Estimation of Production Function Parameters Combining Time-Series and Cross-Section Data

Econometrica ◽  
1962 ◽  
Vol 30 (1) ◽  
pp. 34 ◽  
Author(s):  
Irving Hoch
Keyword(s):  
Time Series ◽  
Production Function ◽  
Cross Section ◽  
Cross Section Data ◽  
Section Data

A Production Function for Florida Foliage Nurseries From Time-Series and Cross-Section Data

1977 ◽  
Vol 9 (1) ◽  
pp. 151-156
Author(s):  
Dan L. Gunter ◽  
Robert D. Emerson
Keyword(s):  
Time Series ◽  
Production Function ◽  
Cross Section ◽  
Analysis Program ◽  
Cross Section Data ◽  
Commercial Agriculture ◽  
Section Data ◽  
Agricultural Income ◽  
Business Analysis ◽  
Production Area

The foliage industry is the most rapidly expanding segment of commercial agriculture in Florida [1]. The industry accounted for about $13 million of the agricultural income in 1966 and over $187 million in 1975. The area in production in the state has more than doubled in the last ten years; it was increased from about 26 million square feet in 1966 to just over 65 million square feet in 1975. Nurserymen were expected to expand their production area by about 8.6 million square feet during 1976 [14].This rapid increase in production area has been from expansion of established producers and entry of new growers into the industry. The producers increased from 163 in 1966 to 262 in 1975. The average foliage nurseryman participating in the Florida Cooperative Extension nursery business analysis program expanded employment from 23 employees in 1970 to 30 in 1975.

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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