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The Institute for the Economy in Transition 5 Gazetny per, Moscow, 125993 Russia Phone./ Fax: 7+(095) 229 6596, E-mail: www.iet.ru Bulletin of Model Analysis of Short-Term Forecasts of Socio-Economic Indicators in the Russian Federation December 2005 M. Turuntseva, . Yudin, Buzayev, . Yevtifieva, S. Kovbasyuk, . Paliy, D. Chetverikov, . Scherbakova The Institute for the Economy in Transition (www.iet.ru) Bulletin of model analysis of short-term forecasts of socio-economic indicators in the Russian Federation, December 2005 Table of contents in this issue:

Introduction to all issues............................................................................................................ 3 Industrial production and retail trade turnover........................................................................... 5 Industrial production................................................................................................. 5 Retail trade turnover................................................................................................. 6 Capital investments.................................................................................................................... 7 Foreign trade indicators.............................................................................................................. 7 Price Movement......................................................................................................................... 8 Consumer price indices and producer price indices................................................. 8 Cost movement of the minimum set of food products............................................ 10 Cargo transportation rate indices............................................................................ 10 Movement of prices of various types of raw materials in the world market.......... 11 Monetary indicators.................................................................................................................. RF gold and foreign exchange reserves................................................................................... Foreign exchange rates............................................................................................................. Living standard indicators........................................................................................................ Economically active population and total unemployment indicators...................................... Appendix. Diagrams of time series of economic indicators in the Russian Federation: Actual and forecast values................................................................................................................... The Institute for the Economy in Transition (www.iet.ru) Bulletin of model analysis of short-term forecasts of socio-economic indicators in the Russian Federation, December Introduction to all issues This bulletin provides estimates of values of different economic indicators in the Russian Federation for 1Q06, which are based on time series models developed during the studies carried out by the IET over the last few years1. The forecasting method in use belongs to the group of formal or statistic methods. In other words, the obtained values represent estimates of prospective values of a particular economic indicator made on the basis of ARIMA (p, d, q) formal time series models by taking into account the existing trend and in some cases its significant variations, rather than reflect the view or expert assessment of a researcher. The forecasts herein under are of inertial nature, because the corresponding models take into account the data movement before the forecast was made and depend largely on the trends typical of time series in the period immediately preceding the time frame to be forecasted. These assessments of prospective values of economic indicators in the Russian Federation can be used to support decision-making on economic policy, provided that general trends that were observed prior to the forecast remain the same, i.e. neither serious shocks nor changes in the prevailing long-term trends will take place in the future.

In sprite of a considerable volume of data available on the pre-crisis period of 1998, the analysis and forecast models were made for the time frame following August 1998 only. This can be explained by the results of the previous studies2 which led to a key conclusion that taking into account the data relating to the pre-crisis period impairs the forecast quality in most cases.

The models of the reviewed economic indicators were assessed with the help of the standard time series analysis techniques. The first stage included analysis of the correlograms of the series under study and first-order differences with a view to determine a maximum number of delayed values to be included into model specification. Then, all time series were tested for weak stationarity (or stationarity near determinate trend) by using the Dickey-Fuller test as based on the results of the analysis of correlograms. Time series were also tested for stationarity near segmented trend with the help of the Perron or Zivot-Andrews tests for endogenous structural brakes in several cases3.

Upon breaking up the time series into weak stationary, stationary near determinate trend and stationary near segmented trend or stationary in differences groups, the models corresponding to each of these groups were assessed (in levels and, if appropriate, by including trend or segmented trend or in differences). The best model was selected on the basis of the Akaike and Schwarz information criteria, as well as characteristics of residuals of models (non-autocorrelation, homoscedasticity, normality) and quality of forecasts for these models. The predictive values were calculated on the basis of the best model constructed for each economic indicator.

See, for example, Entov R.., Drobyshevsky V.P., Nosko S.., Yudin .D. Econometric Analysis of Time Series of the Key Macroeconomic Indicators. ., IET, 2001; .. Entov, Nosko S.., Yudin .D, P.A.

Kadochnikov, S.S. Ponomarenko. Challenges in Forecasting of Various Macroeconomic Indicators. ., IET, 2002; Nosko S.., . Buzayev, P.A. Kadochnikov, S.S. Ponomarenko. Making Analysis of Forecast Specifics of Structural Models and Models Including Results of the Polls at Enterprises. ., IET, 2003.

Ibidem See: Perron, P. Further Evidence on Breaking Trend Functions in Macroeconomic Variables, Journal of Econometrics, 1997, 80, pp. 355-385; Zivot, E. and D.W.K. Andrews. Further Evidence on the Great Crash, the Oil-Price Shock, and Unit-Root Hypothesis. Journal of Business and Economic Statistics, 1992, 10, pp. 251- The Institute for the Economy in Transition (www.iet.ru) Bulletin of model analysis of short-term forecasts of socio-economic indicators in the Russian Federation, December In addition, the Bulletin provides calculations of the prospective values of monthly consumer price index indicators, import volumes from all countries and export volumes to all countries on the basis of structural models (SM) developed at the IET. The predictive values obtained on the basis of structural models can give better results in some cases as compared to ARIMA models, because additional information on the movement of exogenous variables are used in their construction. Besides, structural forecast that was included into the average forecast (i.e. forecasts obtained as an average for several models) can facilitate improvement of the predictive values.

The consumer price index movement was modeled with the help of theoretical hypotheses arising from the monetary theory. Supply of money, volume of issue and nominal RUR/USD exchange rate, which reflect movement in the alternative cost of money keeping, were used as explanatory variables. The consumer price index model also included the price index in electric power industry, because this indicator has a significant effect on manufacturers costs.

The real exchange rate should be highlighted as a key indicator which may effect through its fluctuations a relative movement of prices of domestic and imported goods.

However, the effect of this indicator is not significant in econometric models. It is the world prices of exported resources, in particular oil prices, that have a significant impact on export movement: any price growth results in growth of exports of goods. The household income level in the economy (value of labor power) is used as a characteristic of relative competitiveness of Russian goods. D12 and D01 dummy variables which are equal to one in February and March correspondingly and zero in other periods were introduced so that seasonal fluctuations of exports can be taken into account. Household and corporate incomes have an effect on imports movement, their growth leading to an increase in demand for all goods, including the imported ones. The real disposable cash income reflects the household income, while the industrial production index reflects the corporate income.

Predictive values of explanatory variables required for making forecast on the basis of structural models were calculated on the basis of ARIMA models (p, d, q).

This paper also presents calculations of values of industrial production indices, producer price index, and total unemployment index, which were made on the basis of the results of conjuncture polls made by the IET. Empirical studies reveal4 that the use of conjuncture polls series as explanatory variables5 in prognostic models improves an average accuracy of the forecast. The prospective values of these indices were calculated on the basis of ADL models (by adding seasonal autoregressive delays).

All calculations were made with the use of the Eviews econometric package.

See, for example: V. Nosko, . Buzayev, P. Kadochnikov, S.S. Ponomarenko. Analysis of Prognostic Features of Structural Models and Models Including the Results of the Polls Conducted at Enterprises. ., IET, 2003.

5 The following conjuncture polls series were used as explanatory variables: current/expected changes in production, expected changes in purchasing power, current/expected changes in prices and expected changes in employment.

The Institute for the Economy in Transition (www.iet.ru) Bulletin of model analysis of short-term forecasts of socio-economic indicators in the Russian Federation, December Industrial production and retail trade turnover Industrial production The forecast was made on the basis of ARIMA models with the use of the series of monthly data on basic industrial production indices for the period between October 1998 and November 2005 published by the Center for Economic Analysis (CEA) under the RF Government (the value of 1993 was taken as 100%). Furthermore, predictive values of the CEAs industrial production index, as well as the industrial production index6 obtained from the Federal State Statistic Service (FSSS), were calculated by using the results of the conjuncture polls (CP)7. The final estimates are listed in Table 1.

Table Predictive values of industrial production indices8, (%) Predictive growth rates against the corresponding month of the preceding year January 2006 5.3 6.7 6.8 8.1 16.7 4.1 13.9 1.9 1.2 2.4 8.0 -3.February 2006 6.6 6.9 6.9 7.0 11.5 5.9 12.2 2.2 2.5 1.3 6.9 -1.March 2006 5.7 6.5 6.2 1.3 6.2 4.1 9.6 2.0 0.6 -0.5 5.7 -5.For reference: actual growth rates in 2005 against the corresponding month in January 2005 -0.6 3.8 2.9 -14.7 -0.1 -4.1 3.9 5.9 -2.8 1.9 -14.February 2005 1.2 5.0 2.2 1.7 -1.1 -1.1 1.7 1.6 -3.2 3.9 -9.March 2005 1.8 3.7 5.5 -0.3 -1.5 2.9 3.9 2.0 -0.6 1.6 -9.Note: the industrial production indices series in industry as a whole, metal working industries, chemical and petrochemical industries, building materials producing industry, non-ferrous metallurgy, timber and woodworking industry and food processing industry are trend stationary with a marked seasonal factor (except for the series of the industry as a whole) within the time frame between October 1998 and November 2005. The industrial production indices series of ferrous metallurgy, fuel and energy industry and light industry are identified as processes being stationary in first-order differences taking into account that the industrial production index of fuel and energy industry includes a seasonal component.

As illustrated in Table 1, the value of the industrial production index is expected to grow9 by an average of 5.9% in the industry as a whole in the 1Q06 as compared to the Since January 2005, the FSSS discontinued the calculation method of the industrial production index by using the system of Russian Classificatory of National Economy Sectors (OKONKH) and started using a new system of Russian Classificatory of Types of Economic Activity (OKVED) for the same purpose. The OKVEDs industrial production index series is available for the period between January 1999 and October 2005.

The models are constructed for the time frame between January 1999 and November 2005 for the CECs industrial production index and between January 1999 and October 2005 for the FSSSs industrial production index.

It should be noted that since the so-called raw indices (without regard to seasonal and calendar adjustments) were used for the forecast, most of the models take into account seasonal factors and, as a consequence, the final results reflect seasonal movement of the series.

The average growth on the industrial production indices means the average value of these indicators over three forecasted months.

The Institute for the Economy in Transition (www.iet.ru) CP) Month industry industry industry ARIMA) industries (CEA, CP) Industry total Chemical and Light industry Fuel and energy Food processing Metal fabricating and woodworking Building materials producing industry Ferrous metallurgy Industry total (CEA, Timber, paper-pulp Industry total (FSSS, Non-ferrous metallurgy petrochemical industries Bulletin of model analysis of short-term forecasts of socio-economic indicators in the Russian Federation, December corresponding period of the previous year ( while its value is expected to account for 6.7% for the FASTs industrial production index ).

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