A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series

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

doi:10.2139/ssrn.687782

Cited by 230 publications

(259 citation statements)

References 30 publications

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“…Both ways provide us with T +h and we simulate draws of y +h conditional on T +h to approximate the predictive density. 7 The alternative would be to use direct forecasting, but recent papers such as Marcellino, Stock and Watson (2006) tend to find that iterated forecasts are better. Direct forecasting would also require re-estimating the model for different choices of h and would not necessarily remove the need for predictive simulation since the researcher may wish to simulate T +h from (1) when h > 1.…”

Section: Forecast Comparisonmentioning

confidence: 99%

Large time-varying parameter VARs

Koop

Korobilis

2013

Journal of Econometrics

363

385

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In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden through the use forgetting factors. We then extend the TVP-VAR so that its dimension can change over time. For instance, we can have a large TVP-VAR as the forecasting model at some points in time, but a smaller TVP-VAR at others. A final extension lies in the development of a new method for estimating, in a timevarying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs.These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output and interest rates demonstrates the feasibility and usefulness of our approach.

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Section: Forecast Comparisonmentioning

confidence: 99%

Large time-varying parameter VARs

Koop

Korobilis

2013

Journal of Econometrics

363

385

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“…Bhansali, 1999 andIng, 2003). However, it appears that, empirically (Marcellino, Stock and Watson, 2006), the robustness and bias reduction obtained using the "h-step ahead" forecasts have to be balanced with the price paid in terms of increased sampling variance. This tradeo¤ is nevertheless irrelevant for our purposes of comparing aggregation of forecasts and forecasts of aggregates as the forecasting approach is the same across methods.…”

Section: Forecasting Modelsmentioning

confidence: 99%

“…[ Table 8 reports the forecast results at the industrial economies' level using these three approaches 15 . In 11 cases out of 16, the direct method using disaggregate information outperforms the two other approaches and in 10 cases out of 16, the direct method using aggregate information outperforms the bottom-up one.…”

Section: The Role Of Disaggregate Information In Forecasting Aggregatmentioning

confidence: 99%

Forecasting World Trade: Direct Versus “Bottom-Up” Approaches

Burgert

Dées

2007

Open Econ Rev

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“…Granger & Teräsvirta (1993, Ch. 8) and Marcellino, Stock & Watson (2005), but the computational savings in our real time exercise with several series are substantial.…”

Section: Forecasting Methodsmentioning

confidence: 98%

Forecasting euro area variables with German pre‐EMU data

Brüggemann

Lütkepohl

Marcellino

2008

Journal of Forecasting

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It is investigated whether Euro-area variables can be forecast better based on synthetic time series for the pre-Euro period or by using just data from Germany for the pre-Euro period. Our forecast comparison is based on quarterly data for the period 1970Q1 -2003Q4 for ten macroeconomic variables. The years 2000 -2003 are used as forecasting period. A range of different univariate forecasting methods is applied. Some of them are based on linear autoregressive models and we also use some nonlinear or time-varying coefficient models. It turns out that most variables which have a similar level for Germany and the Euro-area such as prices can be better predicted based on German data while aggregated European data are preferable for forecasting variables which need considerable adjustments in their levels when joining German and EMU data. These results suggest that for variables which have a similar level for Germany and the Euro-area it may be reasonable to consider the German pre-EMU data for studying economic problems in the Euro-area.

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A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series

Cited by 230 publications

References 30 publications

Large time-varying parameter VARs

Large time-varying parameter VARs

Forecasting World Trade: Direct Versus “Bottom-Up” Approaches

Forecasting euro area variables with German pre‐EMU data

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