Academic literature on the topic 'Production function estimation'
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Journal articles on the topic "Production function estimation":
Mundlak, Yair. "Production Function Estimation: Reviving the Primal." Econometrica 64, no. 2 (March 1996): 431. http://dx.doi.org/10.2307/2171790.
Bernini, Cristina, Marzia Freo, and Attilio Gardini. "Quantile estimation of frontier production function." Empirical Economics 29, no. 2 (May 1, 2004): 373–81. http://dx.doi.org/10.1007/s00181-003-0173-5.
Sakellaris, Plutarchos. "Production Function Estimation with Industry Capacity Data." Finance and Economics Discussion Series 2001, no. 06 (2001): 1–16. http://dx.doi.org/10.17016/feds.2001.06.
Adonijah Maxwell, Awoingo, and Isaac Didi Essi. "Econometric Estimation of Production Function with Applications." Academic Journal of Applied Mathematical Sciences, no. 56 (June 15, 2019): 57–61. http://dx.doi.org/10.32861/ajams.56.57.61.
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, no. 2 (2021): 77–105. http://dx.doi.org/10.18559/rielf.2021.2.4.
Bhujel, RB, and SP Ghimire. "Estimation of Production Function of Hiunde (Boro) Rice." Nepal Agriculture Research Journal 7 (May 22, 2009): 88–97. http://dx.doi.org/10.3126/narj.v7i0.1874.
Coelli, T. J. "A computer program for frontier production function estimation." Economics Letters 39, no. 1 (May 1992): 29–32. http://dx.doi.org/10.1016/0165-1765(92)90096-h.
Grieco, Paul L. E., Shengyu Li, and Hongsong Zhang. "PRODUCTION FUNCTION ESTIMATION WITH UNOBSERVED INPUT PRICE DISPERSION." International Economic Review 57, no. 2 (April 28, 2016): 665–90. http://dx.doi.org/10.1111/iere.12172.
Rovigatti, Gabriele. "Production Function Estimation in R: The prodest Package." Journal of Open Source Software 2, no. 18 (October 9, 2017): 371. http://dx.doi.org/10.21105/joss.00371.
Henningsen, Arne, and Géraldine Henningsen. "On estimation of the CES production function—Revisited." Economics Letters 115, no. 1 (April 2012): 67–69. http://dx.doi.org/10.1016/j.econlet.2011.12.007.
Dissertations / Theses on the topic "Production function estimation":
Demirer, Mert. "Essays on production function estimation." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127028.
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 193-201).
This first chapter develops a new method for estimating production functions with factor-augmenting technology and assesses its economic implications. The method does not impose parametric restrictions and generalizes prior approaches that rely on the CES production function. I first extend the canonical Olley-Pakes framework to accommodate factor-augmenting technology. Then, I show how to identify output elasticities based on a novel control variable approach and the optimality of input expenditures. I use this method to estimate output elasticities and markups in manufacturing industries in the US and four developing countries. Neglecting labor-augmenting productivity and imposing parametric restrictions mismeasures output elasticities and heterogeneity in the production function. My estimates suggest that standard models (i) underestimate capital elasticity by up to 70 percent (ii) overestimate labor elasticity by up to 80 percent.
These biases propagate into markup estimates inferred from output elasticities: markups are overestimated by 20 percentage points. Finally, heterogeneity in output elasticities also affects estimated trends in markups: my estimates point to a much more muted markup growth (about half) in the US manufacturing sector than recent estimates. The second chapter develops partial identification results that are robust to deviations from the commonly used control function approach assumptions and measurement errors in inputs. In particular, the model (i) allows for multi-dimensional unobserved heterogeneity,(ii) relaxes strict monotonicity to weak monotonicity, (iii) accommodates a more flexible timing assumption for capital. I show that under these assumptions production function parameters are partially identified by an 'imperfect proxy' variable via moment inequalities. Using these moment inequalities, I derive bounds on the parameters and propose an estimator.
An empirical application is presented to quantify the informativeness of the identified set. The third chapter develops an approach in which endogenous networks is a source of identification in estimations with network data. In particular, I study a linear model where network data can be used to control for unobserved heterogeneity and partially identify the parameters of the linear model. My method does not rely on a parametric model of network formation. Instead, identification is achieved by assuming that the network satisfies latent homophily - the tendency of individuals to be linked with others who are similar to themselves. I first provide two definitions of homophily: weak and strong homophily. Then, based on these definitions, I characterize the identified sets and show that they are bounded under weak conditions.
Finally, to illustrate the method in an empirical setting, I estimate the effects of education on risk preferences and peer effects using social network data from 150 Chinese villages.
by Mert Demirer.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Economics
Gstach, Dieter. "Scale Efficiency: Where Data Envelopment Analysis Outperforms Stochastic Production Function Estimation." Inst. für Volkswirtschaftstheorie und -politik, WU Vienna University of Economics and Business, 1993. http://epub.wu.ac.at/6298/1/WP_23.pdf.
Series: Department of Economics Working Paper Series
Glórias, Ludgero Miguel Carraça. "Estimating a knowledge production function and knowledge spillovers : a new two-step estimation procedure of a Spatial Autoregressive Poisson Model." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20711.
Vários estudos econométricos procuram explicar os determinantes da criação de conhecimento usando como variável dependente o número de patenteamentos numa região. Alguns destes procuram captar os efeitos de Knowledge Spillovers através de modelos lineares que incorporam dependência espacial. No entanto, nenhum estudo foi encontrado que captasse este efeito, tendo em atenção a natureza discreta da variável dependente. Este trabalho pretende preencher essa lacuna propondo um novo estimador de máxima verosimilhança a dois passos para um modelo Poisson Autorregressivo Espacial. As propriedades do estimador são avaliadas num conjunto de simulações de Monte Carlo. Os resultados sugerem que este estimador tem menor Bias e menor RMSE, na generalidade, que outros estimadores propostos, sendo que apenas mostra piores resultados quando a dependência espacial é próxima da unidade. Um exemplo empírico, empregando o novo estimador e um conjunto de estimadores alternativos, é realizado, sendo que a criação de conhecimento em 234 NUTS II de 24 países europeus é analisada. Os resultados evidenciam que existe uma forte dependência espacial na criação de inovação entre as regiões. Conclui-se também que o ambiente socioeconómico é essencial para o processo de formação de conhecimento e que contrariamente às instituições públicas, as empresas privadas são eficientes na produção de inovação. É de realçar, que regiões com menor capacidade em transformar despesas R&D em patenteamentos apresentam maior capacidade de absorção e segregação de conhecimento, evidenciando que regiões vizinhas menos eficientes na produção de conhecimento tendem a criar relações fortalecidas na partilha de conhecimento.
Several econometric studies seek to explain the determinants of knowledge production using as dependent variable the number of patents in a region. Some of these capture the effects of knowledge spillovers through linear models with spatial autorregressive term. However, no study has been found that estimates such effect while also considering the discrete nature of the dependent variable: a count variable. This essay aims to fill this gap by proposing a new Two-step Maximum Likelihood estimator for a Spatial Autorregressive Poisson model. The properties of this estimator are evaluated in a set of Monte Carlo Experiments. The simulation results suggest that this estimator presents lower Bias and lower RMSE than the alternative estimators proposed, only showing worse results when the spatial dependence is close to the unit. An empirical example, using the new estimator and a set of alternative estimators, is executed, where the creation of knowledge in 234 NUTS II from 24 European countries is analyzed. The results show that there is a strong spatial dependence on the creation of innovation. It is also concluded that the socio-economic environment is essential for the knowledge formation and, unlike public R&D institutions, private companies are efficient in producing innovation. It should be noted that regions with less capacity to transform R&D expenses into new patents, have greater capacity for absorption and segregation of knowledge, which shows that neighboring regions less efficient in the production of knowledge tend to create strong relations with each other taking advantage of the knowledge sharing process.
info:eu-repo/semantics/publishedVersion
Bauer, Arthur. "Essays on Firms Production Function, Markups, and the Share of their Income Going to Workers." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAG006.
Firms production function link their use of input factors to their production level. Production function estimates are at the same time important and untrustworthy. Important, because key indicators for policy design, such as the measure of aggregate markups, are derived from those estimates. Untrustworthy because they rely on identification assumptions.This project studies the assumptions underlying the usual techniques for estimating production functions: both their functional forms and the often assumed inputs flexibility. It then leverages production function estimates, to assess how firms ability to price over marginal income and the share of their income going to workers have evolved over the last 30 years.In Chapter 1 we provide evidence on the input flexibility assumption grounding production function estimation: the quasi instantaneous adjustment of either material or labor and delayed adjustment of capital hold. We rely on the existence of notches; values where after-tax profits decrease in before-tax sales in the French tax code.We identify which type of firms adjust their size in response to a transient notch. We do this by studying the ex-ante characteristics of firms below the tax cutoff. We find that firms who bunch tend to have larger elasticity of output with respect to materials and lower elasticity of output with respect to capital. Consistently, we also show that to adjust their remaining production firms, tend to primarily reduce spending on material.In Chapter 2 we leverage evidence on inputs flexibility to recover firm level markups of the universe of firms in France over the 1984-2016 period. De Loecker and Warzynski (2012) show that a firm’s markup proportionates the inverse of one of its flexible inputs revenue share. We analyze the evolution of aggregate markups in France and document that the rise of concentration correlates with a reallocation of market share towards high markup firms. We also show that the evolution of the labor share mirrors the evolution of markups: reallocation tends to decrease labor share while within firms, labor share rises.The choice of a functional form to describe firms production process is a compromise between theory and empirics. The workhorse production function is Cobb-Douglas and imposes a constant (and equal to 1) elasticity of substitution. Recent evidence in the empirical literature has however estimated a micro-elasticity of substitution significantly lower than one. While CES production functions allow for non-unit elasticity of substitution, they assume constant elasticity within industry and imply that the ratio of input use doesn’t depend on firm size.In Chapter 3, we show that the latter production function cannot account for IT inputs use in firms.With detailed data on software and hardware investments among French firms, we document that the firm-level demand for IT inputs relative to other inputs grows in the firm’s scale of operation. Theoretically, a non-homothetic CES production function helps rationalizing this empirical fact.We then analyze how the interaction of the fall of IT prices and the non-homothetic characteristics of IT inputs also help rationalize the empirical facts documented in chapter 2. First, since larger firms are more IT intensive in the cross-section, they benefit disproportionally from the fall in IT prices, rationalizing the rise of concentration. Similarly, since larger firms are more IT intensive in the cross-section, they operate at lower returns to scale and therefore have higher profit shares and lower labor shares. This explains how the rise in concentration drives a decline in aggregate labor shares. Finally, the comparative statistics of the model predicts that the fall of IT prices imply that when firms substitute toward IT they operate at higher returns to scale and therefore tend to have larger labor share, explaining the positive contribution to aggregate labor share of the within component
Aizcorbe, Ana M. "The competitiveness of U.S. automobile firms : a neoclassical cost function estimation of the production costs of U.S. and Japanese firms." Thesis, Boston College, 1986. http://hdl.handle.net/2345/1749.
Thenon, Arthur. "Utilisation de méta-modèles multi-fidélité pour l'optimisation de la production des réservoirs." Electronic Thesis or Diss., Paris 6, 2017. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2017PA066100.pdf.
Performing flow simulations on numerical models representative of oil deposits is usually a time consuming task in reservoir engineering. The substitution of a meta-model, a mathematical approximation, for the flow simulator is thus a common practice to reduce the number of calls to the flow simulator. It permits to consider applications such as sensitivity analysis, history-matching, production estimation and optimization. This thesis is about the study of meta-models able to integrate simulations performed at different levels of accuracy, for instance on reservoir models with various grid resolutions. The goal is to speed up the building of a predictive meta-model by balancing few expensive but accurate simulations, with numerous cheap but approximated ones. Multi-fidelity meta-models, based on co-kriging, are thus compared to kriging meta-models for approximating different flow simulation outputs. To deal with vectorial outputs without building a meta-model for each component of the vector, the outputs can be split on a reduced basis using principal component analysis. Only a few meta-models are then needed to approximate the main coefficients in the new basis. An extension of this approach to the multi-fidelity context is proposed. In addition, it can provide an efficient meta-modelling of the objective function when used to approximate each production response involved in the objective function definition. The proposed methods are tested on two synthetic cases derived from the PUNQ-S3 and Brugge benchmark cases. Finally, sequential design algorithms are introduced to speed-up the meta-modeling process and exploit the multi-fidelity approach
Thenon, Arthur. "Utilisation de méta-modèles multi-fidélité pour l'optimisation de la production des réservoirs." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066100/document.
Performing flow simulations on numerical models representative of oil deposits is usually a time consuming task in reservoir engineering. The substitution of a meta-model, a mathematical approximation, for the flow simulator is thus a common practice to reduce the number of calls to the flow simulator. It permits to consider applications such as sensitivity analysis, history-matching, production estimation and optimization. This thesis is about the study of meta-models able to integrate simulations performed at different levels of accuracy, for instance on reservoir models with various grid resolutions. The goal is to speed up the building of a predictive meta-model by balancing few expensive but accurate simulations, with numerous cheap but approximated ones. Multi-fidelity meta-models, based on co-kriging, are thus compared to kriging meta-models for approximating different flow simulation outputs. To deal with vectorial outputs without building a meta-model for each component of the vector, the outputs can be split on a reduced basis using principal component analysis. Only a few meta-models are then needed to approximate the main coefficients in the new basis. An extension of this approach to the multi-fidelity context is proposed. In addition, it can provide an efficient meta-modelling of the objective function when used to approximate each production response involved in the objective function definition. The proposed methods are tested on two synthetic cases derived from the PUNQ-S3 and Brugge benchmark cases. Finally, sequential design algorithms are introduced to speed-up the meta-modeling process and exploit the multi-fidelity approach
JULIAN, JACK DEANE. "ESTIMATING EDUCATIONAL PRODUCTION FUNCTIONS IN A MULTIPLE-OUTPUT FRAMEWORK: ISSUES AND TOPICS." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1022681028.
Heshmati, Almas. "Estimating technical efficiency, productivity growth and selectivity bias using rotating panel data : an application to Swedish agriculture /." Göteborg : Nationalekonomiska institutionen, Handelshögsk, 1994. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=006407760&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Oliveira, Henrique Faria de. "Avaliação de modelos de estimativa de produtividade da cana-de- açúcar irrigada em Jaíba-MG." Universidade Federal de Viçosa, 2010. http://locus.ufv.br/handle/123456789/5240.
The sugarcane crop is subjected during their growth to different environmental conditions, and agricultural yield directly affected by these conditions. Yield models become important tools in order to supply estimates of crop yield along to characterize management alternatives, increasing the efficiency of management and strategic decisions. Information technology is an important tool in this process and has been increasingly used for collecting and analyzing data that are used as the basis of their decisions. The objective of this work was included in the software Irriplus estimated yield of crops models, using the Stewart and Mantovani models to estimate the real productivity and Method of Agroecological Zone (MAZ) to estimate the maximum productivity. Besides the models, a methodology of multiple linear regressions was developed to explain the factors that are influencing the yield crop and generate yield models from historical data. To evaluate the models, was used descriptive analysis and analysis tests comparing the real and estimated yield. Statistical tests were paired t-test, relative error percentage (REP) and mean absolute error (MAE). Was used real yield data of irrigated sugarcane RB 86-7515, crops xiv 2007/2008 and 2008/2009, in the Jaíba city of Minas Gerais state. The Stewart Model requires as input the maximum yield that was estimated by MAZ in two crops. In the 2007/2008 crop, the model estimated the average productivity at 113.58 t ha-1, while the real yield average was 113.47 t ha-1, the MAE was equal 10.10. In the 2008/2009 crop, the model estimated the average productivity at 121.81 t ha-1, while the real yield average was 121.81 t ha-1, the MAE was equal 8.02. In both crops the paired t-test showed no significant difference between the average yields. The Mantovani model used the same maximum yield of the model of Stewart, estimated by MAZ. In the 2007/2008 crop, the model estimated the average productivity at 198.13 t ha-1, while the real yield average was 113.47 t ha-1, the MAE was equal 84.66. In the 2008/2009 crop, the model estimated the average productivity at 154.81 t ha-1, while the real yield average was 121.81 t ha-1, the MAE was equal 32.72. In both crops the paired t-test showed significant difference between the average yields and the yield estimated overestimated the real yield. An equation was fitted by multiple linear regression using data from the 2007/2008 crop, related variables: total irrigation required, total capacity of the soil water, available soil water, reference evapotranspiration, crop evapotranspiration and maximum crop evapotranspiration. The equation was evaluated in the 2008/2009 crop for yield estimated. The equation estimated the average productivity at 122.41 t ha-1, while the real yield average was 121.81 t ha-1, the MAE was equal 7.07. The paired t-test showed no significant difference between the average yields.
A cultura da cana-de-açúcar é submetida durante o seu desenvolvimento a diferentes condições ambientais, sendo o rendimento agrícola afetado diretamente por estas condições. Modelos de produtividade tornam-se ferramentas importantes objetivando suprir estimativas de rendimento ao longo das safras visando à caracterização de alternativas de manejo, aumentando a eficácia das decisões gerenciais e estratégicas. A tecnologia da informação é uma importante ferramenta nesse processo e tem sido cada vez mais utilizada para coleta e análise de dados que são utilizados como base nas suas decisões. O objetivo deste trabalho foi incluir no software Irriplus modelos de estimativa de produtividades de culturas agrícolas, utilizando os modelos de Stewart e Mantovani para estimar a produtividade real e o Método da Zona Agroecológica (MZA) para estimar a produtividade máxima. Além dos modelos, foi desenvolvida uma metodologia de regressão linear múltipla para explicar os fatores que estão influenciando a produtividade da cultura e gerar modelos de produtividade a partir de dados históricos. Para avaliar os modelos, foi utilizada análise descritiva e testes de análise comparativa entre a produtividade estimada e observada em campo. Os testes estatísticos utilizados foram: teste-t pareado, erro relativo percentual (ERP) e erro médio absoluto (MAE). Foram utilizados dados reais de produtividade da cana-de-açúcar RB 86-7515 irrigada, safras 2007/2008 e 2008/2009, do município de Jaíba do estado de Minas Gerais. O modelo de Stewart requer como dado de entrada a produtividade máxima, que foi estimada pelo MZA nas duas safras. Na safra 2007/2008, o modelo estimou a produtividade média em 113,58 t ha-1, enquanto a produtividade média observada em campo foi 113,47 t ha-1, o MAE foi igual a 10,10. Na safra 2008/2009 o modelo estimou a produtividade média em 121,81 t ha-1, enquanto a produtividade média observada em campo foi 121,81 t ha-1, o MAE foi igual a 8,02. Nas duas safras o teste-t pareado não demonstrou diferença significativa entre as médias de produtividade. O modelo de Mantovani utilizou a mesma produtividade máxima do modelo de Stewart estimada pelo MZA. Na safra 2007/2008, o modelo estimou a produtividade média em 198,13 t ha-1, enquanto a produtividade média observada em campo foi 113,47 t ha-1, o MAE foi igual a 84,66. Na safra 2008/2009, o modelo estimou a produtividade média em 154,81 t ha-1, enquanto a produtividade média observada em campo foi 121,81 t ha-1, o MAE foi igual a 32,72. Nas duas safras, o teste-t pareado demonstrou diferença significativa entre as médias de produtividade e a estimativa do modelo superestimou produtividade observada em campo. Foi ajustada uma equação por regressão linear múltipla, com dados da safra 2007/2008, relacionada com as variáveis: irrigação total necessária, capacidade total de água no solo, água disponível no solo, evapotranspiração de referência, evapotranspiração da cultura e evapotranspiração máxima da cultura. A equação foi avaliada na safra 2008/2009 para estimativa da produtividade. A equação estimou a produtividade média em 122,41 t ha-1, enquanto a produtividade média observada em campo foi 121,81 t ha-1, o MAE foi igual a 7,07. O teste-t pareado não demonstrou diferença significativa entre as médias de produtividade.
Books on the topic "Production function estimation":
Ray, Amit Shovon. Drivers of academic research and patenting in India: Econometric estimation of the research production function. New Delhi: Indian Council for Research on International Economic Relations, 2010.
Hellerstein, Judith K. Production function and wage equation estimation with heterogeneous labor: Evidence from a new matched employer-employee data set. Cambridge, MA: National Bureau of Economic Research, 2004.
Kim, Hŭi-sam. Hagŏp sŏngch'wido punsŏk ŭl t'onghan ch'ojungdŭng kyoyuk ŭi kaesŏn panghyang yŏn'gu: Estimation of education production function focusing on school effects and policy directions for primary and secondary education. 8th ed. Sŏul T'ŭkpyŏlsi: Han'guk Kaebal Yŏn'guwŏn, 2012.
Harrigan, James. Estimation of cross-country differences in industry production functions. [New York, N.Y.]: Federal Reserve Bank of New York, 1998.
Harrigan, James. Estimation of cross-country differences in industry production functions. [New York]: Federal Reserve Bank of New York, 1997.
Harrigan, James. Estimation of cross-country differences in industry production functions. Cambridge, MA: National Bureau of Economic Research, 1997.
Field, K. F. Estimation of direct and indirect production functions: An example from Indian paddy production. Glasgow: Dept. of Economics, Univ. of Strathclyde, 1986.
Field, K. F. An estimation of direct and indirect production functions: An example from Indian paddy production. Glasgow: Department of Economics, University of Strathclyde, 1986.
Levinsohn, James Alan. Estimating production functions using inputs to control for unobservables. Cambridge, MA: National Bureau of Economic Research, 2000.
Sengupta, Jatikumar. Efficiency analysis by production frontiers: The nonparametric approach. Dordrecht: Kluwer Academic, 1989.
Book chapters on the topic "Production function estimation":
Khayyat, Nabaz T. "Production Function Models Estimation." In Energy Demand in Industry, 109–28. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9953-9_7.
Heshmati, Almas, and Jungsuk Kim. "Stochastic Frontier Production Function Model Specification and Estimation Results." In Efficiency and Competitiveness of International Airlines, 75–121. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1017-0_4.
Sepúlveda, Juan M., and Rodrigo Barraza. "Estimation of an Overall Value Function in a Sustainable Network of Suppliers: Case Study in a Small Mining Sector." In Proceedings of the 11th International Conference on Production Research – Americas, 607–13. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36121-0_77.
Tamborini, Marco. "Data, Aesthetics, and Visualizations of Deep Time." In Historiographies of Science, 1–20. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-030-92679-3_18-1.
Puggioni, Daniela, and Spiro E. Stefanou. "Estimating production functions." In The Routledge Handbook of Agricultural Economics, 581–99. New York, NY : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315623351-31.
Tinbergen, Jan. "Some Extensions and Refinements of Gottschalk’s Estimation of Production Functions." In Regional Econometric Modeling, 3–12. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-3267-8_1.
Kang, M. Z., and P. De Reffye. "A Mathematical Approach Estimating Source and Sink Functioning of Competing Organs." In Functional-Structural Plant Modelling in Crop Production, 65–74. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/1-4020-6034-3_6.
Baghli, Mustapha, Christophe Cahn, and Jean-Pierre Villetelle. "Estimating Potential Output with a Production Function for France, Germany and Italy." In Convergence or Divergence in Europe?, 161–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-32611-1_9.
Thiry, Bernard, and Henry Tulkens. "Allowing for Inefficiency in Parametric Estimation of Production Functions for Urban Transit Firms." In International Applications of Productivity and Efficiency Analysis, 41–61. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-017-1923-0_4.
Atack, Jeremy. "The Aggregate Regional Production Function." In Estimation of Economies of Scale in Nineteenth Century United States Manufacturing, 121–90. Routledge, 2018. http://dx.doi.org/10.4324/9781315124094-7.
Conference papers on the topic "Production function estimation":
Yang, Xu, Xiaohou Shao, Tingchao Zhao, Gang Wang, Qian Wang, and Jianfang Guang. "Estimation of Water Production Function of Tobacco K326 by Jensen Model." In 2015 4th International Conference on Sensors, Measurement and Intelligent Materials. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/icsmim-15.2016.41.
Qi, Wang, Su Yingsheng, and Ji Pengfei. "On the Random Disturbance Term and Parameter Estimation of C-D Production Function." In 2010 International Symposium on Information Science and Engineering (ISISE). IEEE, 2010. http://dx.doi.org/10.1109/isise.2010.110.
Li, Huan, Xuewu Liu, Jinhang Wang, Lihua Chen, Yin Xu, and Meng Wu. "Reference Velocity Estimation with Variable Gain Based on Powertrain Dynamics for Production Hybrid Electric Vehicle." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2147.
Al-Salali, Yousef Zaid, Mansour Al-Awadhi, Abrar Hajjeyah, Muna Al-Shuaib, Alaa Al-Saleh, Marcos Useche, Carlos Vargas, Aditya Saxena, and Osaretin Greg Idele. "Automatic Production Rate Estimation Workflow Considering the Reservoir Flow Regime - Kuwait Integrated Digital Oilfield." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22258-ea.
Li, Dan, Wei Yan, Wenyuan Li, and Tao Chen. "Estimation of the probability density function of renewable power production using a hybrid method of minimum frequency and maximum entropy." In 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2016. http://dx.doi.org/10.1109/pmaps.2016.7764071.
Esawi, Amal M. K., and Michael F. Ashby. "Cost Estimation for Process Selection." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dfm-8919.
Lopez-Puiggene, Eva, Nubia Aurora Gonzalez-Molano, Jose Alvarellos-Iglesias, Jose M. Segura, and M. R. Lakshmikantha. "Numerical Modeling of Sand Production Potential Estimation and Passive Control Optimization: A Case Study." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77851.
Molina, Oscar, Laura Santos, Francisco Herrero, Agustin Monaco, and Darren Schultz. "Is Decline Curve Analysis the Right Tool for Production Forecasting in Unconventional Reservoirs?" In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206152-ms.
Singh, P. J. "Gas Deliverability Monitoring and Reserves Quantification without Shut-In the Well: Application of Coupled Material Balance - Nodal Analysis Approach in Main Zone of Tunu Field, Mahakam." In Indonesian Petroleum Association 44th Annual Convention and Exhibition. Indonesian Petroleum Association, 2021. http://dx.doi.org/10.29118/ipa21-e-127.
Grelli, Erica, Fabrizio Ursini, Emanuele Vignati, and Andrea Piccolo. "Advanced Thermo-Fluid Dynamic Well Model for RTVFM Flow Rates Estimation." In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212685-ms.
Reports on the topic "Production function estimation":
Collard-Wexler, Allan, and Jan De Loecker. Production Function Estimation and Capital Measurement Error. Cambridge, MA: National Bureau of Economic Research, July 2016. http://dx.doi.org/10.3386/w22437.
Bond, Steve, Arshia Hashemi, Greg Kaplan, and Piotr Zoch. Some Unpleasant Markup Arithmetic: Production Function Elasticities and their Estimation from Production Data. Cambridge, MA: National Bureau of Economic Research, April 2020. http://dx.doi.org/10.3386/w27002.
Baca Campodónico, Jorge F., and Gerardo Reyes-Tagle. Econometric Estimation of the Capital Stock and the Production Function: The Case of the Bahamas, Barbados, Jamaica, and Suriname. Inter-American Development Bank, February 2023. http://dx.doi.org/10.18235/0004749.
Dhyne, Emmanuel, Amil Petrin, Valerie Smeets, and Frederic Warzynski. Theory for Extending Single-Product Production Function Estimation to Multi-Product Settings. Cambridge, MA: National Bureau of Economic Research, December 2022. http://dx.doi.org/10.3386/w30784.
Hellerstein, Judith, and David Neumark. Production Function and Wage Equation Estimation with Heterogeneous Labor: Evidence from a New Matched Employer-Employee Data Set. Cambridge, MA: National Bureau of Economic Research, February 2004. http://dx.doi.org/10.3386/w10325.
Rossi, José Luiz, José Eduardo Gonçalves de Sousa, and Jose Alejandro Gutiérrez Briceño. Minding the Output Gap: A Hamilton Filter Approach and Updated Estimates for the Brazilian Economy. Inter-American Development Bank, June 2023. http://dx.doi.org/10.18235/0004981.
Levinsohn, James, and Amil Petrin. Estimating Production Functions Using Inputs to Control for Unobservables. Cambridge, MA: National Bureau of Economic Research, August 2000. http://dx.doi.org/10.3386/w7819.
Jaumandreu, Jordi, and Jacques Mairesse. Innovation and Welfare: Results from Joint Estimation of Production and Demand Functions. Cambridge, MA: National Bureau of Economic Research, July 2010. http://dx.doi.org/10.3386/w16221.
Huang, Guofang, and Yingyao Hu. Estimating production functions with robustness against errors in the proxy variables. Institute for Fiscal Studies, November 2011. http://dx.doi.org/10.1920/wp.cem.2011.3511.
Kaestner, Robert, and Luis Faundez. Estimating a Theoretically Consistent Human Capital Production Function With an Application to Head Start. Cambridge, MA: National Bureau of Economic Research, April 2023. http://dx.doi.org/10.3386/w31199.