nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒10‒17
four papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. The boosted HP filter is more general than you might think By Ziwei Mei; Peter C. B. Phillips; Zhentao Shi
  2. Manfred Deistler and the General Dynamic Factor Model Approach to the Analysis of High-Dimensional Time Series By Marc Hallin
  3. Macroeconomic Forecasting in a Multi-country Context By Yu Bai; Andrea Carriero; Todd E. Clark; Massimiliano Marcellino
  4. Local Projection Inference in High Dimensions By Robert Adamek; Stephan Smeekes; Ines Wilms

  1. By: Ziwei Mei; Peter C. B. Phillips; Zhentao Shi
    Abstract: The global financial crisis and Covid recession have renewed discussion concerning trend-cycle discovery in macroeconomic data, and boosting has recently upgraded the popular HP filter to a modern machine learning device suited to data-rich and rapid computational environments. This paper sheds light on its versatility in trend-cycle determination, explaining in a simple manner both HP filter smoothing and the consistency delivered by boosting for general trend detection. Applied to a universe of time series in FRED databases, boosting outperforms other methods in timely capturing downturns at crises and recoveries that follow. With its wide applicability the boosted HP filter is a useful automated machine learning addition to the macroeconometric toolkit.
    Date: 2022–09
  2. By: Marc Hallin
    Abstract: For more than half a century, Manfred Deistler has been contributing to the construction of the rigorous theoretical foundations of the statistical analysis of time series and more general stochastic processes. Half a century of unremitting activity is not easily summarized in a few pages. In thisshort note, we chose to concentrate on a relatively little-known aspect of Manfred’s contribution which nevertheless had quite an impact on the development of one of the most powerful tools of contemporary time series and econometrics: dynamic factor models.
    Keywords: High-dimensional time series, General Dynamic Factor Models, spiked covariance model, reduced-rank process.
    Date: 2022–09
  3. By: Yu Bai; Andrea Carriero; Todd E. Clark; Massimiliano Marcellino
    Abstract: In this paper we propose a hierarchical shrinkage approach for multi-country VAR models. In implementation, we consider three different scale mixtures of Normals priors — specifically, Horseshoe, Normal- Gamma, and Normal-Gamma-Gamma priors. We provide new theoretical results for the Normal-Gamma prior. Empirically, we use a quarterly data set for the G7 economies to examine how model specifications and prior choices affect the forecasting performance for GDP growth, inflation, and a short-term interest rate. We find that hierarchical shrinkage, particularly as implemented with the Horseshoe prior, is very useful in forecasting inflation. It also has the best density forecast performance for output growth and the interest rate. Adding foreign information yields benefits, as multi-country models generally improve on the forecast accuracy of single-country models.
    Keywords: Multi-country VARs; Macroeconomic forecasting; Hierarchical shrinkage; Scale mixtures
    JEL: C11 C33 C53 C55
    Date: 2021–02–03
  4. By: Robert Adamek; Stephan Smeekes; Ines Wilms
    Abstract: In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local projections, while leaving the impulse response parameter of interest unpenalized. We establish the uniform asymptotic normality of the proposed estimator under general conditions. Finally, we demonstrate small sample performance through a simulation study and consider two canonical applications in macroeconomic research on monetary policy and government spending.
    Date: 2022–09

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