati Ram
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Developing Countries ◽  
Cross Section ◽  
Cross Section Data ◽  
Section Data

scholarly journals Pengaruh Perkembangan Bank Umum Syariah Terhadap Pertumbuhan Ekonomi Indonesia Periode 2010-2015

2017 ◽  
Vol 4 (12) ◽  
pp. 952
Author(s):  
Firmansyah Putra ◽  
Muhammad Nafik Hadi Ryandono
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Cross Section ◽  
Annual Report ◽  
Secondary Data ◽  
Islamic Banking ◽  
Islamic Banks ◽  
Cross Section Data ◽  
Production Index ◽  
Section Data

The study aims to examines the influence of Islamic banks on economic growth in Indonesia during the period 2010–2015. Secondary data that were used in this research were in the form of monthly time series and cross-section data during the year 2010–2015. The data were obtained based on monthly statistical banking report from Bank Indonesia (BI) andmonthly statistical reports of the Monthly Industrial Production Index in the Large and Medium scale from Central Bureau of Statistics (BPS) of Indonesia and also annual report from each islamic banks that conclude in this observation. Total assets, and total financing as variables that are representing the Islamic bank. GDP (Gross Domestic Product) is the variable that representing economic growth. By using Multiple Regression Analysis, the result shows that generally, Islamic banking affects economic growth in Indonesia.

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.

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 Random Coefficient Models for Time-Series—Cross-Section Data: Monte Carlo Experiments

2007 ◽  
Vol 15 (2) ◽  
pp. 182-195 ◽  
Author(s):  
Nathaniel Beck ◽  
Jonathan N. Katz
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Cross Section ◽  
Random Coefficient ◽  
Model Parameters ◽  
Cross Section Data ◽  
Likelihood Estimator ◽  
Random Coefficient Models ◽  
Section Data

This article considers random coefficient models (RCMs) for time-series—cross-section data. These models allow for unit to unit variation in the model parameters. The heart of the article compares the finite sample properties of the fully pooled estimator, the unit by unit (unpooled) estimator, and the (maximum likelihood) RCM estimator. The maximum likelihood estimator RCM performs well, even where the data were generated so that the RCM would be problematic. In an appendix, we show that the most common feasible generalized least squares estimator of the RCM models is always inferior to the maximum likelihood estimator, and in smaller samples dramatically so.

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