Abstract
This paper investigates whether monetarypolicy regime changes affect the success of forecasting inflation. The forecasting performances of some linear and nonlinear univariate models are analyzed for 14 different countries that have adopted inflationtargeting (IT) monetary regimes at some point in their economic history. The results show that forecasting performance is generally superior under an IT monetary regime compared to nonIT (NIT) periods. In more than half of the countries covered in this study, superior forecasting accuracy can be achieved in IT periods regardless of the model used. In contrast, among most of the remaining countries, the results remain ambiguous, and the evidence on the superiority of NIT is limited to very few countries.
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Notes
In addition to the special role given to IT, the prominence of inflation forecasting has been raised by the recent formalization of the New Keynesian optimal policy. The New Keynesian model has been used to demonstrate that the optimal choice of policy will depend on the optimal forecasts (see Svensson 2005; Faust and Wright 2012).
See Brito and Bystedt (2010) for a counter argument that claims that there is no evidence that an IT improves economic performance, as measured by the behavior of inflation and output growth in developing countries.
This interpretation is not directly in contrast with D’Agostino and Surico’s results, which are mentioned above. These results provide evidence that a policy regime that successfully stabilizes inflation in the US makes it harder to improve upon the forecasts that are based on “naïve” models. However, the evidence that we provide here can be interpreted thusly: a policy regime that successfully stabilizes inflation (i.e. an IT regime) makes it easier to forecast inflation irrespective of the underlying model that is used for forecasting.
The effect of IT regimes on inflation volatility will be studied in future research.
Deciding whether the direct or the iterated approach is better is an empirical matter because it involves a trade off between the estimation efficiency and the robustnesstomodel misspecification; see Elliott and Timmermann (2008). Marcellino et al. (2006) address these points empirically using a dataset of 170 US monthly macroeconomic time series. They find that the iterated approach generates the lowest MSEvalues, particularly if lengthy lags of the variables are included in the forecasting models and if the forecast horizon is long.
This process involves replacing \(y_{t}\) with \(y_{t+h}\) on the lefthand side of Eq. (4) and running the regression using data up to time \(t\) to fitted values for corresponding forecasts.
Indeed, \(d_{t}\) is convex in \(y_{t1}\) whenever \(y_{t1}<c\) and \(d_{t}\) is convex whenever \(y_{t1}>c\). Therefore, by Jensen’s inequality, naive estimation underestimates \(d_{t}\) if \(y_{t1}<c\), and it overestimates \(d_t\) if \( y_{t1}>c\).
A detailed exposition of approaches for forecasting from a SETAR model can be found in van Dijk et al. (2003).
See Franses and Dijk (2000) for a review of feedforwardtype neural network models.
For the sake of brevity, we only provide the results of the iterative forecasts. The results that were obtained with direct forecasts are qualitatively similar and available upon request.
As long as we assume the same variance for both periods, the DM test is still valid in this case. However, one may object to this assumption by indicating that IT can reduce the variance of the inflation.
Monthly inflation forecasts are scaled by 100. As a caveat, one should keep in mind that comparing two MSE series via DieboldMariano statistics is a scaledependent process, i.e. the statistics change under the multiplication of two series by a constant. Here, we scale the monthly inflation figures by 100 to express them in terms of monthly percentages. See Clark and West (2006) for a detailed discussion on the effects of scaling the outofsample MSEbased tests.
To find out whether any specific model particularly performs better or worse than the others in IT or NIT periods, we have tested the relative forecast accuracy of the utilized models in each period against the benchmark of the random walk model. The null hypothesis was that the forecasts of the model under consideration are no better than those of the random walk model; the alternative is that random walk forecasts more accurately than the model under consideration. The associated DM test statistics indicated mixed results that vary across countries and forecasting horizons. Hence, we cannot assert that forecasts from a particular model are superior to random walk forecasts in most countries and horizons. Furthermore, comparing the results in IT and NIT periods did not lead us to conclude that one or more models overwhelmingly provide better forecasts in one period in comparison to other periods. Therefore, to save some space, we do not report these results here, which can be found in the working paper version and also available upon request.
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Appendix: A note on Monte Carlo numerical method
Appendix: A note on Monte Carlo numerical method
As mentioned in the text, a version of Monte Carlo approach, which was first suggested by Lin and Granger (1994), is adopted for numerical computation of the multistep iterative forecasts within LSTAR and ARNN models. The main reason behind this choice is computational speed and accuracy of Monte Carlo simulation against the alternative approach of numerical integration: as the forecasting steps get higher, numerical integration becomes significantly slower.
Computing more than one step forecasts via Monte Carlo framework for nonlinear models in general consists of the following steps:

Step 1: Compute \(\hat{y}_{t+1t}\) by directly plugging in \(y_t, y_{t1},\ldots \) into the estimated equation.

Step 2: Generate \(n\) normal random variates with a mean of zero and a variance of \(\hat{\sigma }^2\) to form a vector of simulated \({\varepsilon }_{t+1t}\) values.

Step 3: Compute simulated \(y_{t+2t}\)s by plugging in the simulated values of \({\varepsilon }_{t+1t}\) along with \(y_{t+1t}\) and \(y_t, y_{t1},\ldots \, n\)times.

Step 4: Compute the Monte Carlo estimation of \({y}_{t+2t}\) which is \(\hat{y}_{t+2t}\).

Step 5: Repeat Steps 2, 3, and 4 to increase \( t \) for getting the higher step forecasts until the end of the forecast horizon.
Notice that, in order to apply a Monte Carlo scheme for forecasting it is necessary to assume a probability distribution for the error terms \( \{ \varepsilon _{t} \} \). Here, in all models {\({\varepsilon }_{t}\}\) is assumed to be i.i.d. \(N(0, {\sigma }^2)\) for all \(t\).
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Özkan, H., Yazgan, M.E. Is forecasting inflation easier under inflation targeting?. Empir Econ 48, 609–626 (2015). https://doi.org/10.1007/s0018101307933
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DOI: https://doi.org/10.1007/s0018101307933
Keywords
 Inflation targeting
 Forecasting inflation
 Forecast accuracy
JEL Classification
 C45
 C53
 E31
 E37
 E42
 E47
 E52
 E61
 E65