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Does foreign sector help forecast domestic variables in DSGE models?

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  • Marcin Kolasa
  • Michal Rubaszek

Abstract

Estimated dynamic stochastic general equilibrium (DSGE) models are now used around the world for policy analysis. They have become particularly popular in central banks, some of which successfully applied them to generate macroeconomic forecasts. Arguably, one of the key drivers behind this trend was growing evidence that DSGE model-based forecasts can be competitive with those obtained with flexible time series models such as vector autoregressions (VAR), and also with expert judgement.See e.g. Smets and Wouters (2007), Edge et al. (2010), Kolasa et al. (2012) and Del Negro and Schorfheide (2012). The vast majority of these studies focus on the US economy as it allows to evaluate the forecast quality over a relatively large number of periods, and also makes the convenient closed economy assumption acceptable. The open economy applications that use the New Open Macroeconomics (NOEM) framework originating from Obstfeld and Rogoff (1995) and extended by Devereux and Engel (2003) and Gali and Monacelli (2005) do exist, but usually base their conclusions on a rather short evaluation sample. The earliest contribution to this literature is Bergin (2003) who tests small open economy DSGE models for Australia, Canada and the United Kingdom, and Bergin (2006) where a two-country model for the US and G7 is considered. However, only in-sample forecasts are evaluated in these papers. The literature testing open economy DSGE model-base forecasts out of sample include: Adolfson et al. (2007) and Christoffel et al. (2010) for the euro area, Adolfson et al. (2008) for Sweden, Matheson (2010) for Australia, Canada and New Zealand, Gupta et al. (2010) and Alpanda et al. (2011) for South Africa, Marcellino et al. (2014) for Luxemburg within the euro area. Following the literature working with closed economy models, the common practice is to evaluate forecasts generated with a NOEM framework relative to those obtained with some variants of Bayesian VARs. The overall finding is that open economy DSGE models are quite competitive, even though the conclusions differ by variables and countries. However, none of these studies tells us how much we actually gain by accounting for a foreign block in DSGE models. Since this question is essentially about the empirical validity of the key NOEM ingredients, i.e. those theoretical additions over the standard quantitative business cycle framework that are related to an open economy dimension, we argue that not having an aswer to it can be considered an important gap. Actually, there are reasons to be sceptical about the empirical success of the NOEM framework. In an influential paper Justiniano and Preston (2010) demonstrate that estimated small open economy DSGE models fail to account for the substantial influence of foreign shocks identified in many reduced-form studies. They show that capturing the observed comovement between domestic and foreign variables generates counterfactual implications for other variables, especially for the real exchange rate and terms of trade. It is also well-known that NOEM models have difficulties in explaining swings in the exchange rates and current account balances (Engel, 2014; Gourinchas and Rey, 2014). On the bright sight, Ca'Zorzi et al. (2016) show that real exchange rate forecasts from small open economy DSGE models beat the random walk at medium and long horizons and are very competitive with tougher benchmarks. In this paper we evaluate the forecasting performance of a state-of-the-art small open economy NOEM model developed by Justiniano and Preston (2010) relative to its associated New Keynesian (NK) closed economy benchmark. We focus on the forecast accuracy for three standard macrovariables showing up in both models: output, inflation and the short-term interest rate. Several variants of the NOEM model are considered that differ in the set of foreign sector variables used in estimation, which include the real exchange rate, terms of trade, current account balance, as well as foreign output, inflation and interest rates. Our conclusions are based on evidence from three economies, i.e. Australia, Canada and the United Kingdom, for which we can collect data that date back far enough to make our evaluation sample large when compared to the previous studies. Our findings are mainly negative. When we consider the full NOEM model, its point and density forecasts for domestic variables are statistically indistinguishable from, and in most cases even significantly less accurate than, those produced by the standard NK benchmark. Alternative model variants that leave either terms of trade or both terms of trade and foreign variables unobservable do not fare much better, and also do not offer much improvement relative to the closed economy variant. We show that these results are consistent with evidence from BVARs as expanding their dimension with the foreign sector variables also does not usually lead to improvement in their forecasting performance. However, this feature of BVARs is not surprising in light of the earlier literature stressing that small-scale VARs can forecast much better than large-scale VARs (Gurkaynak et al., 2013) as the number of estimated parameters grows very fast with the number of variables included in this class of models. In contrast, the open economy DSGE model considered in this paper is still very scarcely parameterized, hence its lack of competitiveness relative to the closed economy benchmark points at important misspecification of the underlying theoretical structure. We provide support for this claim by showing that even the richly specified NOEM model generates several counterfactual predictions about the comovement between domestic and foreign sector variables.

Suggested Citation

  • Marcin Kolasa & Michal Rubaszek, 2016. "Does foreign sector help forecast domestic variables in DSGE models?," EcoMod2016 9393, EcoMod.
  • Handle: RePEc:ekd:009007:9393
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    Cited by:

    1. Ca’ Zorzi, Michele & Kolasa, Marcin & Rubaszek, Michał, 2017. "Exchange rate forecasting with DSGE models," Journal of International Economics, Elsevier, vol. 107(C), pages 127-146.
    2. Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
    3. Van Nguyen, Phuong, 2020. "The Vietnamese business cycle in an estimated small open economy New Keynesian DSGE model," Dynare Working Papers 56, CEPREMAP.
    4. Michał Rubaszek, 2019. "Forecasting crude oil prices with DSGE models," GRU Working Paper Series GRU_2019_024, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    5. Van Nguyen, Phuong, 2020. "Evaluating the forecasting accuracy of the closed- and open economy New Keynesian DSGE models," Dynare Working Papers 59, CEPREMAP.
    6. Sergii Kiiashko, 2018. "Applications for DSGE Models in Central Banking: Key Issues Explored During Research Workshop of the National Bank of Ukraine," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 246, pages 4-9.

    More about this item

    Keywords

    Australia; Canada; United Kingdom; Forecasting and projection methods; General equilibrium modeling;

    JEL classification:

    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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