Bušs, Ginters (2009): Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach.
This is the latest version of this item.

PDF
MPRA_paper_16832.pdf Download (357kB)  Preview 
Abstract
This paper contributes to the literature by comparing predictive accuracy of oneperiod realtime simple seasonal ARIMA forecasts of Latvia's Gross Domestic Product (GDP) as well as by comparing a direct forecast of Latvia's GDP versus three kinds of indirect forecasts. Four main results are as follows. Direct forecast of Latvia's Gross Domestic Product (GDP) seems to yield better precision than an indirect one. AR(1) model tends to give more precise forecasts than the benchmark movingaverage models. An extra regular differencing appears to help better forecast Latvia's GDP in an economic downturn. Finally, only AR(1) gives forecasts with better precision compared to a naive Random Walk model.
Item Type:  MPRA Paper 

Original Title:  Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach 
Language:  English 
Keywords:  realtime forecasting; seasonal ARIMA; Direct versus indirect forecasting; Latvia's GDP 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General 
Item ID:  16832 
Depositing User:  Ginters Buss 
Date Deposited:  18. Aug 2009 00:11 
Last Modified:  28. Apr 2015 11:38 
References:  [1] Ajevskis, V. and Davidsons, G. (2008), "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product", Working Paper, 2/2008, Latvijas Banka [2] Anderson, B.D.O., and Moore, J.B. (1979), Optimal Filtering, Englewood Cliffs, New Jersey: PrenticeHall [3] Barhoumi, K., Darne, O. and Ferrara, L. (2009), "Are disaggregate data useful for factor analysis in forecasting French GDP?", Document de travail No 232, Banque de France [4] Bell, W.R. and Hillmer, S.C (1991), "Initializing the Kalman Filter for Nonstationary Time Series Models", Journal of Time Series Analysis 12, pp.283300 [5] Benkovskis, K. (2008), "Shortterm Forecasts of Latvia's eal Gross Domestic Product Growth Using Monthly Indicators", Working Paper, 5/2008, Latvijas Banka [6] Boivin, J. and Ng, S. (2006), "Are more data always better for factor analysis?", Journal of Econometrics 132, pp.169194 [7] Box, G.E.P. and Jenkins, G.M. (1970), Time Series Analysis: Forecasting and Control, San Francisco: HoldenDay [8] Brockwell, P. and Davis, R. (1987), Time Series: Theory and Methods, Berlin: SpringerVerlag [9] Burmeister, E., Wall, K.D. and Hamilton, J.D. (1986), "Estimation of Unobserved Expected Monthly In°ation Using Kalman Filtering", Journal of Business and Economic Statistics 4, pp.14760 [10] Caggiano, G., Kapetanios, G. and Labhard, V. (2009), "Are more data always better for factor analysis? Results for the Euro Area, the sex largest Euro Area countries and the UK", Working Paper Series No 1051/May 2009, European Central Bank [11] Caines, P.E. (1988), Linear Stochastic Systems, New York: Wiley [12] Casals, J., Jerez, M. and Sotoca, S. (2000), "Exact Smoothing for Stationary and Nonstationary Time Series," International Journal of Forecasting, vol. 16, pp.5969 [13] Chen, C. and Liu, L.M. (1993), "Joint Estimation of Model Parameters and Outlier Effects in Time Series", Journal of the American Statistical Association 88, pp.284297 [14] De Jong, P. (1991), "The Di®use Kalman Filter", Annals of Statistics 19, pp.10731083 [15] Dempster, A.P., Laird, N. M. and Rubin, D.B. (1977), "Maximum Likelihood from Incomplete Data via the EM Algorithm", Journal of the Royal Statistical Society Series B, 39, pp.138 [16] Diebold, F.X. and Mariano, R.S. (1995), "Comparing Predictive Accuracy", Journal of Business and Economic Statistics 13, pp. 253365 [17] Dreger, C. and Schumacher, C. (2002), "Estimating largescale factor models for economic activity in Germany: Do they outperform simpler models?", HWWA Discussion Paper 199, Hamburg Institute of International Economics [18] Eickmeier, S. and Ng, T. (2009), "Forecasting national activity using lots of international predictors: an application to New Zealand", Discussion Paper, Series 1: Economic Studies, No 11/2009, Deutsche Bundesbank [19] Fischer, B. and Planas, C. (1998), "Large Scale Fitting of ARIMA Models and Stylized Facts of Economic Time Series", Eurostat Working Paper 9/1998/A/8 [20] Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2003), "The Generalized Dynamic Factor Model, Onesided Estimation and Forecasting", LEM Working Paper Series, 2003/13 [21] Gevers, M. and Wertz, V. (1984), "Uniquely Identi¯able StateSpace and ARMA Parameterization for Multivariable Linear Systems", Automatica 20, pp.33347 [22] Ghosh, D. (1989), "Maximum Likelihood Estimation of the Dynamic ShockError Model", Journal of Econometrics 41, pp.12143 [23] Gourieroux, C. and Monfort, A. (1990), Series Temporelles et Modeles Dynamiques, Paris: Economica [24] Gomez, V. and Maravall, A. (2000a), "Automatic Modelling Methods for Univariate Series", Working Paper 9808, Research Department, Bank of Spain [25] Gomez, V. and Maravall, A. (2000b), "Seasonal Adjustment and Signal Extraction in Economic Time Series", Working Paper 9809, Research Department,Bank of Spain [26] Gomez, V. and Maravall, A. (1996), "Programs TRAMO and SEATS; Instruction for the User", Working Paper 9628, Research Department, Bank of Spain [27] Gomez, V. and Maravall, A. (1994), "Estimation, Prediction and Interpolation for Nonstationary Series with the Kalman filter", Journal of American Statistical Association 89, pp.611624 [28] Hamilton, J.D. (1985), "Uncovering Financial Market Expectations of Inflation", Journal of Political Economy 93, pp.122441 [29] Hamilton, J.D. (1994): Time Series Analysis, Princeton, New Yersey: Princeton University Press [30] Hannan, E.J. (1971), "The Identification Problem for Multiple Equation Systems with Moving Average Errors", Econometrica 39, pp.751 65 [31] Harvey, A.C. (1989), Forecasting Structural Time Series and the Kalman Filter, Cambridge: Cambridge University Press [32] Harvey, A. and Phillips, G.D.A (1979), "Maximum Likelihood Estimation of Regression Models with AutoregressiveMoving Average Disturbances", Biometrika 66, pp.3545 [33] Hendry, D.F. (1995), Dynamic Econometrics, Oxford: Oxford University Press [34] Hillmer, S.C., Bell, W.R. and Tiao, G.C. (1983), "Modelling Considerations in the Seasonal Adjustment of Economic Time Series", in Zellner, A. (ed.), Applied Time Series Analysis of Economic Data, Washington, D.C.: U.S. Department of CommerceBureau of the Census, pp.74100 [35] Hylleberg, S., Engle, R.F., Granger, C.W.J. and Yoo, B.S (1990), "Seasonal Integration and Cointegration", Journal of Econometrics 44, pp.215238 [36] Kaiser, R. and Maravall, A. (2001), Measuring Business Cycles in Economic Time Series, New York: SpringerVerlag [37] Kalman, R.E. (1960), "A New Approach to Linear Filtering and Prediction problems", Journal of Basic Engineering, Transactions of the ASME Series D, 82, pp.3545 [38] Kohn, R. and Ansley, C.F. (1986), \Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data", Journal of the American Statistical Association 81, pp.751761 [39] Kuzin, V., Marcellino, M. and Schumacher, C. (2009), "MIDAS versus mixedfrequency VAR: nowcasting GDP in the euro area", Discussion Paper, Series 1: Economic Studies, No 07/2009, Deutsche Bundesbank [40] Kuzin, V., Marcellino, M. and Schumacher, C. (2009), "Pooling versus model selction for nowcasting with many predictors: an application to German GDP", Discussion Paper, Series 1: Economic Studies, No 03/2009, Deutsche Bundesbank [41] Maravall, A. (2000), "An application of TRAMO and SEATS", Working Paper 9914, Research Department, Bank of Spain [42] Masten, I., Benerjee, A. and Marcellino, M. (2009), "Forecasting with factoraugmented error correction models", EUI Working Papers, RSCAS 2009/32 [43] Morf, M., Sidhu, G.S., and Kailath, T. (1974), "Some New Algorithms for Recursive Estimation on Constant, Linear, DiscreteTime Systems", IEEE Transactions on Automatic Control, AC 19, pp.315323 [44] Newbold, P. (1983), "Model Checking in Time Series Analysis", in A. Zellner (ed.) Applied Time Series Analysis of Economic Data, Washington, D.C.: U.S. Department of CommerceBureau of the Census, pp.133143 [45] Pagan, A. (1980), "Some Identification and Estimation Results for Regression Models with Stochastically Varying Coeffcients", Journal of Econometrics 13, pp.34163 [46] Rothenberg, T.J. (1971), "Identiffcation in Parametric Models", Econometrica 39, pp.57791 [47] Schumacher, C. (2009), "Factor forecasting using international targeted predictors: the case of German GDP", Discussion Paper, Series 1: Economic Studies, No 10/2009, Deutsche Bundesbank [48] Shumway, R.H. and Stoffer, D.S. (1982), "An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm", Journal of Time Series Analysis 3, pp.25364 [49] Stock, J.H., and Watson, M.W. (2004), "Forecasting with many predictors", Prepared for the Handbook of Economic Forecasting [50] Stock, J.H., and Watson, M.W. (2003), "Combination Forecasts of Output Growth", Working Paper [51] Stock, J.H., and Watson, M.W. (2002), "Macroeconomic Forecasting Using Diffusion Indexes", Journal of Business and Economic Statistics, April 2002, Vol. 20, No. 2 [52] Stock, J.H., and Watson, M.W. (1998), "Diffusion Indexes", NBER Working Paper Series, w6702 [53] Stock, J.H., and Watson, M.W. (1991), "A Probability Model of the Coincident Economic Indicators", in Lahiri, K. and Moore, G.H. (eds.) Leading Economic Indicators: New Approaches and Forecasting Records, Cambridge, England: Cambridge University Press [54] Tiao, G.C. and Tsay, R.S. (1983), "Cosistency Properties of Least Squares Estimates of Autoregressive Parameters in ARMA Models", The Annals of Statistics 11, pp.856871 [55] Tiao, G.C. and Tsay, R.S. (1989), "Model Specification in Multivariate Time Series", Journal of the Royal Statistical Society B, 51,pp.132141 [56] Wall, K.D. (1987), "Identification Theory for Varying Coe±cient Regression Models", Journal of Time Series Analysis 8, pp.35971 [57] Wang, M. (2008), "Comparing the DSGE model with the factor model: an outofsample forecasting experiment", Discussion Paper, Series 1: Economic Studies, No 04/2008, Deutsche Bundesbank [58] Watson, M.W. (1989), "Recursive Solution Methods for Dynamic Linear Rational Expectations Models", Journal of Econometrics 41, pp.6589 [59] Watson, M.W. and Engle, R.F. (1983), "Alternative Algorithms for the Estimation of Dynamic Factor, MIMIC, and Varying Coeffcient Regression Models", Journal of Econometrics 23, pp. 385400 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/16832 
Available Versions of this Item

Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach. (deposited 10. Aug 2009 09:26)

Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach. (deposited 18. Aug 2009 00:11)
 Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach. (deposited 18. Aug 2009 00:11) [Currently Displayed]

Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach. (deposited 18. Aug 2009 00:11)