Identification of Unknown Common Factors: Leaders and Followers

Parker, Jason; Sul, Donggyu

doi:10.1080/07350015.2015.1026439

Cited by 18 publications

(23 citation statements)

References 23 publications

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“…The common factor representation has successfully been used as the statistical foundation for modeling comovements across exchange rates, but because the factors are not identified, the economic interpretation for the underlying mechanism is not obvious. To address this issue, Bai and Ng () and Parker and Sul () develop econometric methods to identify the unobserved common factors with observed economic variables. In this section, we draw on these methods to identify the common factors for exchange rate returns.…”

Section: Common Factors In Exchange Rate Variationmentioning

confidence: 99%

“…This implies that the residuals

Δ s_{it}^{o}

, from regressions of

Δ {\overset{s}{true}}_{italicit}

(f_{jt}^{p}, f_{st}^{p})

Δ {\overset{s}{true}}_{italicit} = a_{i} + b_{i 1} f_{jt}^{p} + b_{i 2} f_{st}^{p} + Δ s_{it}^{o}

have no common factors. We are guided by the following two results, established by Parker and Sul (): (1)If there are no (zero) common factors in the panel of residuals

Δ s_{it}^{o}

, then

(f_{jt}^{p}, f_{st}^{p})

are the true common factors. (2)If there are one or more common factors in the panel of residuals

Δ s_{it}^{o}

, then either

(f_{jt}^{p}

f_{st}^{p} false)

or both

(f_{jt}^{p}, f_{st}^{p})

are not the true common factors. …”

Section: Common Factors In Exchange Rate Variationmentioning

confidence: 99%

“…have no common factors. We are guided by the following two results, established by Parker and Sul (2016):…”

Section: Common Factors In Exchange Rate Variationmentioning

confidence: 99%

“…In this article, we obtain factor identification using econometric methods developed by Bai and Ng (, ) and Parker and Sul (). Our analysis identifies a two‐factor structure consisting of a dollar factor and a euro factor.…”

Section: Introductionmentioning

confidence: 99%

“…Bai and Ng (), Hallin and Liska (), Onatski (, 2010), and Ahn and Horenstein () propose alternative methods to determine the number of common factors. We employ Bai and Ng's () IC 2 because Parker and Sul () show that it has good robustness properties.…”

mentioning

confidence: 99%

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Identifying Exchange Rate Common Factors

Greenaway‐McGrevy

Mark

Sul

et al. 2018

Int Economic Review

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Using recently developed model selection procedures, we determine that exchange rate returns are driven by a two‐factor model. We identify them as a dollar factor and a euro factor. Exchange rates are thus driven by global, U.S., and euro‐zone stochastic discount factors. The identified factors can also be given a risk‐based interpretation. Identification motivates multilateral models for bilateral exchange rates. Out‐of‐sample forecast accuracy of empirically identified multilateral models dominates the random walk and a bilateral purchasing power parity fundamentals prediction model. Twenty‐four‐month‐ahead forecast accuracy of the multilateral model dominates those of a principal components forecasting model.

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Section: Common Factors In Exchange Rate Variationmentioning

confidence: 99%

“…This implies that the residuals

Δ s_{it}^{o}

, from regressions of

Δ {\overset{s}{true}}_{italicit}

(f_{jt}^{p}, f_{st}^{p})

Δ {\overset{s}{true}}_{italicit} = a_{i} + b_{i 1} f_{jt}^{p} + b_{i 2} f_{st}^{p} + Δ s_{it}^{o}

have no common factors. We are guided by the following two results, established by Parker and Sul (): (1)If there are no (zero) common factors in the panel of residuals

Δ s_{it}^{o}

, then

(f_{jt}^{p}, f_{st}^{p})

are the true common factors. (2)If there are one or more common factors in the panel of residuals

Δ s_{it}^{o}

, then either

(f_{jt}^{p}

f_{st}^{p} false)

or both

(f_{jt}^{p}, f_{st}^{p})

are not the true common factors. …”

Section: Common Factors In Exchange Rate Variationmentioning

confidence: 99%

“…have no common factors. We are guided by the following two results, established by Parker and Sul (2016):…”

Section: Common Factors In Exchange Rate Variationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Identifying Exchange Rate Common Factors

Greenaway‐McGrevy

Mark

Sul

et al. 2018

Int Economic Review

Self Cite

View full text Add to dashboard Cite

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Detecting granular time series in large panels

Brownlees

Mesters

2021

Journal of Econometrics

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Large economic and financial panels often contain time series that influence the entire cross-section. We name such series granular. In this paper we introduce a panel data model that allows to formalize the notion of granular time series. We then propose a methodology, which is inspired by the network literature in statistics and econometrics, to detect the set of granulars when such set is unknown. The influence of the i-th series in the panel is measured by the norm of the i-th column of the inverse covariance matrix. We show that a detection procedure based on the column norms allows to consistently select granular series when the cross-section and time series dimensions are large. Importantly, the methodology allows to consistently detect granulars also when the series in the panel are influenced by common factors. A simulation study shows that the proposed procedures perform satisfactorily in finite samples. Our empirical studies demonstrate, among other findings, the granular influence of the automobile sector in US industrial production.

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Estimation and inference in spatial models with dominant units

Pesaran

Yang

2021

Journal of Econometrics

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In spatial econometrics literature estimation and inference are carried out assuming that the matrix of spatial or network connections has uniformly bounded absolute column sums in the number of units, n, in the network. This paper relaxes this restriction and allows for one or more units to have pervasive effects in the network. The linear-quadratic central limit theorem of Kelejian and Prucha ( 2001) is generalized to allow for such dominant units, and the asymptotic properties of the GMM estimators are established in this more general setting. A new biascorrected method of moments (BMM) estimator is also proposed that avoids the problem of weak instruments by self-instrumenting the spatially lagged dependent variable. Both cases of homoscedastic and heteroskedastic errors are considered and the associated estimators are shown to be consistent and asymptotically normal, depending on the rate at which the maximum column sum of the weights matrix rises with n. The small sample properties of GMM and BMM estimators are investigated by Monte Carlo experiments and shown to be satisfactory. An empirical application to sectoral price changes in the US over the pre-and post-2008 financial crisis is also provided. It is shown that the share of capital can be estimated reasonably well from the degree of sectoral interdependence using the input-output tables, despite the evidence of dominant sectors being present in the US economy.

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Identification of Unknown Common Factors: Leaders and Followers

Cited by 18 publications

References 23 publications

Identifying Exchange Rate Common Factors

Identifying Exchange Rate Common Factors

Detecting granular time series in large panels

Estimation and inference in spatial models with dominant units

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