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Thematische Bibliographien / Economic forecasting / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Economic forecasting“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 29. Juli 2024

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1

Clark, Mary E. „Economic Forecasting“. Science 246, Nr. 4926 (06.10.1989): 10. http://dx.doi.org/10.1126/science.246.4926.10.b.

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2

Lieberman, Bernhardt. „Economic Forecasting“. Science 246, Nr. 4926 (06.10.1989): 10. http://dx.doi.org/10.1126/science.246.4926.10.a.

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3

CLARK, M. E. „Economic Forecasting“. Science 246, Nr. 4926 (06.10.1989): 10. http://dx.doi.org/10.1126/science.246.4926.10-a.

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4

Vogelsang, Timothy J. „Economic Forecasting“. Journal of the American Statistical Association 96, Nr. 453 (März 2001): 339–55. http://dx.doi.org/10.1198/jasa.2001.s386.

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5

Elliott, Graham, und Allan Timmermann. „Economic Forecasting“. Journal of Economic Literature 46, Nr. 1 (01.02.2008): 3–56. http://dx.doi.org/10.1257/jel.46.1.3.

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Forecasts guide decisions in all areas of economics and finance and their value can only be understood in relation to, and in the context of, such decisions. We discuss the central role of the loss function in helping determine the forecaster's objectives. Decision theory provides a framework for both the construction and evaluation of forecasts. This framework allows an understanding of the challenges that arise from the explosion in the sheer volume of predictor variables under consideration and the forecaster's ability to entertain an endless array of forecasting models and time-varying specifications, none of which may coincide with the “true” model. We show this along with reviewing methods for comparing the forecasting performance of pairs of models or evaluating the ability of the best of many models to beat a benchmark specification.

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6

Суворов, Anatoliy Suvorov, Ивантер, Viktor Ivantyer, Сутягин und Valyeriy Sutyagin. „The Main Objectives and Principles of Socio-Economic Forecasting“. Administration 3, Nr. 1 (17.03.2015): 8–17. http://dx.doi.org/10.12737/8785.

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The methodological foundations of socio-economic forecasting are considered in this paper. The forecasting’s definition is given; the forecasting’s role and place in national economy regulation are considered. Forecasts types’ classification and the forecasting’s basic principles have been presented. Forecasting models’ structure and classification, as well as socio-economic forecasts’ elements and development stages have been considered.

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7

Clements, Michael P., und David F. Hendry. „Forecasting economic processes“. International Journal of Forecasting 14, Nr. 1 (März 1998): 111–31. http://dx.doi.org/10.1016/s0169-2070(97)00057-5.

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8

Bradley, M. E. „Forecasting Oilfield Economic Performance“. Journal of Petroleum Technology 46, Nr. 11 (01.11.1994): 965–71. http://dx.doi.org/10.2118/26054-pa.

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9

Hall, Stephen G., K. Holden, D. A. Peel und J. L. Thompson. „Economic Forecasting: An Introduction.“ Economica 59, Nr. 233 (Februar 1992): 132. http://dx.doi.org/10.2307/2555081.

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10

Swanson, Norman R., Michael P. Clements und David F. Hendry. „Forecasting Economic Time Series“. Journal of the American Statistical Association 95, Nr. 450 (Juni 2000): 687. http://dx.doi.org/10.2307/2669429.

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11

Teguh, Muhammad, und Abdul Bashir. „Indonesia’s Economic Growth Forecasting“. SRIWIJAYA INTERNATIONAL JOURNAL OF DYNAMIC ECONOMICS AND BUSINESS 3, Nr. 2 (10.07.2019): 134. http://dx.doi.org/10.29259/sijdeb.v3i2.134-145.

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The high economic growth is very important for Indonesia to accelerate the development process at this period. Although, the growth rate was reached at 5.17 % in 2018 is likely high enough, some domestic economists even point out, it really can be raised to a higher level. This research tries to investigate and formulate again Indonesia’s economic growth rate in 2018 and forecast it for 2019. By doing analysis recent real GDP data by industrial origin and by type of expenditures, and also consider all of the available potential economic resources, this research shows that Indonesia’s economic growth rate could stand at 6.03 % in 2018 and also at 6.03 % in 2019. Anyway, the government need a good economic plan and consistently performing appropriate strategies which are suited to targets in order to have rapid and stable economic growth rate

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12

Suiter, Mary C., und Diego Mendez-Carbajo. „FREDcast: Economic forecasting game“. Journal of Economic Education 49, Nr. 3 (07.06.2018): 296. http://dx.doi.org/10.1080/00220485.2018.1464988.

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13

FKIRIN, M. A., und I. A. AL-TURKI. „FORECASTING AGRICULTURAL ECONOMIC SYSTEMS“. Cybernetics and Systems 22, Nr. 1 (Januar 1991): 17–24. http://dx.doi.org/10.1080/01969729108902268.

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14

Schorfheide, Frank. „FORECASTING ECONOMIC TIME SERIES“. Econometric Theory 16, Nr. 3 (Juni 2000): 441–50. http://dx.doi.org/10.1017/s0266466600003066.

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The prediction of future events and developments is an exciting and perhaps mysterious task, often associated with the aura of prophets and seers instead of probabilistic models and computer screens. The reality of macroeconomic forecasting, however, is quite mundane. Predictions of macroeconomic aggregates play an important role in the decision making of private enterprises, central banks, and governments. In general, forecasts become less popular if they turn out to be inaccurate ex post, and the postwar history of macroeconomic forecasting has had its share of disappointments. For instance, in the early 1980's, economists tested inflation forecasts taken over the previous 20 years and found that the forecasts were poor, partly as a result of the oil price shocks in the 1970's. A recent study (Croushore, 1998) with data up to 1996 provides a more favorable assessment of the quality of inflation forecasts.

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15

Kunst, Robert M., und Martin Wagner. „Economic forecasting: editors’ introduction“. Empirical Economics 58, Nr. 1 (Januar 2020): 1–5. http://dx.doi.org/10.1007/s00181-019-01820-3.

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16

Silver, Stephen. „Economic forecasting for business“. International Journal of Forecasting 4, Nr. 3 (Januar 1988): 499–500. http://dx.doi.org/10.1016/0169-2070(88)90118-5.

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17

Koehler, Anne B. „Forecasting economic time series“. International Journal of Forecasting 5, Nr. 1 (Januar 1989): 137–38. http://dx.doi.org/10.1016/0169-2070(89)90073-3.

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18

Fisher, Paul. „Economic forecasting: an introduction“. International Journal of Forecasting 7, Nr. 3 (November 1991): 388–89. http://dx.doi.org/10.1016/0169-2070(91)90014-m.

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19

Briscoe, G., und R. Wilson. „Forecasting economic activity rates“. International Journal of Forecasting 8, Nr. 2 (Oktober 1992): 201–17. http://dx.doi.org/10.1016/0169-2070(92)90119-t.

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20

Allen, P. Geoffrey. „Economic forecasting in agriculture“. International Journal of Forecasting 10, Nr. 1 (Juni 1994): 81–135. http://dx.doi.org/10.1016/0169-2070(94)90052-3.

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21

Bullard, Peter, K. Holden, D. A. Peel und J. L. Thompson. „Economic Forecasting: An Introduction.“ Statistician 42, Nr. 1 (1993): 79. http://dx.doi.org/10.2307/2348126.

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22

Pliskin, Jeffrey, C. W. J. Granger und Paul Newbold. „Forecasting Economic Time Series.“ Journal of the American Statistical Association 83, Nr. 402 (Juni 1988): 574. http://dx.doi.org/10.2307/2288897.

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23

Focardi, Sergio M., und Frank J. Fabozzi. „Economics: An Empirical Science Capable of Forecasting Economic Events?“ Journal of Portfolio Management 41, Nr. 4 (31.07.2015): 145–51. http://dx.doi.org/10.3905/jpm.2015.41.4.145.

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24

Amoroso, Nicola, Loredana Bellantuono, Alfonso Monaco, Francesco De Nicolò, Ernesto Somma und Roberto Bellotti. „Economic Interplay Forecasting Business Success“. Complexity 2021 (19.03.2021): 1–12. http://dx.doi.org/10.1155/2021/8861267.

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A startup ecosystem is a dynamic environment in which several actors, such as investors, venture capitalists, angels, and facilitators, are the protagonists of a complex interplay. Most of these interactions involve the flow of capital whose size and direction help to map the intricate system of relationships. This quantity is also considered a good proxy of economic success. Given the complexity of such systems, it would be more desirable to supplement this information with other informative features, and a natural choice is to adopt mathematical measures. In this work, we will specifically consider network centrality measures, borrowed by network theory. In particular, using the largest publicly available dataset for startups, the Crunchbase dataset, we show how centrality measures highlight the importance of particular players, such as angels and accelerators, whose role could be underestimated by focusing on collected funds only. We also provide a quantitative criterion to establish which firms should be considered strategic and rank them. Finally, as funding is a widespread measure for success in economic settings, we investigate to which extent this measure is in agreement with network metrics; the model accurately forecasts which firms will receive the highest funding in future years.

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25

Ericsson, Neil R., und Jaime R. Marquez. „A Framework for Economic Forecasting“. International Finance Discussion Paper 1998, Nr. 626 (Oktober 1998): 1–48. http://dx.doi.org/10.17016/ifdp.1998.626.

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26

Ericsson, Neil R. „Predictable Uncertainty in Economic Forecasting“. International Finance Discussion Paper 2000, Nr. 695 (Dezember 2000): 1–30. http://dx.doi.org/10.17016/ifdp.2000.695.

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27

Greszta, Michał, und Wojciech Maciejewski. „Combined Economic Forecasting in Poland“. Gospodarka Narodowa 200, Nr. 5-6 (30.06.2005): 49–61. http://dx.doi.org/10.33119/gn/101524.

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28

Smaghi, Lorenzo Bini. „Economic Forecasting and Monetary Policy“. Vierteljahrshefte zur Wirtschaftsforschung 75, Nr. 2 (April 2006): 54–64. http://dx.doi.org/10.3790/vjh.75.2.54.

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29

Clements, Michael P., und David F. Hendry. „Macro-Economic Forecasting and Modelling“. Economic Journal 105, Nr. 431 (Juli 1995): 1001. http://dx.doi.org/10.2307/2235166.

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30

Cai, Zongwu, Yongmiao Hong und Shouyang Wang. „Econometric Modeling and Economic Forecasting“. Journal of Management Science and Engineering 3, Nr. 4 (Dezember 2018): 178–82. http://dx.doi.org/10.3724/sp.j.1383.304010.

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31

Ericsson, Neil R., und Jaime Marquez. „A framework for economic forecasting“. Econometrics Journal 1, Nr. 1 (01.06.1998): C228—C266. http://dx.doi.org/10.1111/1368-423x.00011.

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32

Donihue, Michael R. „Teaching Economic Forecasting to Undergraduates“. Journal of Economic Education 26, Nr. 2 (April 1995): 113–21. http://dx.doi.org/10.1080/00220485.1995.10844863.

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33

Degiannakis, Stavros, und George Filis. „Forecasting European economic policy uncertainty“. Scottish Journal of Political Economy 66, Nr. 1 (03.01.2019): 94–114. http://dx.doi.org/10.1111/sjpe.12174.

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34

FOURIE, F. C. v. N. „Second Thoughts on Economic Forecasting“. South African Journal of Economics 54, Nr. 3 (September 1986): 196–202. http://dx.doi.org/10.1111/j.1813-6982.1986.tb00880.x.

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35

Wennberg, Karl, und Bjorn Nykvist. „The psychology of economic forecasting“. Global Business and Economics Review 9, Nr. 2/3 (2007): 211. http://dx.doi.org/10.1504/gber.2007.013702.

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36

Sherman, Howard J. „Forecasting Financial and Economic Cycles“. Journal of Economic Issues 29, Nr. 3 (September 1995): 956–57. http://dx.doi.org/10.1080/00213624.1995.11505725.

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37

Loomis, David G., und James E. Cox. „Principles for Teaching Economic Forecasting“. International Review of Economics Education 2, Nr. 1 (2003): 69–79. http://dx.doi.org/10.1016/s1477-3880(15)30151-1.

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38

Breece, James H. „Business forecasting and economic cycles“. International Journal of Forecasting 7, Nr. 4 (März 1992): 533–35. http://dx.doi.org/10.1016/0169-2070(92)90038-b.

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39

Chen, Dean T. „Economic forecasting in agriculture: Comment“. International Journal of Forecasting 10, Nr. 4 (Dezember 1994): 597–99. http://dx.doi.org/10.1016/0169-2070(94)90027-2.

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40

Alien, P. Geoffrey. „Economic forecasting in agriculture: Reply“. International Journal of Forecasting 10, Nr. 4 (Dezember 1994): 601–2. http://dx.doi.org/10.1016/0169-2070(94)90028-0.

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41

Bessler, David A. „Economic forecasting in agriculture: Discussion“. International Journal of Forecasting 10, Nr. 1 (Juni 1994): 137–38. http://dx.doi.org/10.1016/0169-2070(94)90053-1.

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42

Tomek, William G. „Economic forecasting in agriculture: Comment“. International Journal of Forecasting 10, Nr. 1 (Juni 1994): 143–45. http://dx.doi.org/10.1016/0169-2070(94)90055-8.

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43

Mullineux, Andy W. „Forecasting financial and economic cycles“. International Journal of Forecasting 11, Nr. 1 (März 1995): 191–92. http://dx.doi.org/10.1016/0169-2070(95)90023-3.

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44

Donihue, Michael R. „Teaching Economic Forecasting to Undergraduates“. Journal of Economic Education 26, Nr. 2 (1995): 113. http://dx.doi.org/10.2307/1183366.

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45

FKIRIN, M. A., und F. A. AL-TURKI. „Forecasting of economic dynamic systems“. International Journal of Systems Science 22, Nr. 12 (Dezember 1991): 2685–89. http://dx.doi.org/10.1080/00207729108910826.

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46

Kenneth Alexander. „The uncertainties of economic forecasting“. Futures 20, Nr. 3 (Juni 1988): 307–12. http://dx.doi.org/10.1016/0016-3287(88)90083-3.

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47

Holden, Kenneth. „Centre for economic forecasting, economic and financial review“. International Journal of Forecasting 4, Nr. 2 (Januar 1988): 290–91. http://dx.doi.org/10.1016/0169-2070(88)90084-2.

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48

HOLLY, SEAN. „Economic Viewpoint: Economic Models and Economic Forecasting: Ptolemaic or Copernican?“ Economic Outlook 15, Nr. 9 (Juni 1991): 32–38. http://dx.doi.org/10.1111/j.1468-0319.1991.tb00647.x.

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49

Ramatov, Jumaniyoz, Sharofat Rakhmanova und Nasiba Sh. Yunusova. „Philosophical Analysis of Socio-Economic Forecasting of the Future“. International Journal of Psychosocial Rehabilitation 24, Special Issue 1 (28.02.2020): 991–99. http://dx.doi.org/10.37200/ijpr/v24sp1/pr201244.

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50

Udhaya, P. „Forecasting Models: An Economic and Environmental Applications“. International Journal of Research and Review 10, Nr. 7 (19.07.2023): 373–86. http://dx.doi.org/10.52403/ijrr.20230749.

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Forecasting plays a critical role in the development of organizational business strategies. Despite a considerable body of research in the area of forecasting, the focus has largely been on the financial and economic outcomes of the forecasting process as opposed to societal benefits. Our motivation in this study is to promote the latter, with a view to using the forecasting process to advance social and environmental objectives such as equality, social justice and sustainability. We refer to such forecasting practices as Forecasting for Social Good (FSG) where the benefits to society and the environment take precedenceover economic and financial outcomes. We conceptualize FSG and discuss its scope and boundaries in the context of the “Doughnut theory”. We present some key attributes that qualify a forecasting process as FSG: it is concerned with a real problem, it is focused on advancing social and environmental goals and prioritizes these over conventional measures of economic success, and it has a broad societal impact. We also position FSG in the wider literature on forecasting and social good practices. We propose an FSG maturity framework as the means to engage academics and practitioners with research in this area. Keywords: Forecasting Models, Forecasting for Social Good, FSG, Economic and Environmental Applications

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