For all major economies, statistics offices publish wide arrays of economic data series, often with changing definitions, elaborate adjustments, multiple revisions and occasional large distortions. Monitoring economic data consistently is tedious and expensive. Most professional investors find it easier to trade on data surprises than on actual macro trends. It is not uncommon for investment managers to consider an economic report only with respect to its presumed effect on other investors’ expectations and positions and to subsequently forget its contents within hours of its release.
What makes monitoring economies difficult is that there is usually no single series that represents a broad macroeconomic trend on its own in a timely and consistent fashion. To begin with, conventional economic data are published with considerable lags, subject to frequent revisions, and often their true history is very hard to reconstruct for financial market backtesting. Moreover, many important types of macro information for markets are not produced by central agencies. For example, equilibrium real interest rates and long-term inflation trends are essential factors for fixed income strategies (view post here). Yet neither of these is available as an official reliable data series since such estimation requires strong judgment and macroeconomic modelling (view post here). Even something apparently simple such as an inflation trend demands watching many different data series at the same time, such as consumer price growth, “core” inflation measures, price surveys, wage increases, labour market conditions, household spending, exchange rates and inflation derivatives in financial markets. In practice, the use of economic data for macro trading requires  producing special tradable economic data,  formulating a plausible and logical theory to create meaningful indicators, and  applying statistical methods.
Published economic data cannot be easily and directly plugged into systematic trading strategies. Unlike financial market data, which are intensively used for algorithmic and systematic trading, economic data come with a number of inconvenient features such as low frequency of updating, lack of point-in-time recording and backward revisions. Therefore, economics statistics and other quantifiable information must be brought into a form that is suitable for systematic research. One can call this form tradable economic data (view post here).
Theoretical structure establishes a plausible relation between the observed data and the conceived macroeconomic trend. This is opposite to data mining and requires that we set out a formula based on our understanding of the data and the economy before we explore the actual data.
- As a most simple example, different sectoral production reports can be combined by adding them in accordance with the weight of the sectors in the economy.
- The monetary policy stance in a regime with sizeable asset purchase programs can be estimated as a single “implied” short-term interest rates based on the actual short-term interest rate and the equivalent effect of compression of term premia, based on a yield curve factor model (view post here).
- As a more advanced example, we can extend measures of consumer price inflation by indicators of concurrent aggregate demand. This helps to distinguish between supply and demand shocks to prices, making it easier to judge whether a price pressure will last or not (view post here).
- Even modern academic macroeconomic theory can help. True, dynamic stochastic general equilibrium models are often too complex and ambiguous for practical insights. However, simplified static models of the New Keynesian type incorporate important features of dynamic models, while still allowing us to analyze the effect of macro shocks on interest rates, exchange rates and asset prices in simple diagrams (view post here for interest rates and here for exchange rates).
Statistical methods become useful where our prior knowledge of data structure ends. They necessarily rely on the available data sample. In respect to economic trends, they can accomplish two major goals: dimension reduction and nowcasting.
- Dimension reduction condenses the information content of a multitude of data series into a small manageable set of factors or functions. This reduction is important for forecasting with macro variables because many data series have only limited and highly correlated information content. (view post here).
- Nowcasting tracks a meaningful macroeconomic trend in a timely and consistent fashion. An important challenge for macro trend indicators is timeliness. Unlike financial market data, economic series have monthly or quarterly frequency, giving only 4-12 observations per year. For example, GDP growth, the broadest measure of economic activity, is typically only published quarterly with one to three months delay. Hence, it is necessary to integrate lower and higher-frequency indicators and to make use of data releases with different time lags.
In recent years, dynamic factor models have become a popular method for both dimension reduction and nowcasting. Dynamic factor models extract the communal underlying factor behind timely economic reports and translate the information of many data series into a single underlying trend (view post here and here). This single underlying trend is then interpreted conceptually, for example as “broad economic growth” or “inflation expectations”. Also, financial conditions of an economy can be estimated by using dynamic factor models that distil a broad array of financial variables (view post here).
It is important to measure local macroeconomic trends with a global perspective. Just looking at domestic indicators is almost never appropriate in an integrated global economy. As a simple example, inflation trends have increasingly become a global phenomenon, as a consequence of globalization and convergent monetary policy regimes. Over the past three decades local inflation has typically been drifting towards global trends in the wake of deviations (view post here). As an example of the global effects of small-country shocks, “capital flow deflection” is a useful conceptual factor for emerging markets that stipulates that one country’s capital inflow restrictions are likely to increase the inflows into other similar countries (view post here). In order to measure this effect, one needs to build a time series of capital controls in all major economies in order to distil the specific impact on a single currency.