Revealing Cluster Structures Based on Mixed Sampling Frequencies

04/21/2020
by   Yeonwoo Rho, et al.
0

This paper proposes a new nonparametric mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel dataset of mixed sampling frequencies. The nonparametric MIDAS estimation method is more flexible but substantially less costly to estimate than existing approaches. The proposed clustering algorithm successfully recovers true membership in the cross-section both in theory and in simulations without requiring prior knowledge such as the number of clusters. This methodology is applied to estimate a mixed-frequency Okun's law model for the state-level data in the U.S. and uncovers four clusters based on the dynamic features of labor markets.

READ FULL TEXT
research
05/23/2023

DIVA: A Dirichlet Process Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder

Generative model-based deep clustering frameworks excel in classifying c...
research
02/18/2022

A new LDA formulation with covariates

The Latent Dirichlet Allocation (LDA) model is a popular method for crea...
research
10/31/2018

On the True Number of Clusters in a Dataset

One of the main challenges in cluster analysis is estimating the true nu...
research
01/27/2020

A Proposed Method for Assessing Cluster Heterogeneity

Assessing how adequate clusters fit a dataset and finding an optimum num...
research
11/11/2020

Clustering of Big Data with Mixed Features

Clustering large, mixed data is a central problem in data mining. Many a...
research
09/02/2018

A Study of Dynamic Multipath Clusters at 60 GHz in a Large Indoor Environment

The available geometry-based stochastic channel models (GSCMs) at millim...
research
12/11/2017

Dynamic Mixed Frequency Synthesis for Economic Nowcasting

We develop a novel Bayesian framework for dynamic modeling of mixed freq...

Please sign up or login with your details

Forgot password? Click here to reset