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1

Forker, Diana. "More than just a modal particle." Functions of Language 27, no. 3 (2020): 340–72. http://dx.doi.org/10.1075/fol.17011.for.

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Abstract Modal particles have been intensively studied in German and a few other European languages, but investigations of modal particles from little-known languages are rare. This paper examines in detail the morphosyntactic and the semantic properties of the Sanzhi Dargwa (Nakh-Daghestanian) modal particle =q’al. It is shown that the particle possesses the morphosyntactic properties that are commonly assumed for modal particles. The particle is then analyzed as presupposition trigger that interacts with focus and marks clauses as declarative sentences. It triggers two presuppositions, namely uncontroversiality and contrast/correction. Furthermore, it can express finiteness. The analysis suggests that accounting for modal particles as grammatical rather than lexical items with head status seems promising for further research.
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2

Quadackers, Luc, and Zanten Marike Van. "Preliminary research findings of three FAR-studies: what has been found so far?" Maandblad Voor Accountancy en Bedrijfseconomie 92, no. (7/8) (2018): 205–9. https://doi.org/10.5117/mab.92.30161.

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Based on the 2016 call for research proposals, six research projects were launched as a first beacon on the FAR research agenda, strengthening the collaboration between auditing academics and practitioners. During the second plenary session of the conference, representatives of two of the research teams presented initial insights of their studies. These presentations were provided by Therese Grohnert and Wim Gijselaers (concerning learning culture) and Anna Gold (regarding group audits). Furthermore, Olof Bik and Jan Bouwens, who are working on a FAR project in a so-called ‘joint working group’, presented their study onpartner incentives and performance. A summary of the findings of the three studies follows next, supplemented with a short overview of the discussion with the audience.
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3

Quadackers, Luc, and Zanten Marike Van. "Three new FAR research projects: what's going to happen?" Maandblad Voor Accountancy en Bedrijfseconomie 92, no. (7/8) (2018): 211–15. https://doi.org/10.5117/mab.92.30162.

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The 2017 call for research proposals resulted in twelve submitted proposals. Seven projects were eventually selected for a grant from the FAR.[i]During the conference, three of these seven FAR-studies were presented. The FAR-board aims at striking a balance in selecting research proposals, in order to optimize both practical and academic usefulness of the studies. Therefore, a multi-method, multi-theme mix also makes up the 2017 &lsquo;cohort&rsquo; of studies. The fourth plenary session of the conference is a sample of the broad set of studies that currently make up the FAR research portfolio, with projects grounded in, for example, economics, accounting and psychology and using archival and (field) experimental methods. Mark Peecher, Joseph Gerakos, and Jeroen Suijs successively presented the research proposals of their projects. Below you will find a short summary of the accepted research proposals they presented during the conference, to provide insight into these planned studies, as well as a short summary of the discussion with the participants. <strong>Note</strong>[i]An overview of the FAR research projects can be found at: http://foundationforauditingresearch.org/far-research-projects/
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4

Russell, Bill, and Anindya Banerjee. "A markup model for forecasting inflation for the euro area." Journal of Forecasting 25, no. 7 (2006): 495–511. http://dx.doi.org/10.1002/for.1000.

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5

Ulvila, Jacob W. "Decision trees for forecasting." Journal of Forecasting 4, no. 4 (1985): 377–85. http://dx.doi.org/10.1002/for.3980040406.

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6

Bhattacharyya, Malay, Dileep Kumar M, and Ramesh Kumar. "Optimal sampling frequency for volatility forecast models for the Indian stock markets." Journal of Forecasting 28, no. 1 (2009): 38–54. http://dx.doi.org/10.1002/for.1080.

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7

Todd, Richard M. "Algorithms for explaining forecast revisions." Journal of Forecasting 11, no. 8 (1992): 675–85. http://dx.doi.org/10.1002/for.3980110805.

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8

Choi, In-Bong, and Masanobu Taniguchi. "Misspecified prediction for time series." Journal of Forecasting 20, no. 8 (2001): 543–64. http://dx.doi.org/10.1002/for.807.

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9

Liu, Shu-Ing. "Bayesian forecasts for cointegrated models." Journal of Forecasting 21, no. 3 (2002): 167–80. http://dx.doi.org/10.1002/for.826.

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10

Brooks, Chris, and Gita Persand. "Volatility forecasting for risk management." Journal of Forecasting 22, no. 1 (2003): 1–22. http://dx.doi.org/10.1002/for.841.

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11

Salo, Ahti, Tommi Gustafsson, and Ramakrishnan Ramanathan. "Multicriteria methods for technology foresight." Journal of Forecasting 22, no. 2-3 (2003): 235–55. http://dx.doi.org/10.1002/for.850.

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12

Artis, Michael J., Anindya Banerjee, and Massimiliano Marcellino. "Factor forecasts for the UK." Journal of Forecasting 24, no. 4 (2005): 279–98. http://dx.doi.org/10.1002/for.957.

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13

Hwang, Ruey-Ching, K. F. Cheng, and Jack C. Lee. "A semiparametric method for predicting bankruptcy." Journal of Forecasting 26, no. 5 (2007): 317–42. http://dx.doi.org/10.1002/for.1027.

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14

Lo, Shin‐Lian, Fu‐Kwun Wang, and James T. Lin. "Forecasting for the LCD monitor market." Journal of Forecasting 27, no. 4 (2008): 341–56. http://dx.doi.org/10.1002/for.1055.

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15

Lin, Eric S., Ping-Hung Chou, and Ta-Sheng Chou. "Testing for the usefulness of forecasts." Journal of Forecasting 30, no. 5 (2010): 469–89. http://dx.doi.org/10.1002/for.1180.

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16

Angelini, Giovanni, and Luca De Angelis. "PARX model for football match predictions." Journal of Forecasting 36, no. 7 (2017): 795–807. http://dx.doi.org/10.1002/for.2471.

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17

Kawakatsu, Hiroyuki. "Direct multiperiod forecasting for algorithmic trading." Journal of Forecasting 37, no. 1 (2017): 83–101. http://dx.doi.org/10.1002/for.2488.

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18

Edmundson, R. H. "Decomposition; a strategy for judgemental forecasting." Journal of Forecasting 9, no. 4 (1990): 305–14. http://dx.doi.org/10.1002/for.3980090403.

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19

Meade, Nigel, and Towhidul Islam. "Prediction intervals for growth curve forecasts." Journal of Forecasting 14, no. 5 (1995): 413–30. http://dx.doi.org/10.1002/for.3980140502.

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20

Pollock, D. S. G. "Filters for short non-stationary sequences." Journal of Forecasting 20, no. 5 (2001): 341–55. http://dx.doi.org/10.1002/for.791.

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21

Koreisha, Sergio G., and Yue Fang. "Updating ARMA predictions for temporal aggregates." Journal of Forecasting 23, no. 4 (2004): 275–96. http://dx.doi.org/10.1002/for.913.

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22

Polanski, Arnold, and Evarist Stoja. "Dynamic density forecasts for multivariate asset returns." Journal of Forecasting 30, no. 6 (2010): 523–40. http://dx.doi.org/10.1002/for.1192.

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23

Rua, António. "A wavelet approach for factor-augmented forecasting." Journal of Forecasting 30, no. 7 (2010): 666–78. http://dx.doi.org/10.1002/for.1200.

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24

Tsai, Chih-Fong, and Yu-Feng Hsu. "A Meta-learning Framework for Bankruptcy Prediction." Journal of Forecasting 32, no. 2 (2011): 167–79. http://dx.doi.org/10.1002/for.1264.

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25

Chu, Chi-Hsiang, Mong-Na Lo Huang, Shih-Feng Huang, and Ray-Bing Chen. "Bayesian structure selection for vector autoregression model." Journal of Forecasting 38, no. 5 (2019): 422–39. http://dx.doi.org/10.1002/for.2573.

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26

Wall, Kent D., and Charles Correia. "A preference-based method for forecast combination." Journal of Forecasting 8, no. 3 (1989): 269–92. http://dx.doi.org/10.1002/for.3980080311.

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27

Guerard, John B. "Composite model building for foreign exchange rates." Journal of Forecasting 8, no. 3 (1989): 315–29. http://dx.doi.org/10.1002/for.3980080313.

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28

Harvey, A. C., and S. Peters. "Estimation procedures for structural time series models." Journal of Forecasting 9, no. 2 (1990): 89–108. http://dx.doi.org/10.1002/for.3980090203.

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29

Attwell, D. N., and J. Q. Smith. "A bayesian forecasting model for sequential bidding." Journal of Forecasting 10, no. 6 (1991): 565–77. http://dx.doi.org/10.1002/for.3980100603.

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30

Barbosa, Emanuel, and Jeff Harrison. "Variance estimation for multivariate dynamic linear models." Journal of Forecasting 11, no. 7 (1992): 621–28. http://dx.doi.org/10.1002/for.3980110704.

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31

Dua, Pami, and Subhash C. Ray. "A BVAR model for the connecticut economy." Journal of Forecasting 14, no. 3 (1995): 167–80. http://dx.doi.org/10.1002/for.3980140303.

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32

Koskinen, Lasse, and Lars-Erik Öller. "A classifying procedure for signalling turning points." Journal of Forecasting 23, no. 3 (2004): 197–214. http://dx.doi.org/10.1002/for.905.

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33

Wang, Yan, and Yudong Yao. "Measuring downside risk and severity for global output." Journal of Forecasting 26, no. 1 (2007): 23–32. http://dx.doi.org/10.1002/for.1004.

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34

Kolsrud, Dag. "Time-simultaneous prediction band for a time series." Journal of Forecasting 26, no. 3 (2007): 171–88. http://dx.doi.org/10.1002/for.1020.

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35

Fukuda, Kosei. "Forecasting real-time data allowing for data revisions." Journal of Forecasting 26, no. 6 (2007): 429–44. http://dx.doi.org/10.1002/for.1032.

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36

Skintzi, Vasiliki D., and Spyros Xanthopoulos-Sisinis. "Evaluation of correlation forecasting models for risk management." Journal of Forecasting 26, no. 7 (2007): 497–526. http://dx.doi.org/10.1002/for.1036.

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37

Andrawis, Robert R., and Amir F. Atiya. "A new Bayesian formulation for Holt's exponential smoothing." Journal of Forecasting 28, no. 3 (2009): 218–34. http://dx.doi.org/10.1002/for.1094.

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38

Kışınbay, Turgut. "The use of encompassing tests for forecast combinations." Journal of Forecasting 29, no. 8 (2009): 715–27. http://dx.doi.org/10.1002/for.1170.

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39

Cubadda, Gianluca, and Alain Hecq. "Testing for common autocorrelation in data-rich environments." Journal of Forecasting 30, no. 3 (2010): 325–35. http://dx.doi.org/10.1002/for.1186.

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40

Lorca, Pedro, Manuel Landajo, and Javier De Andrés. "Nonparametric Quantile Regression-Based Classifiers for Bankruptcy Forecasting." Journal of Forecasting 33, no. 2 (2013): 124–33. http://dx.doi.org/10.1002/for.2280.

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41

Yang, Hongxia, Jonathan R. M. Hosking, and Yasuo Amemiya. "Dynamic Latent Class Model Averaging for Online Prediction." Journal of Forecasting 34, no. 1 (2014): 1–14. http://dx.doi.org/10.1002/for.2315.

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42

Clements, Adam, Ayesha Scott, and Annastiina Silvennoinen. "On the Benefits of Equicorrelation for Portfolio Allocation." Journal of Forecasting 34, no. 6 (2015): 507–22. http://dx.doi.org/10.1002/for.2357.

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43

Jouini, Tarek. "Efficient Multistep Forecast Procedures for Multivariate Time Series." Journal of Forecasting 34, no. 7 (2015): 604–18. http://dx.doi.org/10.1002/for.2363.

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44

Angers, Jean-François, Atanu Biswas, and Raju Maiti. "Bayesian Forecasting for Time Series of Categorical Data." Journal of Forecasting 36, no. 3 (2016): 217–29. http://dx.doi.org/10.1002/for.2426.

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45

Andersson, Michael K., Ted Aranki, and André Reslow. "Adjusting for information content when comparing forecast performance." Journal of Forecasting 36, no. 7 (2017): 784–94. http://dx.doi.org/10.1002/for.2470.

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46

Sun, Xinyu, Tao Liu, and Jiayin Wang. "A Bayesian structural model for predicting algal blooms." Journal of Forecasting 38, no. 8 (2019): 788–802. http://dx.doi.org/10.1002/for.2583.

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47

Bucci, Andrea. "Cholesky–ANN models for predicting multivariate realized volatility." Journal of Forecasting 39, no. 6 (2020): 865–76. http://dx.doi.org/10.1002/for.2664.

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48

Aczel, Amir D., and Norman H. Josephy. "Using the bootstrap for improved ARIMA model identification." Journal of Forecasting 11, no. 1 (1992): 71–80. http://dx.doi.org/10.1002/for.3980110107.

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49

Jarrell, Stephen B., and T. D. Stanley. "A note on diagrams for turning-point diagnostics." Journal of Forecasting 11, no. 4 (1992): 325–30. http://dx.doi.org/10.1002/for.3980110406.

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50

Scott Armstrong, J., and Fred Collopy. "Causal forces: Structuring knowledge for time-series extrapolation." Journal of Forecasting 12, no. 2 (1993): 103–15. http://dx.doi.org/10.1002/for.3980120205.

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