How far ahead can we forecast? Evidence from cross-country surveys

Isiklar, Gultekin; Lahiri, Kajal

doi:10.1016/j.ijforecast.2007.01.003

Cited by 66 publications

(63 citation statements)

References 25 publications

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“…This empirical observation leads us to believe that over these horizons, forecasters do not receive much dependable information to revise their forecasts systematically. Second, the initial 24-month ahead forecasts for all countries seem to be 6 Similar graphs for the evolution of real GDP consensus forecasts for the earlier years, i.e., 1991-2002 can be found in Isiklar and Lahiri (2007).…”

Section: Datamentioning

confidence: 62%

Learning and Heterogeneity in GDP and Inflation Forecasts

Lahiri

Sheng

2008

SSRN Journal

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We estimate a Bayesian learning model with heterogeneity aimed at explaining the evolution of expert disagreement in forecasting real GDP growth and inflation over 24 monthly horizons for G7 countries during 1990-2007. Professional forecasters are found to begin and have relatively more success in predicting inflation than real GDP at significantly longer horizons; forecasts for real GDP contain little information beyond 6 quarters, but forecasts for inflation have predictive value beyond 24 months and even 36 months for some countries. Forecast disagreement arises from two primary sources in our model: differences in the initial prior beliefs of experts, and differences in the interpretation of new public information. Estimated model parameters, together with two separate case studies on (i) the dynamics of forecast disagreement in the aftermath of the 9/11 terrorist attack in the U.S. and (ii) the successful inflation targeting experience in Italy after 1997, firmly establish the importance of these two pathways to expert disagreement.

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Section: Datamentioning

confidence: 62%

Learning and Heterogeneity in GDP and Inflation Forecasts

Lahiri

Sheng

2008

SSRN Journal

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“…We have learned that forecasts for a horizon longer than 12-18 months might not be valuable. (Vuchelen and Guitierrez, 2005b;Isiklar and Lahiri, 2007). We also know more the causes of bias.…”

Section: E What Have We Learned? 19mentioning

confidence: 96%

“…The pattern of the errors can then provide a way of understanding the forecasters' learning process about the impact of this structural break. Isiklar and Lahiri (2007) use forecast revisions to explain the behavioral characteristics of forecasters, i.e. how they react to news and when is news important.…”

Section: E What Have We Learned? 19mentioning

confidence: 99%

Perspectives on Evaluating Macroeconomic Forecasts

Stekler¹

2011

Advances in Economic Forecasting

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“…Depending on their experience, some forecasters may underreact or overreact to the arrival of new information without intention (Isiklar and Lahiri, 2007). Nordhaus (1987) discusses a scenario in which forecasters may wish to cultivate a reputation for producing stable forecasts and hence intentionally introduce errors that are irrelevant to the target.…”

Section: Implicit Forecast Errormentioning

confidence: 99%

“…The magnitude of M SF R t|s,l can be interpreted as the amount of effort that forecasters made in the revision process, and −∆M SF E t|s,l measures the reward of the forecast revisions in terms of forecast accuracy. Isiklar and Lahiri (2007) compare the effort and reward to assess whether rational forecasters react to news in an optimal way. By allowing for an implicit error component contained in the multi-horizon forecasts, we extend their interpretation of the comparison.…”

Section: For Rational and Implicit Forecastsmentioning

confidence: 99%

A State Space Approach to Evaluate Multi-Horizon Forecasts

Goodwin

Tian

2017

SSRN Journal

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SummaryWe propose a state space modeling framework to evaluate a set of forecasts that target the same variable but are updated along the forecast horizon. The approach decomposes forecast errors into three distinct horizon-specific processes, namely, bias, rational error and implicit error, and attributes forecast revisions to corrections for these forecast errors. We derive the conditions under which forecasts that contain error that is irrelevant to the target can still present the second moment bounds of rational forecasts. By evaluating multi-horizon daily maximum temperature forecasts for Melbourne, Australia, we demonstrate how this modeling framework analyzes the dynamics of the forecast revision structure across horizons. Understanding forecast revisions is critical for weather forecast users to determine the optimal timing for their planning decision.JEL classification: C32; C53

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How far ahead can we forecast? Evidence from cross-country surveys

Cited by 66 publications

References 25 publications

Learning and Heterogeneity in GDP and Inflation Forecasts

Learning and Heterogeneity in GDP and Inflation Forecasts

Perspectives on Evaluating Macroeconomic Forecasts

A State Space Approach to Evaluate Multi-Horizon Forecasts

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