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Published in: Empirical Economics 2/2015

01-03-2015

Is forecasting inflation easier under inflation targeting?

Authors: Harun Özkan, M. Ege Yazgan

Published in: Empirical Economics | Issue 2/2015

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Abstract

This paper investigates whether monetary-policy 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 inflation-targeting (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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Appendix
Available only for authorised users
Footnotes
1
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).
 
2
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.
 
3
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.
 
4
The effect of IT regimes on inflation volatility will be studied in future research.
 
5
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 robustness-to-model 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 MSE-values, particularly if lengthy lags of the variables are included in the forecasting models and if the forecast horizon is long.
 
6
This process involves replacing \(y_{t}\) with \(y_{t+h}\) on the left-hand side of Eq. (4) and running the regression using data up to time \(t\) to fitted values for corresponding forecasts.
 
7
Indeed, \(d_{t}\) is convex in \(y_{t-1}\) whenever \(y_{t-1}<c\) and \(-d_{t}\) is convex whenever \(y_{t-1}>c\). Therefore, by Jensen’s inequality, naive estimation underestimates \(d_{t}\) if \(y_{t-1}<c\), and it overestimates \(d_t\) if \( y_{t-1}>c\).
 
8
A detailed exposition of approaches for forecasting from a SETAR model can be found in van Dijk et al. (2003).
 
9
See Franses and Dijk (2000) for a review of feed-forward-type neural network models.
 
10
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.
 
11
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.
 
12
Monthly inflation forecasts are scaled by 100. As a caveat, one should keep in mind that comparing two MSE series via Diebold-Mariano statistics is a scale-dependent 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 out-of-sample MSE-based tests.
 
13
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.
Table 3
The p-values of the DM tests where the null hypothesis is that IT forecasts are no better than NIT forecasts
M
\(h\)
Canada
Chile
Colombia
Hungary
Israel
S.Korea
Mexico
Norway
Poland
S. Africa
Sweden
Thailand
Turkey
UK
RW
1
0.371
0.000
0.000
0.000
0.000
0.045
0.000
0.952
0.000
0.876
0.031
0.960
0.000
0.178
2
0.334
0.000
0.000
0.001
0.000
0.040
0.000
0.967
0.000
0.886
0.042
0.956
0.000
0.192
3
0.371
0.000
0.000
0.002
0.000
0.039
0.000
0.961
0.000
0.921
0.060
0.966
0.000
0.215
12
0.370
0.000
0.000
0.017
0.000
0.055
0.000
0.935
0.000
0.957
0.208
0.959
0.000
0.224
24
0.497
0.000
0.000
0.073
0.000
0.110
0.000
0.731
0.000
0.884
0.523
0.994
0.000
0.611
AR
1
0.999
0.000
0.000
0.289
0.000
0.001
0.000
0.997
0.000
0.539
0.005
0.970
0.184
0.626
2
0.999
0.000
0.000
0.203
0.000
0.008
0.000
0.997
0.000
0.488
0.005
0.976
0.280
0.618
3
0.999
0.000
0.000
0.229
0.000
0.021
0.000
0.995
0.000
0.490
0.005
0.970
0.291
0.599
12
0.993
0.000
0.000
0.265
0.000
0.127
0.000
0.985
0.000
0.404
0.002
0.926
0.209
0.442
24
0.980
0.037
0.000
0.994
0.001
0.161
0.001
0.738
0.000
0.601
0.074
0.304
0.006
0.609
ARMA
1
0.998
0.065
0.000
0.473
0.000
0.000
0.000
0.999
0.000
0.540
0.003
0.952
0.000
0.591
2
0.999
0.114
0.000
0.425
0.000
0.091
0.000
0.999
0.000
0.491
0.003
0.966
0.287
0.582
3
0.999
0.181
0.001
0.484
0.000
0.114
0.000
0.997
0.000
0.501
0.003
0.946
0.002
0.554
12
0.981
0.242
0.000
0.308
0.000
0.023
0.000
0.989
0.000
0.416
0.001
0.887
0.020
0.478
24
0.966
0.503
0.000
0.991
0.001
0.244
0.000
0.600
0.000
0.663
0.140
1.000
0.003
0.606
SETAR
1
0.994
0.197
0.000
0.004
0.000
0.045
0.000
0.983
0.000
0.617
0.004
0.988
0.000
0.908
2
0.992
0.240
0.000
0.893
0.000
0.132
0.000
0.983
0.000
0.712
0.004
0.971
0.000
0.883
3
0.990
0.344
0.000
0.893
0.000
0.032
0.000
0.983
0.000
0.710
0.004
0.973
0.000
0.927
12
0.954
0.963
0.000
0.001
0.005
0.237
0.000
0.946
0.000
0.561
0.003
0.943
0.000
0.807
24
0.722
0.096
0.000
0.069
0.021
0.208
0.000
0.795
0.000
0.639
0.064
0.941
0.000
0.849
LSTAR
1
0.998
0.000
0.000
0.099
0.001
0.001
0.000
0.955
0.000
0.164
0.018
0.980
0.022
0.855
2
0.998
0.000
0.000
0.129
0.002
0.003
0.000
0.957
0.000
0.173
0.019
0.980
0.006
0.848
3
0.998
0.001
0.000
0.118
0.001
0.008
0.000
0.935
0.000
0.201
0.019
0.978
0.001
0.824
12
0.984
0.014
0.000
0.216
0.034
0.037
0.000
0.928
0.000
0.215
0.040
0.948
0.001
0.715
24
0.877
0.650
0.000
0.763
0.017
0.046
0.000
0.772
0.000
0.287
0.069
0.872
0.001
0.834
ARNN
1
0.992
0.000
0.000
0.233
0.000
0.000
0.000
0.998
0.000
0.528
0.003
0.937
0.000
0.625
2
0.999
0.013
0.004
0.098
0.000
0.020
0.000
1.000
0.000
0.520
0.003
0.931
0.000
0.641
3
0.998
0.000
0.006
0.728
0.000
0.036
0.000
0.995
0.000
0.468
0.003
0.928
0.000
0.462
12
0.990
0.000
0.002
0.563
0.000
0.090
0.000
0.989
0.000
0.393
0.001
0.971
0.093
0.614
24
0.945
0.198
0.000
0.875
0.000
0.142
0.045
0.983
0.000
0.542
0.092
1.000
0.602
0.631
MS-AR
1
0.998
0.110
0.000
0.029
0.000
0.008
0.007
0.998
0.000
0.560
0.021
0.993
0.000
0.696
2
0.998
0.050
0.001
0.027
0.000
0.003
0.012
0.998
0.000
0.589
0.034
0.995
0.003
0.706
3
0.998
0.100
0.010
0.020
0.000
0.007
0.010
0.993
0.000
0.623
0.033
0.994
0.021
0.763
12
0.999
0.058
0.000
0.140
0.000
0.691
0.009
0.973
0.000
0.575
0.097
0.967
0.019
0.775
24
0.993
0.415
0.000
0.651
0.001
0.019
0.004
0.991
0.001
0.618
0.331
0.986
0.147
0.912
Emboldened items refer to the cases where the null hypothesis is rejected at 5 % level of significance
 
Literature
go back to reference Al-Qassam MS, Lane JA (1989) Forecasting exponential autoregressive models of order 1. J Time Ser Anal 91(2):95–113CrossRef Al-Qassam MS, Lane JA (1989) Forecasting exponential autoregressive models of order 1. J Time Ser Anal 91(2):95–113CrossRef
go back to reference Ball LM, Sheridan N (2004) Does inflation targeting matter? In: The inflation-targeting debate. NBER Chaps, National Bureau of Economic Research Inc., Cambridge, pp 249–282 Ball LM, Sheridan N (2004) Does inflation targeting matter? In: The inflation-targeting debate. NBER Chaps, National Bureau of Economic Research Inc., Cambridge, pp 249–282
go back to reference Brito RD, Bystedt B (2010) Inflation targeting in emerging economies: panel evidence. J Dev Econ 91(2):198–210CrossRef Brito RD, Bystedt B (2010) Inflation targeting in emerging economies: panel evidence. J Dev Econ 91(2):198–210CrossRef
go back to reference Clark TE, West KD (2006) Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis. J Econom 135(1–2):155–186CrossRef Clark TE, West KD (2006) Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis. J Econom 135(1–2):155–186CrossRef
go back to reference D’Agostino A, Surico P (2012) A century of inflation forecasts. Rev Econ Stat 94(4):1097–1106CrossRef D’Agostino A, Surico P (2012) A century of inflation forecasts. Rev Econ Stat 94(4):1097–1106CrossRef
go back to reference D’Agostino A, Giannone D, Surico P (2006) (Un)Predictability and macroeconomic stability. Working Paper Series 605, European Central Bank D’Agostino A, Giannone D, Surico P (2006) (Un)Predictability and macroeconomic stability. Working Paper Series 605, European Central Bank
go back to reference D’Agostino A, Gambetti L, Giannone D (2011) Macroeconomic forecasting and structural change. J Appl Econom 26(7):82–101 D’Agostino A, Gambetti L, Giannone D (2011) Macroeconomic forecasting and structural change. J Appl Econom 26(7):82–101
go back to reference De Gooijer JG, De Bruin PT (1998) On forecasting SETAR processes. Stat Probab Lett 37(1):7–14CrossRef De Gooijer JG, De Bruin PT (1998) On forecasting SETAR processes. Stat Probab Lett 37(1):7–14CrossRef
go back to reference Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13(3):253–263 Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13(3):253–263
go back to reference de Mendonça HF, de Guimarães e Souza GJ (2012) Is inflation targeting a good remedy to control inflation? J Dev Econ 98(2):178–191CrossRef de Mendonça HF, de Guimarães e Souza GJ (2012) Is inflation targeting a good remedy to control inflation? J Dev Econ 98(2):178–191CrossRef
go back to reference Elliott G, Timmermann A (2008) Economic forecasting. J Econ Lit 46(1):3–56CrossRef Elliott G, Timmermann A (2008) Economic forecasting. J Econ Lit 46(1):3–56CrossRef
go back to reference Faust J, Wright JH (2012) Forecasting inflation, unpublished manuscript Faust J, Wright JH (2012) Forecasting inflation, unpublished manuscript
go back to reference Franses P, Dijk D (2000) Nonlinear time series models in empirical finance. Cambridge University Press, CambridgeCrossRef Franses P, Dijk D (2000) Nonlinear time series models in empirical finance. Cambridge University Press, CambridgeCrossRef
go back to reference Gonçalves CES, Salles JaM (2008) Inflation targeting in emerging economies: What do the data say? J Dev Econ 85(1–2):312–318CrossRef Gonçalves CES, Salles JaM (2008) Inflation targeting in emerging economies: What do the data say? J Dev Econ 85(1–2):312–318CrossRef
go back to reference Hamilton J (1994) Time series analysis. Princeton University Press, Princeton Hamilton J (1994) Time series analysis. Princeton University Press, Princeton
go back to reference Kock AB, Teräsvirta T (2011) Forecasting with nonlinear time series models. In: Clements MP, Hendry DF (eds) Oxford handbook on economic forecasting. Oxford University Press, Oxford, pp 61–87 Kock AB, Teräsvirta T (2011) Forecasting with nonlinear time series models. In: Clements MP, Hendry DF (eds) Oxford handbook on economic forecasting. Oxford University Press, Oxford, pp 61–87
go back to reference Lin JL, Granger CWJ (1994) Forecasting from non-linear models in practice. J Forecast 13:1–9CrossRef Lin JL, Granger CWJ (1994) Forecasting from non-linear models in practice. J Forecast 13:1–9CrossRef
go back to reference Lin S, Ye H (2007) Does inflation targeting really make a difference? Evaluating the treatment effect of inflation targeting in seven industrial countries. J Monet Econ 54(8):2521–2533CrossRef Lin S, Ye H (2007) Does inflation targeting really make a difference? Evaluating the treatment effect of inflation targeting in seven industrial countries. J Monet Econ 54(8):2521–2533CrossRef
go back to reference Marcellino M, Stock JH, Watson MW (2006) A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. J Economet 135(1–2):499–526CrossRef Marcellino M, Stock JH, Watson MW (2006) A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. J Economet 135(1–2):499–526CrossRef
go back to reference Rossi B, Sekhposyan T (2010) Have economic models’ forecasting performance for US output growth and inflation changed over time, and when? Int J Forecast 26(4):808–835CrossRef Rossi B, Sekhposyan T (2010) Have economic models’ forecasting performance for US output growth and inflation changed over time, and when? Int J Forecast 26(4):808–835CrossRef
go back to reference Stock JH, Watson MW (2007) Why has US inflation become harder to forecast? J Money Credit Banking 39(1):3–33CrossRef Stock JH, Watson MW (2007) Why has US inflation become harder to forecast? J Money Credit Banking 39(1):3–33CrossRef
go back to reference Stock JH, Watson MW (2009) Phillips curve inflation forecasts. In: Fuhrer J, Kodrzycki YK, Little J, Olivei GP (eds) Understanding inflation and the implications for monetary policy: a Phillips curve retrospective. The MIT Press, Cambridge Stock JH, Watson MW (2009) Phillips curve inflation forecasts. In: Fuhrer J, Kodrzycki YK, Little J, Olivei GP (eds) Understanding inflation and the implications for monetary policy: a Phillips curve retrospective. The MIT Press, Cambridge
go back to reference Svensson LE (2010) Inflation targeting. In: Friedman BM, Woodford M (eds) Handbook of monetary economics, handbook of monetary economics, vol 3, 22nd edn. Elsevier, Amsterdam, pp 1237–1302 Svensson LE (2010) Inflation targeting. In: Friedman BM, Woodford M (eds) Handbook of monetary economics, handbook of monetary economics, vol 3, 22nd edn. Elsevier, Amsterdam, pp 1237–1302
go back to reference Svensson LEO (2005) Monetary policy with judgment: forecast targeting. Int J Cent Bank 1(1):1–54 Svensson LEO (2005) Monetary policy with judgment: forecast targeting. Int J Cent Bank 1(1):1–54
go back to reference Teräsvirta T (2006) Forecasting economic variables with nonlinear models. In: Elliott G, Granger C, Timmermann A (eds) Handbook of economic forecasting, handbook of economic forecasting, vol 1, 8th edn. Elsevier, Cambridge, pp 413–457CrossRef Teräsvirta T (2006) Forecasting economic variables with nonlinear models. In: Elliott G, Granger C, Timmermann A (eds) Handbook of economic forecasting, handbook of economic forecasting, vol 1, 8th edn. Elsevier, Cambridge, pp 413–457CrossRef
go back to reference van Dijk D, Franses PH, Clements MP, Smith J (2003) On SETAR non-linearity and forecasting. J Forecast 22(5):359–375CrossRef van Dijk D, Franses PH, Clements MP, Smith J (2003) On SETAR non-linearity and forecasting. J Forecast 22(5):359–375CrossRef
go back to reference White H (2006) Approximate nonlinear forecasting methods. In: Elliott G, Granger C, Timmermann A (eds) Handbook of economic forecasting, vol 1, 9th edn. Elsevier, Cambridge, pp 459–512CrossRef White H (2006) Approximate nonlinear forecasting methods. In: Elliott G, Granger C, Timmermann A (eds) Handbook of economic forecasting, vol 1, 9th edn. Elsevier, Cambridge, pp 459–512CrossRef
Metadata
Title
Is forecasting inflation easier under inflation targeting?
Authors
Harun Özkan
M. Ege Yazgan
Publication date
01-03-2015
Publisher
Springer Berlin Heidelberg
Published in
Empirical Economics / Issue 2/2015
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-013-0793-3

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