Status of national intellectual property rights (IPRs) systems and its impact to agricultural development: a time series cross section data analysis of TRIPS member-countries

2012 ◽  
Vol 5 (1) ◽  
pp. 82 ◽  
Author(s):  
Jane Payumo ◽  
Howard Grimes ◽  
Philip Wandschneider
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Intellectual Property ◽  
Property Rights ◽  
Cross Section ◽  
Intellectual Property Rights ◽  
Agricultural Development ◽  
Cross Section Data ◽  
Section Data

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.

A Lot More to Do: The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications

2007 ◽  
Vol 15 (2) ◽  
pp. 101-123 ◽  
Author(s):  
Sven E. Wilson ◽  
Daniel M. Butler
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Cross Section ◽  
Cross Section Data ◽  
Section Data ◽  
The Past ◽  
Simple Alternative ◽  
Common Problems ◽  
Statistical Issues

In 1995, Beck and Katz (B&K) instructed the profession on “What to do (and not to do) with time-series, cross-section data,” and almost instantly their prescriptions became the new orthodoxy for practitioners. Our assessment of the intellectual aftermath of this paper, however, does not inspire confidence in the conclusions reached during the past decade. The 195 papers we reviewed show a widespread failure to diagnose and treat common problems of time-series, cross-section (TSCS) data analysis. To show the importance of the consequences of the B&K assumptions, we replicate eight papers in prominent journals and find that simple alternative specifications often lead to drastically different conclusions. Finally, we summarize many of the statistical issues relative to TSCS data and show that there is a lot more to do with TSCS data than many researchers have apparently assumed.

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 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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