Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa

Otrok, Christopher; Whiteman, Charles H.

doi:10.2307/2527349

Cited by 166 publications

(153 citation statements)

References 17 publications

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“…We estimate the dynamic factor model using the Bayesian MCMC algorithm of Whiteman (2003, 2008), which builds on the procedures developed by Otrok and Whiteman (1998) and Chib and Greenberg (1994). The algorithm constructs a Markov chain with data augmentation, whose limiting distribution is the target posterior density of the parameters.…”

Section: Estimationmentioning

confidence: 99%

“…The ‡exibility of the model comes at the cost of being high dimensional: for two types of ‡ows and 55 countries, it requires estimation of 397 parameters. We estimate the parameters of the dynamic factor model using the Bayesian MCMC algorithm of Whiteman (2003, 2008), which builds on the procedures developed by Otrok and Whiteman (1998). Bayesian estimation o¤ers the advantage of dealing e¤ectively with the high dimension of the model and making estimation feasible and e¢ cient.…”

Section: Introductionmentioning

confidence: 99%

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What drives international portfolio flows?

Sarno

Tsiakas

Ulloa³

2016

Journal of International Money and Finance

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent AbstractUnderstanding what drives international portfolio ‡ows has important policy implications for countries wishing to exert some control on the size, direction and volatility of the ‡ows. This paper empirically assesses the relative contribution of common (push) and country-speci…c (pull) factors to the variation of bond and equity ‡ows from the US to 55 other countries. Using a Bayesian dynamic latent factor model, we …nd that more than 80% of the variation in bond and equity ‡ows is due to push factors from the US to other countries. Hence global economic forces seem to prevail over domestic economic forces in explaining movements in international portfolio ‡ows. The dynamics of push and pull factors can be partially explained by US and foreign economic fundamentals.

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Section: Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What drives international portfolio flows?

Sarno

Tsiakas

Ulloa³

2016

Journal of International Money and Finance

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“…At the moment, Bayesian analysis of dynamic factor models still do not allow for cross-sectionally correlated errors. In preparing the monte carlo study, we also obtained Bayesian factor estimates using the method discussed in Otrok and Whiteman (1998), as well as the one described in Kim and Nelson (2000). The Bayesian methods give posterior means very similar results to the principal component estimates, but are tremendously more time consuming to compute with little to no gain in precision.…”

Section: How Precise Are the Factor Estimates?mentioning

confidence: 99%

Large Dimensional Factor Analysis

Bai

2008

FNT in Econometrics

286

190

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Econometric analysis of large dimensional factor models has been a heavily researched topic in recent years. This review surveys the main theoretical results that relate to static factor models or dynamic factor models that can be cast in a static framework. Among the topics covered are how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy of observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, * We are grateful to an anonymous referee for careful reading and constructive comments. This survey was presented by the second author as an invited lecture at the 2007 annual meeting of the Society of Computational Economics in Montreal. Financial support from the National Science Foundation (SES055127 and SES-0549978) is gratefully acknowledged.and how to estimate panel cointegration models. The fundamental result that justifies these analyses is that the method of asymptotic principal components consistently estimates the true factor space. We use simulations to better understand the conditions that can affect the precision of the factor estimates.

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“…Our econometric methods will also use DFMs. DFMs have become an increasingly common way of quantifying the extent of co-movements in macroeconomic variables (e.g., among others, Otrok and Whiteman, 1998;Kose, Otrok and Whiteman, 2003;Crucini, Kose and Otork, 2011;Mumtaz, Simonelli and Surico, 2011) and …nancial time series (e.g., among others, Aguilar and West, 2000;Gourieroux and Jasiak, 2001;Diebold, Rudebusch and Aruoba, 2006;and, Koopman, Lucas and Schwaab, 2012). In our setting, DFMs can quantify the degree of co-movement in employment growth across industries and regions and allow us to determine the sources of ‡uctuations in employment growth, i.e., how much of the ‡uctuations in employment growth can be attributed to industry factors, regional factors, national factors or external factors.…”

Section: Introductionmentioning

confidence: 99%

A new look at variation in employment growth in Canada: The role of industry, provincial, national and external factors

Campolieti

Gefang

Koop

2014

Journal of Economic Dynamics and Control

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We examine ‡uctuations in employment growth using Canadian data from 1976 to 2010. We consider a wide range of models and examine the sensitivity of our …ndings to modeling assumptions. The results from our most preferred model, which we selected using the Bayesian Information Criteria, indicate that most of the variance in employment growth that Campolieti gratefully acknowledges the …nancial support of the Social Sciences and Humanities Research Council of Canada (SSHRC). Please address correspondence to Michele Campolieti, Centre for Industrial Relations, University of Toronto, 121 St. George Street, Toronto, Ontario, M5S 2E8, Canada; email: campolie@chass.utoronto.ca. 1 is not due to the idiosyncratic error comes from domestic sources, with most of this coming from industry and provincial factors. Overall, we …nd external and national factors play a much smaller role in employment ‡uctuations than earlier research. We provide some possible explanations for these di¤erences.

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Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa

Cited by 166 publications

References 17 publications

What drives international portfolio flows?

What drives international portfolio flows?

Large Dimensional Factor Analysis

A new look at variation in employment growth in Canada: The role of industry, provincial, national and external factors

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