- Assessing time series models for forecasting international migration: Lessons from the United Kingdom. Journal of Forecasting 38(5): 470–487. doi:10.1002/for.2576.
Paper not yet in RePEc: Add citation now
Azose, J.J. and Raftery, A.E. (2015). Bayesian probabilistic projection of international migration. Demography 52(5): 1627–1650. doi:10.1007/s13524-015-0415-0.
Azose, J.J. and Raftery, A.E. (2019). Estimation of emigration, return migration, and transit migration between all pairs of countries. Proceedings of the National Academy of Sciences 116(1): 116–122. doi:10.1073/pnas.1722334116.
- Azose, J.J., Ševčı́ková, H., and Raftery, A.E. (2016). Probabilistic population projections with migration uncertainty. Proceedings of the National Academy of Sciences 113(23): 6460–6465. doi:10.1073/pnas.1606119113.
Paper not yet in RePEc: Add citation now
Bell, M., Blake, M., Boyle, P., Duke-Williams, O., Rees, P., Stillwell, J., and Hugo, G. (2002). Cross-national comparison of internal migration: Issues and measures.
- Bergmeir, C. and Benı́tez, J.M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences 191: 192–213.
Paper not yet in RePEc: Add citation now
Bergmeir, C., Hyndman, R.J., and Koo, B. (2018). A note on the validity of crossvalidation for evaluating autoregressive time series prediction. Computational Statistics and Data Analysis 120: 70–83. doi:10.1016/j.csda.2017.11.003.
- Bijak, J. (2010). Forecasting international migration in Europe: A Bayesian view, vol. 24. Dordrecht: Springer Science and Business Media.
Paper not yet in RePEc: Add citation now
Bijak, J. and Bryant, J. (2016). Bayesian demography 250 years after Bayes. Population Studies 70(1): 1–19. doi:10.1080/00324728.2015.1122826.
- Box, G. and Jenkins, G. (1976). Time series analysis: Forecasting and control. San Francisco: Holden–Day.
Paper not yet in RePEc: Add citation now
Canova, F. (1998). Detrending and business cycle facts. Journal of Monetary Economics 41(3): 475–512. doi:10.1016/S0304-3932(98)00006-3.
- Congdon, P. (2000). A Bayesian approach to prediction using the gravity model, with an application to patient flow modeling. Geographical Analysis 32(3): 205–224. doi:10.1111/j.1538-4632.2000.tb00425.x. de Beer, J. (1988). Predictability of demographic variables in the short run. European Journal of Population/Revue européenne de Démographie 4(4): 283–296.
Paper not yet in RePEc: Add citation now
- de Jong, A., Alders, M., Feijten, P., Visser, P., Deerenberg, I., Van Huis, M., and Leering, D. (2005). Achtergronden en veronderstellingen bij het model PEARL. Naar een nieuwe regionale bevolkings-en allochtonenprognose. Den Haag: Ruimtelijk Planbureau /Centraal Bureau voor de Statistiek.
Paper not yet in RePEc: Add citation now
- De Livera, A.M., Hyndman, R.J., and Snyder, R.D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association 106(496): 1513–1527. doi:10.1198/jasa.2011.tm09771.
Paper not yet in RePEc: Add citation now
Durbin, J. and Koopman, S.J. (2012). Time series analysis by state space methods. Oxford: Oxford University Press.
- Fotheringham, A.S. and O’Kelly, M.E. (1989). Spatial interaction models: Formulations and applications. Dordrecht: Kluwer Academic Publishers.
Paper not yet in RePEc: Add citation now
- Fotheringham, A.S. and Wong, D.W. (1991). The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A 23(7): 1025–1044. doi:10.1068/a231025.
Paper not yet in RePEc: Add citation now
Hamilton, J.D. (2018). Why you should never use the Hodrick-Prescott filter. Review of Economics and Statistics 100(5): 831–843. doi:10.1162/resta00706.
Harvey, A.C. (1990). Forecasting, structural time series models and the Kalman filter. Cambridge: Cambridge University Press.
- Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
Paper not yet in RePEc: Add citation now
Holt, C.C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting 20(1): 5–10. doi:10.1016/j.ijforecast.2003.09.015.
- http://www.demographic-research.org Husby & Visser: Short- to medium-run forecasting of mobility with dynamic linear models doi:10.1007/BF01797130.
Paper not yet in RePEc: Add citation now
Husby, T.G., de Groot, H.L., Hofkes, M.W., and Dröes, M.I. (2014). Do floods have permanent effects? Evidence from the Netherlands. Journal of Regional Science 54(3): 355–377. doi:10.1111/jors.12112.
- Hyndman, R.J. and Athanasopoulos, G. (2018). Forecasting: Principles and practice. Melbourne: OTexts.
Paper not yet in RePEc: Add citation now
Hyndman, R.J. and Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software 26(3): 1–22. doi:10.18637/jss.v027.i03.
Hyndman, R.J. and Koehler, A.B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting 22(4): 679–688. 894 http://www.demographic-research.org Demographic Research: Volume 45, Article 28 doi:10.1016/j.ijforecast.2006.03.001.
Hyndman, R.J., Koehler, A.B., Snyder, R.D., and Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting 18(3): 439–454. doi:10.1016/S0169-2070(01)00110-8.
- Kalman, R.E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana 5(2): 102–119.
Paper not yet in RePEc: Add citation now
Kaplan, G. and Schulhofer-Wohl, S. (2017). Understanding the long-run decline in interstate migration. International Economic Review 58(1): 57–94. doi:10.1111/iere.12209.
- Koopman, S.J. and Ooms, M. (2011). Forecasting economic time series using unobserved components time series models. In: Clements, M.P. and Hendry, D.F. (eds.). The Oxford handbook of economic forecasting. Oxford: Oxford University Press.
Paper not yet in RePEc: Add citation now
Lee, R. and Anderson, M. (2002). Malthus in state space: Macro economic-demographic relations in English history, 1540 to 1870. Journal of Population Economics 15(2): 195–220. doi:10.1007/s001480100091.
Li, J.S.H., Zhou, K.Q., Zhu, X., Chan, W.S., and Chan, F.W.H. (2019). A Bayesian approach to developing a stochastic mortality model for China. Journal of the Royal Statistical Society: Series A (Statistics in Society) 182(4): 1523–1560. doi:10.1111/rssa.12473.
Matthews, S.A. and Parker, D.M. (2013). Progress in spatial demography. Demographic Research 28(10): 271–312. doi:10.4054/demres.2013.28.10.
- Monahan, J.F. (1984). A note on enforcing stationarity in autoregressive-moving average models. Biometrika 71(2): 403–404. doi:10.1093/biomet/71.2.403.
Paper not yet in RePEc: Add citation now
Mulder, C.H. (2018). Putting family centre stage: ties to nonresident family, internal migration, and immobility. Demographic Research 39(43): 1151–1180.
- Ordorica-Mellado, M. and Garcı́a-Guerrero, V.M. (2016). Estimating the demographic dynamic of small areas with the Kalman filter. In: Schoen, R. (ed.). Dynamic demographic analysis. Cham: Springer: 261–271.
Paper not yet in RePEc: Add citation now
- Petris, G., Petrone, S., and Campagnoli, P. (2009). Dynamic linear models with R. New York: Springer.
Paper not yet in RePEc: Add citation now
- Raftery, A.E., Li, N., Ševčı́ková, H., Gerland, P., and Heilig, G.K. (2012). Bayesian probabilistic population projections for all countries. Proceedings of the National Academy of Sciences 109(35): 13915–13921. doi:10.1073/pnas.1211452109.
Paper not yet in RePEc: Add citation now
- Raymer, J., Willekens, F., and Rogers, A. (2019). Spatial demography: A unifying http://www.demographic-research.org Husby & Visser: Short- to medium-run forecasting of mobility with dynamic linear models core and agenda for further research. Population, Space and Place 25(4): e2179. doi:10.1002/psp.2179.
Paper not yet in RePEc: Add citation now
Rueda, C. and Rodrı́guez, P. (2010). State space models for estimating and forecasting fertility. International Journal of Forecasting 26(4): 712–724. doi:10.1016/j.ijforecast.2009.09.008.
- Smith, S.K., Tayman, J., and Swanson, D.A. (2013). A practitioner’s guide to state and local population projections. Dordrecht: Springer.
Paper not yet in RePEc: Add citation now
Tashman, L.J. (2000). Out-of-sample tests of forecasting accuracy: An analysis and review. International Journal of Forecasting 16(4): 437–450. doi:10.1016/S01692070 (00)00065-0.
- te Riele, S., Huisman, C., Stoeldraijer, L., de Jong, A., van Duin, C., and Husby, T. (2019). PBL/CBS Regionale bevolkings- enhuishoudensprognose 2019–2050: Belangrijkste uitkomsten. Den Haag: Statistische Trends.
Paper not yet in RePEc: Add citation now
- West, M. and Harrison, J. (2006). Bayesian forecasting and dynamic models. New York: Springer Science and Business Media.
Paper not yet in RePEc: Add citation now
Winters, P.R. (1960). Forecasting sales by exponentially weighted moving averages.
Wiśniowski, A., Smith, P.W., Bijak, J., Raymer, J., and Forster, J.J. (2015). Bayesian population forecasting: Extending the Lee-Carter method. Demography 52(3): 1035– 1059. doi:10.1002/psp.2179.
- Young, P.C., Ng, C.N., Lane, K., and Parker, D. (1991). Recursive forecasting, smoothing and seasonal adjustment of non-stationary environmental data. Journal of Forecasting 10(1-2): 57–89. doi:10.1002/FOR.3980100105.
Paper not yet in RePEc: Add citation now
Zietz, J. and Traian, A. (2014). When was the US housing downturn predictable? A comparison of univariate forecasting methods. The Quarterly Review of Economics and Finance 54(2): 271–281. doi:10.1016/j.qref.2013.12.004. 896 http://www.demographic-research.org Demographic Research: Volume 45, Article 28