혼합자료 샘플링 모형을 통한 GDP 예측
Forecasting GDP with a Mixed Data Sampling Model
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초록
경제전망을 위해 다양한 예측 방법을 활용한 GDP의 예측이 논의되어 왔다. 본 연구에서는혼합주기 샘플링(MIDAS) 모형과 AR-MIDAS 모형을 이용한 GDP의 단기전망을 실시하였다. 예측을 개선하는 방법으로 제시되는 MIDAS 모형은 월별 거시경제변수를 활용하여 당분기, 1 분기 혹은 2분기 후의 GDP 예측에 월별 자료의 정보가 유용함을 실증적으로 보여준다. 이는MIDAS 모형이 다른 경쟁 모형에 비해 월별 자료가 갖는 풍부한 정보를 좀 더 효과적으로 예측에이용하기 때문인 것으로 보인다. 개별 거시경제변수를 이용한 MIDAS의 예측은 선택된 변수에따라 상대적 우열이 다르게 나타나지만, 단순평균, 가중평균 혹은 중위값 등의 방법에 의한결합예측의 경우 예측력이 뚜렷하게 개선되는 것으로 나타났다. 본 연구를 통해 GDP 예측에서월별 자료를 이용한 MIDAS 모형의 예측방법 및 이들 예측치의 정보를 종합하는 결합예측 방법의사용은 GDP의 단기예측의 예측력을 크게 개선시키는 것으로 나타났다.
Abstract
Many methods have been developed to forecast GDP. In this study, we use the MIDAS and AR-MIDAS models to forecast GDP at short forecast horizon. We find that the MIDAS model uses monthly macroeconomic variables more efficiently compared to other models for nowcasting and forecasting quarterly GDP growth. The MIDAS model contends for superiority in forecasting GDP when using monthly macroeconomic variables. However, when we utilize combined forecast by the simple average, weighted average or median methods, we find that most of the combinations perform well relative to the benchmark. In conclusion, we find that the pooling of MIDAS or AR-MIDAS perform better in terms of nowcasting and forecasting quarterly GDP growth.
참고문헌 (28)
- 박강우, "GDP 예측력 제고를 위한 동태요인모형 구축", 한국은행 조사통계월보, pp.27~60 (2009)
- 최병재, "GDP 예측력 제고를 위한 동태요인모형 구축", 한국은행조사통계월보, pp.16~48 (2014)
- Andreou, E., "Should macroeconomic forecasters use daily financial data and how?", Journal of Business and Economic Statistics, 31(2), pp.240~251 (2013)
- Bai, J., "State space models and MIDAS regressions", Econometric Reviews, 32(7), pp.779~813 (2013)
- Bańbura, M., "A look into the factor model black box : publication lags and the role of hard and soft data in forecasting GDP", International Journal of Forecasting, 27(2), pp.333~346 (2011)
- Bańbura, M. M., "Now-casting and the real-time data flow", Handbook of Economic Forecasting, 2, pp.195~237 (2013)
- Chauvet, M., "Sunspots, animal spirits, and economic fluctuations", Macroeconomic Dynamics, 7(1), pp.140~169 (2003)
- Chauvet, M., "Forecasting output", Handbook of Economic Forecasting, 2(Part A), pp.141~194 (2013)
- Clements, M. P., "Macroeconomic forecasting with mixedfrequency data : Forecasting output growth in the United States", Journal of Business and Economic Statistics, 26(4), pp.546~554 (2008)
- Clements, M. P., "Forecasting US output growth using leading indicators : An appraisal using MIDAS models", Journal Applies Econometrics, 24, pp.1187~1206 (2009)
- Dhrymes, P. J., "Distributed lags: problems of estimation and formulation" (1971), Holden-Day
- Evans, M. D., "Where Are We Now? Real-Time Estimates of the Macroeconomy", International Journal of Central Banking, 1(2), pp.127~175 (2005)
- UMIDAS(unrestricted MIDAS), "Foroni, C.", ~ (2011)
- A Survey of Econometric Method for Mixed-Frequency Data, "Foroni, C.", ~ (2013)
- The MIDAS touch: Mixed Data Sampling regression models, "Ghysels, E.", ~ (2004)
- Ghysels, E., "Predicting volatility: getting the most out of return data sampled at different frequencies", Journal of Econometrics, 131, pp.59~95 (2006)
- Ghysels, E., "MIDAS regressions: Further results and new directions", Econometric Reviews, 26(1), pp.53~90 (2007)
- Giannone, D., "Nowcasting: The real-time informational content of macroeconomic data", Journal of Monetary Economics, 55(4), pp.665~676 (2008)
- Giannone, D., "Handbook of Economic Forecasting SET 2A" (2013), Elsevier
- Kuzin, V., "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area", International Journal of Forecasting, 27(2), pp.529~542 (2011)
- Kuzin, V., "Pooling versus model selection for nowcasting GDP with many predictors: Empirical evidence for six industrialized countries", Journal of Applied Econometrics, 28(3), pp.392~411 (2013)
- Marcellino, M., "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series", Journal of Econometrics, 135, pp.499~526 (2006)
- Marcellino, M., "Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP", Oxford Bulletin of Economics and Statistics, 72(4), pp.518~550 (2010)
- Nelson, C. R., "The prediction performance of the FRB-MIT-PENN model of the US economy", The American Economic Review, 62(5), pp.902~917 (1972)
- Runstler, G., "Short-Term Forecasting of GDP Using Large Datasets: A Pseudo Real-Time Forecast Evaluation Exercise", Journal of Forecasting, 28, pp.595~611 (2009)
- Schumacher, C., "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data", International Journal of Forecasting, 24(3), pp.386~398 (2008)
- Stock, J. H., "How Did Leading Indicator Forecasts Perform During the 2001 Recession", Federal Reserve Bank of Richmond Economic Quarterly, 89(3), pp.71~90 (2003)
- Wang, M. C., "Comparing the DSGE model with the factor model: an out of-sample forecasting experiment", Journal of Forecasting, 28(2), pp.167~182 (2009)