Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?

1. Allan, G. (2013). Evaluating the usefulness of forecasts of relative growth.Sire Discussion Paper,2013-10, 1-26.Search in Google Scholar

2. Barnichon, R., Nekarda, C. J., Hatzius, J., Stehn, S. J., &Petrongolo, B. (2012). The Ins and Outs of Forecasting Unemployment: Using Labor Force Flows to Forecast the Labor Market [with Comments and Discussion]. Brookings Papers on Economic Activity, 83-131. http://dx.doi.org/10.1353/eca.2012.001810.1353/eca.2012.0018Search in Google Scholar

3. Bratu, M.(2012).The reduction of uncertainty in making decisions by evaluating the macroeconomic forecasts performance in Romania.Economic Research-Scientific Journal, 25(2), 239-262.10.1080/1331677X.2012.11517506Search in Google Scholar

4. Camba-Mendez, G. (2012). Conditional forecasts on SVAR models using theKalman filter.Economics Letters, 115(3), 376-378. http:// dx.doi.org/10.1016/j.econlet.2011.12.08710.1016/j.econlet.2011.12.087Search in Google Scholar

5. Clements, M. P., & Hendry, D. F. (2003). On the limitations of comparing mean squared forecast errors (with discussion). Journal of Forecasting, 12, 617-639. http://dx.doi.org/10.1002/for.398012080210.1002/for.3980120802Search in Google Scholar

6. Franses, P. H., McAleer, M., &Legerstee, R. (2012).Evaluating macroeconomic forecasts: a concise review of some recent developments. Kier Discussion Paper Series, 821, 1-29.Search in Google Scholar

7. Franses, P. H., Paap, R., &Vroomen, B. (2004).Forecasting unemployment using an autoregression with censored latent effects parameters.International Journal of Forecasting, 20(2), 255-271. http://dx.doi.org/10.1016/j.ijforecast.2003.09.00410.1016/j.ijforecast.2003.09.004Search in Google Scholar

8. Heilemann, U., &Stekler, H. O. (2013). Has The Accuracy of Macroeconomic Forecasts for Germany Improved?.German Economic Review, 14(2), 235-253. http://dx.doi.org/10.1111/j.1468-0475.2012.00569.x10.1111/j.1468-0475.2012.00569.xSearch in Google Scholar

9. Hodrick, R., & Prescott, E. C. (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking, 1-16. http://dx.doi.org/10.2307/295368210.2307/2953682Search in Google Scholar

10. Kishor, N. K., & Koenig E. F. (2012).VAR estimation and forecasting when data are subject to revision.Journal of Business & Economic Statistics, 30(2), 181-190.Search in Google Scholar

11. Koopman, S.J., Shephard, N.&Doornik, J.A. ( 1999). Statistical algorithms for models in state space using SsfPack 2.2.Econometrics Journal, 2(1), 107-160. http://dx.doi.org/10.1111/1368-423X.0002310.1111/1368-423X.00023Search in Google Scholar

12. Kurita, T. (2010).A forecasting model for Japan’s unemployment rate.Eurasian Journal of Business and Economics, 3(5), 127-134.Search in Google Scholar

13. Lack, C. (2006).Forecasting Swiss inflation using VAR models.Swiss National Bank Economic Studies, 2.Search in Google Scholar

14. Milas, C.,&Rothman, F. (2008). Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts.International Journal of Forecasting, 24(1), 101-121. http://dx.doi.org/10.1016/j.ijforecast.2007.12.00310.1016/j.ijforecast.2007.12.003Search in Google Scholar

15. Proietti, T. (2003). Forecasting the US unemployment rate.Computational Statistics & Data Analysis, 42(3), 451-476. http://dx.doi. org/10.1016/S0167-9473(02)00230-X10.1016/S0167-9473(02)00230-XSearch in Google Scholar

16. Razzak, W. (1997). The Hodrick-Prescott technique: A smoother versus a filter: An application to New Zealand GDP.Economics Letters, 57(2), 163-168. http://dx.doi.org/10.1016/S0165-1765(97)00178-X10.1016/S0165-1765(97)00178-XSearch in Google Scholar

17. Robinson, W. (1998). Forecasting inflation using VAR Analysis.Bank of Jamaica.Retrieved from http://www.boj.org.jm/uploads/pdf/papers_pamphlets/papers_pamphlets_forecasting_inflation_using_var_analysis.pdfSearch in Google Scholar

18. Sermpinis, G., Stasinakis, C., &Karathanasopoulos, A. (2013).Kalmanfilter and SVR combinations in forecasting US unemployment. Artificial Intelligence Applications and InnovationsIFIP Advances in Information and Communication Technology, 412, 506-515.Search in Google Scholar

19. vanDijk, D., Teräsvirta, T., &Franses, P.H. (2000). Smooth transition autoregressive models-Asurvey of recent developments. Working Paper Series in Economics andFinance, No. 380, Stockholm School of EconomicsSearch in Google Scholar