Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach

Proietti, Tommaso; Giovannelli, Alessandro; Ricchi, Ottavio; Citton, Ambra; Tegami, Christían; Tinti, Cristina

doi:10.1016/j.ijforecast.2021.04.003

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…Within the context of Madagascar, the leading indicators listed in Table 1 are regarded as those capable of anticipating potential short-term fluctuations in the GDP. Moreover, these indicators are consistent with existing empirical research on real-time monitoring of economic activities [7,15,23,30,44,52,53].…”

Section: Dataset and Featuressupporting

confidence: 87%

Nowcasting Madagascar's real GDP using machine learning algorithms

Ramaharo,

Rasolofomanana

2023

Preprint

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We investigate the predictive power of different machine learning algorithms to nowcast Madagascar's gross domestic product (GDP). We trained popular regression models, including linear regularized regression (Ridge, Lasso, Elastic-net), dimensionality reduction model (principal component regression), k-nearest neighbors algorithm (k-NN regression), support vector regression (linear SVR), and tree-based ensemble models (Random forest and XGBoost regressions), on 10 Malagasy quarterly macroeconomic leading indicators over the period 2007Q1-2022Q4, and we used simple econometric models as a benchmark. We measured the nowcast accuracy of each model by calculating the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Our findings reveal that the Ensemble Model, formed by aggregating individual predictions, consistently outperforms traditional econometric models. We conclude that machine learning models can deliver more accurate and timely nowcasts of Malagasy economic performance and provide policymakers with additional guidance for data-driven decision making.

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Section: Dataset and Featuressupporting

confidence: 87%

Nowcasting Madagascar's real GDP using machine learning algorithms

Ramaharo,

Rasolofomanana

2023

Preprint

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“…The crucial gap in the literature is the lack of a systematic and validated approach to rescale changes in electricity load into economic indicators (Fezzi and Fanghella 2021). Most often, electricity data (such as consumption, export, import, and production) are used as one of the key inputs to nowcast GDP (Fezzi and Fanghella 2021;Proietti et al 2021;Eraslan and Götz 2021) or economic activity (Wegmüller et al 2023). Lehmann and Sascha (2022) used weekly and monthly electricity consumption data for the monthly growth rate of industrial production.…”

Section: Introductionmentioning

confidence: 99%

Nowcasting Economic Activity Using Electricity Market Data: The Case of Lithuania

et al. 2023

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Traditional forecasting methods usually rely on historical macroeconomic indicators with significant delays. To address this problem, new opportunities for economic modeling and forecasting are emerging by using real-time data and making nowcasting of economic activity. This research aims to assess the usefulness of electricity market data to nowcast the economic activity in Lithuania. Various MIDAS regression models are used to nowcast nine monthly macroeconomic indicators. In general, electricity market indicators are useful to nowcast certain macroeconomic indicators. Electricity consumption is the most useful among electricity market indicators and brings benefits when nowcasting imports, industrial production, consumer confidence, wholesale and retail trade, and the repair of motor vehicles and motorcycles. Electricity production is beneficial in nowcasting the industrial production. Meanwhile, electricity price is useful for nowcasting exports, exports of goods of Lithuanian origin, imports, and industrial production. Meanwhile, electricity market data do not improve the prediction of the unemployment rate, economic sentiment indicator, and CPI-based consumer price in comparison with an autoregressive model.

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“…See among othersGiannone et al (2008),Camacho and Perez-Quiros (2010), Ba ńbura and Rünstler (2011), orBok et al (2018). Incidentally, this literature has typically focused on forecasting GDP taken as a whole, whereas papers on individual subcomponents, to build a bottom-up forecast for GDP, are scarcer (seeBaffigi et al, 2004;Foroni & Marcellino, 2014;Hahn & Skudelny, 2008;or Proietti et al, 2021). However, in most of these papers, government consumption is either forecast by means of univariate methods or is considered a residual (seeGrassi et al, 2015;Esteves, 2013).…”

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confidence: 99%

Fiscal targets. A guide to forecasters?

Paredes

Pérez

Pérez‐Quirós

2023

J of Applied Econometrics

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Should rational agents take into consideration government policy announcements? A skilled agent (an econometrician) could set up a model to combine the following two pieces of information in order to anticipate the future course of fiscal policy in real-time: (i) the ex-ante path of policy as published/announced by the government; (ii) incoming, observed data on the actual degree of implementation of ongoing plans. We formulate and estimate empirical models for a number of EU countries (Germany, France, Italy and Spain) to show that government (consumption) targets convey useful information about ex-post policy developments when policy changes significantly (even if past credibility is low) and when there is limited information about the implementation of plans (e.g. at the beginning of a fiscal year). In addition, our models are instrumental in unveiling the current course of policy in real time. Our approach complements a well-established branch of the literature that finds politically motivated biases in policy targets.

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Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach

Cited by 11 publications

References 35 publications

Nowcasting Madagascar's real GDP using machine learning algorithms

Nowcasting Madagascar's real GDP using machine learning algorithms

Nowcasting Economic Activity Using Electricity Market Data: The Case of Lithuania

Fiscal targets. A guide to forecasters?

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