Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

Bencivelli, Lorenzo; Marcellino, Massimiliano; Moretti, Gianluca

doi:10.2139/ssrn.2154928

Cited by 30 publications

(8 citation statements)

References 122 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach is commonly used in the literature on bridge models; see for example Bencivelli et al . ().…”

Section: The Mixed Frequency Three‐pass Regression Filtermentioning

confidence: 97%

“…For example, if the last two observations of the variable i are missing, we group the available observations in the vector x obs i and formulate the relationship between observed (x obs i ) and not fully observed (x i ) variables as The second method is similar to the simplified version of the first: it simply requires fitting time series models to the variables with ragged edges (auto-regressive AR(2), for example), and then replacing the missing observations at the end of each time series with their forecast values. This approach is commonly used in the literature on bridge models; see for example Bencivelli et al (2017).…”

Section: Ragged Edgesmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Gross Domestic Product Growth with large Unbalanced Data Sets: The Mixed Frequency Three-pass Regression Filter

Hepenstrick

Marcellino

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

Summary Gross domestic product (GDP) is a key summary of macroeconomic conditions and it is closely monitored both by policy makers and by decision makers in the private sector. However, it is only available on a quarterly frequency, and in many countries it is released with a substantial delay. There are, however, many higher frequency and more timely economic and financial indicators that could be used for nowcasting and short‐term forecasting GDP. Against this backdrop, we propose a modification of the three‐pass regression filter to make it applicable to large mixed frequency data sets with ragged edges in a forecasting context. The resulting method, labelled MF‐3PRF, is very simple but compares well with alternative mixed frequency factor estimation procedures in terms of theoretical properties and actual GDP nowcasting and forecasting for the USA and a variety of other countries.

show abstract

“…This approach is commonly used in the literature on bridge models; see for example Bencivelli et al . ().…”

Section: The Mixed Frequency Three‐pass Regression Filtermentioning

confidence: 97%

Section: Ragged Edgesmentioning

confidence: 99%

Forecasting Gross Domestic Product Growth with large Unbalanced Data Sets: The Mixed Frequency Three-pass Regression Filter

Hepenstrick

Marcellino

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

show abstract

“…Bridge models, which were considered in such studies as Baffigi et al (2004), Diron (2008) and Bencivelli et al (2012), relate the period t value of the quarterly variable of interest, such as GDP growth, to the period t quarterly average of key monthly indicators. The period t average of each monthly indicator is obtained with data that are available within the quarter and forecasts for other months of the quarter (obtained typically from an AR model for the monthly indicator).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility

Carriero¹,

Clark²,

Marcellino³

2012

Working Paper (Federal Reserve Bank of Cleveland)

View full text Add to dashboard Cite

Summary. The paper develops a method for producing current quarter forecasts of gross domestic product growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest rates. In light of existing evidence of time variation in the variances of shocks to gross domestic product, we consider versions of the model with both constant variances and stochastic volatility. We use Bayesian methods to estimate the model, to facilitate providing shrinkage on the (possibly large) set of model parameters and conveniently generate predictive densities. We provide results on the accuracy of nowcasts of realtime gross domestic product growth in the USA from 1985 through 2011. In terms of point forecasts, our proposal improves significantly on auto-regressive models and performs comparably with survey forecasts. In addition, it provides reliable density forecasts, for which the stochastic volatility specification is quite useful.

show abstract

“…The selection of the monthly indicators included in the bridge model is usually based on a general-to-speci…c methodology and relies on di¤erent in-sample or out-of-sample criteria, like information criteria or RMSE performance. Bencivelli, Marcellino and Moretti (2012) propose an alternative procedure based on Bayesian Model Averaging (BMA) that performs quite well empirically.…”

Section: Bridge Equationsmentioning

confidence: 99%

“…In what follows, we depict the main features of the bridge models, often employed in central banks and other policy making institutions, especially for nowcasting and short-term forecasting, see e.g. Ba¢ gi, Golinelli and Parigi (2004), Diron (2008) and Bencivelli, Marcellino and Moretti (2012). We then move to one of the main strands of the literature, mixed-data sampling (MIDAS) models, parsimonious speci…cations based on distributed lag polynomials, which ‡exibly deal with data sampled at di¤erent frequencies and provide a direct forecast of the low-frequency variable (see e.g.…”

Section: Introductionmentioning

confidence: 99%