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Use of Business Tendency Survey Results for Forecasting Industry Production in Slovakia Use of Business Tendency Survey Results for Forecasting Industry.

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Presentation on theme: "Use of Business Tendency Survey Results for Forecasting Industry Production in Slovakia Use of Business Tendency Survey Results for Forecasting Industry."— Presentation transcript:

1 Use of Business Tendency Survey Results for Forecasting Industry Production in Slovakia Use of Business Tendency Survey Results for Forecasting Industry Production in Slovakia Jana Juriová INFOSTAT – Institute of Informatics and Statistics, Slovakia

2 industry production indicators are very important for construction of GDP flash estimates, because the share of industrial production comprises more than 30% of GDP in Slovakia nowadays flash estimates of GDP are being prepared within 45 days after the end of reference quarter (at the time T+45), but perspectively it should be shortened up to T+30 days using results from business tendency surveys (BTS) can help to obtain a very early estimate of industrial production, as these qualitative data are known at the time T-2 before the end of reference quarter

3 monthly industrial production is represented by two quantitative indicators: industrial production index (IPI) - finished production index of new orders in industry (INOI) - potential production (which is expected to be realized in the near future) Similarity of development for quarterly time series of GDP and indices of industrial production for the period from the 1 st quarter 2000 to 2 nd quarter 2009 Correlation analysis has also confirmed the strong dependence between GDP and industrial production; correlation coefficient achieved: cca 0.91 between GDP and IPI cca 0.82 between GDP and INOI

4 Methodology 2 different methodological approaches are used, but explanatory variables of both are only qualitative data from BTS: econometric model with error correction term - explains separately the long-term and short-term influence of explanatory variables on dependent variable - solves the problem of so-called “spurious” regression (non-stationary time series used in classical regression models can produce spurious results) ARIMA and ARIMAX models - - univariate ARIMA models are widely used in forecasting practice - - the basic concept of this type of models is stationarity - - ARIMAX are ARIMA models with input variables or regression models with ARMA errors

5 Error Correction Model (ECM) : The short-term dynamics of variable Y t is joined with the long-term equilibrium relationship in the form of ECM: where the parameter α represents the short-run effect that a change in X t will have on a change in Y t, π or the parameter of so-called error correction term is the adjustment effect and shows how much of disequilibrium from the previous period is being corrected this period, u t-1 is random variable from long-term relationship: (if u t is stationary, variables X t and Y t are cointegrated and u t-1 can be used in the ECM form) and ε t is random variable with attributes of white noise.

6 Error Correction Model (ECM) the initial hypothesis: industrial production in the form of fixed-base index (IP05) is growing over time at more or less constant rate and fluctuations around the trend (time) are changing in dependence on the balance statistics from business tendency surveys (BBTS) or Residuals obtained from estimation of long-term relationship defined this way are tested on stationarity by Augmented Dickey-Fuller (ADF) test of unit root.

7 ARIMA and ARIMAX models ARIMA combines three processes: AutoRegressive (AR), dIfferences (I) and Moving Average (MA) The resulting general linear model is in the following form: are autoregressive parameters (i=1,…,p), θ j are moving average parameters (j=1,…,q), Z t is obtained by differencing original time series d-times, a t is white noise component. Very strong seasonality of both analyzed time series indicates the need of seasonal ARIMA models (or SARIMA models) and they are denoted as ARIMA(p,d,q)(P,D,Q)s where (P,D,Q) are the parameters of seasonal part of the model based on the same principles as the non-seasonal part and s represents the seasonality. ARIMAX models are ARIMA models with eXogenous variables (X). In this case ARIMA modelling identification is applied to residual series from the regression model (these residuals should be stationary). Exogenous variables in our case are balances from BTS.

8 Industry production index The time series of industrial production index (IPI) was increasing almost all the time regardless the trend break in the end of In the whole analyzed period the average monthly growth rate was approximately 5.8% year-on-year. The industrial confidence indicator (ICI) - composed of 3 balances: order books, stocks of finished products and production expectations - was proved as the main explanatory variable for models of industrial production index.

9 Industry production index – Error correction model The long-term relation: LOG(IPI)= *TIME+0.007*ICI(-1) explains almost 88% of variability for the dependent variable and its residuals are stationary. The short-term relation: DLOG(IPI)=0.002*D(ICI(-1))-0.382*RESID_LT- 1)+0.048*D(SD3)+0.031*D(SD6)-0.04*D(SD8)+0.056*D(SD10)+0.067*D(SD11) where RESID_LT are residuals from the long-term relation and SD i are seasonal variables. The estimated EC model explains about 60% of variability for the dependent variable. The parameter of error correction term represents how much of the imbalance from the previous month is being corrected this month, so it is 38% of imbalance in this case. The Durbin-Watson statistic (2.26) confirms that there is no autocorrelation in the model. Conclusions: - industrial confidence indicator is statistically significant for explaining of deviations of industrial production index from its long-term trend being approximated by linear trend; - industrial confidence indicator is statistically significant from short-term view as well.

10 Industry production index –ARIMAX model Transformation of non-stationary time series of IPI: logarithmic transformation, 1 st unseasonal and 1 st seasonal differences; transformed time series: The final seasonal model ARIMA(0,1,1)(0,1,1) 12 explains about 34.8% of variability for dependent variable. The most suitable balance from business tendency surveys that has improved the model is production expectations (EXPROD) over the next 3 months. The final ARIMAX model explains a greater part of variability for dependent variable; it increased almost up to 40%. Variable EXPROD was transformed by means of first differences: D(EXPROD)=EXPROD t -EXPROD t-1

11 Industry production index – Compared forecasts : Static simulation ex post for the period from January 2008 to June 2009: The lower RMSE (8.67%) was achieved by ECM model, because RMSE for ARIMAX model is 9.65%. (RMSE – Root Means Square Error or standard deviation of forecasts’ errors) The conclusion is that ECM model achieves better forecasts for industrial production index than ARIMAX model.

12 Industrial new orders index The time series of index of new orders in industry (INOI) has an increasing trend (like in the case of IPI), but its variability and slope is more distinct. The break in trend appeared a little earlier than for the time series of IPI; INOI (y-o-y) has been continuously achieving minus numbers from August Average growth rate was almost 12% during all analyzed period. The main explanatory variable for the error correction model of industrial new orders index is the balance of order books.

13 Industrial new orders index - Error correction model The long-term relation: LOG(INOI)= *TIME+0.010*ORDERBOOKS explains almost 80% of variability for dependent variable and its residuals are stationary. The short-term relation: DLOG(INOI05)=0.001*D(ORDERBOOKS)-0.206*RESID_LT(-1)+0.058*D(SD3) *D(SD7)-0.134*D(SD8)-0.108*D(SD12) The estimated EC model explains about 44% of variability for the dependent variable. The Durbin-Watson statistic (2.22) confirms that there is no autocorrelation in the model. Conclusions: - balance of order books is statistically significant for explaining of deviations of industrial new orders index from its long-term trend being approximated by linear trend; - industrial confidence indicator is statistically significant from short-term view as well. Industrial new orders index –ARIMAX model The same transformation of non-stationary time series of INOI was used like for IPI. The final seasonal model ARIMA(3,1,)(0,1,1)12 explains almost 50% of variability for dependent variable. The final ARIMAX model with balance of order books as input variable explains a greater part of variability for dependent variable; it increased to about 55.5%.

14 Industrial new orders index – Compared forecasts : Static simulation ex post for the period from January 2008 to June 2009: The lower RMSE was achieved by ARIMAX model % (10.12% for ECM model). The conclusion is that ARIMAX model has better forecasting accuracy than ECM model for index of new orders in industry; the opposite result like for IPI. Both model approaches are useful for forecasting industrial production using results from BTS.

15 Thank you for attention.


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