Quarterly Journal of Economics and Modelling Shahid Beheshti University * ** ( )

Transcription

1 392 Quarterly Journal of Economics and Modelling Shahid Beheshti University * ** 93/2/20 93//25 m_noferesti@sbuacir mahboubehbaiat@gmailcom ( ) * **

2 E27 C53 C22 JEL -

3 3 (989) 3 (2006) 2 (2004) 4 (MIDAS) Klein and Sojo 2 Ghysels, E, P Santa-Clara, and R Valkanov 3 Ghysels, E, A Sinko, and R Valkanov 4 MIxed frequency DAta Sampling MIDAS

4 (MIDAS) -2 y t { x } { y t } t t s t m=/s t- t s m t = m t {y t } t m=3 {x } {y t } t m s =/4 = ( ) ( ) = 4 x y t K

5 5 - ( ) ) (ARDL) ( (2006) = + ( ; ) ( ) + ( j max = 5) = + ( ; ) = + ( ) ( ) + + ( ) + + ( ) + Aggregation

6 ( ; ) ( = ) j ) ( ; ) = ( ; ) ( ; ) ( ; ) ( ; ) (max j ( ) ( ; ) ( ) = ( ; ) ( ; ) = p (2) ( ( ; ) ( ) = ) 2 (2009) 4 3 Eric Ghysels 2 Almon lag polynomial specifcation 3 Normalized exponential Almon lag polynomial 4 Normalized beta probability density function

7 7-2 ( ; ) = ( ) = = 0 ( ) =, ; = (, ; ) =, ;, ; ( ) ( ) ( ) ( )

8 = = ( ) ( + ) NLS = ( ( ; ) ) (2004) ( ( ; ) ) - 2 p = + + ( ; ) ( ) + = ( ; ) = + + ( ; ) + + ( ; ) + Ghysels, E, P Santa-Clara, and R Valkanov 2 AR-MIDAS

9 9 - = + + ( ) + = ( ; ) = + + ( ) + + d () - RMSPE Root Mean Square Predict Error

10 (2004) (2006) 2 (2008) (2008) 5 (2008) 4 (2007) 6 (2006) (2009) (2008) (2006) (2006) ( ) Alper, CE, S Fendoglu, and B Saltoglu 2 Engle, RF, E Ghysles, and B Sohn 3 Forsberg, L, and E Ghysels 4 Leon, A, JM Nave, and G Rubio 5 Clements, MP, AB Galvao, and JH Kim 6 Clements, M, and A Galvao 7 Tay, A 8 Marcellino, M, and C Schumacher

11 (2009) (VAR) 2 (200) 3 (200) Kuzin, V, M Marcellino, and C Schumacher 2 Bai, J, E Ghysels, and J Wright 3 Armesto, MT, KM Engemann, and MT Owyang 4 Bessec, M and Bouabdallah, O 5 Tsui, A K, C Y Xu, and Z Y Zhang

12 = ( ; ) ( ( ; ) ) + ( ; ) ( ) + ( ) + ( )

13 3 ( ) ( ) ( ; )

14 R midasr 392 I(0) (204) y3 y2 y s3 Ghysels, E, V Kvedaras and V Zemlys

15 5 ( ) ) m3 m2 m (m) ( % 95 midasr Estimate Std Error t value Pr(> t ) sig level Intercept ** y E-5 *** y < 2e-6 *** y E-09 *** s s s * m * m *** m E-05 *** o * o o o e e e * d E-05 *** dd E-06 *** Signif codes *** 000 ** 00 * R2= , R2adj= , DurbinWatson= 8405 Shapiro-Wilk normality test =09824 (p= 0209), hah =2725 (p=09704)

16 dd2 d % 90 o4 o /97 hahtest = /56-5/06-4/22-5/07 RMSPE 0/5096

17 7 % 3/ % 3/76 9/4 (2) %-0/27

18 /729-9/ 486-9/ 443 3/76 3 / 087-0/

19 9 5/6 4/ 6 4/ 4 / 6 /5 / 7 / 8 4/3 3 / 7-2/ (4) /9 -/ 9-6/ 8 4/ 3 6 / (4)

20 %4/6 %4/3 %3/7 %5/6 %-2/2 %/5 392 Alper, CE, S Fendoglu, and B Saltoglu, (2008), Forecasting Stock Market Volatilities UsingMIDAS Regressions An Application to the Emerging Markets, Discussion paper, MPRA Paper, 7460

21 2 2 Andreou, Elena, Eric Ghysels, and Andros Kourtellos, (200), Regression models with mixed sampling frequencies, Journal of Econometrics 58 3 Armesto, MT, KM Engemann, and MT Owyang, 200, Forecasting with Mixed Frequencies, Federal Reserve Bank of St Louis Review 92 4 Bai, J, E Ghysels, and J Wright (2009), State space models and MIDAS regressions, Working paper, NY Fed, UNC and Johns Hopkins 5 Bessec, M and Bouabdallah, O (204), Forecasting gdp over the business cycle in a multi-frequency and data-rich environment, Oxford Bulletin of Economics and Statistics doi 0/obes Clements, MP, AB Galvao, and JH Kim, (2008) Quantile forecasts of daily exchange rate returns from forecasts of realized volatility, Journal of Empirical Finance 5 7 Clements, M, and A Galvao, (2008), Macroeconomic Forecasting with Mixed Frequency Data Forecasting US output growth, Journal of Business and Economic Statistics 26 8 Clements, M, and A Galvao (2009) Forecasting US Output Growth Using Leading IndicatorsAn Appraisal Using Midas Models,Journal of Applied Econometrics 24 9 Clements, M and A Galvao (2006) Macroeconomic forecasting with mixed frequency data forecasting US output growth and inflation Warwick Economic Research Paper No 773,University of Warwick 0 Engle, R F, E Ghysels, and B Sohn, (2008), On the Economic Sources of Stock Market Volatility, Discussion Paper NYU and UNC Forsberg, L, and E Ghysels, (2006), Why do absolute returns predict volatility so well?,journal of Financial Econometrics 5 2 Ghysels, E, P Santa-Clara, and R Valkanov; (2004) The MIDAS Touch Mixed frequency Data Sampling Regressions, manuscript, University of NorthCarolina and UCLA 3 Ghysels, E, A Sinko, and R Valkanov; (2006) MIDAS regressions Further results and new directions, Econometric Reviews, 2007, 26 4 Ghysels, E, V Kvedaras and V Zemlys;(204) Mixed Frequency Data Sampling Regression Models the R Package midasr, Journal of Statistical Software 5 Klein, LR and E Sojo; (989), Combinations of High and Low Frequency Data in Macroeconomic Models, in LR Klein and

22 JMarquez (eds), Economics in Theory & PracticeAn Eclectic ApproachKluwer Academic Publisherspp 36 6 Kuzin, V, M Marcellino, and C Schumacher, (20) MIDAS versus mixed-frequency VARNowcasting GDP in the Euro Area Deutsche Bundesbank Discussion Paper 07/ Leon, A, JM Nave, and G Rubio, 2007, The relationship between risk and expected return in Europe, Journal of Banking and Finance 3 8 Marcellino, M, and C Schumacher, (200) Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data A Model Comparison for German GDP,, Oxford Bulletin of Economics and Statistics 72 9 Tay, A (2006) Financial Variables as Predictors of Real Output Growth, Discussion Paper SMU 20 Tsui, A K, C Y Xu, and Z Y Zhang, (203) Forecasting Singapore economic growth with mixed-frequency data, presented at 20th International Congress on Modelling and Simulation, Adelaide, Australia, 6 December 203

23 I(0) y I(0) o I(0) m I(0) e I(0) s 0/0 pvalue