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Journal articles on the topic 'Demand approach'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Janda, K., J. Mikolášek, and M. Netuka. "Complete almost ideal demand system approach to the Czech alcohol demand." Agricultural Economics (Zemědělská ekonomika) 56, No. 9 (September 21, 2010): 421–34. http://dx.doi.org/10.17221/117/2009-agricecon.

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Tax interventions into alcohol beverages market are an important and recently discussed tool of the Czech fiscal policy. The impact of any such measure would be strongly dependent on the microeconomic behavior of the consumers. The aim of this paper is to provide a reliable set of income, own-price, and cross-price elasticities of demand for the key alcohol beverages based on the Almost Ideal Demand System model applied on the most relevant Czech data set of the Household Budget Statistics.

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2

Fisher, Douglas. "Money-Demand Variability: A Demand-Systems Approach." Journal of Business & Economic Statistics 10, no. 2 (April 1992): 143. http://dx.doi.org/10.2307/1391673.

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3

Fisher, Douglas. "Money-Demand Variability: A Demand-Systems Approach." Journal of Business & Economic Statistics 10, no. 2 (April 1992): 143–51. http://dx.doi.org/10.1080/07350015.1992.10509894.

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4

Li, Sheng, Richard Vogel, and Nanda Viswanathan. "Demand for saltwater recreational fishing: A generalized demand approach." Ocean & Coastal Management 179 (September 2019): 104820. http://dx.doi.org/10.1016/j.ocecoaman.2019.104820.

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5

Westcott, R. "A scenario approach to demand forecasting." Water Supply 4, no. 3 (June 1, 2004): 45–56. http://dx.doi.org/10.2166/ws.2004.0042.

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The Environment Agency's 2001 national water resources strategy, Water resources for the future, provides a secure framework for the management of water that protects the long-term future of the water environment while encouraging sustainable development. Underpinning the strategy are a suite of scenario based forecasts developed to explore the impact of key drivers of demand within different sectors of water use across England and Wales. This paper explains the approach used to produce these forecasts, summarises how the individual components of demand were considered and highlights opportunities for future application and development of this approach. Using the premise that total water demand can mask conflicting trends between sectors, it is essential to consider each sector and its micro-components independently to understand the specific drivers of demand and consequently determine how these might best be managed. Four scenarios reflecting different possible futures of socio-economic and governmental structure were created to test “how”, “why” and “where” these water demands may change by 2025. Such an approach provides an opportunity to test the implications of macro drivers of demand, such as, economic growth and regulatory reform, on the micro-components of water use, linking disparate sectors to a common set of assumptions about the future.

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6

Thiagarajan, Rajesh, Mustafizur Rahman, Don Gossink, and Greg Calbert. "A Data Mining Approach To Improve Military Demand Forecasting." Journal of Artificial Intelligence and Soft Computing Research 4, no. 3 (July 1, 2014): 205–14. http://dx.doi.org/10.1515/jaiscr-2015-0009.

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Abstract Accurately forecasting the demand of critical stocks is a vital step in the planning of a military operation. Demand prediction techniques, particularly autocorrelated models, have been adopted in the military planning process because a large number of stocks in the military inventory do not have consumption and usage rates per platform (e.g., ship). However, if an impending military operation is (significantly) different from prior campaigns then these prediction models may under or over estimate the demand of critical stocks leading to undesired operational impacts. To address this, we propose an approach to improve the accuracy of demand predictions by combining autocorrelated predictions with cross-correlated demands of items having known per-platform usage rates. We adopt a data mining approach using sequence rule mining to automatically determine cross-correlated demands by assessing frequently co-occurring usage patterns. Our experiments using a military operational planning system indicate a considerable reduction in the prediction errors across several categories of military supplies.

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7

Laurila, Ilkka P. "Demand for food products in Finland: A demand system approach." Agricultural and Food Science 3, no. 4 (July 1, 1994): 315–420. http://dx.doi.org/10.23986/afsci.72709.

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The study was concerned with the estimation of food-demand parameters in a system context. The patterns of food consumption in Finland were presented over the period 1950-1991, and a complete demand system of food expenditures was estimated. Price and expenditure elasticities of demand were derived, and the results were used to obtain projections on future consumption. While the real expenditure on food has increased, the budget share of food has decreased. In the early 19505, combined Food-at-Home and Food-away-from-Home corresponded to about 40% of consumers’ total expenditure. In 1991 the share was 28%. There was a shift to meals eaten outside the home. While the budget share of Food-away-from-Home increased from 3% to 7% over the observation period, Food-at-Home fell from 37% to 21%, and Food-at-Home excluding Alcoholic Drinks fell from 34% to 16%. Within Food-at-Home, the budget shares of the broad aggregate groups, Animalia (food from animal sources), Beverages, and Vegetablia (food from vegetable sources), remained about the same over the four decades, while structural change took place within the aggregates. Within Animalia, consumption shifted from Dairy Products (other than Fresh Milk) to Meat and Fish. Within Beverages, consumption shifted from Fresh Milk and Hot Drinks to Alcoholic Drinks and Soft Drinks. Within Vegetablia, consumption shifted from Flour to Fruits, while the shares of Bread and Cake and Vegetables remained about the same. As the complete demand system, the Almost Ideal Demand System (AIDS) was employed. The conventional AIDS was extended by developing a dynamic generalisation of the model and allowing for systematic shifts in structural relationships over time. A four-stage budgeting system was specified, consisting of seven sub-systems (groups), and covering 18 food categories. Tests on parameter restrictions and misspecification tests were used to choose the most preferred model specification for each group. Generally, the estimated models did not satisfy the Slutsky conditions. The goodness-of-fit measures were good, and, compared to static specifications, dynamics usually provided a better fit. The misspecification tests indicated that the dynamic specification was correct, but some form of misspecification was found. The structural change in parameters indicated that the modelling failed to track a stable preference structure - if there is one. The estimated demand system was employed in projecting the future consumption of food products in Finland to the year 2000. The approach was to choose a certain change in the real total consumption expenditure and alternative sets of relative prices for the forecast period. Four different options of price variables were defined. Three of the options relied on the historical price trends recorded in Finland, whereas one option measured the expected consequences of Finland's possible membership in the European Union. A predicted consequence of the membership in the European Union is that the share of food in consumers’ budget would decrease. The expected decrease is somewhat faster than the decrease that would take place if future price developments were based on the historical trends. If Finland joins the Union, the budget share of Food-at-Home would decrease from 21% in 1991 to 18% in 2000, whereas the budget share of Food-at-Home excluding Alcoholic Drinks would decrease from 16% in 1991 to 14% in 2000.

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8

Brown, Mark G., Jonq-Ying Lee, and James L. Seale. "Demand Relationships Among Juice Beverages: A Differential Demand System Approach." Journal of Agricultural and Applied Economics 26, no. 2 (December 1994): 417–29. http://dx.doi.org/10.1017/s1074070800026341.

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AbstractNielsen ScanTrack data were used to study how income and prices influenced consumer juice beverage demand in the United States during the period from 1988-89 through 1991-92. Alternative differential demand models combining the features of the Rotterdam model and the almost ideal demand system (AIDS) were tested. Results indicate the CBS type demand responses describe consumer behavior better than the other specifications for this particular data set.

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9

Capt, Tallen, Ali Mirchi, Saurav Kumar, and W. Shane Walker. "Urban Water Demand: Statistical Optimization Approach to Modeling Daily Demand." Journal of Water Resources Planning and Management 147, no. 2 (February 2021): 04020105. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0001315.

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10

Aksoy, H. Kıvanc, and Asli Guner. "A Bayesian Approach to Demand Estimation." Procedia Economics and Finance 26 (2015): 777–84. http://dx.doi.org/10.1016/s2212-5671(15)00844-8.

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11

Gracia, A., J. M. Gil, and A. M. Angulo. "Spanish food demand: a dynamic approach." Applied Economics 30, no. 10 (October 1, 1998): 1399–405. http://dx.doi.org/10.1080/000368498325011.

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12

Khoshnevis Yazdi, Soheila, and Bahman Khanalizadeh. "Tourism demand: a panel data approach." Current Issues in Tourism 20, no. 8 (April 27, 2016): 787–800. http://dx.doi.org/10.1080/13683500.2016.1170772.

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13

Gray, Paul. "A Demand-Side Approach to Telecommuting." Information Systems Management 14, no. 4 (January 1997): 21–28. http://dx.doi.org/10.1080/10580539708907071.

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14

Rothstein, Donna S. "An Economic Approach to Abortion Demand." American Economist 36, no. 1 (March 1992): 53–64. http://dx.doi.org/10.1177/056943459203600109.

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This paper uses econometric multiple regression techniques in order to analyze the socioeconomic factors affecting the demand for abortion for the year 1985. A cross-section of the 50 states and Washington D.C. is examined and a household choice theoretical framework is utilized. The results suggest that average price of abortion, disposable personal per capita income, percentage of single women, whether abortions are state funded, unemployment rate, divorce rate, and if the state is located in the far West, are statistically significant factors in the determination of the demand for abortion.

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15

de Freitas, Luciano Charlita, and Shinji Kaneko. "Ethanol demand in Brazil: Regional approach." Energy Policy 39, no. 5 (May 2011): 2289–98. http://dx.doi.org/10.1016/j.enpol.2011.01.039.

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16

Fuchs-Seliger, Susanne. "An axiomatic approach to compensated demand." Journal of Economic Theory 52, no. 1 (October 1990): 111–22. http://dx.doi.org/10.1016/0022-0531(90)90069-v.

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17

Murjani, Ahmadi. "Energy Goods Demand in Tabalong Regency: Almost-Ideal Demand System Approach." Jurnal Bina Praja 9, no. 2 (November 28, 2017): 307–19. http://dx.doi.org/10.21787/jbp.09.2017.307-319.

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18

Lazaridis, Panagiotis. "Household meat demand in Greece: A demand systems approach using microdata." Agribusiness 19, no. 1 (January 2003): 43–59. http://dx.doi.org/10.1002/agr.10044.

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19

Bilgic, Abdulbaki, and Steven T. Yen. "Household food demand in Turkey: A two-step demand system approach." Food Policy 43 (December 2013): 267–77. http://dx.doi.org/10.1016/j.foodpol.2013.09.004.

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20

Suárez-Varela, Marta. "Modeling residential water demand: An approach based on household demand systems." Journal of Environmental Management 261 (May 2020): 109921. http://dx.doi.org/10.1016/j.jenvman.2019.109921.

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21

Raza, Ali. "Credit Demand among Small Farmers: A District Level Approach, Pakistan." Asian Journal of Agriculture and Rural Development 9, no. 1 (May 10, 2019): 47–61. http://dx.doi.org/10.18488/journal.1005/2019.9.1/1005.1.47.61.

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In the current study, determinants of credit demand among small farmers in the Mandi Bahauddin district of Pakistan were investigated. For this purpose, interviews with 123 small farmers in six villages of this district were conducted. Both qualitative and quantitative techniques were used to examine factors that affect credit demands. Seven determinants were devised and tested, and a probit model was employed to analyze the effects of education, household size, and income on the credit demand. Through qualitative methods, factors like informal lending, interest rate, consumption smoothing, and transaction cost were analyzed. It was noted that informal borrowing, higher interest rates, and high transaction costs crowded out formal lending. A positive correlation was observed between education and credit demand. Household size and all types of incomes did not significantly correlate with credit demand.

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22

Park, Kyungchul, Kyungsik Lee, and Sungsoo Park. "AN LP-BASED APPROACH TO THE RING LOADING PROBLEM WITH INTEGER DEMAND SPLITTING." Journal of the Operations Research Society of Japan 52, no. 1 (2009): 35–45. http://dx.doi.org/10.15807/jorsj.52.35.

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23

Han, Min Suk, Jae Wan Jeon, and Sang Young Jei. "The Estimation of Demand Elasticity for Transport Fuel by Demand System Approach." Korean Data Analysis Society 21, no. 5 (October 31, 2019): 2453–64. http://dx.doi.org/10.37727/jkdas.2019.21.5.2453.

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24

Khoiriyah, Nikmatul, Ratya Anindita, Nuhfil Hanani, and Abdul Wahib Muhaimin. "Animal Food Demand in Indonesia: A Quadratic Almost Ideal Demand System Approach." Agris on-line Papers in Economics and Informatics 12, no. 2 (June 30, 2020): 85–97. http://dx.doi.org/10.7160/aol.2020.120208.

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25

Mekonnen, Dawit Kelemework, Esendugue Greg Fonsah, and Boris Borgotti. "US import demand for apple: source differentiated almost ideal demand system approach." International Journal of Trade and Global Markets 4, no. 4 (2011): 372. http://dx.doi.org/10.1504/ijtgm.2011.042862.

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26

Filippini, Massimo, and Lester C. Hunt. "US residential energy demand and energy efficiency: A stochastic demand frontier approach." Energy Economics 34, no. 5 (September 2012): 1484–91. http://dx.doi.org/10.1016/j.eneco.2012.06.013.

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27

Clastres, Cédric, and Haikel Khalfallah. "An analytical approach to activating demand elasticity with a demand response mechanism." Energy Economics 52 (December 2015): 195–206. http://dx.doi.org/10.1016/j.eneco.2015.10.013.

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28

Blokker, E. J. M., J. H. G. Vreeburg, H. Beverloo, M. Klein Arfman, and J. C. van Dijk. "A bottom-up approach of stochastic demand allocation in water quality modelling." Drinking Water Engineering and Science Discussions 3, no. 1 (January 4, 2010): 1–24. http://dx.doi.org/10.5194/dwesd-3-1-2010.

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Abstract. An "all pipes" hydraulic model of a DMA-sized drinking water distribution system was constructed with two types of demand allocations. One is constructed with the conventional top-down approach, i.e. a demand multiplier pattern from the booster station is allocated to all demand nodes with a correction factor to account for the average water demand on that node. The other is constructed with a bottom-up approach of demand allocation, i.e., each individual home is represented by one demand node with its own stochastic water demand pattern. The stochastic water demand patterns are constructed with an end-use model on a per second basis and per individual home. The flow entering the test area was measured and a tracer test with sodium chloride was performed to measure travel times. The two models were evaluated on the predicted sum of demands and travel times, compared with what was measured in the test area. The new bottom-up approach performs at least as well as the conventional top-down approach with respect to total demand and travel times, without the need for any flow measurements or calibration measurements. The bottom-up approach leads to a stochastic method of hydraulic modelling and gives insight into the variability of travel times as an added feature beyond the conventional way of modelling.

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29

Firbank, Les, and A. P. Gutierrez. "Applied Population Ecology: A Supply-Demand Approach." Journal of Ecology 85, no. 3 (June 1997): 400. http://dx.doi.org/10.2307/2960516.

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30

Wood, S. N., and A. P. Gutierrez. "Applied Population Ecology: A Supply-Demand Approach." Biometrics 53, no. 1 (March 1997): 388. http://dx.doi.org/10.2307/2533130.

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31

Bauwens, Luc, Denzil G. Fiebig, and Mark F. J. Steel. "Estimating End-Use Demand: A Bayesian Approach." Journal of Business & Economic Statistics 12, no. 2 (April 1994): 221. http://dx.doi.org/10.2307/1391485.

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32

Jain, Parul, and Choon-Geol Moon. "Sectoral Money Demand: A Co-Integration Approach." Review of Economics and Statistics 76, no. 1 (February 1994): 196. http://dx.doi.org/10.2307/2109839.

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33

Li, Caihong, Senhui Mu, Jianzhou Wang, Yi Yang, and Lian Li. "A novel approach for electricity demand forecasting." Nonlinear Theory and Its Applications, IEICE 5, no. 2 (2014): 184–97. http://dx.doi.org/10.1587/nolta.5.184.

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34

Bernard, Jean-Thomas, Denis Bolduc, and Donald Belanger. "Quebec Residential Electricity Demand: A Microeconometric Approach." Canadian Journal of Economics 29, no. 1 (February 1996): 92. http://dx.doi.org/10.2307/136153.

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35

Yüksel, Sedat. "An integrated forecasting approach to hotel demand." Mathematical and Computer Modelling 46, no. 7-8 (October 2007): 1063–70. http://dx.doi.org/10.1016/j.mcm.2007.03.008.

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36

Nguyen, Ruby Thuy, Tomer Fishman, Fu Zhao, D. D. Imholte, and T. E. Graedel. "Analyzing critical material demand: A revised approach." Science of The Total Environment 630 (July 2018): 1143–48. http://dx.doi.org/10.1016/j.scitotenv.2018.02.283.

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37

Taratukhin, E. O. "BIOPSYCHOSOCIAL APPROACH — A MODERN DEMAND FOR INTERDISCIPLINARITY." Russian journal of Cardiology, no. 9 (November 27, 2015): 80. http://dx.doi.org/10.15829/1560-4071-2015-9-80-83.

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38

Freckleton, R. P., and A. P. Gutierrez. "Applied Population Ecology: A Supply-Demand Approach." Journal of Applied Ecology 33, no. 6 (December 1996): 1588. http://dx.doi.org/10.2307/2404800.

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39

Kelly, T. "A demand-based approach to menu pricing." Cornell Hotel and Restaurant Administration Quarterly 35, no. 1 (February 1994): 48–52. http://dx.doi.org/10.1016/0010-8804(94)90064-7.

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40

Wu, Zhou, and Xiaohua Xia. "A Portfolio Approach of Demand Side Management." IFAC-PapersOnLine 50, no. 1 (July 2017): 171–76. http://dx.doi.org/10.1016/j.ifacol.2017.08.029.

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41

Žukauskas, Vytautas, and Jörg Guido Hülsmann. "Financial asset valuations: The total demand approach." Quarterly Review of Economics and Finance 72 (May 2019): 123–31. http://dx.doi.org/10.1016/j.qref.2018.11.004.

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42

Bauwens, Luc, Denzil G. Fiebig, and Mark F. J. Steel. "Estimating End-use Demand: A Bayesian Approach." Journal of Business & Economic Statistics 12, no. 2 (April 1994): 221–31. http://dx.doi.org/10.1080/07350015.1994.10510009.

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43

Arbel, Avner, and S. Abraham Ravid. "On recreation demand: a time-series approach." Applied Economics 17, no. 6 (December 1985): 979–90. http://dx.doi.org/10.1080/00036848500000062.

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44

Gupta, Punit, Harshit Ladia, Kabir Kakkar, Kriti Rai, Yogesh Agrawal, Rishika Mamgain, and Navaditya Gaur. "Implementation of Demand Forecasting – A Comparative Approach." Journal of Physics: Conference Series 1714 (January 2021): 012003. http://dx.doi.org/10.1088/1742-6596/1714/1/012003.

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45

Chu, Fong-Lin. "Forecasting tourism demand: a cubic polynomial approach." Tourism Management 25, no. 2 (April 2004): 209–18. http://dx.doi.org/10.1016/s0261-5177(03)00086-4.

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46

Kelly, Thomas J., Nicholas M. Kiefer, and Kenneth Burdett. "A Demand-Based Approach to Menu Pricing." Cornell Hospitality Quarterly 50, no. 3 (July 28, 2009): 383–87. http://dx.doi.org/10.1177/1938965509339038.

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47

Nor, M. E., A. I. M. Nurul, and M. S. Rusiman. "A Hybrid Approach on Tourism Demand Forecasting." Journal of Physics: Conference Series 995 (April 2018): 012034. http://dx.doi.org/10.1088/1742-6596/995/1/012034.

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48

Smith, Matthew. "Demand-led Growth Theory: A Historical Approach." Review of Political Economy 24, no. 4 (October 2012): 543–73. http://dx.doi.org/10.1080/09538259.2012.729931.

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49

Ono, Yoshiyasu, and Junichiro Ishida. "On Persistent Demand Shortages: A Behavioural Approach." Japanese Economic Review 65, no. 1 (May 23, 2013): 42–69. http://dx.doi.org/10.1111/jere.12016.

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50

Koijen, Ralph S. J., and Motohiro Yogo. "A Demand System Approach to Asset Pricing." Journal of Political Economy 127, no. 4 (August 2019): 1475–515. http://dx.doi.org/10.1086/701683.

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