Department of Economics and Business Economics

Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure

Research output: Working paper/Preprint Working paper

Standard

Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure. / Rodríguez-Caballero, Carlos Vladimir.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2016.

Research output: Working paper/Preprint Working paper

Harvard

APA

Rodríguez-Caballero, C. V. (2016). Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure. Institut for Økonomi, Aarhus Universitet. CREATES Research Papers No. 2016-31

CBE

MLA

Rodríguez-Caballero, Carlos Vladimir Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2016-31). 2016., 42 p.

Vancouver

Author

Rodríguez-Caballero, Carlos Vladimir. / Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure. Aarhus : Institut for Økonomi, Aarhus Universitet, 2016. (CREATES Research Papers; No. 2016-31).

Bibtex

@techreport{dc29964326104ac3b4e504b52fbb1582,
title = "Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure",
abstract = "A panel data model with a multi-level cross-sectional dependence is proposed. The factor structure is driven by top-level common factors as well as non-pervasive factors. I propose a simple method to filter out the full factor structure that overcomes limitations in standard procedures which may mix up both levels of unobservable factors and may hamper the identification of the model. The model covers both stationary and non-stationary cases and takes into account other relevant features that make the model well suited to the analysis of many types of time series frequently addressed in macroeconomics and finance. The model makes it possible to examine the time series and cross-sectional dynamics of variables allowing for a rich fractional cointegration analysis. A Monte Carlo simulation is conducted to examine the finite sample features of the suggested procedure. Findings indicate that the methodology proposed works well in a wide variety of data generation processes and has much lower biases than the alternative estimation methods either in the I(0) or I(d) cases.",
keywords = "Cross-section dependence; Multi-level factor models; Large panels; Long memory; Fractional cointegration; Common correlated effects",
author = "Rodr{\'i}guez-Caballero, {Carlos Vladimir}",
year = "2016",
month = nov,
day = "1",
language = "English",
series = "CREATES Research Papers",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
number = "2016-31",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure

AU - Rodríguez-Caballero, Carlos Vladimir

PY - 2016/11/1

Y1 - 2016/11/1

N2 - A panel data model with a multi-level cross-sectional dependence is proposed. The factor structure is driven by top-level common factors as well as non-pervasive factors. I propose a simple method to filter out the full factor structure that overcomes limitations in standard procedures which may mix up both levels of unobservable factors and may hamper the identification of the model. The model covers both stationary and non-stationary cases and takes into account other relevant features that make the model well suited to the analysis of many types of time series frequently addressed in macroeconomics and finance. The model makes it possible to examine the time series and cross-sectional dynamics of variables allowing for a rich fractional cointegration analysis. A Monte Carlo simulation is conducted to examine the finite sample features of the suggested procedure. Findings indicate that the methodology proposed works well in a wide variety of data generation processes and has much lower biases than the alternative estimation methods either in the I(0) or I(d) cases.

AB - A panel data model with a multi-level cross-sectional dependence is proposed. The factor structure is driven by top-level common factors as well as non-pervasive factors. I propose a simple method to filter out the full factor structure that overcomes limitations in standard procedures which may mix up both levels of unobservable factors and may hamper the identification of the model. The model covers both stationary and non-stationary cases and takes into account other relevant features that make the model well suited to the analysis of many types of time series frequently addressed in macroeconomics and finance. The model makes it possible to examine the time series and cross-sectional dynamics of variables allowing for a rich fractional cointegration analysis. A Monte Carlo simulation is conducted to examine the finite sample features of the suggested procedure. Findings indicate that the methodology proposed works well in a wide variety of data generation processes and has much lower biases than the alternative estimation methods either in the I(0) or I(d) cases.

KW - Cross-section dependence; Multi-level factor models; Large panels; Long memory; Fractional cointegration; Common correlated effects

M3 - Working paper

T3 - CREATES Research Papers

BT - Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -