Forecasting growth and inflation in an enlarged euro area

Flavin, Thomas; Panopoulou, Ekaterini; Pantelidis, Theologos

doi:10.1002/for.1117

Cited by 3 publications

(3 citation statements)

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“…3 We assess the forecasting abilities of our MIDAS models through an application to U.S. inflation (all items). The performances of these models are contrasted with that of the AR model, which we regard as a benchmark in accordance with well-established practice (e.g., Bruneau, De Bandt, Flageollet, & Michaux, 2007, Flavin, Panopoulou, & Pantelidis, 2009, Smith, 2015. Our results suggest that the MIDAS models combined with the daily BPP CPI generally provide robust estimates of the time period considered, and contribute to significantly large increases in the forecast accuracy of consumer price inflation.…”

Section: Introductionsupporting

confidence: 74%

How can big data enhance the timeliness of official statistics?

Harchaoui

Janssen

2018

International Journal of Forecasting

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The daily consumer price index (CPI) produced by the Billion Prices Project (BPP CPI) offers a glimpse of the direction taken by consumer price inflation in real time. This is in contrast to the official U.S. CPI, which is compiled monthly and released with an average of a three-week delay following the end of the reference month. A recent body of research contended that the movements of online prices are representative of those of offline retail prices, making the BPP CPI a natural candidate for accurately improving the timeliness of the official CPI. We assess the predictive content of the BPP CPI using a variety of MIDAS models that accommodate data sampled at different frequencies. These models generate estimates that remain robust to the variety of time periods considered and, by the standard of the existing literature, contribute to a significant upgrade in the forecast accuracy of official consumer price inflation figures. The paper then sketches the broad implications of BPP CPI for the consumer price statistics maintained by national statistics offices and discusses how the proposed improvement in the timeliness of the official CPI fits in this perspective.

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Section: Introductionsupporting

confidence: 74%

How can big data enhance the timeliness of official statistics?

Harchaoui

Janssen

2018

International Journal of Forecasting

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“…We show that estimated MPCs and forecasts indirectly constructed by aggregating the countryspecific outcomes (hereafter indirect estimates/forecasts) are close to those using directly the data at the aggregated euro area level (direct estimates/forecasts). What is new compared to other studies reporting on country aggregation bias (Pesaran, and Smith, 1995;Marcellino et al, 2003;Ruth, 2008;Flavin et al, 2009;Monteforte and Siviero, 2010) is that our application uses a large number of private consumption model specifications rather than focus on one or a few specifications. In addition, other studies (Monteforte, 2007;Zeng, 2016) illustrate the country aggregation bias by aggregating across the three largest euro area countries, whereas we aggregate across the four largest euro area countries (hereafter EA4).…”

Section: Introductionmentioning

confidence: 99%

Euro Area Income and Wealth Effects: Aggregation Issues*

et al. 2021

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This empirical study for private consumption in the euro area and the four largest euro area countries validates the need to separate components of income and supports the common practice to split wealth. A focus on income and wealth components avoids an aggregation bias in estimated income and wealth effects. It finds little evidence in favour of an extensive country aggregation issue given direct euro area estimates of income and wealth propensities and forecasts for consumption are not too different from those indirectly aggregated across the four largest euro area countries.JEL Classification numbers: C53, D12, E21, E27. *All views expressed in this paper are those of the authors and do not necessarily reflect the position of the European Central Bank or the Central Bank of Ireland. We thank two anonymous referees and the editor as well as participants of Eurosystem meetings and of various conferences for their valuable comments.

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“…The general literature on inflation forecasting is copious; see Stock and Watson (, ), Granger and Jeon (), Hubrich (), Sephton (), Marcellino (), Flavin et al . (), Wright () and Beechey and Österholm () for a few recent examples and Faust and Wright () for further references.…”

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confidence: 99%

Forecasting Inflation Using Constant Gain Least Squares

Antipin¹,

Boumédiène

Österholm

2014

Australian Economic Papers

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This paper assesses the usefulness of constant gain least squares when forecasting inflation. An out-of-sample forecast exercise is conducted, in which univariate autoregressive models for inflation in Australia, Sweden, the United Kingdom and the United States are used. The results suggest that it is possible to improve the forecast accuracy by employing constant gain least squares instead of ordinary least squares. In particular, when using a gain of 0.05, constant gain least squares generally outperforms the corresponding autoregressive model estimated with ordinary least squares. In fact, at longer forecast horizons, the root mean square forecast error is reliably lowered for all four countries and for all lag lengths considered in the study.

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Forecasting growth and inflation in an enlarged euro area

Cited by 3 publications

References 25 publications

How can big data enhance the timeliness of official statistics?

How can big data enhance the timeliness of official statistics?

Euro Area Income and Wealth Effects: Aggregation Issues*

Forecasting Inflation Using Constant Gain Least Squares

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