Factor models are generally subject to a rotational indeterminacy, meaning that individual factors are only identified up to a rotation. In the presence of local factors, which only affect a subset of the outcomes, we show that the implied sparsity of the loading matrix can be used to solve this...

The Internet Appendix collects the proofs and additional results that support the main text. We show in simulations that our estimators perform well relative to alternative estimators and can be improved even further with an iterative approach. We also confirm that the distribution results,...

We propose a dynamic factor model which we use to analyze the relationship between education participation and national unemployment, as well as to forecast the number of students across the many different types of education. By clustering the factor loadings associated with the dynamic...

In this paper, the factor model is used to explore the importance of the monthly macroeconomic variables and Google Trend index in forecasting the Taipei House Price Index. In particular, We consider different settings of constructing the factors, including the traditional factor model, squared...

In this article, we propose a cointegration-based Permanent-Transitory decomposition for non-stationary Dynamic Factor Models. Our methodology exploits the cointegration relations among the observable variables and assumes they are driven by a common and an idiosyncratic component. The common...

Special Purpose Acquisition Companies (“SPACs”) are shell companies formed for the purpose of acquiring an existing operating company. A typical characteristic of a SPAC is that it has no specific business plan at the time of establishment or that it is still unclear of its target company. A...

This article analyzes the steps to solve economic problems using multivariate regression and correlation methods. The paper has mathematical modelling of the economic problems and the solutions with different parameters

We propose a new approach to sample unobserved states conditional on available data in (conditionally) linear unobserved component models when some of the observations are missing. The approach is based on the precision matrix of the states and model variables, which is sparse and banded in many...

Factors estimated from large macroeconomic panels are being used in an increasing number of applications. However, little is known about how the size and the composition of the data affect the factor estimates. In this paper, we question whether it is possible to use more series to extract the...