Showing 31 - 40 of 21,639
In this paper, the authors comment on the Monte Carlo results of the paper by Lucchetti and Veneti (A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics), 2020)) that studies and compares the performance of the...
Persistent link: https://www.econbiz.de/10012211628
The authors replicate and extend the Monte Carlo experiment presented in Doz, Giannone and Reichlin (A Quasi-Maximum Likelihood Approach For Large, Approximate Dynamic Factor Models, Review of Economics and Statistics, 2012) on alternative (time-domain based) methods for extracting dynamic...
Persistent link: https://www.econbiz.de/10012227625
Identifying narratives in texts is a challenging task, as not only narrative elements such as the factors and events have to be identified but their semantic relation has to be explained as well. Despite this complexity, an effective technique to extract narratives from texts can have a great...
Persistent link: https://www.econbiz.de/10013366012
We demonstrate that regression models can be estimated by working independently in a row-wise fashion. We document a simple procedure which allows for a wide class of econometric estimators to be implemented cumulatively, where, in the limit, estimators can be produced without ever storing more...
Persistent link: https://www.econbiz.de/10014469687
This paper analyses the contribution of survey data, in particular various sentiment indicators, to nowcasts of quarterly euro area GDP. It uses a genuine real-time dataset that is constructed from original press releases in order to transform the actual dataflow into an interpretable flow of...
Persistent link: https://www.econbiz.de/10011786083
The recent rise of big data and artificial intelligence (AI) is changing markets, politics, organizations, and societies. It also affects the domain of research. Supported by new statistical methods that rely on computational power and computer science --- data science methods --- we are now...
Persistent link: https://www.econbiz.de/10012893773
This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of...
Persistent link: https://www.econbiz.de/10012894061
This article introduces the xtivdfreg command in Stata, which implements a general Instrumental Variables (IV) approach for estimating large panel data models with unobserved common factors or interactive effects, as developed by Norkute et al. (2020) and Cui et al. (2020a). The underlying idea...
Persistent link: https://www.econbiz.de/10012826354
Stata is fast, often very fast. However, when performing regressions on small sub-samples within a large host dataset (more than 1 million observations) performance can deteriorate by many orders of magnitude. For example, an OLS regression on a sub-sample of 100 consecutive observations takes...
Persistent link: https://www.econbiz.de/10013055687
mewto is an R package that allows users to experiment with different thresholds for classification of prediction results in the case of binary classification problems and to interactively visualize model evaluation metrics, confusion matrices, the ROC and PR curves. It can also calculate the...
Persistent link: https://www.econbiz.de/10013239045