Journal number 2 ∘ Lia Totladze ∘
DIFFUSION INDEX FOR GEORGIA: SELECTION LEADING ECONOMIC INDICATORS

Summary

Cyclical feature of economies in a violent environment is forcing researchers to search for early signals of turning points. The most appropriate tools to solve this problem are the leading indicators and indexes based on leading indicators. It is difficult to rank which types of indicators have most weight in foretelling the course of the economy, and subsequently, its impact on currency market.  Given the volatile nature of transformed economy, there is a need an accurate leading indicator of economic performance (In particular for Georgia). In this paper we try collect leading economic indicators, study their features and engage diffusion index for Georgia.

Keywords: Leading Indicators,Diffusion Index, Composite Index, Time Series;

JEL Classification:  C52, C53, C18, C18, E37.

1. Introduction

For many years a system of leading economic indicators, first developed by the National Bureau of Economic Research (NBER), has been widely used in the United States to appraise the state of the business cycle.  A few years ago the Organization for Economic Cooperation and Development (OECD) set up a working party to develop this type of analysis and most of the member countries participated.

Georgia is country with transitional economy. Given the volatile nature of transformed economic, there is a need for businesses and government agencies to have access to an accurate leading indicator of transformed economic performance (In particular for Georgia).

While such analysis is well established in advanced economies, it has received relatively little attention in many emerging market and developing economies, reflecting in part the lack of sufficient historical data to determine the reliability of these indicators. This paper presents an econometric approach to deriving diffusion indexes of leading indicators for a small open economy. The results show that, even with limited monthly observations, it is possible to establish meaningful economic and statistically significant relations between indicators from different sectors of the economy and the present and future direction of economic activity.

The main problems were short time series and the instability of macroeconomic conditions connected mainly with the economic transition.

2. Literature Review

The NBER first developed an approach to monitor economic variables that are sensitive to cyclical change in the 1930s. A research team at the NBER led by Burns and Mitchell studied a group of economic variables to see if fluctuations in those variables persistently led, coincided with, or lagged turning points in U.S. business cycles.

Moore and Shishkin (1967) first developed and applied a formal weighting scheme by scoring variables in terms of their economic significance, statistical adequacy, cyclical timing and business cycle conformity. The choice of variables and the weights associated with them, however, was purely subjective and did not involve a formal econometric analysis.

The OECD started publishing leading indicators in 1987 and now publishes such indexes on a monthly basis for all member countries and for six aggregate geographical zones. The NBER-CB and the OECD methods are very similar. Both methods are based on the analysis of turning points, namely the analysis of expansions (i.e., peaks) and recessions (i.e., troughs). The main difference is that  the NBER-CB does not rely on trend adjustments, whereas the OECD method estimates long-term trends using a modified version of the Phase Average Trend method (PAT) first developed by the NBER. The difference between the two methods reflects a more fundamental difference in the definition of the business cycle.

Leading indicators are systematically collected data on an activity or condition that is related to a subsequent and valued outcome, as well as both the processes surrounding the investigation of those data and the associated responses.This definition captures several important attributes of leading indicators. First, leading indicators are antecedents to important events that predict or foreshadow those events. Second, leading indicators are not fixed characteristics of individuals or systems; rather, they are conditions or activities that can be changed by action. Third, the search for leading indicators catalyzes a productive inquiry that results in the rethinking of organizational resources or supports. Fourth, the search for leading indicators may help identify or develop more relevant and precise indicators.

Leading indicators share some meaning with terms such as correlates, predictors, and risk factors, but are distinctive. The term correlates describe the connection between variables, but does not convey the antecedent nature of a leading indicator. While leading indicators can be predictors and convey risk factors, they are distinct from these concepts in that they always represent an actionable concept, whereas predicators and risk factors may convey immutable qualities of individuals or groups.

According NBER methodology there are ten indicators for USA. This types of indicators are different by OECD methodology. Differences depend on country’s features. For example for Belgium there are: New passenger car registration; Employment (manufacturing) future trend; Export orders inflow; Demand; Production; Consumer confidence indicator. Also different are LEI for Poland: Real effective exchange rate; Interest rate; Production; Unfilled job vacancies; Production of coal. But in case Netherlands there are 5 leading indicators in total, four of which are confidence indicators. 

3.Data and Empirical Results

Calculating the Diffusion Indexes of Leading Indicators. The composite indexes measure the volume of overall business activity based on the percentage changes in selected indicators. The diffusion indexes measure the proportion of the indicators that are improving. If the proportion of indicators improving is more than 50%, the economy is expanding and if they are below 50%, the economy is contracting.

The indexes of business conditions are summary measures for aggregate economic activity. They are designed to be a useful tool for analyzing current conditions, and for forecasting future economic conditions. They are indexes that combine the behavior of key cyclical indicators that represent widely differing activities of the economy such as production, employment, and many more. The composite indexes are used to identify the volume of overall business activities by composing percentage changes of selected indicators. On the other hand, diffusion indexes are used to determine turning points of the business cycle, among other purposes, by counting changes in directions of selected indicators.

The diffusion indexes are used to determine the turning points in the business cycles. When the diffusion indexes are above the 50 percent threshold, the economy can be interpreted to be in an expansion phase; when below, in a contraction phase.

Compute Diffusion Indexes. Diffusion indexes measure the proportion of the components that contribute positively to the index. The first step in computing the diffusion indexes is to calculate if a component increased, decreased, or had no change. Components that rise more than 0.05 percent are given a value of 1, components that change less than 0.05 percent are given a value of 0.5, and components that fall more than 0.05 percent are given a value of 0. Next, sum the values of the components. Third, divide by the number of components. Finally, multiply by 100.

The Choice of Leading Variables. How can we choice target variables for the leading indicators.Since the pioneering work of Mitchell and Burns (1938), variable selection has rightly attracted considerable attention in the leading indicator literature, see, e.g., Zarnowitz and  Boschan (1975a,b) for a review of early procedures at the NBER and Department of Commerce.

Moore and Shiskin (1967) formalized an often quoted scoring system (see, e.g., Boehm (2001), Phillips (1998-99)), based mostly upon (i) consistent timing as a leading indicator (i.e., to systematically anticipate peaks and troughs in the target variable, possibly with a rather constant lead time); (ii) conformity to the general business cycle (i.e., have good forecasting properties not only at peaks and troughs); (iii) economic significance (i.e., being supported by economic theory either as possible causes of business cycles or, perhaps more importantly, as quickly reacting to negative or positive shocks); (iv) statistical reliability of data collection (i.e., provide an accurate measure of the quantity of interest); (v) prompt availability without major later revisions (i.e., being timely and regularly available for an  early evaluation of the expected economic conditions, without requiring subsequent modifications of the initial statements); (vi) smooth month to month changes (i.e., being free of major high frequency movements) (Marcellino, 2005).

We also can select indicators, which are closely related to GDP. In this regard we select six time series ready to enter the leading indicator. The data availability for Georgia is limited therefore we consider the period from 01.2012 to 04.2015 (40 observations). The basis for selection of reference series and construction of the diffusion indicator for the Georgia economy is the database of monthly time series. The DLI database contains time series released by the official statistics (National Statistics Office of Georgia, National Bank of Georgia). They all contain information on business cycles, which is the basic precondition for the components of DLI. The database is built of quantitative and qualitative monthly data (Business and Consumer Tendency Surveys). The containing time series come from different areas of economy (real and financial sector).

The given criteria yielded six indicators with best scores:

1. CPI
2. Monetary Aggregate (M1);
3. Spread of Interest Rates;
4. LCI for OECD Countries;
5. Residential Transactions;
6. Consumer Confidence Indicator;

CPI – Consumer price index provides an overall picture of price rises. The CPI is the central indicator to judge monetary value trends. Data are available through the Georgian Statistic Department.

Money M1 – is a prime mover, contains information about the monetary policy. M1 (Narrow money) measures cover highly liquid forms of money. Measures of the money supply have exhibited fairly close relationship with important economic variable such as GDP. Decrease or increase of money supply influences economic activities of various subjects. Data are available through the National Bank of Georgia.

The Interest Rate Spread – is a key determinant of a financial institution profitability. The role of financial sector in facilitating economic growth and development is well acknowledged. Data are available through the National Bank of Georgia.

LCI for OECD Countries – Georgia is country with small open economies and widely depend on import. Therefore we use Leading Composite Index for OECD countries as one of the indicator. Data are available through the OECD statistics.

Residential Transactions – are evidence of economic activity. An increase in housing drives economic activity in two ways. First, it induces investment in new residential construction. Second, it leads some household to spend, either for home improvement or consumption. Housing market (As existing home sales as new home sales) is a better indicator of future economic activity. For residential transactions we take the information collected by “Colliers International Georgia”.

Consumer Confidence Index for Georgia - Consumer Confidence Survey by ISET follows the standard EU methodology: There are randomly sample 300-350 individuals on a monthly basis and question them about the past, current and future financial situation of their families and the country as a whole. Consumer confidence is the degree of optimism that consumers feel about the overall state of the economy and their personal financial situation. How confident people feel about stability of their incomes determines their spending activity and therefore serves as one of the key indicators for the overall shape of the economy.   In essence, if consumer confidence is higher, consumers are making more purchases, boosting the economic expansion. On the other hand, if confidence is lower, consumers tend to save more than they spend, prompting the contraction of the economy. Data are available through the ISET survey.

Construction the DLI for Georgia. The methodological starting point for the DLI construction is the OECD system of leading indicators, which is mainly based on a system approach developed by NBER. We calculated DLI for Georgia according methodology described above. As we see diffusion index level indicate that economic of Georgia will be growth.

4.Conclusions

In this paper we have evaluated the forecasting performance of diffusion index-based methods. In this regard, diffusion indexes of leading economic indicators provide useful summary statistics to analyze the current and future direction of economic activity. This numbers indices some aspects of economic development. They might be used for forecasting for a small open economy such Georgia. A regular updating of these indexes could provide a useful tool to the Georgian authorities for policy formulation.

The indexes proposed in this paper should be considered experimental for the time being and should be used operationally only after a testing period that can confirm their   performance. The first experiment with DLI construction can be assumed as successful.  The next phases of research will be based on verification of the results. However, verification of its reliability and predicative ability requires a long-term experimental application, including many revisions as confirmed by experience of other countries. The accuracy of different forecasting methods is a topic of continuing interest and research.

In addition, a comprehensive assessment of future economic activity will always need to take account of other information that cannot be quantified, including the effects of geopolitical uncertainties and macroeconomic policy changes. 

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