Measuring real activity using a weekly economic index

Lewis, Daniel; Mertens, Karel; Stock, James H.; Trivedi, Mihir

doi:10.1002/jae.2873

Cited by 36 publications

(37 citation statements)

References 26 publications

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“…Looking at the underlying month-on-month estimates of true GDP, y M t , we observe the biggest falls in May 2020. This contrasts slightly with the NBER's assessment that the trough (of the business cycle) was April 2020, but less so with the weekly economic indicator of Lewis et al (2021), which is lowest in the last week of April. In any case, we are explicitly measuring true GDP rather than the "business cycle" or "real activity.…”

Section: Nowcasting True Monthly Gdp During the Coronavirus Pandemiccontrasting

confidence: 84%

“…The argument for reconciliation, not just of quarterly GDP E and GDP I data but also for exploiting the wealth of monthly indicators that take the pulse of the economy, is that the reconciled monthly GDP estimates incorporate more information. Unlike index-based measures of economic activity, such as those developed by ADS or Lewis et al (2021), estimates of higher-frequency (here monthly) GDP have a natural interpretation: when aggregated to a quarterly frequency, they can be compared (and evaluated) directly against the BEA's own estimates.…”

Section: Discussionmentioning

confidence: 99%

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Reconciled Estimates of Monthly GDP in the US*

Koop

McIntyre

Mitchell

et al. 2022

Working Paper (Federal Reserve Bank of Cleveland)

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In the US, income and expenditure-side estimates of GDP (GDP I and GDP E ) measure "true" GDP with error and are available at a quarterly frequency. Methods exist for using these proxies to produce reconciled quarterly estimates of true GDP. In this paper, we extend these methods to provide reconciled historical true GDP estimates at a monthly frequency. We do this using a Bayesian mixed frequency vector autoregression (MF-VAR) involving GDP E , GDP I , unobserved true GDP, and monthly indicators of short-term economic activity. Our MF-VAR imposes restrictions that reflect a measurement-error perspective (that is, the two GDP proxies are assumed to equal true GDP plus measurement error). Without further restrictions, our model is unidentified. We consider a range of restrictions that allow for point and set identification of true GDP and show that they lead to informative monthly GDP estimates. We illustrate how these new monthly data contribute to our historical understanding of business cycles and we provide a real-time application nowcasting monthly GDP over the pandemic recession.

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Section: Nowcasting True Monthly Gdp During the Coronavirus Pandemiccontrasting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Reconciled Estimates of Monthly GDP in the US*

Koop

McIntyre

Mitchell

et al. 2022

Working Paper (Federal Reserve Bank of Cleveland)

View full text Add to dashboard Cite

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“…We report results for a total of 6 different combinations of macroeconomic and financial indicators. The weekly activity indicators used correspond to most of those in the weekly economic activity index of Lewis et al (2021). Table 1 lists the variables and our calendar assumptions.…”

Section: General Design Of the Forecast Calendar And Data Setmentioning

confidence: 99%

“…We smooth the consumer comfort measure by using a 4-week average of the weekly data. In light of noisiness and strong seasonality in weekly indicators of steel production, utility output, car loadings, fuel sales, and Redbook retail sales, we follow Ferrara and Simoni (2019) and Lewis et al (2021) and rely on 52-week growth rates.…”

Section: General Design Of the Forecast Calendar And Data Setmentioning

confidence: 99%

“…Weekly indicators, such as initial claims for unemployment insurance, weekly retail sales from Redbook, raw steel production, and output of electric utilities, began to draw attention for the light they could more quickly shed on the emerging downturn. Lewis et al (2021) develop a weekly economic index formed as a principal component of 10 underlying series, now published by the New York Fed.…”

Section: Introductionmentioning

confidence: 99%

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Nowcasting tail risk to economic activity at a weekly frequency

Carriero

Clark

Marcellino

2022

J of Applied Econometrics

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This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information used to produce nowcasts on a weekly basis. We consider Bayesian mixed frequency regressions with stochastic volatility and Bayesian quantile regressions. Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward within a quarter, making additional data available, with monthly data more important to accuracy than weekly data. Accuracy also typically improves with the use of financial indicators in addition to a base set of macroeconomic indicators.

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Nowcasting the state of the Italian economy: The role of financial markets

Ceci

Silvestrini

2023

Journal of Forecasting

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This paper compares several methods for constructing weekly nowcasts of recession probabilities in Italy, with a focus on the most recent period of the Covid‐19 pandemic. The common thread of these methods is that they use, in different ways, the information content provided by financial market data. In particular, a battery of probit models are estimated after extracting information from a large dataset of more than 130 financial market variables observed at a weekly frequency. The accuracy of these models is explored in a pseudo out‐of‐sample nowcasting exercise. The results demonstrate that nowcasts derived from probit models estimated on a large set of financial variables are, on average, more accurate than those delivered by standard probit models estimated on a single financial covariate, such as the slope of the yield curve. The proposed approach performs well even compared with probit models estimated on single time series of real economic activity variables, such as industrial production, business tendency survey data or composite PMI indicators. Overall, the financial indicators used in this paper can be easily updated as soon as new data become available on a weekly basis, thus providing reliable early estimates of the Italian business cycle.

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Measuring real activity using a weekly economic index

Cited by 36 publications

References 26 publications

Reconciled Estimates of Monthly GDP in the US*

Reconciled Estimates of Monthly GDP in the US*

Nowcasting tail risk to economic activity at a weekly frequency

Nowcasting the state of the Italian economy: The role of financial markets

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