Testing Gibrat's Law: An Empirical Evidence from Indian Industry

2008 ◽  
Vol 4 (4) ◽  
pp. 38-52 ◽  
Author(s):  
Priyanka Aggarwal ◽  
Subhash Chander
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Empirical Evidence ◽  
Time Series Data ◽  
Series Data ◽  
Gibrat’S Law ◽  
Market Capitalization ◽  
Indian Industry ◽  
Gibrat's Law ◽  
Corporate Growth

According to Gibrat's law of Proportionate Effect, the growth rate of a given firm is independent of its size at the beginning of the examined period. The objective of this paper is to empirically analyze the pattern of corporate growth for an emerging economy, namely India. Balanced time series data of approximately 300 firms over the years 1991–92 to 2005–06 has been used to explore the size and growth relationship. The size of a firm has been measured in terms of net sales, total assets, and market capitalization. The data for various attributes has been taken from ‘PROWESS’ a database of CMIE. SPSS 10.05 has been used to perform the statistical applications.

scholarly journals Aquaculture Production and Consumption of Fish in India

2021 ◽  
Vol 10 (1) ◽  
pp. 21-26
Author(s):  
Dhanya Sai Das ◽  
R Govindasamy
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Food Security ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Food Protein ◽  
Fish Production ◽  
Total Fish ◽  
The Government

Aquaculture and fisheries emerged as an important source of food, protein, nutrition, livelihood and employment for the majority of the rural population. The fisheries sector has registered a sustainable and astounding growth rate over the last decade. The sector offers an attractive and promising future for employment, livelihood and food security. The study is based on the available secondary data from different aspects of fishery statistics published in Handbook on Fisheries Statistics 2020 by the Government of India and other related articles. Data for the time series analysis was taken from 2001-02 to 2017-18. It is found that the world per capita apparent consumption of fish has been increased by 10.4 kg from the 1960s (i.e., 9.9 kg) to 2016 (i.e., 20.30 kg). By analysing the time-series data, it is evident that the total fish production, including both marines and inland, has shown an astounding growth with a Compound Growth Rate of 4.58. The regression equation was Y = 5.182X – 12267, R2 value was 0.9414 where Y is the total fish production (dependent variable) and X is the total fish seed production (independent variable). There exists a positive relationship between fish seed and fish production in the country. It can be concluded that aquaculture plays a significant role in the country’s GDP rate and food security.

scholarly journals Early detection of seasonality and second-wave prediction in the COVID-19 pandemic

2020 ◽  
Author(s):  
Márcio Watanabe
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Time Series Data ◽  
Seasonal Effects ◽  
Series Data ◽  
Seasonal Effect ◽  
Social Distancing ◽  
The Mean

AbstractSeasonality plays an essential role in the dynamics of many infectious diseases. Its confirmation in an emerging infectious disease is usually done using time series data from several years. By using statistical regression methods for time-series data pooled from more than 50 countries from both hemispheres, we show how to determine its presence in a pandemic at the onset of the seasonal period. We measure its expected effect in the mean transmission rate of SARS-coV-2 and predict when further epidemic outbreaks of COVID-19 will occur. The obtained result in the Northern Hemisphere shows that seasonality reduced the mean growth rate in 222.5% in April 2020. A relative reduction greater than 100% should be interpreted as a reduction changing an increasing rate to a decreasing one. In contrast, at the same moment, the seasonal effect in the Southern Hemisphere increased the mean growth rate in 740.3%. Our analysis simultaneously considers other confounding factors to properly separate them from seasonal effects and, in addition, we measure the mean global effect of social-distancing interventions and its relation with income. Future COVID-19 waves are expected to occur in autumn/winter seasons, typically between September and March in the Northern Hemisphere, and between April and September in the Southern Hemisphere. Simulations of a seasonal SEIR model with a social distancing effect are shown to describe the behavior of COVID-19 outbreaks in several countries. These results provide vital information for policy makers to plan their actions against the new coronavirus disease, particularly in the optimization of social-distancing interventions and vaccination schedules. Ultimately, our methods can be used to identify and measure seasonal effects in a future pandemic.

scholarly journals Impact of Price and Price Variability on Acreage Allocation in Rice and Wheat Production in Bangladesh

2012 ◽  
Vol 10 (1) ◽  
pp. 23-30
Author(s):  
Basanta Kumar Barmon ◽  
Muntasir Chaudhury
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Time Series Data ◽  
Wholesale Price ◽  
Series Data ◽  
Wheat Crop ◽  
Price Variability ◽  
Wheat Production ◽  
Long Run ◽  
Short Run

The present study was conducted to estimate the impacts of price and price variability on acreage allocation of rice and wheat production in Bangladesh. Time series data of price and acreage allocation of rice and wheat production during 1983-84 to 2007-08, collected from Bangladesh Bureau of Statistics (BBS) were used in this study. Compound growth rate and Nerlovian models were used. The study indicated that the wholesale price of rice and wheat had significant impact on the allocation of land for rice and wheat production. Significant price variability was found both in case of rice and wheat crop in short-run (SR) and long-run (LR). The values of Nerlovian coefficients of adjustment were found low, which means that although the farmers were adjusting to the changing levels of price, price variability, yield, etc the adjustment was not rapid. Therefore, it may be concluded that the price of rice and wheat should be adjusted rapidly along with allocation of rice and wheat production in Bangladesh.DOI: http://dx.doi.org/10.3329/agric.v10i1.11061The Agriculturists 2012; 10(1): 23-30

scholarly journals Variability and Sources of Output Growth in Major Oilseeds of Rajasthan

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Kailash Chand Bairwa
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Time Series Data ◽  
Output Growth ◽  
The State ◽  
Series Data ◽  
Oilseed Crops ◽  
Significant Growth ◽  
Area Effect ◽  
Relative Contribution

Rajasthan state is the second largest oilseeds producer and land coverage in the country. The share of oilseed crops is scheduled the significant growth in area and output in latest 20 years. Nevertheless, compare to wheat and gram, the growth rate of area and production of several oilseeds is less significant and there exist wide instability in their productivity in scattered part of the state. This study investigates to growth, its contributors and variability in area, production and productivity of major oilseed crops. The study period from 1990-91 to 2019-20 was divided into three sub-periods viz., period-I (1990-91 to 2004-05); period-II (2005-06 to 2019-20) and Overall study Period (1990-91 to 2018-19). Time series data were collected from various public E-sources to compute the growth, instability and decomposition in oilseeds production. It was revealed from the analysis that growth of kharif oilseeds was higher than rabi oilseeds. The highest instability (31.78) in production and productivity was reported in period-I for kharif oilseeds. In case of relative contribution, the area effect (416.85) and yield effects (211.10) were more effective in production of taramira and sesame crops, respectively. This analysis suggested that during period –I and II area effect was dominant in changing output of taramira and rapeseed-mustard.

scholarly journals Growth Factor: an Important Factor in Determining the Fate of Outbreaks

2021 ◽  
Author(s):  
Yousef Alimohamadi ◽  
Mojtaba Sepandi ◽  
Taher Teymouri ◽  
Hadiseh Hosamirudsari
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Growth Factor ◽  
Exponential Growth ◽  
Inflection Point ◽  
Time Series Data ◽  
Healthy People ◽  
Series Data ◽  
The Social ◽  
Effective Role

Introduction: Epidemic curves are a type of time series data consisting of the number of events that occur over a period of time. The time unit in this data can be a day, a week, or a month, etc. Methods: In the current letter, the authors tried to explain the growth factor and its effect on epidemic curves by using some literature. Results: In the outbreaks setting, the number of cases can increase with different patterns. When the number of cases is increasing exponentially, it means that the number of cases is increasing at a certain speed, which is determined by a factor called an exponential growth factor. When this factor is greater than one, it means that the cases are increasing exponentially, and when this coefficient is equal to 1, it means that we have reached an inflection point that we will face a change in the growth rate of the cases. Conclusion: Some factors such as reducing the contact between infected and healthy people, run the social distancing program, and so on can have an effective role in decreasing epidemic growth factor and controlling the epidemic.

Growth Rate of Pulses in Eastern Uttar Pradesh a Zone-wise Analysis

2021 ◽  
Vol 66 (3) ◽  
Author(s):  
Ekta Pandey
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Time Series Data ◽  
Uttar Pradesh ◽  
Series Data ◽  
The North ◽  
North Eastern ◽  
Eastern Uttar Pradesh ◽  
Negative Growth ◽  
Made In

Attempts are made in this paper to investigate the trend of pulses in Eastern Uttar Pradesh, as well as their instability and non-linear model. This time series data on pulses pertains to the period 1980-1981 to 2014-15 and includes information on the area, production, and productivity of pulses. Pulses have had negative growth in terms of area, production, and productivity in all three zones of Eastern Uttar Pradesh, namely, the North Eastern plain zone, the Eastern plain zone, and the Vindhyan zone. Since 1980-81, there has been a rise in the area and output of pulses in the Vindhyan zone, as seen by the percentage change. The Eastern plain zone has the most stable pulse crop in terms of instability

Performance of Millets in Bundelkhand Region of UP State

2021 ◽  
Vol 57 (4) ◽  
pp. 120-125
Author(s):  
Uma Sah ◽  
G. P. Dixit ◽  
Hemant Kumar ◽  
Jitendra Ojha ◽  
Mohit Katiyar ◽  
...  
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Pearl Millet ◽  
Time Series Data ◽  
Uttar Pradesh ◽  
Series Data ◽  
Instability Index

Time series data on area, production and productivity of major millets (2000-01 to 2019- 20) was analyzed for Bundelkhand region of Uttar Pradesh state. Sorghum was the most important millet crop that accounted for highest area (36.6%) and (34%) of total millet area and production in UP state, respectively. Chitrakoot district contributed highest area (31.6%) under millets. Among all the millets, pearl millet recorded highest growth rate in area (0.97%), production (3.57%) and productivity (1.59%) with low instability index for area. The overall area and production of millet crops recorded decline in all the seven districts of Bundelkhand region during overall study period (2000-20). Lalitpur district recorded highest decline in area (-22.02%), followed by Chitrakoot (-10.82%) and Jhansi district (- 10.63%) during overall study period, while during the same period Banda district recorded a growth in area (1.16%) and production (4.77 %) of pearl millet. The overall area, production and productivity of millets registered a decline in Bundelkhand region during 2000-20. This calls for aggressive promotional activities for enhancing millets production in the region.

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