Articoli di riviste: "Production function estimation"

1

Mundlak, Yair. "Production Function Estimation: Reviving the Primal". Econometrica 64, n. 2 (marzo 1996): 431. http://dx.doi.org/10.2307/2171790.

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Bernini, Cristina, Marzia Freo e Attilio Gardini. "Quantile estimation of frontier production function". Empirical Economics 29, n. 2 (1 maggio 2004): 373–81. http://dx.doi.org/10.1007/s00181-003-0173-5.

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Sakellaris, Plutarchos. "Production Function Estimation with Industry Capacity Data". Finance and Economics Discussion Series 2001, n. 06 (2001): 1–16. http://dx.doi.org/10.17016/feds.2001.06.

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Adonijah Maxwell, Awoingo, e Isaac Didi Essi. "Econometric Estimation of Production Function with Applications". Academic Journal of Applied Mathematical Sciences, n. 56 (15 giugno 2019): 57–61. http://dx.doi.org/10.32861/ajams.56.57.61.

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This study focuses on Monte Carlo Methods in parameter estimation of production function. The ordinary least square (OLS) method is used to estimate the unknown parameters. The Monte Carlo simulation methods are used for the data generating process. The Cobb-Douglas production model with multiplicative error term is fitted to the data generated. From tables 1.1 to 1.3, the mean square error (MSE) of 1 are 0.007678, 0.001972 and 0.001253 respectively for sample sizes 20, 40 and 80. Our finding showed that the mean square error (MSE) value varies with the sum of the powers of the input variables.

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Thioune, Thierno. "Écart de production dans la zone UEMOA : analyse comparative d'une estimation par la fonction de production, le filtre de Kalman et le var structurel bayésien". Revue Internationale des Économistes de Langue Française 6, n. 2 (2021): 77–105. http://dx.doi.org/10.18559/rielf.2021.2.4.

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The potential output and output gap concepts are important tools for central banks, and in particular the Central Bank of West African States (BCEAO), to forecast inflation in pursuit of their priority objective of inflation control. The choice of a method for estimating inflation is a delicate one. This paper proposes an estimation of potential output by the unobservable component methods, Watson's (1986) and Kuttner's (1994) approach, and by an economic modelling method, namely the Bayesian structural VAR. It also proposes a comparison of these different methods with the production function, which is widely used in the literature and recognized as the best method for estimating potential output for WAEMU countries. The results indicate that the different approaches as well as the production function explain the different crisis periods identified within the union. The comparative analysis, against all expectations, reveals that only the output gap obtained by the production function does not explain inflation.

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Bhujel, RB, e SP Ghimire. "Estimation of Production Function of Hiunde (Boro) Rice". Nepal Agriculture Research Journal 7 (22 maggio 2009): 88–97. http://dx.doi.org/10.3126/narj.v7i0.1874.

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Hiunde (Boro) rice has not been popularized due to least attention given to this crop in Nepal. Inorder to estimate the production function of this crop, a field survey in Morang district during2002/2003 was carried out using a semi-structured questionnaire. The primary information wascollected through face to face interview. The result of the empirical model of Cobb-Douglasproduction function revealed the model significant at 1% level and defined 95% variation inHiunde rice production due to variation in independent variables included in the model. Thecoefficient of area, nitrogen, phosphorous, and tractor hour were found significant at 1% levelwhile the dummy for more than 10 times of irrigation was significant at 5% level and up to 10times of irrigation and potash was significant at 10% level. The effect of human and bullocklabor was found non-significant. Among the sampled farmers the average cropping intensity was194% and average yield of Hiunde rice was 4802.50 kg/ha. On an average 131 kg of nitrogen,phosphorous and potash was applied for one ha and 15 irrigations in average. The net benefitfrom Hiunde rice was found to be Rs. 14507.41/ha with 1.73 benefit cost ratio. About 31% costwas incurred in land preparation and transplanting which was highest among the operations. Itwas followed by the costs incurred in fertilizers and agrochemicals which counted 23%.Key words: Estimate; Hiunde rice; production functionDOI: 10.3126/narj.v7i0.1874Nepal Agriculture Research Journal Vol.7 2006 pp.88-97

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Coelli, T. J. "A computer program for frontier production function estimation". Economics Letters 39, n. 1 (maggio 1992): 29–32. http://dx.doi.org/10.1016/0165-1765(92)90096-h.

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Grieco, Paul L. E., Shengyu Li e Hongsong Zhang. "PRODUCTION FUNCTION ESTIMATION WITH UNOBSERVED INPUT PRICE DISPERSION". International Economic Review 57, n. 2 (28 aprile 2016): 665–90. http://dx.doi.org/10.1111/iere.12172.

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Rovigatti, Gabriele. "Production Function Estimation in R: The prodest Package". Journal of Open Source Software 2, n. 18 (9 ottobre 2017): 371. http://dx.doi.org/10.21105/joss.00371.

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Henningsen, Arne, e Géraldine Henningsen. "On estimation of the CES production function—Revisited". Economics Letters 115, n. 1 (aprile 2012): 67–69. http://dx.doi.org/10.1016/j.econlet.2011.12.007.

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Adri, Adri, Firdaus Firdaus, Suharyon Suharyon, Busra BS, Yarda Yarda, Rachmat Hendayana e Syafri Edi. "ESTIMATION OF RICE SEED PRODUCTION EFFICIENCY USING THE STOCHASTIC FRONTIER PRODUCTION FUNCTION". Jurnal Ilmiah Ilmu Terapan Universitas Jambi 7, n. 2 (17 dicembre 2023): 150–58. http://dx.doi.org/10.22437/jiituj.v7i2.30412.

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Production efficiency is a crucial factor in producing seeds. This paper aims to estimate the level of efficiency of rice seed production and determine the factors that influence it using a frontier stochastic production function approach. The rice varieties developed are Inpara3, Inpari Nutri Zink and Baroma. The research was conducted in Rawa Medang Village, Batang Asam District, West Tanjung Jabung Regency, Jambi Province, in December 2021. Data collection was carried out through observation and in-depth interviews with seed breeders. By using the frontier production function, the conclusion is obtained: The efficiency level of rice farming for superior varieties is relatively good, with the efficiency level at level 6. All rice seed breeders show effective performance. To increase the efficiency of rice farming in the future, it is necessary to sharpen the use of production input inputs. The consideration is that the breeder's orientation is to produce quality seed for use by the farming community. Therefore, more intensive technological assistance is needed.

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Shashidara, P. S., N. Nagesha e Badagi Raghavendrachari. "Estimation of Production Function for a Rice-Mill Industry". International Journal of Darshan Institute on Engineering Research & Emerging Technology 11, n. 1 (15 luglio 2022): 13–18. http://dx.doi.org/10.32692/ijdi-eret/11.1.2022.2203.

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Patalinghug, Jason C. "Estimation of a Production Function for Philippine Retail Stores". ECONOMICS 7, n. 1 (1 giugno 2019): 69–79. http://dx.doi.org/10.2478/eoik-2019-0006.

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Abstract The purpose of this paper is the estimation of a production function for retail stores in the Philippines. A generalized Cobb-Douglas production function is utilized for this purpose. Ordinary Least Squares is used in obtaining the coefficients for labor and capital. The results show that the marginal product of labor to be higher than the marginal product of capital and the hypothesis of constant returns to scale is not rejected.

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Ornaghi, Carmine. "Price deflators and the estimation of the production function". Economics Letters 99, n. 1 (aprile 2008): 168–71. http://dx.doi.org/10.1016/j.econlet.2007.06.020.

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15

Huang, Clief J., e Jin-Tan Liu. "Estimation of a non-neutral stochastic frontier production function". Journal of Productivity Analysis 5, n. 2 (giugno 1994): 171–80. http://dx.doi.org/10.1007/bf01073853.

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Fajar, Muhammad, Oktya Putri Gitaningtyas, Muhammad Muhtoni e Purwaning Dhahari. "The Estimation of Production Function and Technical Efficiency Shallot Farming". Jurnal Matematika "MANTIK" 5, n. 1 (31 maggio 2019): 50–59. http://dx.doi.org/10.15642/mantik.2019.5.1.50-59.

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Shallot is one of the potential horticultural commodities. The purpose of this study is to estimate the production function and efficiency of shallot farming. The method used in the study is the estimation of production functions using stochastic frontier. The data used in this study were shallot production (kg), harvested area (m2), labor used (HOK), use of seeds (kg), fertilizer (kg), pesticides used (kg), sourced from SHR2014 which conducted by the Central Statistics Agency. In the estimation process, all variables are transformed by natural logarithms. The results showed that the estimation of the function of shallot production for both the dry season and the wet season with independent variables included harvested area, labor, seeds, fertilizers, and significant pesticides in the model, so that formed model was valid for further use. The average technical efficiency of shallot farming in the dry and wet season is 0.6626 and 0.6627, respectively, which means that in general, shallot farming in Indonesia is not efficient on the technical side. That is, there are indications that the optimal processing technology of production inputs in the business has not been carried out optimally.

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Hilmer, Christiana E., e Matthew T. Holt. "Estimating Indirect Production Functions with a More General Specification: An Application of the Lewbel Model". Journal of Agricultural and Applied Economics 37, n. 3 (dicembre 2005): 619–34. http://dx.doi.org/10.1017/s1074070800027127.

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Whereas consumer theory employs several different empirical specifications for estimating indirect utility functions, producer theory has relied on the Translog specification to estimate the indirect production function. In this paper, we apply Lewbel's more general functional specification and investigate its implications for the estimation of indirect production functions in productivity analysis. An attractive feature of the Lewbel model is that it nests both the Translog and the almost ideal supply system, offering a method to assess the empirical validity of all three specifications. Aggregate U.S. production data are used to examine the performance of the three models in an empirical application.

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Mohammed, M. A., Sundus N. Al-Aziz, Eateraf M. A. Al Sumati e Emad E. Mahmoud. "Bayesian Estimation of Different Scale Parameters Using a LINEX Loss Function". Computational Intelligence and Neuroscience 2022 (30 aprile 2022): 1–12. http://dx.doi.org/10.1155/2022/4822212.

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The LINEX loss function, which climbs exponentially with one-half of zero and virtually linearly on either side of zero, is employed to analyze parameter analysis and prediction problems. It can be used to solve both underestimation and overestimation issues. This paper explained the Bayesian estimation of mean, Gamma distribution, and Poisson process. First, an improved estimator for μ 2 is provided (which employs a variation coefficient). Under the LINEX loss function, a better estimator for the square root of the median is also derived, and an enhanced estimation for the average mean in such a negatively exponential function. Second, giving a gamma distribution as a prior and a likelihood function as posterior yields a gamma distribution. The LINEX method can be used to estimate an estimator λ B L ^ using posterior distribution. After obtaining λ B L ^ , the hazard function h B L ^ and D B L ^ the function of survival estimators are used. Third, the challenge of sequentially predicting the intensity variable of a uniform Poisson process with a linear exponentially (LINEX) loss function and a constant cost of production time is investigated using a Bayesian model. The APO rule is offered as an approximation pointwise optimal rule. LINEX is the loss function used. A variety of prior distributions have already been studied, and Bayesian estimation methods have been evaluated against squared error loss function estimation methods. Finally, compare the results of Maximum Likelihood Estimation (MLE) and LINEX estimation to determine which technique is appropriate for such information by identifying the lowest Mean Square Error (MSE). The displaced estimation method under the LINEX loss function was also examined in this research, and an improved estimation was proposed.

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De-Graft Acquah, Henry. "Comparing ols and rank-based estimation techniques for production analysis: An application to Ghanaian maize farms." Applied Studies in Agribusiness and Commerce 10, n. 4-5 (31 dicembre 2016): 125–30. http://dx.doi.org/10.19041/apstract/2016/4-5/16.

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This paper introduces the rank-based estimation method to modelling the Cobb-Douglas production function as an alternative to the least squares approach. The intent is to demonstrate how a nonparametric regression based on a rank-based estimator can be used to estimate a Cobb-Douglas production function using data on maize production from Ghana. The nonparametric results are compared to common parametric specification using the ordinary least squares regression. Results of the study indicate that the estimated coefficients of the CobbDouglas Model using the Least squares method and the rank-based regression analysis are similar. Findings indicated that in both estimation techniques, land and Equipment had a significant and positive influence on output whilst agrochemicals had a significantly negative effect on output. Additionally, seeds which also had a negative influence on output was found to be significant in the robust rank-based estimation, but insignificant in the ordinary least square estimation. Both the least squares and rank-based regression suggest that the farmers were operating at an increasing returns to scale. In effect this paper demonstrate the usefulness of the rank-based estimation in production analysis. JEL CODE: Q18, D24, Q12, C1 and C67

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Rovigatti, Gabriele, e Vincenzo Mollisi. "Theory and Practice of Total-Factor Productivity Estimation: The Control Function Approach using Stata". Stata Journal: Promoting communications on statistics and Stata 18, n. 3 (settembre 2018): 618–62. http://dx.doi.org/10.1177/1536867x1801800307.

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Alongside instrumental-variables and fixed-effects approaches, the control function approach is the most widely used in production function estimation. Olley and Pakes (1996, Econometrica 64: 1263–1297), Levinsohn and Petrin (2003, Review of Economic Studies 70: 317–341), and Ackerberg, Caves, and Frazer (2015, Econometrica 83: 2411–2451) have all contributed to the field by proposing two-step estimation procedures, whereas Wooldridge (2009, Economics Letters 104: 112–114) showed how to perform a consistent estimation within a single-step generalized method of moments framework. In this article, we propose a new estimator based on Wooldridge's estimation procedure, using dynamic panel instruments à la Blundell and Bond (1998, Journal of Econometrics 87: 115–143), and we evaluate its performance by using Monte Carlo simulations. We also present the new command prodest for production function estimation, and we show its main features and strengths in a comparative analysis with other community-contributed commands. Finally, we provide evidence of the numerical challenges faced when using the Olley–Pakes and Levinsohn–Petrin estimators with the Ackerberg–Caves–Frazer correction in empirical applications, and we document how the generalized method of moments estimates vary depending on the optimizer or starting points used.

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Monk, David H. "The Education Production Function: Its Evolving Role in Policy Analysis". Educational Evaluation and Policy Analysis 11, n. 1 (marzo 1989): 31–45. http://dx.doi.org/10.3102/01623737011001031.

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Production research in education has been dominated by attempts to estimate the structural parameters of what has been called the education production function. These estimation attempts are viewed in this article as only one way the production function concept can be drawn upon to inform debates over education policy. After exploring what it means to posit the existence of the education production function, the article critically reviews past estimation efforts and gives examples of how the production function can be used as a source of insight to guide policy-relevant inquiries into education productivity.

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Bond, Steve, Arshia Hashemi, Greg Kaplan e Piotr Zoch. "Some unpleasant markup arithmetic: Production function elasticities and their estimation from production data". Journal of Monetary Economics 121 (luglio 2021): 1–14. http://dx.doi.org/10.1016/j.jmoneco.2021.05.004.

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Cheng, Maolin, e Yun Han. "Parameter Estimation of a Mixed Production Function Model Based on Improved Firefly Algorithm and Model Application". Journal of Systems Science and Information 6, n. 4 (26 settembre 2018): 336–48. http://dx.doi.org/10.21078/jssi-2018-336-13.

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Abstract In the analysis on economic growth factors, researchers usually use the production function model to calculate and measure influencing factors’ contribution rates to economic growth. Common production functions include the CD (Cobb-Douglas) production function, the CES (Constant Elasticity of Substitution) production function, the VES (Variable Elasticity of Substitution) production function, and so on. In consideration of the diversity and complementarity of models, the paper combines the CD production function with the CES production function and then proposes a mixed production function. With regard to the parameter estimation of model, the paper gives an improved firefly algorithm with the high precision and a fast rate of convergence. With regard to the calculation of factors’ contribution rates, traditional methods generally have big errors and are not applicable to complicated models, so the paper offers a new method which can calculate contribution rates scientifically. Finally, the paper calculates the contribution rates of factors affecting Chinese economic growth and gets a good result.

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Zeytoon-Nejad, Ali, e Barry K. Goodwin. "Econometric Modeling of Input-Driven Output Risk through a Versatile CES Production Function". Journal of Risk and Financial Management 16, n. 2 (6 febbraio 2023): 100. http://dx.doi.org/10.3390/jrfm16020100.

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The conventional functional form of the Constant-Elasticity-of-Substitution (CES) production function is a general production function nesting a number of other forms of production functions. Examples of such functions include Leontief, Cobb–Douglas, and linear production functions. Nevertheless, the conventional form of the CES production specification is still restrictive in multiple aspects. One example is the fact that the marginal effect of increasing input use always has to be to increase the variability of output quantity by the conventional construction of this function. This paper proposes a generalized variant of the CES production function that allows for various input effects on the probability distribution of output. Failure to allow for this possible input–output risk structure is indeed one of the limitations of the conventional form of the CES production function. This limitation may result in false inferences about input-driven output risk. In light of this, the present paper proposes a solution to this problem. First, it is shown that the familiar CES formulation suffers from very restrictive structural assumptions regarding risk considerations, and that such restrictions may lead to biased and inefficient estimates of production quantity and production risk. Following the general theme of Just and Pope’s approach, a CES-based production-function specification that overcomes this shortcoming of the original CES production function is introduced, and a three-stage Nonlinear Least-Squares (NLS) estimation procedure for the estimation of the proposed functional form is presented. To illustrate the proposed approaches in this paper, two empirical applications in irrigation and fertilizer response using the famous Hexem–Heady experimental dataset are provided. Finally, implications for modeling input-driven production risks are discussed.

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Sperlich, Stefan, Dag Tjøstheim e Lijian Yang. "NONPARAMETRIC ESTIMATION AND TESTING OF INTERACTION IN ADDITIVE MODELS". Econometric Theory 18, n. 2 (aprile 2002): 197–251. http://dx.doi.org/10.1017/s0266466602182016.

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We consider an additive model with second-order interaction terms. Both marginal integration estimators and a combined backfitting-integration estimator are proposed for all components of the model and their derivatives. The corresponding asymptotic distributions are derived. Moreover, two test statistics for testing the presence of interactions are proposed. Asymptotics for the test functions and local power results are obtained. Because direct implementation of the test procedure based on the asymptotics would produce inaccurate results unless the number of observations is very large, a bootstrap procedure is provided, which is applicable for small or moderate sample sizes. Further, based on these methods a general test for additivity is developed. Estimation and testing methods are shown to work well in simulation studies. Finally, our methods are illustrated on a five-dimensional production function for a set of Wisconsin farm data. In particular, the separability hypothesis for the production function is discussed.

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Chertkov, A. A., S. V. Saburov e Ya N. Kask. "Algorithm for identifying parameters of production function models using a neural network". Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova 15, n. 6 (7 febbraio 2024): 1096–104. http://dx.doi.org/10.21821/2309-5180-2023-15-6-1096-1104.

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The purpose of the work is to ensure the monitoring and reliability of statistical data used for the analysis and forecasting of the production development on the basis of generalized indicators that take into account the volume of resources used and the ways of their use. An algorithm for parametric identification of production functions models from time series of statistical data using generalized regression, which provides the best estimation of the parametric optimization error by the method of least squares, is proposed. In contrast to existing assessment methods, neural networks are used to build models, which significantly expand the technical capabilities of modeling and contribute to improving the accuracy of calculations through the use of neural network technologies. It is shown that in order to solve the problems of this class, it is advisable to use generalized regression neural networks with simple training modes and high modeling accuracy. As a result, it is possible to propose an algorithm for quantitative assessment of the production functions parameters, which consists in the construction of a neural model with its subsequent use to fit the trajectory of the production model of a given structure to the obtained data by means of recurrent estimates of the vector of the desired coefficients at a given initial approximation. The proposed algorithm is demonstrated by estimating the parameters of the Cobb-Douglas production function and the discrete-dynamic model of the consumption function according to the corresponding statistical series. The calculations are performed using the functions of the Neural Networks package of the MATLAB software environment. The algorithm is applicable for quantitative estimation of the production models parameters with complex logical-probabilistic relationships, as well as for obtaining numerical values of target indicators and indicators for assessing the inland water transport development by statistical series and monitoring.

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Oluwade, Bukola B., e Augustine Adu Frimpong. "Estimating Louisiana Crawfish Hatchery Production Function: An Application of Cobb-Douglas Production Function". International Journal of Social Sciences and Humanities Invention 5, n. 1 (24 gennaio 2018): 4331–36. http://dx.doi.org/10.18535/ijsshi/v5i1.14.

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It is an undeniable fact that the Louisiana Crawfish production over the years has been produced from wild habitats, mainly from the Atchafalaya River basin, which seems to be a factor of seasonality. Following the statistics of the total Crawfish production between 2004 and 2005 seasons, it was underscored that more than 82 million pounds of crawfishes were produced. Out of the 82 million capacity of crawfishes produced between 2004 and 2005, approximately 74 million pounds of Crawfishes were obtain from regular crawfish farms and more than 8 million pounds of crawfishes were harvested from natural habitats by the activities of approximately 1,100 fishermen. However, many of the crawfish farmers rely on natural reproduction system by unharnessed crawfish from the previous year or on mature crawfish that are stocked to produce young naturally. In addition, it is a fact that Crawfish aquaculture in the Louisiana State does not rely on hatchery system of operations. This has been a hindrance on the part of the crawfish farmers to stabilize the populating capacities of their ponds with crawfishes now (or currently) and in the near future. Therefore, this current study tends to model and investigate the Louisiana crawfish production with a control variable of hatching system by following the Cobb-Douglas production function/ theory. The study adopted a cross-sectional data collection procedure to sampled 320 crawfish farmers in the State of Louisiana with the help of Cluster random sampling technique. The study revealed that the engagement/involvement of crawfish farmers' in the hatchery system of production in the State of Louisiana are associated with the influence of labor cost and its availability; the cost of hatchery equipment; the size of the storage facility; initial estimation cost of capital; genetic effect of interbreeding; government regulation; and climatic conditions. The study recommended that the Louisiana State government and the other key stakeholders should do a frequent evaluation of the goals and performance of hatchery programs in a transparent manner to the benefit of the crawfish farmers, and also make available to the farmers with Federal and local government funds and expertise guidelines. Again, it was also recommended that policymakers should place a priority on research that will aid in developing solutions to potential problems and quantify factors affecting the relative reproductive success, and the long-term fitness of populations in order to influence the hatchery systems.

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KINUGASA, Tatsuo. "Estimation of the Production Function of Government Enterprises in Japan". Studies in Regional Science 38, n. 3 (2008): 599–614. http://dx.doi.org/10.2457/srs.38.599.

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ESKANDER, H., M. ALHAMDANI e Q. JASSAM. "ESTIMATION OF TRANSLOG COST FUNCTION OF BARLEY PRODUCTION IN WASIT". Mesopotamia Journal of Agriculture 46, n. 2 (1 giugno 2018): 1–6. http://dx.doi.org/10.33899/magrj.2018.161438.

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Datta, K. K., V. P. Sharma e D. P. Sharma. "Estimation of a production function for wheat under saline conditions". Agricultural Water Management 36, n. 1 (febbraio 1998): 85–94. http://dx.doi.org/10.1016/s0378-3774(97)00015-2.

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Manjón, Miguel, e Juan Mañez. "Production Function Estimation in Stata Using the Ackerberg–Caves–Frazer Method". Stata Journal: Promoting communications on statistics and Stata 16, n. 4 (dicembre 2016): 900–916. http://dx.doi.org/10.1177/1536867x1601600406.

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We present a new e-class command, acfest, that implements the method of Ackerberg, Caves, and Frazer (2015, Econometrica 83: 2411–2451) to estimate production functions. This method deals with the functional dependence problems that may arise in the methods proposed by Olley and Pakes (1996, Econometrica 64: 1263–1297) and, particularly, Levinsohn and Petrin (2003, Review of Economic Studies 70: 317–341) (implemented in Stata by Yasar, Raciborski, and Poi [2008, Stata Journal 8: 221–231] and Petrin, Poi, and Levinsohn [2004, Stata Journal 4: 113–123], respectively). In particular, the acfest command yields (nonlinear, robust) generalized method of moments estimates using a Mata function and two specification tests (Wald and Sargan–Hansen). After estimation, predict provides the estimated productivity of the firms in the sample.

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bonga-bonga, lumengo. "THE SOUTH AFRICAN AGGREGATE PRODUCTION FUNCTION: ESTIMATION OF THE CONSTANT ELASTICITY OF SUBSTITUTION FUNCTION". South African Journal of Economics 77, n. 2 (giugno 2009): 332–49. http://dx.doi.org/10.1111/j.1813-6982.2009.01208.x.

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Issa, Nahidh Naji, e Hassan Thamer Zanzal. "Estimation the Cost function in long-run derived from the Cobb Douglas production function and estimating the resources demand function and the output supply function for Calves fattening projects in Baghdad Governorate". Tikrit journal for agricultural sciences 22, n. 3 (30 settembre 2022): 20–28. http://dx.doi.org/10.25130/tjas.22.3.3.

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Despite the seriousness individual attempts made by some breeders to develop and improve methods of breeding and fattening to reduce costs, but most breeders follow the traditional methods of breeding and fattening their livestock, and this is what prompted me to do this study, A questionnaire for a sample of (100) breeders in Mahmoudia district (case study) for the year 2021, the study aimed to derive the cost function in long-run from the output of Cobb-Douglas production function, as well as deriving the supply function from the cost function in long-run derived from the Cobb-Douglas production function, and derivation the supply function From the production function estimated for Calves fattening projects, in addition to deriving the two demand functions for labor and capital Resources which use in the production process of Calves fattening projects, The cost function in long-run was estimated, and the results of the study showed that the demand for labor and capital in Calves fattening projects was up to (160.7) man /day for labor and (12,776.874) thousand dinars for capital. The displayed quantity of production is about (4040.77) kg, the study recommends the necessity of providing feed, especially concentrated feed, in the necessary quantities and qualities during different seasons by providing hybrid fodder crops such as fodder corn to obtain the desired weight gain.

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Cheng, Maolin, e Bin Liu. "A novel method for calculating the contribution rates of economic growth factors". AIMS Mathematics 8, n. 8 (2023): 18339–53. http://dx.doi.org/10.3934/math.2023932.

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<abstract><p>The common production functions include the Leontief production function, the Cobb-Douglas (C-D) production function, the constant elasticity of substitution (CES) production function, the variable elasticity of substitution (VES) production function and so on. With different elasticity of substitution of factor, the production functions have different ranges of applications. In the production functions, the C-D production function is used the most widely because of its simple form, while the CES production function and the VES production function have limitations in applications due to their complicated forms. However, the C-D production function has the elasticity of substitution of factors of 1, and the CES production function has the elasticity of substitution of factors which is not 1 but a constant, so the two production functions both have limitations in applications. The VES production function with the variable elasticity of substitution is more practical in some application cases. This paper studies the applications of the VES production function model and gives a method of calculating the contribution rates of economic growth factors scientifically. As for the parameter estimation of the model, this paper gives an improved Sine Cosine Algorithm (SCA) to enhance the convergence rate and precision. Finally, the paper makes an empirical analysis on the contribution rates of economic growth factors of Shanghai City, China, using the method proposed.</p></abstract>

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Mila Ramadhani Putri Daulay, Saidin Nainggolan e Elwamendri. "Production function estimation analysis of black grape farming in Buleleng District-Indonesia". GSC Advanced Research and Reviews 14, n. 2 (28 febbraio 2023): 039–46. http://dx.doi.org/10.30574/gscarr.2023.14.2.0035.

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This study aims to estimate the production function of black grape farming. The research was conducted in Buleleng Regency. This area was taken purposively. The sample village is Kalianget Village. The sample size was 33 farmers. Sampling was done by Simple Random Sampling method. The data used were primary data and secondary data. Data analysis method used Cobb-Douglas Stochastic Frontier production function model. The results showed that cultivation technology is relatively traditional, the use of production factors is not in accordance with the recommended dosage. The use of production inputs of urea fertilizer 1622 kg/ha/year, urea fertilizer 714 kg/ha/year, and Organic fertilizer 1544 kg/ha/year. The production achieved was only 18.49 kg/ha/year. The use of production inputs simultaneously had a significant effect on black grape production. Variables of land area, NPK fertilizer, Urea fertilizer, Organic fertilizer, and labor have a significant effect on black grape production. Technical efficiency; ET = 0.83, meaning that the achievement of black grape farming production is 83% of Frontier production, black grape farming can be concluded for the use of production inputs used by farmers has been technically efficient.

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Spitsin, Vladislav, Elena Akerman, Maxim Bushuev, Sergey Anokhin e Dmitry Anokhin. "Arbitrage Opportunity Estimation: The Case of the Cobb-Douglas Production Function". International Journal of Operational Research 1, n. 1 (2021): 1. http://dx.doi.org/10.1504/ijor.2021.10044099.

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Agarwal, A. L. "On Estimation of Cobb-Douglas Production Function in Selected Indian Industries". Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics 28, n. 2 (1 giugno 1986): 152. http://dx.doi.org/10.21648/arthavij/1986/v28/i2/116330.

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Farhan, M., e Raad Obiad. "ESTIMATION OF COST FUNCTION OF TABLE EGGS PRODUCTION IN BAGHDAD PROVINCE". TANMIYAT AL-RAFIDAIN 28, n. 84 (1 dicembre 2006): 135–47. http://dx.doi.org/10.33899/tanra.2006.161632.

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Choi, Eleanor Jawon, Hyungsik Roger Moon e Geert Ridder. "Estimation of an Education Production Function under Random Assignment with Selection". American Economic Review 104, n. 5 (1 maggio 2014): 206–11. http://dx.doi.org/10.1257/aer.104.5.206.

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This paper estimates an education production function using data on the College Scholastic Ability Test score and high school characteristics from Seoul, Korea, where, on entering high school, students are randomly assigned to schools within each school district. We derive a school production function by aggregating the individuals' potential outcomes under the random assignment and no cohort effect assumption. We find that the school production function coefficients differ between districts and that the single-sex school effect estimate is much larger than that found in previous studies.

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Kumbhakar, Subal C. "Efficiency estimation in a profit maximising model using flexible production function". Agricultural Economics 10, n. 2 (aprile 1994): 143–52. http://dx.doi.org/10.1111/j.1574-0862.1994.tb00297.x.

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Fayissa, Bichaka, e Anca Traian. "Estimation of a Health Production Function: Evidence from East-European Countries". American Economist 58, n. 2 (novembre 2013): 134–48. http://dx.doi.org/10.1177/056943451305800206.

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Saha, Atanu, Arthur Havenner e Hovav Talpaz. "Stochastic production function estimation: small sample properties of ML versus FGLS". Applied Economics 29, n. 4 (1 aprile 1997): 459–69. http://dx.doi.org/10.1080/000368497326958.

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Petrin, Amil, Brian P. Poi e James Levinsohn. "Production Function Estimation in Stata using Inputs to Control for Unobservables". Stata Journal: Promoting communications on statistics and Stata 4, n. 2 (giugno 2004): 113–23. http://dx.doi.org/10.1177/1536867x0400400202.

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Yasar, Mahmut, Rafal Raciborski e Brian Poi. "Production Function Estimation in Stata Using the Olley and Pakes Method". Stata Journal: Promoting communications on statistics and Stata 8, n. 2 (giugno 2008): 221–31. http://dx.doi.org/10.1177/1536867x0800800204.

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Kumbhakar, S. "Efficiency estimation in a profit maximising model using flexible production function". Agricultural Economics 10, n. 2 (aprile 1994): 143–52. http://dx.doi.org/10.1016/0169-5150(94)90003-5.

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Kumbhakar, Subal C., e Efthymios G. Tsionas. "Estimation of production risk and risk preference function: a nonparametric approach". Annals of Operations Research 176, n. 1 (15 novembre 2008): 369–78. http://dx.doi.org/10.1007/s10479-008-0472-5.

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Gaire, Hom Nath. "Estimation of Production Function for Furniture and Pharmaceutical Industries in Nepal". NRB Economic Review 30, n. 2 (1 novembre 2018): 19–34. http://dx.doi.org/10.3126/nrber.v30i2.52279.

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With the application of Robust Regression Method, this paper attempts to estimate the production function for manufacturing industries of Nepal. In this endeavour, the production function for Furniture and Pharmaceutical industries have been estimated using cross-section data of the Census of Manufacturing Establishment (CME) 2011/2012. The coefficients of log-linear form of Cobb-Douglas (C-D) production function reveal that the selected manufacturing industries are operating with decreasing returns to scales. The labour coefficients of both industries are found to be statistically insignificant. Negative labour coefficient of Pharmaceutical Industry indicates capital intensive nature of the production and minimal contribution of labour inputs. Although positive and significant, capital coefficients indicate both industries were running with decreasing returns to capital inputs. Total Factor Productivity (TFP) representing the state of technology and factors other than labour and capital found to be instrumental and significant for both the industries.

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Kibona, Cornel Anyisile, Zhang Yuejie e Lu Tian. "Factors that influence beef meat production in Tanzania. A Cobb-Douglas production function estimation approach". PLOS ONE 17, n. 8 (12 agosto 2022): e0272812. http://dx.doi.org/10.1371/journal.pone.0272812.

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Beef meat production is the key to reducing poverty, achieving food security and nutrition, promoting exports, economic growth, and industrialization. Despite a large number of beef cattle, Tanzania continues to import beef meat and its contribution to GDP is low. Thus, this study used time-series panel data to analyze the beef meat industry in Tanzania from 1990 to 2019, with a particular focus on identifying the reasons and direction of the correlation between beef meat output and its determinants in the production processes. The study applied both descriptive statistics and the Cobb-Douglas production function model, using the Ordinary Least Squares (OLS) based estimator to analyze the data. Descriptive analyses revealed that Tanzania’s beef meat production increased by 283,871 tons (59.3%-a positive trend) between 1990 and 2019. This increase was accompanied by approximately 29.75%, 53.05%, and 42.42% increases in beef cattle yield (carcass weight (hg) per head, beef cattle inventory, and the number of beef cattle slaughtered, respectively). However, the analysis further revealed that a 2.8% decrease in beef cattle extraction (low harvesting) rate due to low market participation, continues to be a critical barrier to increasing beef meat production in Tanzania. Furthermore, econometric estimates showed that the key factors that positively influenced beef meat output at a 5% significance level (P < 0.05) included beef cattle population (inventory), beef cattle yield (carcass weight (kg) per head, and the number of beef cattle slaughtered, with elasticity coefficients of 0.146, 0.469, and 0.564, respectively). While the number of beef cattle exported positively influenced beef meat production at the 10% significance level (P < 0.1) with an elasticity coefficient of 0.028. Surprisingly, invested credit to agriculture (farm credits) and imported pure-bred beef cattle had a negative impact on beef meat output but were statistically insignificant at P < 0.05. The results of this study have implications as to what factors need to be addressed to further improve beef meat production, thereby reducing its reliance on imports. We suggest that the Tanzania government and policymakers need to establish balanced policies for beef farmers and appropriately manage them so that beef meat development can be induced, contributing to poverty reduction, food security, and economic development.

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Jahandideh, Alireza, e Amirhossian Chambaria. "Estimation of Production Function and Labor Productivity Rate in Aghajari Oil and Gas Production Company". Singaporean Journal of Business Economics and Management Studies 5, n. 12 (giugno 2017): 8–21. http://dx.doi.org/10.12816/0039976.

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Ramadhani, Puput, Dwi Ispriyanti e Diah Safitri. "KAPABILITAS PROSES DENGAN ESTIMASI FUNGSI DENSITAS KERNEL PADA PRODUKSI DENIM DI PT APAC INTI CORPORA". Jurnal Gaussian 7, n. 3 (29 agosto 2018): 326–36. http://dx.doi.org/10.14710/j.gauss.v7i3.26665.

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The quality of production becomes one of the basic factors of consumer decisions in choosing a product. Quality control is needed to control the production process. Control chart is a tool used in performing statistical quality control. One of the alternatives used when the data obtained is not known distribution is analyzed by nonparametric approach based on estimation of kernel density function. The most important thing in estimating kernel density function is optimal bandwidth selection (h) which minimizes Cross Validation (CV) value. Some of the kernel functions used in this research are Rectangular, Epanechnikov, Triangular, Biweight, and Gaussian. If the process control chart is statistically controlled, a process capability analysis can be calculated using the process conformity index to determine the nature of the process capability. In this research, the kernel control chart and process conformity index were used to analyze the slope shift of Akira-F style fabric and Corvus-SI style on the production of denim fabric at PT Apac Inti Corpora. The results of the analysis show that the production process for Akira-F style is statistically controlled, but Ypk > Yp is 0.889823 > 0,508059 indicating that the process is still not in accordance with the specified limits set by the company, while for Corvus- SI is statistically controlled and Ypk < Yp is 0.637742 < 0.638776 which indicates that the process is in accordance with the specification limits specified by the company. Keywords: kernel density function estimation, Cross Validation, kernel control chart, denim fabric, process capability

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