Modeling and Estimation of Indirect Manufacturing Agregate Supply and Profit Functions

Yıl 2024, , 152 - 171, 13.08.2024

Mustafa Akal

https://doi.org/10.35343/kosbed.1480210

Öz

In this study, it has been shown that the total supply and profit curves of the manufacturing industry can be obtained as a function of commodity and factor prices and technology level. The manufacting agregate indirect supply and profit curves reached were estimated and evaluations were made. Both correlation analysis results and model estimation results have shown that the total supply and profit of the manufacturing industry, the level of technology, and commodity and factor prices are closely related. The coefficients of determination and F statistics of the estimated models were found to be significant. Therefore, it has been concluded that alternative manufacturing industry production and profit can be indirectly explained by the mentioned variables instead of physical production factors. According to Model1, when the average price of goods increases by 1%, the manufacturing industry supply increases by 0.129%, and according to Model2, when the average price of goods increases by 1%, the manufacturing industry profit increases by 0.096%. When the share of R&D in GDP increases by 1%, the manufacturing industry supply increases by 0.499% and its profit increases by 0.222%. It has been found that a 1% increase in all variables increases the manufacturing industry supply by 1.86% in total and its profits by 1.209%.

Anahtar Kelimeler

manufacturing, supply, profit, estimation, model deduction

Proje Numarası

yazarın kendi çalışmasıdır, proje çalışması değildir.

İmalat Sanayii Toplam Dolaylı Arz ve Kâr Fonksiyonlarının Modellemesi ve Tahmini

Öz

Bu çalışmada imalat sanayi toplam arz ve kâr eğrilerinin mal ve faktör fiyatları ile teknoloji seviyesinin bir fonksiyonu olarak elde edilebileceği gösterilmiştir. Ulaşılan imalat sanayii toplam dolaylı arz ve kâr eğrileri tahmin edilmiş ve değerlendirmelerde bulunulmuştur. Gerek korelasyon analizi sonuçları gerekse model tahmin sonuçları imalat sanayii toplam arz ve kârı ile teknoloji seviyesinin, mal ve faktör fiyatlarının yakın ilişkili olduğunu göstermiştir. Tahmin edilen modellerin belirlilik katsayıları ve F istatistikleri anlamlı bulunmuştur. Dolayısıyla, alternatif imalat sanayii üretimi ve kârı fiziki üretim faktörleri yerine sözü edilen değişkenlerle dolaylı olarak açıklanabileceği sonucuna ulaşılmıştır. Model1’e göre ortalama mal fiyatı %1 artınca imalat sanayi arzı % 0.129, Model2’ye göre kârın ortalama mal fiyatı %1 artınca imalat sanayi kârı % 0.096 artar. ARGE’nin GSYH payı %1 artınca imalat sanayi arzı %0.499, kârı da % 0.222 kadar artar. Bütün değişkelerde %1’lik bir artışın imalat sanayii arzını toplamda %1.86, kârını da % 1.209 kadar artırdığı bulunmuştur.

Anahtar Kelimeler

imalat sanayii, arz, kar, model türetme

Etik Beyan

Yazar kendi hazırlamıştır

Destekleyen Kurum

Yoktur

Proje Numarası

yazarın kendi çalışmasıdır, proje çalışması değildir.

Kaynakça

• Akal, M. (2022). Mikroekonomi: Tüketici, üretici ve piyasa teorisi (3.bs). Seçkin Yayınları.
• Bhaduri, A., Laski, K., & Riese, M. (1998). Effective demand versus profit maximization in aggregate demand/supply analysis from a dynamic perspective. The Vienna Institute for Comparative Economic Studies (WIIW) Working Papers, Article 9. https://wiiw.ac.at/effective-demand-versus-profit-maximization-in-aggregate-demand-supply-analysis-from-a-dynamic-perspective-dlp-4554.pdf.
• Epple, D., Gordon, B., & Sieg, H. (2010). A new approach to estimating the production function for housing. American Economic Review, 100, 905–924. http://www.aeaweb.org/articles.php?doi=10.1257/aer.100.3.905
• Fazzari, S. M., Ferri, P., & Greenberg, E. (1998). Aggregate demand and firm behavior: A new perspective on Keynesian microfoundations. Journal of Post Keynesian Economics, 20(4), 527-558. https://www.jstor.org/stable/4538600
• Hein, E. (2015). The principle of effective demand – Marx, Kalecki, Keynes and Beyond. Institute for International Political Economy Berlin Working Paper, Article 60/2015. https://www.ipe-berlin.org/fileadmin/institut-ipe/Dokumente/Working_Papers/ipe_working_paper_ 60.pdf
• Hendersen, J. M, & Quandt, R. E. (1980). Microeconomic theory (3th ed.). McGraw Hill Publishing Company.
• Hilmer, C. E., & Holt, M. T. (2005). Estimating indirect production functions with a more general specification: An application of the Lewbel model. Journal of Agricultural and Applied Economics, 37(3), 619-634. https://doi.org/10.1017/S1074070800027127
• Kim, H. Y. (1988). Analyzing the indirect production function for U. S. manufacturing. Southern Economic Journal, 55(2), 494-504. https://www.jstor.org/stable/1059121
• Nicholson, W. E. (1989). Microeconomic theory (4th ed.). The Dryden Press.
• Paravastu, S.A.V.B., Muehlen, P., & Chang, I-L. (2021). Coping with unobservables in estimating production functions: An example with US banking data. Sustainable Futures 3, Article 100058. https://doi.org/10.1016/j.sftr.2021.100058
• Petrin, A., Poi, B. P., & Levinsohn, J. (2004). Production function estimation in stata using inputs to control for unobservables. The Stata Journal, 4(2),113–123. https://journals.sagepub.com/doi/pdf/10.1177/1536867X0400400202
• Strateji ve Bütçe Başkanlığı (2023). Bilim ve teknoloji göstergeleri. https://www.sbb.gov.tr/ekonomik-ve-sosyal-gostergeler/#1669637640284-b60b923a-d4d7
• Silberberg, E. (1990). The structure of economics: A mathematical analysis (2nd ed.). Mc-Graw Hill Inc.
• Solow, R. (1957). Technical change and the aggregate production function. Review of Economics and Statistics, 39(3), 312-320. https://doi.org/10.2307/1926047.
• TCMB (Ağustos 2023). İstatistikler. https://www.tcmb.gov.tr/wps/wcm/connect/TR/TCMB+TR/Main+Menu/Istatistikler/Faiz+Istatistikleri/Azami+Mevduat+Faiz/
• TÜİK (2022). İstatistik göstergeler, 1923-2022. https://data.tuik.gov.tr)
• TÜRKSTAT (2022). İstatistik göstergeler, 1923-2022. https://data.tuik.gov.tr/