Anders Bredahl Kock : Citation Profile


Are you Anders Bredahl Kock?

Oxford University (90% share)
Aarhus Universitet (5% share)
Aarhus Universitet (5% share)

8

H index

4

i10 index

179

Citations

RESEARCH PRODUCTION:

14

Articles

21

Papers

RESEARCH ACTIVITY:

   11 years (2009 - 2020). See details.
   Cites by year: 16
   Journals where Anders Bredahl Kock has often published
   Relations with other researchers
   Recent citing documents: 43.    Total self citations: 16 (8.21 %)

MORE DETAILS IN:
ABOUT THIS REPORT:

   Permalink: http://citec.repec.org/pko276
   Updated: 2021-01-16    RAS profile: 2019-11-11    
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Relations with other researchers


Works with:

Caner, Mehmet (4)

Callot, Laurent (2)

Authors registered in RePEc who have co-authored more than one work in the last five years with Anders Bredahl Kock.

Is cited by:

Barigozzi, Matteo (10)

Claveria, Oscar (10)

Audrino, Francesco (10)

Smeekes, Stephan (8)

Camponovo, Lorenzo (7)

Medeiros, Marcelo (7)

Chernozhukov, Victor (4)

Mogliani, Matteo (4)

Brownlees, Christian (4)

Hallin, Marc (4)

Lee, Tae Hwy (4)

Cites to:

Fan, Jianqing (22)

Leeb, Hannes (13)

Pötscher, Benedikt (13)

Chernozhukov, Victor (11)

Teräsvirta, Timo (8)

Tetenov, Aleksey (7)

Hansen, Christian (7)

Caner, Mehmet (7)

Hansen, Bruce (6)

Marcellino, Massimiliano (5)

White, Halbert (5)

Main data


Where Anders Bredahl Kock has published?


Journals with more than one article published# docs
Econometric Reviews3
Journal of Econometrics3
Econometric Theory2

Working Papers Series with more than one paper published# docs
Papers / arXiv.org2
Tinbergen Institute Discussion Papers / Tinbergen Institute2

Recent works citing Anders Bredahl Kock (2021 and 2020)


YearTitle of citing document
2020On LASSO for Predictive Regression. (2018). Lee, Ji Hyung ; Gao, Zhan ; Shi, Zhentao ; Hyung, JI. In: Papers. RePEc:arx:papers:1810.03140.

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2020Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure. (2019). Smeekes, Stephan ; Margaritella, Luca ; Hecq, Alain. In: Papers. RePEc:arx:papers:1902.10991.

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2020Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction. (2019). Mogliani, Matteo. In: Papers. RePEc:arx:papers:1903.08025.

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2020Time series models for realized covariance matrices based on the matrix-F distribution. (2019). Zhu, Ke ; Li, Wai Keung ; Jiang, Feiyu ; Zhou, Jiayuan. In: Papers. RePEc:arx:papers:1903.12077.

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2020Exact Testing of Many Moment Inequalities Against Multiple Violations. (2019). Bekker, Paul ; Koning, Nick. In: Papers. RePEc:arx:papers:1904.12775.

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2020Data-driven covariance estimators for high-dimensional minimum-variance portfolios. (2019). Steinert, Rick ; Shivarova, Antoniya ; Husmann, Sven . In: Papers. RePEc:arx:papers:1910.13960.

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2020Estimation and HAC-based Inference for Machine Learning Time Series Regressions. (2019). Striaukas, Jonas ; Ghysels, Eric ; Babii, Andrii. In: Papers. RePEc:arx:papers:1912.06307.

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2020Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data. (2020). Spindler, Martin ; Luo, YE ; Chen, XI. In: Papers. RePEc:arx:papers:1912.12867.

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2020Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions. (2020). Schweikert, Karsten. In: Papers. RePEc:arx:papers:2001.07949.

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2020Functional Sequential Treatment Allocation with Covariates. (2020). Veliyev, Bezirgen ; Preinerstorfer, David ; Kock, Anders Bredahl. In: Papers. RePEc:arx:papers:2001.10996.

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2020Sharpe Ratio in High Dimensions: Cases of Maximum Out of Sample, Constrained Maximum, and Optimal Portfolio Choice. (2020). Vasconcelos, Gabriel ; Medeiros, Marcelo ; Caner, Mehmet. In: Papers. RePEc:arx:papers:2002.01800.

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2020Wild Bootstrap Inference for Penalized Quantile Regression for Longitudinal Data. (2020). Parker, Thomas ; Lamarche, Carlos. In: Papers. RePEc:arx:papers:2004.05127.

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2020A dynamic conditional approach to portfolio weights forecasting. (2020). Palandri, Alessandro ; Gallo, Giampiero M ; Cipollini, Fabrizio. In: Papers. RePEc:arx:papers:2004.12400.

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2020Treatment recommendation with distributional targets. (2020). Veliyev, Bezirgen ; Preinerstorfer, David ; Kock, Anders Bredahl. In: Papers. RePEc:arx:papers:2005.09717.

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2020Machine learning time series regressions with an application to nowcasting. (2020). Babii, Andrii ; Striaukas, Jonas ; Ghysels, Eric. In: Papers. RePEc:arx:papers:2005.14057.

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2020Lasso Inference for High-Dimensional Time Series. (2020). Smeekes, Stephan ; Wilms, Ines ; Adamek, Robert. In: Papers. RePEc:arx:papers:2007.10952.

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2020An Upper Bound for Functions of Estimators in High Dimensions. (2020). Han, XU ; Caner, Mehmet. In: Papers. RePEc:arx:papers:2008.02636.

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2020Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data. (2020). Tao, Jing ; Peng, Sida ; Ning, Yang. In: Papers. RePEc:arx:papers:2009.03151.

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2020Dimension Reduction for High Dimensional Vector Autoregressive Models. (2020). Hecq, Alain ; Cubadda, Gianluca. In: Papers. RePEc:arx:papers:2009.03361.

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2020Regularization Approach for Network Modeling of German Power Derivative Market. (2020). L'Opez, Brenda ; Hardle, Wolfgang Karl ; Chen, Shi. In: Papers. RePEc:arx:papers:2009.09739.

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2020Online Action Learning in High Dimensions: A New Exploration Rule for Contextual $\epsilon_t$-Greedy Heuristics. (2020). Medeiros, Marcelo C ; Flores, Claudio C. In: Papers. RePEc:arx:papers:2009.13961.

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2020Time-varying Forecast Combination for High-Dimensional Data. (2020). Maung, Kenwin ; Chen, Bin. In: Papers. RePEc:arx:papers:2010.10435.

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2020Learning from Forecast Errors: A New Approach to Forecast Combinations. (2020). Seregina, Ekaterina ; Lee, Tae-Hwy. In: Papers. RePEc:arx:papers:2011.02077.

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2020A Basket Half Full: Sparse Portfolios. (2020). Seregina, Ekaterina. In: Papers. RePEc:arx:papers:2011.04278.

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2020Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network. (2020). Wang, Guanyi ; Kitagawa, Toru. In: Papers. RePEc:arx:papers:2012.04055.

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2020Sparse vector error correction models with application to cointegration‐based trading. (2020). Wang, Xiaohang ; Lu, Renjie . In: Australian & New Zealand Journal of Statistics. RePEc:bla:anzsta:v:62:y:2020:i:3:p:297-321.

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2020A Dynamic Network of Arbitrage Characteristics. (2020). Linton, O. In: Cambridge Working Papers in Economics. RePEc:cam:camdae:2060.

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2020Artificial neural network regression models in a panel setting: Predicting economic growth. (2020). Jahn, Malte. In: Economic Modelling. RePEc:eee:ecmode:v:91:y:2020:i:c:p:148-154.

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2020Forecasting large covariance matrix with high-frequency data using factor approach for the correlation matrix. (2020). Tse, Yiu-Kuen ; Dong, Yingjie. In: Economics Letters. RePEc:eee:ecolet:v:195:y:2020:i:c:s016517652030286x.

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2020Inference for high-dimensional instrumental variables regression. (2020). Lederer, Johannes ; Gold, David ; Tao, Jing. In: Journal of Econometrics. RePEc:eee:econom:v:217:y:2020:i:1:p:79-111.

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2020Statistical inferences for realized portfolio weights. (2020). Lazariv, Taras ; Seifert, Miriam Isabel ; Schmid, Wolfgang ; Golosnoy, Vasyl. In: Econometrics and Statistics. RePEc:eee:ecosta:v:14:y:2020:i:c:p:49-62.

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2020Factor state–space models for high-dimensional realized covariance matrices of asset returns. (2020). Gribisch, Bastian ; Liesenfeld, Roman ; Hartkopf, Jan Patrick. In: Journal of Empirical Finance. RePEc:eee:empfin:v:55:y:2020:i:c:p:1-20.

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2020Volatility spillovers in commodity markets: A large t-vector autoregressive approach. (2020). Wilms, Ines ; Barbaglia, Luca ; Croux, Christophe. In: Energy Economics. RePEc:eee:eneeco:v:85:y:2020:i:c:s0140988319303500.

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2020On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks. (2020). Zhang, Dayong ; Klein, Tony ; Ji, Qiang ; Luo, Jiawen ; Todorova, Neda. In: Energy Economics. RePEc:eee:eneeco:v:89:y:2020:i:c:s0140988320301213.

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2020Realized volatility forecast with the Bayesian random compressed multivariate HAR model. (2020). Chen, Langnan ; Luo, Jiawen. In: International Journal of Forecasting. RePEc:eee:intfor:v:36:y:2020:i:3:p:781-799.

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2021Boosting nonlinear predictability of macroeconomic time series. (2021). Virtanen, Timo ; Kauppi, Heikki. In: International Journal of Forecasting. RePEc:eee:intfor:v:37:y:2021:i:1:p:151-170.

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2020Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions. (2020). Wochner, Daniel. In: KOF Working papers. RePEc:kof:wpskof:20-472.

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2020Most Powerful Test against High Dimensional Free Alternatives. (2020). GAO, Jiti ; Jaidee, Sombut ; He, YI. In: Monash Econometrics and Business Statistics Working Papers. RePEc:msh:ebswps:2020-13.

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2020A Bayesian Covariance Graph And Latent Position Model For Multivariate Financial Time Series. (2020). Ahelegbey, Daniel Felix ; Carvalho, Luis ; Kolaczyk, Eric D. In: DEM Working Papers Series. RePEc:pav:demwpp:demwp0181.

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2020Comparison of macroeconomic indicators nowcasting methods: Russian GDP case. (2020). Stankevich, Ivan. In: Applied Econometrics. RePEc:ris:apltrx:0402.

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2020Modified LASSO estimators for time series regression models with dependent disturbances. (2020). Taniguchi, Masanobu ; Xue, Yujie. In: Statistical Methods & Applications. RePEc:spr:stmapp:v:29:y:2020:i:4:d:10.1007_s10260-020-00506-w.

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2020Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations. (2020). Hungnes, HÃ¥vard. In: Discussion Papers. RePEc:ssb:dispap:931.

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2020Learning from Forecast Errors: A New Approach to Forecast Combination. (2020). Seregina, Ekaterina ; Lee, Tae-Hwy. In: Working Papers. RePEc:ucr:wpaper:202024.

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Works by Anders Bredahl Kock:


YearTitleTypeCited
2009Forecasting with Universal Approximators and a Learning Algorithm In: CREATES Research Papers.
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paper2
2011Forecasting with Universal Approximators and a Learning Algorithm.(2011) In: Journal of Time Series Econometrics.
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This paper has another version. Agregated cites: 2
article
2010Forecasting with nonlinear time series models In: CREATES Research Papers.
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paper14
2010Oracle Efficient Variable Selection in Random and Fixed Effects Panel Data Models In: CREATES Research Papers.
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paper7
2013ORACLE EFFICIENT VARIABLE SELECTION IN RANDOM AND FIXED EFFECTS PANEL DATA MODELS.(2013) In: Econometric Theory.
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This paper has another version. Agregated cites: 7
article
2011Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques In: CREATES Research Papers.
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paper8
2016Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques.(2016) In: Econometric Reviews.
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This paper has another version. Agregated cites: 8
article
2011Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009 In: CREATES Research Papers.
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paper9
2014Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009.(2014) In: International Journal of Forecasting.
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This paper has another version. Agregated cites: 9
article
2012On the Oracle Property of the Adaptive Lasso in Stationary and Nonstationary Autoregressions In: CREATES Research Papers.
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paper9
2012Oracle Inequalities for High Dimensional Vector Autoregressions In: CREATES Research Papers.
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paper51
2015Oracle inequalities for high dimensional vector autoregressions.(2015) In: Journal of Econometrics.
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This paper has another version. Agregated cites: 51
article
2012Oracle Efficient Estimation and Forecasting with the Adaptive LASSO and the Adaptive Group LASSO in Vector Autoregressions In: CREATES Research Papers.
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paper7
2013Oracle inequalities for high-dimensional panel data models In: CREATES Research Papers.
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paper4
2013Lassoing the Determinants of Retirement In: CREATES Research Papers.
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paper0
2016Lassoing the Determinants of Retirement.(2016) In: Econometric Reviews.
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This paper has another version. Agregated cites: 0
article
2013Oracle Inequalities for Convex Loss Functions with Non-Linear Targets In: CREATES Research Papers.
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paper1
2016Oracle Inequalities for Convex Loss Functions with Nonlinear Targets.(2016) In: Econometric Reviews.
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This paper has another version. Agregated cites: 1
article
2014Asymptotically Honest Confidence Regions for High Dimensional Parameters by the Desparsified Conservative Lasso In: CREATES Research Papers.
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paper8
2018Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso.(2018) In: Journal of Econometrics.
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This paper has another version. Agregated cites: 8
article
2014Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice In: CREATES Research Papers.
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paper2
2014Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice.(2014) In: Tinbergen Institute Discussion Papers.
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This paper has another version. Agregated cites: 2
paper
2014Inference in High-dimensional Dynamic Panel Data Models In: CREATES Research Papers.
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paper3
2015Sharp Threshold Detection Based on Sup-norm Error rates in High-dimensional Models In: CREATES Research Papers.
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paper1
2017Sharp Threshold Detection Based on Sup-Norm Error Rates in High-Dimensional Models.(2017) In: Journal of Business & Economic Statistics.
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This paper has another version. Agregated cites: 1
article
2015Sharp Threshold Detection based on Sup-Norm Error Rates in High-dimensional Models.(2015) In: Tinbergen Institute Discussion Papers.
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This paper has another version. Agregated cites: 1
paper
2016Inference in partially identified models with many moment inequalities using Lasso In: CREATES Research Papers.
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paper4
2018Optimal sequential treatment allocation In: Papers.
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2020Functional Sequential Treatment Allocation In: Papers.
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paper4
2016CONSISTENT AND CONSERVATIVE MODEL SELECTION WITH THE ADAPTIVE LASSO IN STATIONARY AND NONSTATIONARY AUTOREGRESSIONS In: Econometric Theory.
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article9
2017Power in High-dimensional testing Problems In: Working Papers ECARES.
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paper2
2019Power in High‐Dimensional Testing Problems.(2019) In: Econometrica.
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This paper has another version. Agregated cites: 2
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2016Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models In: Journal of Econometrics.
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article1
2013Forecasting the Finnish Consumer Price Inflation Using Artificial Neural Network Models and Three Automated Model Selection Techniques In: Finnish Economic Papers.
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article14
2017Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice In: Journal of Applied Econometrics.
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article19

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