Selection of Macroeconomic Forecasting Models: One Size Fits All?

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ABSTRACT

The main distinction between this paper and traditional approach is the assumption that variables affect the economy through different horizons. Under this alternative hypothesis, a variable considered as an unimportant detail from a short-horizon perspective may become an essential factor in a long-horizon standpoint, this paper, therefore, suggests selecting variables specific to the horizon. My findings confirm that a model that allows the variables particular to the horizon has a lower Schwarz Bayesian Information Criterion (SBIC) value than a model that does not. My outcomes also show that the vector autoregression (VAR) model in general forecasts poorly compared with my approach. Likewise, I contribute to the literature by setting predictions equal to the sample mean as a benchmark and showing that the out-of-sample forecasts of the VAR model with lag length higher than one fail to outperform the sample mean. Additionally, I select principal components derived from 190 different time series to forecast a time series as the time horizon varies. Again, the results show that some of the principal components may be more important at some horizons than at others, thus I suggest selecting the principal components in a factor-augmented VAR (FAVAR) model specific to the horizon. According to above results, I conclude that long-horizon and deep-rooted economic problems cannot be fixed with short-horizon and surface-level interventions. I also reach my argument via simulation.

Cite this paper

Lv, Y. (2017) Selection of Macroeconomic Forecasting Models: One Size Fits All?. Theoretical Economics Letters, 7, 643-682. doi: 10.4236/tel.2017.74048.

References

[1] Box, G.E. (1979) Some Problems of Statistics and Everyday Life. Journal of the American Statistical Association, 74, 1-4.
https://doi.org/10.1080/01621459.1979.10481600
[2] Stock, J.H. and Watson, M.W. (1999) Forecasting Inflation. Journal of Monetary Economics, 44, 293-335.
https://doi.org/10.3386/w7023
[3] Lv, Y. Y. (2017) How Can the Error Term Be Correlated with the Explanatory Variables on the R.H.S. of a Model? Theoretical Economics Letters, 7, 448-453.
[4] Sims, C.A. (1980) Macroeconomics and Reality. Econometrica, 48, 1-48.
https://doi.org/10.2307/1912017
[5] Doan, T., Litterman, R.B. and Sims, C.A. (1984) Forecasting and Conditional Projection Using Realistic Prior Distribution. Econometric Review, 3, 1-100.
https://doi.org/10.1080/07474938408800053
[6] Littlerman, R.B. (1986) A Statistical Approach to Economic Forecasting. Journal of Business and Economic Statistics, 4, 1-4.
[7] Stock, J.H. and Watson, M.W. (2005) Implications of Dynamic Factor Models for VAR Analysis. NBER Working Paper, No. W11467.
https://doi.org/10.3386/w11467
[8] Bernanke, B.S., Boivin, J. and Eliasz, P. (2004) Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach. National Bureau of Economic Research, No. W10220.
https://doi.org/10.3386/w10220
[9] Friedman, M. (1961) The Lag in Effect of Monetary Policy. The Journal of Political Economy, 69, 447-466.
https://doi.org/10.1086/258537
[10] Blanchard, O.J. and Quah, D. (1988) The Dynamic Effects of Aggregate Demand and Supply Disturbances. NBER Working Paper, No. 2737.
[11] Kilian, L. (2009) Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market. American Economic Review, 99, 1053-1069.
https://doi.org/10.1257/aer.99.3.1053
[12] Lippi, F. and Nobili, A. (2012) Oil and the Macroeconomy: A Quantitative Structural Analysis. Journal of the European Economic Association, 10, 1059-1083.
https://doi.org/10.1111/j.1542-4774.2012.01079.x
[13] Cassou, S.P. and Vázquez, J. (2014) Small-Scale New Keynesian Model Features That Can Reproduce Lead, Lag and Persistence Patterns. The B.E. Journal of Macroeconomics, 14, 267-300.
https://doi.org/10.1515/bejm-2012-0037
[14] Diebold, F.X. (2015) Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests. Journal of Business & Economic Statistics, 33, 1.
https://doi.org/10.1080/07350015.2014.983236
[15] Lv, Y. Y. (2017) Does the Biased Coefficient Problem Plague the VAR Model? Theoretical Economics Letters, 7, 454-463.
[16] Christiano, L.J., Eichenbaum, M. and Evans, C.L. (2005) Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy. Journal of Political Economy, 113, 1-45.
https://doi.org/10.1086/426038
[17] Stock, J.H. and Watson, M.W. (1996) Evidence on Structural Instability in Macroeconomic Time Series Relations. Journal of Business & Economic Statistics, 14, 11-30.
[18] Bai, J.S. and Ng, S. (2002) Determining the Number of Factors in Approximate Factor Models. Econometrica, 70, 191-221.
https://doi.org/10.1111/1468-0262.00273
[19] Preacher, K.J., Zhang, G., Kim, C. and Mels, G. (2013) Choosing the Optimal Number of Factors in Exploratory Factor Analysis: A Model Selection Perspective. Multivariate Behavioral Research, 48, 28-56.
https://doi.org/10.1080/00273171.2012.710386
[20] Stock, J.H. and Watson, M.W. (2002) Forecasting Using Principal Components from a Large Number of Predictors. Journal of the American Statistical Association, 97, 1167-1179.
https://doi.org/10.1198/016214502388618960
[21] Stock, J.H. and Watson, M.W. (1998) Diffusion Indexes. NBER Working Paper, No. W6702.
https://doi.org/10.3386/w6702
[22] Jordà, ò. (2005) Estimation and Inference of Impulse Responses by Local Projections. American Economic Review, 95, 161-182.
[23] Marcellino, M., Stock, J.H. and Watson, M.W. (2006) A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series. Journal of Econometrics, 135, 499-526.
[24] Baker, D., De Long, J.B. and Krugman, P.R. (2005) Asset Returns and Economic Growth. Brookings Papers on Economic Activity, 2005, 289-330.
https://doi.org/10.1353/eca.2005.0011
[25] Bernanke, B.S., Gertler, M., Watson, M., Sims, C.A. and Friedman, B.M. (1997) Systematic Monetary Policy and the Effects of Oil Price Shocks. Brookings Papers on Economic Activity, 1997, 91-157.
https://doi.org/10.2307/2534702
[26] Hamilton, J.D. (1996) This Is What Happened to the Oil Price—Macroeconomy Relationship. Journal of Monetary Economics, 38, 215-220.
[27] Basu, S., Fernald, J. and Kimball, M. (2006) Are Technology Improvements Contractionary? American Economic Review, 96, 1418-1448.
https://doi.org/10.1257/aer.96.5.1418

  
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