Bulletin of model analysis of short-term forecasts of socio-economic indicators in the Russian Federation

Transcription

1 The Institute for the Economy in Transition 5 Gazetny per, Moscow, Russia Phone./ Fax: 7+(095) , Bulletin of model analysis of short-term forecasts of socio-economic indicators in the Russian Federation November 2005 M. Turuntseva, А. Yudin, А Buzayev, А. Yevtifieva, S. Kovbasyuk, А. Paliy, D. Chetverikov, Е. Scherbakova

2 Table of contents: Introduction to all publications... 3 Industrial production and retail turnover... 5 Industrial production... 5 Trade Retail turnover... 6 Capital investments... 6 Foreign trade indicators... 7 Price Movement... 8 Consumer price indices and producer price indices... 8 Cost movement of the minimum set of food products... 9 Cargo transportation rate indices Movement of prices of various types of raw materials in the world market Monetary indicators RF gold and foreign exchange reserves Foreign exchange rates Living standard indicators Economically active population and total unemployment indicators Annex. RF economic indicators time series curves: Actual and predictive values

3 Introduction to all publications This bulletin provides calculations of values of different economic indicators in the Russian Federation within a period between February 2005 and March 2006, which are based on time series models developed during the studies carried out by the IET over the last few years 1. 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 isionmaking 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 studies 2 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 cases 3. 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. 1 See, for example, Entov R.М., Drobyshevsky V.P., Nosko S.М., Yudin А.D. Econometric Analysis of Time Series Based Upon Macroeconomic Indicators. М., IET, 2001; Р.М. Entov, Nosko S.М., Yudin А.D, P.A. Kadochnikov, S.S. Ponomarenko. Challenges in Forecasting Various Macroeconomic Indicators. М., IET, 2002; Nosko S.М., А. Бузаев, P.A. Kadochnikov, S.S. Ponomarenko. Making Analysis of Forecasting Specifics of Structural Models and Models Including Results of the Polls at Enterprises. М., IET, Ibidem 3 See: Perron, P. Further Evidence on Breaking Trend Functions in Macroeconomic Variables, Journal of Econometrics, 1997, 80, pp ; 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

4 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 reveal 4 that the use of conjuncture polls series as explanatory variables 5 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. 4 See, for example: V. Nosko, А. Buzayev, P. Kadochnikov, S.S. Ponomarenko. Analysis of Prognostic Features of Structural Models and Models Including the Results of the Polls Conducted at Enterprises. М., IET, 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. 4

5 Industrial production and retail 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 October 2005 published by the Center for Economic Analysis (CEA) under the RF Government (the value of 1993 was accepted as 100%). Furthermore, predictive values of the CEA s industrial production index, as well as the industrial production index 6 obtained from the Federal State Statistic Service (FSSS), were calculated by using the results of the conjuncture polls (CP) 7. The final calculation data are listed in Table 1. Table 1 Calculation data on predictive values of industrial production indices 8, (%) Month Industry total (CEA, ARIMA) Industry total (CEA, CP) Industry total (FSSS, CP) Ferrous metallurgy Metal fabricating industries Chemical and petrochemical industries Construction materials producing industry Fuel and energy industry Non-ferrous metallurgy Timber and woodworking industry Food processing industry Predictive growth rates against the corresponding month of the preceding year December February March For reference: actual growth rates in against the corresponding month in December January February Note: the industrial production indices series in industry as a whole, metal fabricating industries, chemical and petrochemical industries, construction materials producing industry, non-ferrous metallurgy, timber and woodworking industry and food processing industry are near-trend stationary with a marked seasonal component (except for the series of the industry as a whole) within the time frame between October 1998 and September 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. Light industry As illustrated in Table 1, the value of the industrial production index is expected to grow by an average of 4.7% in the industry as a whole in winter , as compared to the corresponding period of the previous year ( while its value is expected to account for 5.5% for the FAST s industrial production index ). 6 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 OKVED s industrial production index series is available for the period between January 1999 and October The models are constructed for the time frame between January 1999 and October 2005 for the CEC s industrial production index and between January 1999 September 2005 for the FSSS s industrial production index. 8 It should be noted that since the so-called raw indices (without taking into account 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. 5

6 The same parameter for ferrous and non-ferrous metallurgy is expected to reach 3.2% and 1.6% respectively. Growth is forecasted in food processing industry and chemical and petrochemical industries: 6.9% and 4.9%, respectively, as well as construction materials producing industry and fuel and energy industry: the average growth rates in these industries are expected to account for 9.5% and 2.1% respectively of the corresponding period in the previous year. The average growth in timber, wood-pulp and woodworking industry in predicted to account for 0.8% of the corresponding months of the previous year. Mechanical engineering and metal fabricating industries are expected to grow by 13.6%. An average downfall is forecasted (3.7%) in light industry production as compared to the corresponding period of the previous year. A growth in the industrial production (within a range of 1.3% in ferrous metallurgy and 12% in mechanical engineering and metal fabricating industries) is forecasted for all industries on an annualized basis in 2005, except for only timber, wood-pulp and woodworking and light industries with an annual downfall being expected at the level of 1.4% and 5.2% respectively. Trade Retail turnover This section (see Table 2) presents forecasts of monthly trade retail turnover volumes as based on the FSSS s monthly data in the period between March 1999 and September Calculation data on predictive values of retail turnover volume Table 2 Predictive values according to ARIMA model (billion rubles) December February March For reference: actual values over corresponding months in (billion rubles) December January February Predictive growth rates against the corresponding month in (%) December February March Note: retail turnover series is stationary within the time frame between March 1999 and September It is evident from Table 2 that the retail turnover is estimated to average to total about 622 bln rubles in winter The nominal monthly growth in the retail turnover volume is expected to be average about 19% as compared to the corresponding period of the previous year. In terms of real growth, the annual growth in the retail turnover is expected to account for 7.4% by the end of Capital investments Listed in Table 3 are the calculation data of predictive values of capital investments in the period between December 2005 and February The forecast was made on the basis of time series according to the FSSS s data relating to the period between January 1999 and September

7 Calculation data of predictive values of capital investment volumes Table 3 Predictive values on ARIMA model (billion rubles) December February March For reference: actual values over corresponding months in 2005 (billion rubles) December January February Forecasting nominal growth rates against the corresponding month of the previous year (%) December February March Note: investment series within the time frame between January 1999 and September 2005 belong to the DS-type series. The estimates listed in Table 3 predict a growth in capital investments in winter , as compared to the corresponding period of the previous year. The average growth in capital investments is expected to amount to about 10.5% per month. The annual capital investments growth is expected 10.2% at year-end The value of the annual capital investment indicator is expected to line by 0.8% in real terms in Foreign trade indicators Model analysis of predictive values of export volumes, export to countries other than CIS member countries, import and import from countries other than CIS member countries was made by using time series models and structural models as assessed on the basis of monthly data within the time frame between September 1998 and September 2005 according to the RF Central Bank s data 9. The final calculation data of the forecast are listed in Table 4. The average growth estimates of export and export to countries other than CIS member countries, import and import from countries other than CIS member countries in the period between December 2005 and February 2006 are predicted to account for 32%, 32%, 18% and 35% respectively, as compared to the corresponding period between 2004 and Export surplus to all countries and countries other than CIS member countries in December 2005 and February 2006 is expected to grow by an average of 45% and 30% respectively, as compared to the corresponding period of the previous year. The volume of export surplus to all countries is predicted to total 38 bln US dollars in the period between December 2005 and February The value of export to all countries and value of import from all countries are expected to grow by an average of 23.5% and 18.5% respectively on an annualized basis, according to the two models. The annual growth in export volumes to and import volumes from countries 9 The data on foreign trade turnover were calculated by the RF Central Bank in accordance with the methodology of making up the foreign balance in exporting country s prices (FOB) in billion US dollars. 7

8 other than CIS member countries is forecasted to account for 29% and 27% respectively in Calculation data of predictive values of foreign trade volumes Table 4 Export total Export to countries other than CIS member countries Import total Import from countries other than CIS member countries Month predictive values (billion US dollars per month) in percentage of actual data on the corresponding month of the preceding year (%) predictive values (billion US dollars per month) in percentage of actual data on the corresponding month of the preceding year (%) predictive values (billion US dollars per month) in percentage of actual data on the corresponding month of the preceding year (%) predictive values (billion US dollars per month) in percentage of actual data on the corresponding month of the preceding year (%) ARIMA SM ARIMA SM ARIMA ARIMA SM ARIMA SM ARIMA December February March For reference: actual values over corresponding months in (billion US dollars) December January February Note: export and import series to countries other than CIS member countries are identified as stationary series with first-order differences, while export and import series to countries other than CIS member countries are identified as near-trend stationary series within the time frame between September 1998 and September Seasonal components were taken into account in model specifications in all cases. Price Movement Consumer price indices and producer price indices This section provides calculation of predictive values of consumer price index and producer price indices (both in industry as a whole and its branches according to the OKVED s classification) obtained on the basis of times series models which were assessed according to the FSSS s data for the time frame between March 1999 and September Listed in Table 5 are model calculation data of predictive values in the period between December 2005 and February 2006 according to the ARIMA models, structural models (SM) and models constructed with the use conjuncture polls (CP). 10 Structural models were assessed for the time frame since October

9 Calculation data of predictive values of price indices Table 5 Producer price indices: Month consumer price index (ARIMA) consumer price index (SM) Industrial product producer price index (ARIMA) Industrial product producer price index (CP) Mining operations Manufacturing industries Production of electric power energy, gas and water Production of food products Textile and garment manufacture Wood fashioning and woodworking Paper-pulp manufacturing Production of coke and oil products Chemical production Metallurgy and production of finished metal products Production of machinery and equipment Production of transportation vehicles and equipment Predictive values according to ARIMA models (in terms of percentage of the preceding month) December January February Predictive values according to ARIMA models (in terms of percentage of February ) December January February For reference: actual value over the corresponding periods 2004 (in terms of percentage of February ) December January February Note: all consumer price index series belong to the TS-type series in the time frame between January 1999 and September The consumer price index belongs to the DS-type series within the time frame between November 1998 and September Monthly inflation rates and the annual inflation rate are estimated to average 1.4% and 13% respectively in the period between February 2005 and March Prices of industrial producers are forecasted to grow at 1.4% per month on average in the same period. Prices of industrial producers are predicted to grow by an average of 22.5% in 2005 according to the two models, which is above the consumer price growth rates. The average monthly OKVED s industrial production indices are forecasted to grow in winter as follows: 3.1% in mining operations, 0.6% in manufacturing industries, 1.2% in production of electric power energy, gas and water, 0.9% in production of food products, 1.0% in textile and garment manufacture, -0.3% in wood fashioning and woodworking, 0.8% in paper-pulp manufacturing, 2.3% in production of coke and oil products, 1.0% in chemical production, 1.3% in metallurgy and production of finished metal products, 0.9% in production of machinery and equipment, and 0.6% in production of transportation vehicles and equipment. Price indices of industries are forecasted to grow at the level of 9.2% on average in terms of annual growth grates, except for the mining operations index, which is predicted to grow at 58.8%, and the price index in production of coke and oil products, which is forecasted to be at the level of 51%. Cost movement of the minimum set of food products This section presents the calculation data of predictive values of the cost of the minimum set of food products in the period between December 2005 and February The forecasts were made on the basis of 9

10 time series according to the FSSS s data in the period between January 2000 and September The calculation data are listed in Table 6. Table 6 Forecast of the cost of the minimum set of food products (per capita per month) ARIMA model predictive values (rubles) December January February For reference: actual values over corresponding months in (rubles) December January February Predictive growth rates against the corresponding month in (%) December January February Note: the minimum set of food products series was used in relation to the corresponding period of the previous year. This series belongs to the DS-type series within the time frame between January 2000 and September As illustrated (in Table 6), the value of the minimum set of food products is expected to grow in the period between February 2005 and March 2006, with its average predicted cost being about 1400 rubles, as compared to the corresponding period of the previous year. The growth in the cost of the minimum set of food products is predicted to average about 11.8% as opposed to the corresponding period of the previous year. The annual growth of the value of the minimum set of food products is forecasted to account for 10.7% in 2005, which is a bit lower than the forecast annual inflation, 11.6%. Cargo transportation rate indices This section provides the calculation data of predictive values of price indices of cargo transportation rates 11 obtained on the basis of times series models which were assessed according to the FSSS s data for the time frame between September 1998 and September Listed in Table 7 are model calculation data of predictive values in the period December 2005 and February It should be noted that some of the indicators under review (for example, the pipeline transportation rate index) are regulable, which makes them difficult to be described by time series models. As a result, the obtained prospective values may be far different from real ones in cases of centralized growth of rates within the forecasted time frame or in absence of such within the forecasted period, if increased the day before. 11 The Bulletin considers the cargo transportation rate composite index and the motor vehicle cargo transportation rate index, as well as the pipeline transportation rate index. The cargo transportation rate composite index is calculated on the basis of cargo transportation indices for various means of transportation: railway, pipeline, sea, domestic water, motor and air transportation (for more details refer to, for example: Prices in Russia. Official publication of the Russian State Statistic Committee (Goskomstat), 1998). 10

11 Calculation data of predictive values of transportation rates Period Cargo transportation rate composite index Motor vehicle cargo transportation rate index Pipeline transportation rate index Table 7 Predictive values according to ARIMA models (in terms of percentage of the preceding month) December January February Predictive values according to ARIMA models (in terms of percentage of February ) December January February For reference: actual value over the corresponding periods (in terms of percentage of the preceding month) December January February Note: the motor vehicle cargo transportation rate index series was identified as a TS-type series within the time frame between October 1998 and September 2005; various dummy variables were used for taking into account special bursts for all series. The composite index of cargo transportation rates is expected to grow by almost three percent in February 2005, thus its growth is expected to be 28.4% in 2005, according to the forecast for the period between February 2005 and March This index is expected to grow at the beginning of 2006, but its growth rate is predicted to slow down. The total growth is expected to be about 2.5% in the period between March and March The index of motor vehicle cargo transportation rates is expected to grow, its average monthly growth rate being about 1.3% at the end of 2005 and at the beginning of The annual growth of index is expected to be 13%. The pipeline transportation rate index is forecasted to grow slightly in February By tracing the growth record of this index throughout the entire 2005, one can find out that it has grown almost 50% over the last year thus showing the best growth over the other indices. Movement of prices of various types of raw materials in the world market This section provides the calculation data of the average monthly values of Brent oil prices (US dollars per barrel), aluminum (US dollars per ton), gold (US dollars per ounce), copper (US dollars per ton) and nickel (US dollars per ton) in the period between December 2005 and February 2006, obtained on the basis of times series models assessed according to the IMF s data for the time frame between March 1993 and September

12 Calculation data of predictive values of prices of natural resources Table 8 Month Brent oil (US dollars per barrel) Aluminum (US dollars per ton) Gold (US dollars per ounce) Copper (US dollars per ton) Nickel (US dollars per ton) Predictive values according to ARIMA models December January February Growth rates а against the corresponding month in (%) December January February For reference: actual values in the corresponding period of December January February Note: price series of oil, nickel, gold, copper and aluminum belong to the DS-type series in the time frame between March 1993 and September As illustrated ( in Table 8 ), prices of oil, gold, nickel and copper in winter are expected to exceed those of the previous year, while the predictive aluminum prices are predicted nearly equal to those in the corresponding period of the previous year, and nickel prices are expected to be significantly lower than at the corresponding period of the previous year. Besides, the average oil prices are expected to amount to nearly 61.5 US dollars per barrel, which is by an average of 43% above the corresponding figures of the previous year. Aluminum prices are forecasted to be nearly at the level of 1865 US dollars per ton. This corresponds to almost zero price level as compared to the corresponding period of the previous year. Gold prices are forecasted to amount to 474 US dollars per ounce. Nickel and copper prices are expected to average and 4000 US dollars per ton respectively. The average growth gold and copper prices is expected to account for nearly 10% and 23% respectively, while the average nickel price is expected to fall by 20% as opposed to the corresponding level of the previous year. The forecasted prices of mineral resources by the end of 2005 are predicted as follows: 57.8 US dollars per barrel of Brent oil, 1895 US dollars per ton of aluminum, 472 per ounce of gold, 11.5 thousand US dollars per ton of copper and 4102 US dollars per ton of nickel. Monetary indicators Prospective values of the monetary base (comprising cash in circulation and credit organizations required reserve balances with the RF Central Bank) and М 2 in the period between December 2005 and February 2006 were calculated on the basis of times series models of the corresponding indicators calculated by the RF Central Bank 12 for the time frame between October 1998 and September Listed in Table 9 are the 12 The data on a particular month are listed in accordance with the RF Central Bank s methodology as of the beginning of the next month. 12

13 calculation data on predictive values and actual values of these indicators over the corresponding period in the previous year. It should be noted that by virtue of that the monetary base is a tool used by the RF Central Bank to pursue its policy, its forecast relying upon the times series models is to a certain extent conditional, because prospective values of this indicator are determined on the basis of the isions made by the RF Central Bank rather than the internal peculiarities of the series. M 2 and monetary base forecast Table 9 Period Monetary base M 2 billion rubles growth against the preceding month, % billion rubles growth against the preceding month, % December January February % For reference: actual values in the corresponding period of (growth against the preceding month, %) December January February Note: all time series of monetary indicators were classified as stationary in first-order differences with a market seasonal component for the time frame between October 1998 and September M 2 is forecasted to move as follows in the period between February 2005 and March 2006: it is expected in increase by almost 10% in February 2005 due to a heavy seasonal impact and fall in March So M 2 is predicted to grow by 9.5% in February 2005 to be fall by 5.7% in March 2006 and slightly increase in March The monetary base is forecasted to behave in the same manner as M 2, while it is expected to grow faster in February 2005 and fall in March The monetary base and M 2 are forecasted to total and bln rubles respectively at the end of RF gold and foreign exchange reserves This section provides statistical assessment data on prospective values of the RF gold and foreign exchange reserves 13, obtained on the basis of assessment of the time series model of the RF gold and foreign exchange reserves according to the RF Central Bank s data for the time frame between October 1998 and October This indicator is forecasted without taking into account reduction in the RF gold and foreign exchange reserves due to the repayment of Russia s foreign debt, which may lead to the fact that the volume of the RF gold and foreign exchange reserves may be overvalued with regard to the months of the foreign debt repayment (or undervalued otherwise) as compared to the actual values. 13 The data on the volume of the RF gold and foreign exchange reserves are listed as of the first date of the next month. 13

14 Period RF gold and foreign exchange reserves forecast Predictive values according to ARIMA models growth against the corresponding billion US dollars month in , % December ,0 0.5 January ,3 2.0 February ,0 6.6 For reference: actual values for the corresponding month in billion US dollars growth against the corresponding month in , % December ,5 6.1 January ,9 0.3 February ,2 7.4 Table 10 Note: the RF gold and foreign exchange reserves series was identified as stationary in differences for the time frame between October 1998 and October The RF gold and foreign exchange reserves are expected to keep growing at the end of 2005 and the beginning of As a result, the RF gold and foreign exchange reserves are expected to total 172 bln US dollars as early as in February and 180 bln USD by the end March. The annual growth of this indicator is expected to be 38.2% in Foreign exchange rates Model calculations of prospective values of the foreign exchange rate (rubles per US dollar) were made on the basis of assessment of the time series models of the corresponding indicators quoted by the RF Central Bank on the last date of the month over the period between October 1998 and November The predictive values of the USD/EURO exchange rate were calculated on the basis of the IMF s data as of the last date of the month in the period between March 1999 and November Period RUR/USD and USD/EUR exchange rates forecast Predictive values of RUR/USD exchange rate (rubles per US dollar) according to ARIMA models Predictive values of USD/EUR exchange rate (US dollar per Euro) according to ARIMA models December January February For reference: actual values in the corresponding months December January February Table The Bulletin includes the IMF s data for the period between January 1999 and September The data on October and October 2005 were obtained from the foreign exchange rate statistics website 14

15 Note: the series under review were identified as the first-order integrated series with a seasonal component within the corresponding time frames. Table 11 provides forecasts of the RUR/USD and USD/EUR exchange rates in winter , as well as actual values of these indicators in the corresponding period between 2004 and The RUR/USD exchange rate is forecasted to vary at the level of 28.5 rubles per US dollar in the period between February 2005 and March This level is 60 kopeks above the average RUR/USD exchange rate in the corresponding period of the previous year. In total, the RUR/USD exchange rate is expected to amount to rubles per US dollar at the end of According to the forecast, the USD/EURO exchange rate is expected to line to 1.16 USD per Euro by the end of 2005 and then reach 1.18 in the period between March and March Living standard indicators This section (see Table 12) presents calculation data of the predictive values of the real wages and real disposable money income indicators obtained on the basis of times series models of the corresponding indicators calculated by the FSSS for the time frame between March 1999 and September These indicators depends to a certain degree upon centralized isions on wage increase for budget-funded workers, as well as isions on increase of pensions, scholarships and benefits, which involves certain adjustments to the movement of the indicators under review. Consequently, the prospective values of real wages and real disposable money income indicators calculated on the basis of the series, the latest of which are considerably higher or lower because of such an increase, may differ largely from those realized in practice. Living standard indicators forecast Period Real disposable Real money money incomes incomes Real wages Predictive values according to ARIMA models (in terms of percentage of the corresponding period in ) December January February For reference: actual values in the corresponding period in (in terms of percentage of the corresponding period in ) December January February Table 12 Note: The real money income and real wages series in relation to the corresponding period of the previous year were used for the calculation. Both series were classified as stationary in differences processes in the time frame under review. The disposable money income series was studied in basic form with March 1999 accepted as the basic period. This series belongs to the near-trend stationary series. The results listed in Table 12 predict growth in living standards of the population in the period between February 2005 and March 2006 as opposed to the corresponding level of the previous year. The average predictive growth in real disposable monetary income is about 11% as compared to the corresponding period of the previous year. The predictive growth in 15

16 real monetary income is about 12% and real wages near 11.5% as compared to the corresponding period of the previous year. As a result, real disposable monetary income, real monetary income and real wages are expected to grow by 8.3%, 9.2% and 12.2% respectively by the end of Economically active population and total unemployment indicators Prospective values of the economically active population and total unemployment indicators were calculated with the help of the time series models assessed for the time frame between October 1998 and September 2005 on the basis of the FSSS s monthly data 15. The total unemployment indicator was also calculated on the basis of the models using the results of the conjuncture polls 16. It should be noted that logical discrepancies 17 that may be found in the forecasts of total employment and total unemployment which are supposed to be equal in total to the value of the economically active population indicator, may be caused by the fact that every series is forecasted separately rather than as the difference between the predictive values of economically active population and other indicator. Table 13 Calculation data of predictive values of total economically active population and total unemployment Month Total economically active population (ARIMA) million persons growth rates against the corresponding period in (%) million persons Total unemployment (ARIMA) growth rates against the corresponding period in (%) in terms of percentage of the indicator relating to the number of economically active population Total unemployment (CP) million persons growth rates against the corresponding period in (%) in terms of percentage of the indicator relating to the number of economically active population December January February For reference: actual value over the corresponding periods in (million persons) December January February Note: the economically active population indicator series is a stochastic process, near-trend stationary within the time frame between October 1998 and September The total unemployment indicator series is a stochastic process, first-order integrated. Both indicators includes a seasonal component. 15 The indicator was calculated as of the end of the month, in accordance with the methodology of The International Labor Organization (ILO). 16 The model was assessed for the time frame between January 1999 and September For example, simultaneous reduction of both economically active population and total unemployment can be considered such a discrepancy. It should be noted, however, that such a situation is possible in principle, provided that the number of economically active population is reduced in strength. 16

17 The average monthly growth in the number of economically active population during the forecast period is expected to account for 1.1% of the corresponding months of the previous year, according to the forecasts made on the basis of the ARIMA models ( see Table 13 ). The number of economically active population is forecasted to total 67.8 million people by the end of The number of the unemployed is expected to rease by an average of 10,3% per month as compared to the corresponding period of the previous year, according to the ARIMA models and by 11.5% per month according to the models constructed with the use of conjuncture polls. The total unemployment is expected to average 5.6 million people by the end of the year. 17

18 Annex. RF economic indicators time series curves: Actual and predictive values. Figure 1. Basic index of industrial production volume in industry as a whole ( January 1993 = 100% ) Jan-03 Jul-03 Feb-04 Aug-04 Mar-05 Sep-05 Figure 2. Basic index of industrial production volume in ferrous metallurgy ( January 1993 = 100% ) Jan-03 Jul-03 Feb-04 Aug-04 Mar-05 Sep-05 Figure 3. Basic index of industrial production volume in metal fabricating industries ( January 1993 = 100% ) Jan-03 Jul-03 Feb-04 Aug-04 Mar-05 Sep-05 18

19 Figure 4. Basic index of industrial production volume in chemical and petrochemical industry ( January 1993 = 100% ) Jan-03 Jul-03 Feb-04 Aug-04 Mar-05 Sep-05 Figure 5. Basic index of industrial production volume in construction materials producing industry ( January 1993 = 100% ) Jan-03 Jul-03 Feb-04 Aug-04 Mar-05 Sep-05 Figure 6. Basic index of industrial production volume in fuel and energy industry ( January 1993 = 100% ) Jan-03 Jul-03 Feb-04 Aug-04 Mar-05 Sep-05 19

20 Figure 7. Basic index of industrial production volume in non-ferrous metallurgy ( January 1993 = 100% ) Jan-03 Jul-03 Feb-04 Aug-04 Mar-05 Sep-05 Figure 8. Basic index of industrial production volume in woodworking and paper-pulp industries ( January 1993 = 100% ) Jan-03 Jul-03 Feb-04 Aug-04 Mar-05 Sep-05 Figure 9. Basic index of industrial production volume in food processing industry ( January 1993 = 100% ) Jan-03 Jul-03 Feb-04 Aug-04 Mar-05 Sep-05 20

21 Figure 10. Basic index of industrial production volume in light industry ( January 1993 = 100% ) янв.03 июл.03 фев.04 авг.04 мар.05 сен.05 Jan 2003 Jul 2003 Feb 2004 Aug 2004 March 2005 Sep 2005 Figure 11. Capital investments ( billion rubles ) Jan-03 Jul-03 Feb-04 Aug-04 Mar-05 Sep-05 Figure 12. Retail turnover volume ( billion rubles ) May-02 December-02 June-03 January-04 August-04 February-05 September-05 March-06 October-06 21

22 Figure 13. Export to all countries ( billion US dollars ) 25,0 23,0 21,0 SM ARIMA 19,0 17,0 15,0 13,0 11,0 9,0 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Figure 14. Export to countries other than CIS member countries ( billion US dollars ) 22,0 20,0 18,0 16,0 14,0 12,0 10,0 ARIMA 8,0 6,0 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Figure 15. Import from all countries ( billion US dollars ) 14,0 13,0 12,0 11,0 10,0 9,0 8,0 7,0 6,0 5,0 4,0 SM ARIMA Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 22

23 Figure 16. Import from countries other than CIS member countries ( billion US dollars ) 11,0 10,0 9,0 8,0 7,0 6,0 5,0 4,0 3,0 ARIMA Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan-05 Mar-05 May-05 Jul-05 Sep-05 Nov-05 Jan-06 Figure 17. Basic index of consumer prices in terms of percentage of December in the preceding year jan feb march apr may june july aug sep oct nov Figure 17а. Basic index of consumer prices in terms of percentage of December in the preceding year ( SM ) Dec- 05 Jan- 06 Feb- 06 Mar- 06 Apr- 06 May- 06 Jun- 06 Jul- 06 Aug- 06 Sep- 06 Oct- 06 Nov- 06 Dec

24 Figure 18. Basic price index of industrial manufacturers in terms of percentage of December in the preceding year jan feb march apr may june july aug sep oct nov Figure 19. Basic price index in mining operations in terms of percentage of December in the preceding year feb apr june aug oct Figure 20. Basic price index in manufacturing industries in terms of percentage of December in the preceding year jan feb march apr may june july aug sep oct nov

25 Figure 21. Basic price index in production and distribution of electric power energy, gas and water in terms of percentage of December in the preceding year feb apr june aug oct Figure 22. Basic price index in food production in terms of percentage of December in the preceding year 114,0 112,0 110,0 108,0 106,0 104,0 102,0 100,0 jan feb march apr may june july aug sep oct nov Figure 23. Basic price index in textile and garment industries in terms of percentage of December in the preceding year feb apr june aug oct

26 Figure 24. Basic price index in wood furnishing and woodwork industries in terms of percentage of December in the preceding year feb apr june aug oct Figure 25. Basic price index in paper-pulp manufacturing in terms of percentage of December in the preceding year feb apr june aug oct Figure 26. Basic price index in production of coke and oil products in terms of percentage of December in the preceding year jan feb march apr may june july aug sep oct nov

27 Figure 27. Basic price index in chemical production in terms of percentage of December in the preceding year feb apr june aug oct Figure 28. Basic price index in metallurgy and production of finished metal products in terms of percentage of December in the preceding year feb apr june aug oct Figure 29. Basic price index in production of machinery and equipment in terms of percentage of December in the preceding year feb apr june aug oct

28 Figure 30. Basic price index in production of transportation vehicles and equipment in terms of percentage of December in the preceding year feb apr june aug oct Figure 31. Cost of the minimum set of food products per capita per month ( rubles ) May-02 December-02 June-03 January-04 August-04 February-05 September-05 March-06 October-06 Figure 32. Basic index of transportation rates ( in terms of percentage of the preceding month for each year ) January February March April May June July Augoust September October November December