An overview of the estimation of large covariance and precision matrices
The estimation of large covariance and precision matrices is fundamental in modern
multivariate analysis. However, problems arise from the statistical analysis of large panel …
multivariate analysis. However, problems arise from the statistical analysis of large panel …
Macroeconomic nowcasting and forecasting with big data
Data, data, data…. Economists know their importance well, especially when it comes to
monitoring macroeconomic conditions—the basis for making informed economic and policy …
monitoring macroeconomic conditions—the basis for making informed economic and policy …
Spectral methods for data science: A statistical perspective
Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
US monetary policy and the global financial cycle
S Miranda-Agrippino, H Rey - The Review of Economic Studies, 2020 - academic.oup.com
US monetary policy shocks induce comovements in the international financial variables that
characterize the “Global Financial Cycle.” A single global factor that explains an important …
characterize the “Global Financial Cycle.” A single global factor that explains an important …
Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics
JH Stock, MW Watson - Handbook of macroeconomics, 2016 - Elsevier
This chapter provides an overview of and user's guide to dynamic factor models (DFMs),
their estimation, and their uses in empirical macroeconomics. It also surveys recent …
their estimation, and their uses in empirical macroeconomics. It also surveys recent …
Forecasting inflation in a data-rich environment: the benefits of machine learning methods
Inflation forecasting is an important but difficult task. Here, we explore advances in machine
learning (ML) methods and the availability of new datasets to forecast US inflation. Despite …
learning (ML) methods and the availability of new datasets to forecast US inflation. Despite …
Forecasting tourism demand with composite search index
Researchers have adopted online data such as search engine query volumes to forecast
tourism demand for a destination, including tourist numbers and hotel occupancy. However …
tourism demand for a destination, including tourist numbers and hotel occupancy. However …
Deep factors for forecasting
Producing probabilistic forecasts for large collections of similar and/or dependent time series
is a practically highly relevant, yet challenging task. Classical time series models fail to …
is a practically highly relevant, yet challenging task. Classical time series models fail to …
Panel Vector Autoregressive Models: A Survey☆ The views expressed in this article are those of the authors and do not necessarily reflect those of the ECB or the …
F Canova, M Ciccarelli - … and applications: Essays in honor of …, 2013 - emerald.com
This article provides an overview of the panel vector autoregressive models (VAR) used in
macroeconomics and finance to study the dynamic relationships between heterogeneous …
macroeconomics and finance to study the dynamic relationships between heterogeneous …
Large covariance estimation by thresholding principal orthogonal complements
The paper deals with the estimation of a high dimensional covariance with a conditional
sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance …
sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance …