Real-Time Forecasting and Scenario Analysis using a Large Mixed-Frequency Bayesian VAR

McCracken, Michael W.; Owyang, Michael T.; Sekhposyan, Tatevik

doi:10.20955/wp.2015.030

Cited by 26 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a detailed analysis of blocked linear systems see Chen, Anderson, Deistler, and Filler (2012). McCracken, Owyang, and Sekhposyan (2013) use this approach for now-casting with a large Bayesian VAR.…”

Section: Mixed Frequency Varmentioning

confidence: 99%

Now-Casting and the Real-Time Data Flow

Banbura

Giannone

Modugno

et al. 2013

Handbook of Economic Forecasting

306

263

View full text Add to dashboard Cite

In 2013 all ECB publications feature a motif taken from the €5 banknote.note: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. All rights reserved. ISSN 1725-2806 (online) EU Catalogue NoQB-AR-13-061-EN-N (online)Any reproduction, publication and reprint in the form of a different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors. Lucrezia ReichlinLondon Business School and CEPR; e-mail: lreichlin@london.edu AbstractThe term now-casting is a contraction for now and forecasting and has been used for a long-time in meteorology and recently also in economics. In this paper we survey recent developments in economic now-casting with special focus on those models that formalize key features of how market participants and policy makers read macroeconomic data releases in real time, which involves: monitoring many data, forming expectations about them and revising the assessment on the state of the economy whenever realizations diverge sizeably from those expectations. (Prepared for G. Elliott and A. Timmermann, eds., Handbook of Economic Forecasting, Volume 2, Elsevier-North Holland). Keywords Non-technical summaryNow-casting is defined as the prediction of the present, the very near future and the very recent past. The term is a contraction for now and forecasting and has been used for a long-time in meteorology and recently also in economics. Now-casting is relevant in economics because key statistics on the present state of the economy are available with a significant delay. This is particularly true for those collected on a quarterly basis, with Gross Domestic Product (GDP) being a prominent example. For instance, the first official estimate of GDP in the United States (US) or in the United Kingdom is published approximately one month after the end of the reference quarter. In the euro area the corresponding publication lag is 2-3 weeks longer. Now-casting can also be meaningfully applied to other target variables revealing particular aspects of the state of the economy and thereby followed closely by markets. An example is labor market variables.The basic principle of now-casting is the exploitation of the information which is published early and possibly at higher frequencies than the target variable of interest in order to obtain an 'early estimate' before the official figure becomes available. If the focus is on tracking GDP, one may look at its expenditure components, like for example personal consumption, which for the US is available at a monthly frequency, or variables related to the production side such as industry output. In addition, one may consider information contained in surveys or in forward looking indicators such as financial variables. The idea here is that both 'hard' information like industrial production and 'soft' information like sur...

show abstract

Section: Mixed Frequency Varmentioning

confidence: 99%

Now-Casting and the Real-Time Data Flow

Banbura

Giannone

Modugno

et al. 2013

Handbook of Economic Forecasting

306

263

View full text Add to dashboard Cite

show abstract

“…The fact that large VARs are being found increasingly useful in an era of Big Data needs little justification. The large VAR literature began with the US macroeconomic application of Bańbura, Giannone, and Reichlin (2010) but large VARs are now used with a variety of macroeconomic and financial data (see, among others, Bańbura, Giannone, & Lenza, 2015; Bloor & Matheson, 2010; Carriero, Kapetanios, & Marcellino, 2010, 2012; Giannone, Lenza, Momferatou, & Onorante, 2014; Jarociński & Maćkowiak, 2017; Kastner & Huber, 2017; Koop & Korobilis, 2016; McCracken, Owyang, & Sekhposyan, 2016).…”

Section: Introductionmentioning

confidence: 99%

Composite likelihood methods for large Bayesian VARs with stochastic volatility

Chan

Eisenstat

Hou

et al. 2020

J of Applied Econometrics

View full text Add to dashboard Cite

Adding multivariate stochastic volatility of a flexible form to large vector autoregressions (VARs) involving over 100 variables has proved challenging owing to computational considerations and overparametrization concerns. The existing literature works with either homoskedastic models or smaller models with restrictive forms for the stochastic volatility. In this paper, we develop composite likelihood methods for large VARs with multivariate stochastic volatility. These involve estimating large numbers of parsimonious models and then taking a weighted average across these models. We discuss various schemes for choosing the weights. In our empirical work involving VARs of up to 196 variables, we show that composite likelihood methods forecast much better than the most popular large VAR approach, which is computationally practical in very high dimensions: the homoskedastic VAR with Minnesota prior. We also compare our methods to various popular approaches that allow for stochastic volatility using medium and small VARs involving up to 20 variables. We find our methods to forecast appreciably better than these as well.

show abstract

“…In order to produce more frequent nowcasts of regional productivity, we use a highdimensional mixed frequency vector autoregressive (MF-VAR). MF-VARs have become a popular nowcasting tool; see, among many others, Eraker et al (2015), Schorfheide and Song (2015), Brave et al (2019), Koop et al (2020b,c), andMcCracken et al (2021). But none of these econometric models can be used directly for nowcasting UK regional productivity due to the specific characteristics of our data set.…”

Section: Introductionmentioning

confidence: 99%

Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates

Koop

McIntyre

Mitchell

et al. 2022

Working Paper (Federal Reserve Bank of Cleveland)

View full text Add to dashboard Cite

Recent decades have seen advances in using econometric methods to produce more timely and higher-frequency estimates of economic activity at the national level, enabling better tracking of the economy in real time. These advances have not generally been replicated at the sub–national level, likely because of the empirical challenges that nowcasting at a regional level presents, notably, the short time series of available data, changes in data frequency over time, and the hierarchical structure of the data. This paper develops a mixed– frequency Bayesian VAR model to address common features of the regional nowcasting context, using an application to regional productivity in the UK. We evaluate the contribution that different features of our model provide to the accuracy of point and density nowcasts, in particular the role of hierarchical aggregation constraints. We show that these aggregation constraints, imposed in stochastic form, play a key role in delivering improved regional nowcasts; they prove to be more important than adding region-specific predictors when the equivalent national data are known, but not when this aggregate is unknown.

show abstract

Real-Time Forecasting and Scenario Analysis using a Large Mixed-Frequency Bayesian VAR

Cited by 26 publications

References 20 publications

Now-Casting and the Real-Time Data Flow

Now-Casting and the Real-Time Data Flow

Composite likelihood methods for large Bayesian VARs with stochastic volatility

Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates

Contact Info

Product

Resources

About