A dynamic factor model for nowcasting Canadian GDP growth

Chernis, Tony; Sekkel, Rodrigo

doi:10.1007/s00181-017-1254-1

Cited by 39 publications

(25 citation statements)

References 31 publications

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“…It is particularly important for policy institutions to …nd ways to incorporate global developments into their forecasting models for the domestic economy (see, e.g., Rossiter, 2010;Stratford, 2013). External forces are typically taken into account by a large share of international predictor variables in a data-rich environment (see, e.g., Chernis and Sekkel, 2017). Here we focus directly on the value of existing global indicators for growth forecasts in small open economies.…”

Section: Panel (B) Ofmentioning

confidence: 99%

A Comparison of Monthly Global Indicators for Forecasting Growth

Baumeister

Guérin

2020

SSRN Journal

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for many useful suggestions. The views expressed in this paper are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, IMF management, or the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Section: Panel (B) Ofmentioning

confidence: 99%

A Comparison of Monthly Global Indicators for Forecasting Growth

Baumeister

Guérin

2020

SSRN Journal

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“…Laurent & Andrey (2014) indicated that forecast validity of the data increases when the probability-based forecasts of the coincident indicator model and the interest rate yield curve model are combined. Chemis & Sekkei (2017) showed that the Dynamic Factor Model outperforms univariate benchmarks as well as other used now-casting models, such as Mixed Data Sampling (MIDAS) and bridge regressions. Forni, Gambetti, Lippi, & Sala (2017) found that noise shocks cause hump-shaped responses of GDP, consumption and investment, and explain a large part of their prediction error variance in business cycles.…”

Section: Literature Reviewmentioning

confidence: 99%

AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries

Kurihara¹,

Fukushima²

2019

AEF

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This paper examines the validity of forecasting economic variables by using machine learning. AI (artificial intelligence) has been improved and entering our society rapidly, and the economic forecast is no exception. In the real business world, AI has been used for economic forecasts, but not so many studies focus on machine learning. Machine learning is focused in this paper and a traditional statistical model, the autoregressive (AR) model is also used for comparison. A comparison of using an AR model and machine learning (LSTM) to forecast GDP and consumer price is conducted using recent cases from G7 countries. The empirical results show that the traditional forecasting AR model is a little more appropriate than the machine learning model, however, there is little difference to forecast consumer price between them.

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“…First, data should be updated frequently (for example, at the monthly frequency) and with a publication delay shorter than the one observed for GDP. Second, the variables need to reflect economic activity in order to be helpful predictors of GDP (see Chernis and Sekkel, 2017). For instance, information on bank loans is more relevant than the interest rate on those operations.…”

Section: Datamentioning

confidence: 99%

“…Various institutions around the world-central banks in particular-use nowcasting models to inform their policy decision-making. For instance, nowcasting applications have been used to forecast the growth rate of the economies of Canada (see Chernis and Sekkel, 2017), Spain (see Cuevas and Quilis, 2012), Mexico (see Tirado, Delajara and Alvarez, 2016), and several Latin American countries (see Liu, Matheson and Romeu, 2012), among many others. Of course, nowcasting models are also used for larger economies such as the United States, in which the nowcasts produced by the Federal Reserve Bank of Atlanta (whose model is denominated "GDPNow") (see Higgins, 2014)…”

Section: Introductionmentioning

confidence: 99%

A Nowcasting Model for the Growth Rate of Real GDP of Ecuador: Implementing a Time-Varying Intercept

González‐Astudillo

Baquero²,

Desarrollo³

2018

FEDS

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This paper proposes a model to nowcast the annual growth rate of real GDP for Ecuador. The specification combines monthly information of 28 macroeconomic variables with quarterly information of real GDP in a mixed-frequency approach. Additionally, our setup includes a time-varying mean coefficient on the annual growth rate of real GDP to allow the model to incorporate prolonged periods of low growth, such as those experienced during secular stagnation episodes. The model produces reasonably good nowcasts of real GDP growth in pseudo out-of-sample exercises and is marginally more precise than a simple ARMA model.

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A dynamic factor model for nowcasting Canadian GDP growth

Cited by 39 publications

References 31 publications

A Comparison of Monthly Global Indicators for Forecasting Growth

A Comparison of Monthly Global Indicators for Forecasting Growth

AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries

A Nowcasting Model for the Growth Rate of Real GDP of Ecuador: Implementing a Time-Varying Intercept

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