1. A real-time regional accounts database for Germany with applications to GDP revisions and nowcasting. (2024). Lehmann, Robert.
   In: Empirical Economics.
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2. Quarterly GDP Estimates for the German States. (2022). Lehmann, Robert ; Wikman, Ida.
   In: MPRA Paper.
   RePEc:pra:mprapa:112642.

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3. Using boosting for forecasting electric energy consumption during a recession: a case study for the Brazilian State Rio Grande do Sul. (2021). Lindenmeyer, Guilherme ; Skorin, Pedro Pablo ; da Silva, Hudson.
   In: Letters in Spatial and Resource Sciences.
   RePEc:spr:lsprsc:v:14:y:2021:i:2:d:10.1007_s12076-021-00268-3.

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4. Nowcasting East German GDP growth: a MIDAS approach. (2020). Holtemöller, Oliver ; Heinisch, Katja ; Holtemoller, Oliver ; Claudio, Joo C.
   In: Empirical Economics.
   RePEc:spr:empeco:v:58:y:2020:i:1:d:10.1007_s00181-019-01810-5.

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5. Das neue ifo Geschäftsklima Ostdeutschland und Sachsen: Hintergründe und Anpassungen. (2018). Wohlrabe, Klaus ; Sauer, Stefan ; Weber, Michael.
   In: ifo Dresden berichtet.
   RePEc:ces:ifodre:v:25:y:2018:i:03:p:20-24.

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1. Andrada-Félix, J., Fernández-Rodríguez, F.: Improving moving average trading rules with boosting and statistical learning methods. J. Forecast. 27(5), 433–449 (2008).
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2. Bühlmann, P., Yu, B.: Boosting with the $$L_2$$ L 2 loss: regression and classification. J. Am. Stat. Assoc. 98(462), 324–339 (2003).
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10. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001).
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11. Henzel, S.R., Lehmann, R., Wohlrabe, K.: Nowcasting regional GDP: the case of the Free State of Saxony. Rev. Econ. 66(1), 71–98 (2015).

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1. Forecasting using cross-section average–augmented time series regressions. (2021). Westerlund, Joakim ; Karabiyik, Hande.
   In: The Econometrics Journal.
   RePEc:oup:emjrnl:v:24:y:2021:i:2:p:315-333..

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2. Dynamic Factor Models. (2019). Fuleky, Peter ; Doz, Catherine.
   In: PSE Working Papers.
   RePEc:hal:psewpa:halshs-02262202.

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3. Dynamic Factor Models. (2019). Fuleky, Peter ; Doz, Catherine.
   In: Working Papers.
   RePEc:hae:wpaper:2019-4.

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4. Boosting High Dimensional Predictive Regressions with Time Varying Parameters. (2019). Ng, Serena ; Yousuf, Kashif.
   In: Papers.
   RePEc:arx:papers:1910.03109.

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5. Inference in additively separable models with a high-dimensional set of conditioning variables. (2018). Kozbur, Damian.
   In: ECON - Working Papers.
   RePEc:zur:econwp:284.

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6. Testing-Based Forward Model Selection. (2018). Kozbur, Damian.
   In: ECON - Working Papers.
   RePEc:zur:econwp:283.

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7. Sharp convergence rates for forward regression in high-dimensional sparse linear models. (2018). Kozbur, Damian.
   In: ECON - Working Papers.
   RePEc:zur:econwp:253.

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8. Monitoring Bank Failures in a Data-Rich Environment. (2018). Moran, Kevin ; Gnagne, Jean Armand.
   In: Cahiers de recherche.
   RePEc:lvl:crrecr:1815.

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9. Forecasting with Many Predictors: How Useful are National and International Confidence Data?. (2018). Rherrad, Imad ; Moran, Kevin ; Nono, Simplice Aime.
   In: Cahiers de recherche.
   RePEc:lvl:crrecr:1814.

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10. Understanding survey-based inflation expectations. (2018). Berge, Travis.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:34:y:2018:i:4:p:788-801.

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11. Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods. (2018). Swanson, Norman ; Kim, Hyun Hak.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:34:y:2018:i:2:p:339-354.

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12. Boosting and regional economic forecasting: the case of Germany. (2017). Wohlrabe, Klaus ; Lehmann, Robert.
    In: Letters in Spatial and Resource Sciences.
    RePEc:spr:lsprsc:v:10:y:2017:i:2:d:10.1007_s12076-016-0179-1.

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13. Model Selection In Factor-augmented Regressions With Estimated Factors. (2017). Djogbenou, Antoine.
    In: Working Paper.
    RePEc:qed:wpaper:1391.

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14. Uncertainty and Forecasts of U.S. Recessions. (2017). Pierdzioch, Christian ; GUPTA, RANGAN.
    In: Working Papers.
    RePEc:pre:wpaper:201732.

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15. Predicting recessions with boosted regression trees. (2017). Pierdzioch, Christian ; Fritsche, Ulrich ; Dopke, Jorg.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:33:y:2017:i:4:p:745-759.

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16. Sufficient forecasting using factor models. (2017). Fan, Jianqing ; Xue, Lingzhou ; Yao, Jiawei.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:201:y:2017:i:2:p:292-306.

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17. Modelling and Forecasting Recessions in Oil-exporting Countries: The Case of Iran. (2017). Fattahi, Shahram ; Sohaili, Kiomars ; Monkaresi, Hamed ; Mehrabi, Fatemeh.
    In: International Journal of Economics and Financial Issues.
    RePEc:eco:journ1:2017-03-75.

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18. Measuring the value of housing services in household surveys: an application of machine learning approach. (2017). Zereyesus, Yacob A ; Embaye, Weldensie T.
    In: 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama.
    RePEc:ags:saea17:252851.

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19. Model selection with factors and variables. (2016). Fosten, Jack.
    In: University of East Anglia School of Economics Working Paper Series.
    RePEc:uea:ueaeco:2016_07.

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20. Comment. (2016). Swanson, Norman.
    In: Journal of Business & Economic Statistics.
    RePEc:taf:jnlbes:v:34:y:2016:i:3:p:348-353.

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21. A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation. (2016). Risse, Marian ; Pierdzioch, Christian ; Rohloff, Sebastian.
    In: Applied Economics Letters.
    RePEc:taf:apeclt:v:23:y:2016:i:5:p:347-352.

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22. A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss. (2016). Risse, Marian ; Pierdzioch, Christian ; Rohloff, Sebastian.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:47:y:2016:i:c:p:95-107.

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23. Econometric estimation with high-dimensional moment equalities. (2016). Shi, Zhentao.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:195:y:2016:i:1:p:104-119.

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24. ifo Konjunkturumfragen und Konjunkturanalyse: Band II. (2016). Nierhaus, Wolfgang ; Wollmershauser, Timo.
    In: ifo Forschungsberichte.
    RePEc:ces:ifofob:72.

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25. Boosting and Regional Economic Forecasting: The Case of Germany. (2016). Wohlrabe, Klaus ; Lehmann, Robert.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_6157.

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26. Bayesian Inference in a Non-linear/Non-Gaussian Switching State Space Model: Regime-dependent Leverage Effect in the U.S. Stock Market. (2015). .
    In: MPRA Paper.
    RePEc:pra:mprapa:67153.

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27. Stock market volatility: Identifying major drivers and the nature of their impact. (2015). Mittnik, Stefan ; Robinzonov, Nikolay ; Spindler, Martin.
    In: Journal of Banking & Finance.
    RePEc:eee:jbfina:v:58:y:2015:i:c:p:1-14.

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28. Forecasting with factor-augmented regression: A frequentist model averaging approach. (2015). Hansen, Bruce ; Cheng, XU.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:186:y:2015:i:2:p:280-293.

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29. Housing Market Forecasting with Factor Combinations. (2015). Rahal, Charles.
    In: Discussion Papers.
    RePEc:bir:birmec:15-05r.

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30. Housing Market Forecasting with Factor Combinations. (2015). Rahal, Charles.
    In: Discussion Papers.
    RePEc:bir:birmec:15-05.

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31. The Forecasting Power of the Yield Curve, a Supervised Factor Model Approach. (2015). Hillebrand, Eric ; Boldrini, Lorenzo .
    In: CREATES Research Papers.
    RePEc:aah:create:2015-39.

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32. Supervision in Factor Models Using a Large Number of Predictors. (2015). Hillebrand, Eric ; Boldrini, Lorenzo .
    In: CREATES Research Papers.
    RePEc:aah:create:2015-38.

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33. Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area, and Germany. (2014). Wohlrabe, Klaus ; Teresa, Buchen .
    In: VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy.
    RePEc:zbw:vfsc14:100626.

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34. Forecasting Aggregates with Disaggregate Variables: Does boosting help to select the most informative predictors?. (2014). Zeng, Jing.
    In: VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy.
    RePEc:zbw:vfsc14:100310.

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35. On the Selection of Common Factors for Macroeconomic Forecasting. (2014). Proietti, Tommaso ; Giovannelli, Alessandro.
    In: MPRA Paper.
    RePEc:pra:mprapa:60673.

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36. Boosting multi-step autoregressive forecasts. (2014). Hyndman, Rob ; Ben Taieb, Souhaib.
    In: Monash Econometrics and Business Statistics Working Papers.
    RePEc:msh:ebswps:2014-13.

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37. Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?. (2014). Zeng, Jing.
    In: Working Paper Series of the Department of Economics, University of Konstanz.
    RePEc:knz:dpteco:1420.

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38. Forecasting with approximate dynamic factor models: The role of non-pervasive shocks. (2014). Luciani, Matteo.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:30:y:2014:i:1:p:20-29.

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39. Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence. (2014). Swanson, Norman ; Kim, Hyun Hak.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:178:y:2014:i:p2:p:352-367.

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40. Variable Selection in Predictive Regressions. (2013). Ng, Serena.
    In: Handbook of Economic Forecasting.
    RePEc:eee:ecofch:2-752.

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41. Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea. (2013). Kim, Hyun Hak.
    In: Working Papers.
    RePEc:bok:wpaper:1326.

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42. Variable selection, estimation and inference for multi-period forecasting problems. (2011). Timmermann, Allan ; Pesaran, Mohammad ; Pick, Andreas.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:164:y:2011:i:1:p:173-187.

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43. Forecasting with many predictors: Is boosting a viable alternative?. (2011). Wohlrabe, Klaus ; Buchen, Teresa.
    In: Economics Letters.
    RePEc:eee:ecolet:v:113:y:2011:i:1:p:16-18.

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44. Forecasting using targeted diffusion indexes. (2010). Rua, António ; Pinheiro, Maximiano ; Dias, Francisco.
    In: Journal of Forecasting.
    RePEc:jof:jforec:v:29:y:2010:i:3:p:341-352.

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45. Variable Selection, Estimation and Inference for Multi-period Forecasting Problems. (2010). Pick, Andreas ; Timmermann, Allan.
    In: Working Papers.
    RePEc:dnb:dnbwpp:250.

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46. Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models. (2009). Marcellino, Massimiliano ; Kapetanios, George ; Carriero, Andrea.
    In: Economics Working Papers.
    RePEc:eui:euiwps:eco2009/31.

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47. Variable Selection and Inference for Multi-period Forecasting Problems. (2009). Timmermann, Allan ; Pesaran, Mohammad ; Pick, Andreas.
    In: CEPR Discussion Papers.
    RePEc:cpr:ceprdp:7139.

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48. IFOCAST: Methoden der ifo-Kurzfristprognose. (2009). Wohlrabe, Klaus ; Mayr, Johannes ; Henzel, Steffen ; Carstensen, Kai.
    In: ifo Schnelldienst.
    RePEc:ces:ifosdt:v:62:y:2009:i:23:p:15-28.

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49. Variable Selection and Inference for Multi-period Forecasting Problems. (2009). Timmermann, Allan ; Pesaran, Mohammad ; Pick, Andreas.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_2543.

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50. Variable Selection and Inference for Multi-period Forecasting Problems. (2009). Timmermann, Allan ; Pesaran, Mohammad ; Pick, A..
    In: Cambridge Working Papers in Economics.
    RePEc:cam:camdae:0901.

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