Empirical Methods in Strategy Research: Regression Analysis and the Use of Cross-Section Versus Pooled Time-Series, Cross-Section Data

1997 ◽  
pp. 201-219 ◽  
Author(s):  
Margarethe F. Wiersema ◽  
Harry P. Bowen
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Cross Section ◽  
Cross Section Data ◽  
Empirical Methods ◽  
Strategy Research ◽  
Section Data ◽  
Section Versus ◽  
Pooled Time Series

The impact of higher quality government regulation of business and greater economic freedom on living standards

2016 ◽  
Vol 5 (1) ◽  
pp. 82-94 ◽  
Author(s):  
Richard Cebula ◽  
Fabrizio Rossi ◽  
Jeff Clark
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Economic Freedom ◽  
Government Regulation ◽  
Living Standards ◽  
Cross Section Data ◽  
Content Type ◽  
Section Data ◽  
Pooled Time Series ◽  
The Impact

Purpose – The purpose of this paper is to evaluate whether two specific forms of government policy influence entrepreneurship and hence the performance economy as a whole. Performance is measured in terms of living standards and growth therein. The policies are, as follows: higher quality government regulation of businesses and higher levels of economic freedom. Design/methodology/approach – The paper first provides a basic model focussing upon the regulation and economic freedom variables. The study then adds a dummy variable for G8 nations, a tax burden variable, and the long-term interest rate and provides six estimates. The empirical analysis involves pooled time-series/cross-section data for the OECD over the period 2003-2007. Findings – The findings indicate that for OECD nations, higher quality public regulation promotes entrepreneurial spirit and performance. Higher economic freedom does the same. The findings are that higher quality government regulation of business and higher levels of economic freedom lead to higher growth rates in the standard of living. Originality/value – The time period studied, i.e., just prior to the Great Recession, the context of the OECD, the adoption of pooled time-series/cross-section data, and the specific choice of variables in the analysis, along with the estimation of possibly unique or close to unique specifications involving the growth rate of living standards per se make this study different.

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 ANALISIS PENGARUH SOSIAL EKONOMI TERHADAP PENGELOLAAN SAMPAH DI SUMATERA BARAT (STUDI KASUS DAERAH PERKOTAAN)

2020 ◽  
Vol 2 (1) ◽  
pp. 107
Author(s):  
Nesyana Dewi ◽  
Melti Roza Adry
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Waste Management ◽  
Cross Section ◽  
Urban Areas ◽  
Secondary Data ◽  
Cross Section Data ◽  
Income Per Capita ◽  
Section Data ◽  
Per Capita

This study aims to determine the effect of education, income per capita, age and knowledge on waste management in urban areas West Sumatera. This study uses secondary data in the form of cross section data of urban West Sumatera. Data obtained from BPS- Susenas West Sumatera. This study uses logistic regression analysis. The result of this study indicate that (1) education has not significant effect on waste management in urban areas West Sumatera (2) income per capita has not significant effect on waste management  in urban areas West Sumatera (3) age has not significant effect on waste management in urban areas West Sumatera (4) knowledge has a significant effect on waste management in urban areas West Sumatera

scholarly journals Conventional and Current way of Interpretation and Cost Forecasting

2020 ◽  
Vol 1 (6) ◽  
pp. 30-33
Author(s):  
Shahin ripon Nazmul ◽  
Riyaaz Sanjoy
Keyword(s):  
Regression Analysis ◽  
Production Process ◽  
Cross Section ◽  
Series Data ◽  
Cross Section Data ◽  
Short Term ◽  
Section Data ◽  
Production Factors ◽  
Cost Forecasting

This study discuses Short-term cost interpretation, regression analysis with time-series data, long term cost interpretation, Regression analysis using cross-section data, cost forecasting and Changes in the productivity of production factors. Short-term cost interpretation lead to short-term decisions, the concept of incramental costs has a very important role which includes variable costs and changes in fixed costs.  Long term cost interpretation to analyze the production function of several different firms, long-run cost estimates can be used. Based on these conditions, the estimation of long-term costs uses cross-section data. Forecasting costs for various levels of output in the coming period requires an assessment of changes in the efficiency of the production process physically, plus changes in the prices of production factors used in the production process.

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.

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