The New CFS Divisia Monetary Aggregates: Design, Construction, and Data Sources

Barnett, William A.; Liu, Jia; Mattson, Ryan S.; Noort, Jeff van den

doi:10.1007/s11079-012-9257-1

Cited by 96 publications

(97 citation statements)

References 8 publications

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“…For the credit card interest rate, , we use the series discussed above. For the yield on the benchmark asset adopted by the CFS and used by us, see Barnett, Liu, Mattson, and van den Noort (2013). Restricted by the credit card interest rate's availability, the nominal user cost of credit card services is available since October 1994.…”

Section: Data Sources For Chow-lin Interpolationmentioning

confidence: 99%

“…The greatest source of risk is credit risk (called Net Credit Loss), but fraud risk along with high operating costs all play a role in the high interest rates on credit card debt. To summarize all the adopted data sources to construct the augmented Divisia Index, we provided table 2 in the Appendix, following the tradition of Barnett, Liu, Mattson, and Van Den Noort (2013) and Anderson and Jones (2011). In addition, a graphical demonstration of the Chow-Lin interpolation is provided in Appendix Figure 1.…”

Section: Data Sources For Chow-lin Interpolationmentioning

confidence: 99%

“…In this paper we make available the results of our search, not only to document the sources for the new CFS augmented Divisia monetary aggregates, but also for reference by future researchers who might want to work in this area and therefore be confronted with similar data search challenges. In this paper, we focus entirely on the credit card components of the new aggregates, since the sources for the other components have been documented by Barnett, Liu, Mattson, and Noort (2013), as was needed for construction of the original CFS aggregates.…”

Section: Introductionmentioning

confidence: 99%

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Data sources for the credit-card augmented Divisia monetary aggregates

Barnett

2017

Research in International Business and Finance

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Abstract:In 2013, the Center for Financial Stability (CFS) initiated its Divisia monetary aggregates database, maintained within the CFS program called Advances in Monetary and Financial Measurement (AMFM), in accordance with Barnett (1980Barnett ( , 2012. The CFS is now making available Divisia monetary aggregates extended to include the transactions services of credit cards. The extended aggregates are called the augmented Divisia monetary aggregates and are available to the public in monthly releases. The new aggregates are also available to Bloomberg terminal users. The theory on which the new aggregates is based is provided in Barnett and Su (2014). 3 In this paper, we provide detailed information on the data sources used in producing the new augmented Divisia monetary aggregates.

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Section: Data Sources For Chow-lin Interpolationmentioning

confidence: 99%

Section: Data Sources For Chow-lin Interpolationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data sources for the credit-card augmented Divisia monetary aggregates

Barnett

2017

Research in International Business and Finance

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“…From these exceptions, the best performing indicators are the 3-Month Treasury Bill (TBILL), the S&P500 index, and Divisia measures of M3 and M4 computed by the Center for Financial Stability (CFS), which relies on the methodology proposed by Barnett (1980). 10 The results are shown in Table 2 We next estimate models with …ve and six variables. 11 The results are reported in Table 3.…”

Section: Additional Indicatorsmentioning

confidence: 99%

“…12 We use the six MFDF models that yield the best predictive performance so far to assess their ability to predict current growth of NGDP, using the exact amount of data available at the time of the prediction, and by taking into account all possible revisions in previous releases of variables. For comparison, we also Barnett et al (2013). 1 1 We have also estimated larger models.…”

Section: Nowcasting With Linear Modelsmentioning

confidence: 99%

Real-Time Nowcasting of Nominal GDP Under Structural Breaks

2014

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. iii Terms of use: Documents in AbstractThis paper provides a framework for the early assessment of current U.S. nominal GDP growth, which has been considered a potential new monetary policy target. The nowcasts are computed using the exact amount of information that policy-makers have available at the time predictions are made. However, real-time information arrives at different frequencies and asynchronously, which poses challenges of mixed frequencies, missing data and ragged edges. This paper proposes a multivariate state-space model that not only takes into account asynchronous information inflow, but also allows for potential parameter instability. We use small-scale confirmatory factor analysis in which the candidate variables are selected based on their ability to forecast nominal GDP. The model is fully estimated in one step using a non-linear Kalman filter, which is applied to obtain optimal inferences simultaneously on both the dynamic factor and parameters. In contrast to principal component analysis, the proposed factor model captures the comovement rather than the variance underlying the variables. We compare the predictive ability of the model with other univariate and multivariate specifications. The results indicate that the proposed model containing information on real economic activity, inflation, interest rates and Divisia monetary aggregates produces the most accurate realtime nowcasts of nominal GDP growth. JEL classification: C32, E27, E31, E32 Bank classification: Business fluctuations and cycles; Econometric and statistical methods; Inflation and prices RésuméDans cette étude, les auteurs proposent un modèle qui permet d'évaluer de façon précoce la croissance actuelle du PIB nominal des États-Unis, laquelle est considérée comme une possible nouvelle cible de politique monétaire. Les prévisions concernant la période en cours sont établies à partir des mêmes informations dont disposent les décideurs à ce moment précis. Toutefois, les renseignements en temps réel leur parviennent à des intervalles différents et de manière asynchrone, ce qui cause plusieurs problèmes : fréquences diverses, données manquantes, valeurs absentes en fin d'échantillon. Dans cette étude, les auteurs proposent un modèle espace d'états multivarié qui non seulement prend en considération le flux asynchrone d'informations, mais tient compte aussi de l'instabilité éventuelle des paramètres. Ils utilisent une analyse factorielle confirmatoire à échelle réduite, le...

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Forecasting inflation and output growth with credit‐card‐augmented Divisia monetary aggregates

Barnett

Park

2022

Journal of Forecasting

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This paper investigates the performance of the Credit-Card-Augmented Divisia monetary aggregates in forecasting U.S. inflation and output growth at the 12-month horizon. We compute recursive and rolling out-of-sample forecasts using an Autoregressive Distributed Lag (ADL) model based on Divisia monetary aggregates. We use the three available versions of those monetary aggregate indices, including the original Divisia aggregates, the credit cardaugmented Divisia, and the credit-card-augmented Divisia inside money aggregates. The source of each is the Center for Financial Stability (CFS). We find that the smallest Root Mean Square Forecast Errors (RMSFE) are attained with the credit-card-augmented Divisia indices used as the forecast indicators. We also consider Bayesian vector autoregression (BVAR) for forecasting annual inflation and output growth.

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The New CFS Divisia Monetary Aggregates: Design, Construction, and Data Sources

Cited by 96 publications

References 8 publications

Data sources for the credit-card augmented Divisia monetary aggregates

Data sources for the credit-card augmented Divisia monetary aggregates

Real-Time Nowcasting of Nominal GDP Under Structural Breaks

Forecasting inflation and output growth with credit‐card‐augmented Divisia monetary aggregates

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