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In this paper, we review a range of approaches used to capture monetary policy in a period of Zero Lower Bound (ZLB). We concentrate here on methods closely linked to interest rates, which include: spreads, synthetic indices from principal component analysis, and different shadow rates. Next, we...
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In this paper, we review a range of approaches used to capture monetary policy in a period of Zero Lower Bound (ZLB). We concentrate here on methods closely linked to interest rates, which include: spreads, synthetic indices from principal component analysis, and different shadow rates. Next, we...
Persistent link: https://www.econbiz.de/10012958901
This paper proposes a new approach to extract quantile-based inflation risk measures using Quantile Autoregressive Distributed Lag Mixed-Frequency Data Sampling (QADL-MIDAS) regression models. We compare our models to a standard Quantile Auto-Regression (QAR) model and show that it delivers...
Persistent link: https://www.econbiz.de/10012141539
This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle...
Persistent link: https://www.econbiz.de/10013238628
This paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled...
Persistent link: https://www.econbiz.de/10013492089
Often, variables are linked to each other via a network. When such a network structure is known, this knowledge can be incorporated into regularized regression settings via a network penalty term. However, when the type of interaction via the network is unknown (that is, whether connections are...
Persistent link: https://www.econbiz.de/10012898830
Covariates in regressions may be linked to each other on a network. Knowledge of the network structure can be incorporated into regularized regression settings via a network penalty term. However, when it is unknown whether the connection signs in the network are positive (connected covariates...
Persistent link: https://www.econbiz.de/10014357781
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