Forecasting with leading economic indicators—a neural network approach

Business Economics, Oct, 2003 by Timotej Jagric

Single-index models and the selection of leading indicator variables are normally based on linear regression methods. Moreover, in statistical modeling of the business cycle, it has been well established that cycles are asymmetric. (See, for example, Kaiser and Maravall 1999, Verbrugge 1997, Kim et al. 1996, Sichel 1993.) Therefore, it is doubtful that linear models can adequately describe them.

The original NBER model (classical model) was built solely within a linear framework. With recent developments in nonlinear time series analysis, several authors have begun to examine the forecasting properties of nonlinear models in economics. Probably the largest share of economic applications of nonlinear models can be found in the field of prediction of time series in capital markets (Meese and Rogoff 1983, Lee et al. 1993). In a study more comparable to ours, Jaditz, Riddick and Sayers (1998) use financial variables to forecast industrial production. They estimated a nonlinear, non-parametric nearest-neighbor regression model. Tkacz (2000) also achieved superior results over linear models in forecasting Canadian GDP growth. Tiao and Tsay (1994) show that a simple threshold autoregressive model is superior to an AR(2) representation for GDP growth. Maasoumi, Khotanzad and Abaye (1994) show that the fourteen macroeconomic series in the Nelson and Ploser (1982) study are nonlinear processes rather than unit root processes.

To avoid the aforementioned pitfalls of widely used linear models, in the present research we adopt neural networks to forecast business cycles. The decision to focus on neural networks arises directly from the features of these models as described by Bishop (1995). First, neural networks are data-driven and can "learn" from, and adapt to, underlying relationships. This property makes them an ideal modeling tool for studies in which there exists little prior knowledge about the appropriate functional representation of the relationship under investigation. Second, when properly specified they are universal functional approximates, implying that they can approximate functional forms to any given degree of accuracy. Finally, neural networks are nonlinear, which seems to be the case for many macroeconomic time series.

Another important issue is the prediction of the future value of the reference series. The classical model of leading indicators can only provide a sign for a turning point in aggregate economic activity. It is not possible to exactly define when the turning point will occur, or how strong the following contraction or expansion will be. Therefore, a reliable composite index of leading indicators should possess the following properties (Fritsche and Stephan 2000): (1) the movements in the index should resemble those in the business cycle reference series, (2) the relationship between the reference series and the indicator should be statistically significant, and (3) the forecasting performance should be stable over time.

Our approach differs from previous studies in several ways. First, we try to modify the classical model with the aim of overcoming the deficiencies of the model. Second, our focus is on constructing a multivariate neural network forecasting model. Third, our model is used for monthly forecasts.

We tested our model on data for a small, open, transition economy. This gave us the opportunity to test the properties of the model under extreme conditions:

* Time series can cover only a short time period. In the case of Slovenia, the data cover a period of nine years.

* As in many other transition economies, Slovenia has faced a deep transformation depression. In the process of restructuring its economy, wild swings in time series occur, which may have a significant impact on chosen indicators.

* In the observed period, the economy is in the process of transforming from a former semi-command socialist economy to a market-oriented economy. The former regional market has changed to a national market. The ownership structure is changing rapidly, and hence it may have important impacts on investment and consumption behavior.

Figure 1 provides a flow chart of our research to develop the model. The paper is organized as follows: in next section the structure of the database and the selection of reference series are explained. Then, the selected input variables of the model are presented. This is followed by a presentation of the model and its results, which we compare with the results of the classical leading indicators model.

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Data

An important step in the process of constructing the model is the development of a broad database, which should cover all crucial fields of economic activity. The database that we used includes 365 time series classified into the categories given in Table 1. In the final version of the database, all time series were transformed into growth rates. All series that were presented in current prices were converted using the CPI (1999=100).

The aim of the model is to forecast a reference variable that is selected to indicate fluctuations in economic activity. The variable must be a monthly reported variable, be available for many countries, and must measure the real sector of the economy. There are two alternative strategies for obtaining a time series that represents current monthly business activity (Dias 1994): either adopt a single series as the variable of interest or use a function of several variables. Both approaches have long traditions in empirical macroeconomics. For example, the empirical literature on the monthly money-income relationship focuses on the predictability of monthly industrial production. Alternatively, Burns and Mitchell (1946) constructed a reference series by averaging several different major aggregate time series. This reference series was then used to date their reference cycles.