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A two-step estimator for large approximate dynamic factor models based on Kalman filtering
This paper shows consistency of a two-step estimation of the factors in a dynamic approximate factor model when the panel of time series is large ( n large). In the first step, the parameters of the model are estimated from an OLS on principal components. In the second step, the factors are estimate...
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| Published in: | Econometrics 2011-09, Vol.164 (1), p.188-205 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c610t-a634511e625db8b3f98a84bd3b4fe041466def62d958132e7d62e52925df9e9f3 |
| container_end_page | 205 |
| container_issue | 1 |
| container_start_page | 188 |
| container_title | Econometrics |
| container_volume | 164 |
| creator | Doz, Catherine Giannone, Domenico Reichlin, Lucrezia |
| description | This paper shows consistency of a two-step estimation of the factors in a dynamic approximate factor model when the panel of time series is large (
n
large). In the first step, the parameters of the model are estimated from an OLS on principal components. In the second step, the factors are estimated via the Kalman smoother. The analysis develops the theory for the estimator considered in
Giannone et al. (2004) and
Giannone et al. (2008) and for the many empirical papers using this framework for nowcasting. |
| doi_str_mv | 10.1016/j.jeconom.2011.02.012 |
| format | article |
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n
large). In the first step, the parameters of the model are estimated from an OLS on principal components. In the second step, the factors are estimated via the Kalman smoother. The analysis develops the theory for the estimator considered in
Giannone et al. (2004) and
Giannone et al. (2008) and for the many empirical papers using this framework for nowcasting.</description><identifier>ISSN: 0304-4076</identifier><identifier>ISSN: 2225-1146</identifier><identifier>EISSN: 1872-6895</identifier><identifier>EISSN: 2225-1146</identifier><identifier>DOI: 10.1016/j.jeconom.2011.02.012</identifier><identifier>CODEN: JECMB6</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Applications ; Distribution theory ; Dynamic models ; Econometric models ; Econometrics ; Estimating techniques ; Estimation ; Exact sciences and technology ; Factor analysis ; Factor models ; Factor models Kalman filter Principal components Large cross-sections ; Inference from stochastic processes; time series analysis ; Insurance, economics, finance ; Kalman filter ; Kalman filters ; Large cross-sections ; Mathematics ; Model testing ; Multivariate analysis ; Panel data ; Principal components ; Principal components analysis ; Probability and statistics ; Probability theory ; Quantitative Finance ; Regression analysis ; Sciences and techniques of general use ; Significance tests ; Statistical Finance ; Statistical models ; Statistics ; Studies ; Time series</subject><ispartof>Econometrics, 2011-09, Vol.164 (1), p.188-205</ispartof><rights>2011 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Elsevier Sequoia S.A. Sep 1, 2011</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c610t-a634511e625db8b3f98a84bd3b4fe041466def62d958132e7d62e52925df9e9f3</citedby><cites>FETCH-LOGICAL-c610t-a634511e625db8b3f98a84bd3b4fe041466def62d958132e7d62e52925df9e9f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S030440761100039X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,309,310,314,776,780,785,786,881,3447,3551,23911,23912,25120,27903,27904,33202,33203,45971,45981</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24462693$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttp://econpapers.repec.org/article/eeeeconom/v_3a164_3ay_3a2011_3ai_3a1_3ap_3a188-205.htm$$DView record in RePEc$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-00844811$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Doz, Catherine</creatorcontrib><creatorcontrib>Giannone, Domenico</creatorcontrib><creatorcontrib>Reichlin, Lucrezia</creatorcontrib><title>A two-step estimator for large approximate dynamic factor models based on Kalman filtering</title><title>Econometrics</title><description>This paper shows consistency of a two-step estimation of the factors in a dynamic approximate factor model when the panel of time series is large (
n
large). In the first step, the parameters of the model are estimated from an OLS on principal components. In the second step, the factors are estimated via the Kalman smoother. The analysis develops the theory for the estimator considered in
Giannone et al. (2004) and
Giannone et al. (2008) and for the many empirical papers using this framework for nowcasting.</description><subject>Applications</subject><subject>Distribution theory</subject><subject>Dynamic models</subject><subject>Econometric models</subject><subject>Econometrics</subject><subject>Estimating techniques</subject><subject>Estimation</subject><subject>Exact sciences and technology</subject><subject>Factor analysis</subject><subject>Factor models</subject><subject>Factor models Kalman filter Principal components Large cross-sections</subject><subject>Inference from stochastic processes; time series analysis</subject><subject>Insurance, economics, finance</subject><subject>Kalman filter</subject><subject>Kalman filters</subject><subject>Large cross-sections</subject><subject>Mathematics</subject><subject>Model testing</subject><subject>Multivariate analysis</subject><subject>Panel data</subject><subject>Principal components</subject><subject>Principal components analysis</subject><subject>Probability and statistics</subject><subject>Probability theory</subject><subject>Quantitative Finance</subject><subject>Regression analysis</subject><subject>Sciences and techniques of general use</subject><subject>Significance tests</subject><subject>Statistical Finance</subject><subject>Statistical models</subject><subject>Statistics</subject><subject>Studies</subject><subject>Time series</subject><issn>0304-4076</issn><issn>2225-1146</issn><issn>1872-6895</issn><issn>2225-1146</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><recordid>eNqFkV-L1DAUxYsoOK5-BKEIIj50zL-m6ZMMy-rKDviiL76ENL3ZTWmbmnRmnW_v7XaYB18M3AQuv3NyLyfL3lKypYTKT922AxvGMGwZoXRL2JZQ9izbUFWxQqq6fJ5tCCeiEKSSL7NXKXWEkFIovsl-7fL5MRRphimHNPvBzCHmDqs38R5yM00x_FnakLen0Qze5s7YBRpCC33KG5OgzcOY35l-MGPufD9D9OP96-yFM32CN-f3Kvv55ebH9W2x__712_VuX1hJyVwYyUVJKUhWto1quKuVUaJpeSMcEEGFlC04ydq6VJQzqFrJoGQ14q6G2vGr7OPq-2B6PUWcNZ50MF7f7vZ66RGihFCUHimyH1YWt_p9wIX14JOFvjcjhEPSqqpJXdYVQ_LdP2QXDnHERbRSHIcSZYVQuUI2hpQiuMv_lOglG93pczZ6yUYTpjEb1N2tuggT2IsI8KzwUXNDpcD7hPUk5bgRNrGm5VUKHUv9MA_o9v48qknW9C6a0fp0cWVCSCZrjtznlcPY4Ogh6mQ9jBZaH8HOug3-P3P_BUxJv3g</recordid><startdate>20110901</startdate><enddate>20110901</enddate><creator>Doz, Catherine</creator><creator>Giannone, Domenico</creator><creator>Reichlin, Lucrezia</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Sequoia S.A</general><general>MDPI</general><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope><scope>1XC</scope><scope>VOOES</scope></search><sort><creationdate>20110901</creationdate><title>A two-step estimator for large approximate dynamic factor models based on Kalman filtering</title><author>Doz, Catherine ; Giannone, Domenico ; Reichlin, Lucrezia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c610t-a634511e625db8b3f98a84bd3b4fe041466def62d958132e7d62e52925df9e9f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Applications</topic><topic>Distribution theory</topic><topic>Dynamic models</topic><topic>Econometric models</topic><topic>Econometrics</topic><topic>Estimating techniques</topic><topic>Estimation</topic><topic>Exact sciences and technology</topic><topic>Factor analysis</topic><topic>Factor models</topic><topic>Factor models Kalman filter Principal components Large cross-sections</topic><topic>Inference from stochastic processes; time series analysis</topic><topic>Insurance, economics, finance</topic><topic>Kalman filter</topic><topic>Kalman filters</topic><topic>Large cross-sections</topic><topic>Mathematics</topic><topic>Model testing</topic><topic>Multivariate analysis</topic><topic>Panel data</topic><topic>Principal components</topic><topic>Principal components analysis</topic><topic>Probability and statistics</topic><topic>Probability theory</topic><topic>Quantitative Finance</topic><topic>Regression analysis</topic><topic>Sciences and techniques of general use</topic><topic>Significance tests</topic><topic>Statistical Finance</topic><topic>Statistical models</topic><topic>Statistics</topic><topic>Studies</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Doz, Catherine</creatorcontrib><creatorcontrib>Giannone, Domenico</creatorcontrib><creatorcontrib>Reichlin, Lucrezia</creatorcontrib><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Econometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Doz, Catherine</au><au>Giannone, Domenico</au><au>Reichlin, Lucrezia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A two-step estimator for large approximate dynamic factor models based on Kalman filtering</atitle><jtitle>Econometrics</jtitle><date>2011-09-01</date><risdate>2011</risdate><volume>164</volume><issue>1</issue><spage>188</spage><epage>205</epage><pages>188-205</pages><issn>0304-4076</issn><issn>2225-1146</issn><eissn>1872-6895</eissn><eissn>2225-1146</eissn><coden>JECMB6</coden><abstract>This paper shows consistency of a two-step estimation of the factors in a dynamic approximate factor model when the panel of time series is large (
n
large). In the first step, the parameters of the model are estimated from an OLS on principal components. In the second step, the factors are estimated via the Kalman smoother. The analysis develops the theory for the estimator considered in
Giannone et al. (2004) and
Giannone et al. (2008) and for the many empirical papers using this framework for nowcasting.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.jeconom.2011.02.012</doi><tpages>18</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Econometrics, 2011-09, Vol.164 (1), p.188-205 |
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| language | eng |
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| source | International Bibliography of the Social Sciences (IBSS); ScienceDirect Freedom Collection; Elsevier SD Backfile Economics; Backfile Package - Mathematics (Legacy) [YMT] |
| subjects | Applications Distribution theory Dynamic models Econometric models Econometrics Estimating techniques Estimation Exact sciences and technology Factor analysis Factor models Factor models Kalman filter Principal components Large cross-sections Inference from stochastic processes time series analysis Insurance, economics, finance Kalman filter Kalman filters Large cross-sections Mathematics Model testing Multivariate analysis Panel data Principal components Principal components analysis Probability and statistics Probability theory Quantitative Finance Regression analysis Sciences and techniques of general use Significance tests Statistical Finance Statistical models Statistics Studies Time series |
| title | A two-step estimator for large approximate dynamic factor models based on Kalman filtering |
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