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1

Janssen, Jacques, ed. Semi-Markov Models. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1.

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2

H, Bakker. Enthalpies in alloys: Miedemas̓ semi-empirical model. Uetikon-Zuerich, Switzerland: Trans Tech Publications, 1998.

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3

United States. National Aeronautics and Space Administration., ed. Cellular monotectic model solidification: Semi-annual report. [Washington, DC: National Aeronautics and Space Administration, 1987.

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4

Janssen, Jacques, and Nikolaos Limnios, eds. Semi-Markov Models and Applications. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4613-3288-6.

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5

Milholen, William E. Computational analysis of semi-span model test techniques. Hampton, Virginia: National Aeronautics and Space Administration, Langley Research Center, 1996.

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6

McCallum, Bennett T. A semi-classical model of price level adjustment. Cambridge, Mass: National Bureau of Economic Research, 1994.

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7

International Symposium on Semi-Markov Processes and Their Applications (1984 Brussels, Belgium). Semi-Markov models: Theory and applications. New York: Plenum Press, 1986.

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8

United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., ed. Model reduction by trimming for a class of semi-Markov reliability models and the corresponding error bound. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1991.

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9

Elliott, K. H. A computer simulation model of semi-worsted yarn manufacture. Christchurch: WRONZ, 1986.

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10

Elliott, K. H. A computer simulation model of semi-worsted yarn manufacture. Christchurch: WRONZ, 1986.

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11

Elliott, K. H. A computer simulation model of semi-worsted yarn manufacture. Christchurch: WRONZ, 1986.

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12

D'Amico, Guglielmo, Giuseppe Di Biase, Jacques Janssen, and Raimondo Manca. Semi-Markov Migration Models for Credit Risk. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119415084.

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13

Almeida, Ricardo Carvalho de. Simulation of atmospheric frontogenesis with a semi-Lagrangian numerical model. Monterey, Calif: Naval Postgraduate School, 1991.

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14

Feinberg, Fred M. Managing market share when consumers seek variety. Cambridge, Mass: The Marketing Center, Massachusetts Institute of Technology, Alfred P. Sloan School of Management, 1985.

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15

M, Tayal, Atomic Energy of Canada Limited. CANDU., and IAEA Technical Committee, eds. INTEGRITY: A semi-mechanistic model for stress corrosion cracking of fuel. Mississauga, Ont: AECL CANDU, 1994.

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16

Henwood, John. The forgotten Winchesters: A history of the models 1905, 1907, and 1910 self-loading rifles. Pacifica, CA (819 Linda Mar Blvd., Pacifica 94044-3450): J. Henwood, 1995.

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17

Barbu, Vlad Stefan. Semi-Markov chains and hidden semi-Markov models toward applications: Their use in reliability and DNA analysis. New York: Springer, 2008.

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18

Shavell, Steven. Suit versus settlement when parties seek nonmonetary judgments. Cambridge, MA: National Bureau of Economic Research, 1992.

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19

McCallum, Bennett T. Performance of operational policy rules in an estimated semi-classical structural model. Cambridge, MA: National Bureau of Economic Research, 1998.

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20

Li, Chol-Won. Endogenous vs. semi-endogenous growth in a two-R&D-sector model. Glasgow: University of Glasgow, Department of Economics., 1999.

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21

Conway, Paul. Estimating potential output: A semi-structural approach. Wellington, N.Z: Reserve Bank of New Zealand, Economics Dept., 1997.

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22

Taliercio, Robert. Designing performance: The semi-autonomous revenue authority model in Africa and Latin America. [Washington, D.C: World Bank, 2004.

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23

Justus, C. G. Additions to Mars global reference atmospheric model (MARS-GRAM): Semi-annual technical report. [Washington, DC: National Aeronautics and Space Administration, 1991.

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24

United States. National Aeronautics and Space Administration., ed. Semi-span model testing in the National Transonic Facility: Semi-annual status report, May 1994, cooperative agreement no. NCC1, 169. [Washington, DC: National Aeronautics and Space Administration, 1994.

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25

Wheater, Howard. Groundwater modelling in arid and semi-arid areas. New York: Cambridge University Press, 2010.

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26

United States. National Aeronautics and Space Administration., ed. Near-wall modelling of compressible turbulent flows: A semi-annual progress report. Tempe, Ariz: College of Engineering & Applied Sciences, Arizona State University, 1991.

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27

University, Arizona State, and Langley Research Center, eds. Near-wall modelling of compressible turbulent flows: A semi-annual progress report. Tempe, Ariz: Arizona State University, 1990.

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28

United States. National Aeronautics and Space Administration., ed. Near-wall modelling of compressible turbulent flows: A semi-annual progress report. Tempe, Ariz: College of Engineering & Applied Sciences, Arizona State University, 1991.

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29

Salfner, Felix. Modeling event-driven time series with generalized hidden semi-Markov models. Berlin: Humboldt-Universität zu Berlin, 2006.

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30

Ferson, Wayne E. Weak and semi-strong form stock return predictability, revisited. Cambridge, MA: National Bureau of Economic Research, 2004.

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31

Ferson, Wayne E. Weak and semi-strong form stock return predictability revisited. Cambridge, Mass: National Bureau of Economic Research, 2005.

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32

Ferson, Wayne E. Weak and semi-strong form stock return predictability, revisited. Cambridge, Mass: National Bureau of Economic Research, 2004.

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33

Fürstenau, Hagen. Semi-supervised semantic role labeling via graph alignment. Saarbrücken: German Research Center for Artificial Intelligence, 2011.

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34

United States. National Aeronautics and Space Administration., ed. Modelling of hydrogen-air diffusion flame: Semi-annual report. Rolla, Mo: University of Missouri, Mechanical and Aerospace Engineering and Engineering Mechanics Dept., 1989.

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35

Harned, Douglas S. Extending the semi-impact MHD method: grid-point models and improved accuracy. New York: Courant Institute of Mathematical Sciences, New York University, 1986.

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36

Ansell, J. I. Practical aspects of modelling of repairable systems data using semi-parametric models. Edinburgh: University of Edinburgh, Department of Business Studies, 1994.

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37

Harned, Douglas S. Extending the semi-implicit MHD method: Grid-point models and improved accuracy. New York: Courant Institute of Mathematical Sciences, New York University, 1986.

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38

Weaver, Robert D. Price and tax policy for semi-subsistence agriculture in Ethiopia. [Washington, D.C.]: International Bank for Reconstruction and Development, 1988.

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39

Bourgeois-Pichat, Jean. La dynamique des populations: Populations stables, semi-stables et quasi-stables. Paris: Presses Universitaires de France, 1994.

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40

V, Kaza K. R., and United States. National Aeronautics and Space Administration., eds. Semi-empirical model for prediction of unsteady forces on an airfoil with application to flutter. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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41

Semi-Markov Models. Elsevier, 2015. http://dx.doi.org/10.1016/c2014-0-02226-5.

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42

Hidden Semi-Markov Models. Elsevier, 2016. http://dx.doi.org/10.1016/c2014-0-02508-7.

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43

Baya'a, Nimer Fayez. Semi-intelligent computer assisted instruction model. 1987.

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44

Baya'a, Nimer Fayez. Semi-intelligent computer assisted instruction model. 1987.

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45

Semi-Markov models and applications. Dordrecht: Kluwer Academic Publishers, 1999.

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46

Janssen, Jacques, and Nikolaos Limnios. Semi-Markov Models and Applications. Springer London, Limited, 2013.

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47

Janssen, Jacques, and Nikolaos Limnios. Semi-Markov Models and Applications. Springer London, Limited, 2011.

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48

Charry, Luisa, Pranav Gupta, and Vimal Thakoor. Introducing a Semi-Structural Macroeconomic Model for Rwanda. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198785811.003.0018.

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Abstract:
We develop a simple semi-structural model for the Rwandan economy to better understand the monetary policy transmission mechanism. A key feature of the model is the introduction of a modified uncovered interest parity condition to capture key structural features of Rwanda’s economy and policy framework, such as the limited degree of capital mobility and managed floating regime. A filtration of the observed data through the model allows us to illustrate the contribution of various factors to inflation dynamics and its deviations from the inflation target. Our results, consistent with evidence for other countries in the region, suggest that food and oil prices as well as the exchange rate have accounted for the bulk of inflation dynamics in Rwanda.
49

Charry, Luisa. Introducing a Semi-Structural Macroeconomic Model for Rwanda. International Monetary Fund, 2014.

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50

Gupta, Pranav, Luisa Ms Charry, and Vimal Thakoor. Introducing a Semi-Structural Macroeconomic Model for Rwanda. International Monetary Fund, 2014.

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