A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series

Marcellino, Massimiliano; Stock, James H.; Watson, Mark W.

doi:10.1016/j.jeconom.2005.07.020

Cited by 584 publications

(227 citation statements)

References 21 publications

(8 reference statements)

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“…The Error Correction Model (ECM) assumes that there is a long run relationship between the level of the exchange rate, s t , and the level of the fundamentals, f t ; thus, in forecasting the rate of growth of the exchange rate, it includes a correction term that captures 34 Note that the forecast is based on a parameter directly estimated from a regression of the current rate of growth of the exchange rate on the lagged value of the fundamental. See Marcellino, Stock and Watson (2006) for a review of direct and iterated forecast methods. 35 See Meese and Rogo¤ (1983a,b) for the monetary model; for the monetary, productivity and net foreign assets measures; and Bacchetta, van Wincoop and Beutler (2010) for the monetary model augmented by unemployment and oil prices.…”

Section: Single-equation Ecm Modelsmentioning

confidence: 99%

Exchange Rate Predictability

Rossi

2013

Journal of Economic Literature

517

333

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The main goal of this article is to provide an answer to the question: "Does anything forecast exchange rates, and if so, which variables?". It is well known that exchange rate ‡uctuations are very di¢ cult to predict using economic models, and that a random walk forecasts exchange rates better than any economic model (the Meese and Rogo¤ puzzle). However, the recent literature has identi…ed a series of fundamentals/methodologies that claim to have resolved the puzzle. This article provides a critical review of the recent literature on exchange rate forecasting and illustrates the new methodologies and fundamentals that have been recently proposed in an upto-date, thorough empirical analysis. Overall, our analysis of the literature and the data suggests that the answer to the question: "Are exchange rates predictable?" is, "It depends" -on the choice of predictor, forecast horizon, sample period, model, and forecast evaluation method. Predictability is most apparent when one or more of the following hold: the predictors are Taylor rule or net foreign assets, the model is linear, and a small number of parameters are estimated. The toughest benchmark is the random walk without drift.

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Section: Single-equation Ecm Modelsmentioning

confidence: 99%

Exchange Rate Predictability

Rossi

2013

Journal of Economic Literature

517

333

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“…Using this criterion in small samples requires special attention, without which the forecasting uncertainty may increase. Therefore, the RMSE criterion is also used for assessing the forecasting accuracy (Marcellinio et al 2006), so that the forecasting error (in terms of RMSE) would be minimized for the minimum Akaike criterion. The following equation is used for AIC computation.…”

Section: Autoregressive Integrated Moving Average (Arima) Modelmentioning

confidence: 99%

Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models

Tavakkoli¹,

Hemmasi²,

Talaeipour³

et al. 2015

BioResources

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The performance of the Autoregressive Integrated Moving Average )ARIMA) model and Double and Holt-winters exponential smoothing techniques for forecasting the consumption of particleboard in Iran are compared. Annual time series data from 1978 to 2009 in the modeling process, and observations from 2010 to 2012 were used to check the accuracy of the models' forecasting performance. Also, the models' performances were calculated in terms of RMSE criterion, and the consumption of particleboard in Iran was forecasted up to the year 2017 using the most appropriate model. The results of comparing different forecast models showed that the ARMA (2,1) model yielded the lowest RMSE value compared to the other two models, which makes it more appropriate for the prediction of consumption of particleboard in Iran. Results also revealed that there might be an increasing trend in the consumption of particleboard, i.e., an average annual increasing rate calculated as 5% for particleboard. Thus, it was predicted that the consumption of particleboard would increase from 901,652 m 3 in 2012 to 1,178,320 m 3 in 2017.

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“…Whether using direct forecasts is better than iterated forecasts is a matter of ongoing debate, see the discussion in e.g. Marcellino et al (2004).…”

Section: Var Modelmentioning

confidence: 99%

Predicting the Term Structure of Interest Rates: Incorporating Parameter Uncertainty, Model Uncertainty and Macroeconomic Information

2007

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We forecast the term structure of U.S. Treasury zero-coupon bond yields by analyzing a range of models that have been used in the literature. We assess the relevance of parameter uncertainty by examining the added value of using Bayesian inference compared to frequentist estimation techniques, and model uncertainty by combining forecasts from individual models. Following current literature we also investigate the benefits of incorporating macroeconomic information in yield curve models. Our results show that adding macroeconomic factors is very beneficial for improving the out-of-sample forecasting performance of individual models. Despite this, the predictive accuracy of models varies over time considerably, irrespective of using the Bayesian or frequentist approach. We show that mitigating model uncertainty by combining forecasts leads to substantial gains in forecasting performance, especially when applying Bayesian model averaging.

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A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series

Cited by 584 publications

References 21 publications

Exchange Rate Predictability

Exchange Rate Predictability

Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models

Predicting the Term Structure of Interest Rates: Incorporating Parameter Uncertainty, Model Uncertainty and Macroeconomic Information

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