Structural VAR identification in asset markets using short-run market inefficiencies

We impose a structure on the short-run market inefficiencies in the asset markets and use this structure to identify a structural vector autoregressive model. This novel identification method is based on more reasonable assumptions than the standard approaches and also gives estimates for inefficiency measures in the markets, which are important on their own. Applying our method on the major European stock markets, we find that while the UK shocks were dominant in Europe until 1999, German innovations have been more important since 1999. We also find that the pattern of inefficiencies are consistent with the rational inattention model of Sims (2003).Structural VAR; Overreaction and Underreaction; Stock Market

Estimating International Transmission of Shocks Using GDP Forecasts: India and Its Trading Partners

Using a Factor Structural Vector Autoregressive (FSVAR) model and monthly GDP growth forecasts during 1995-2003, we find that Indian economy responds largely to domestic and Asian common shocks, and much less to shocks the from the West. However, when we exclude the Asian crisis period from our sample, the Western factor comes out as strong as the Asian factor contributing 16% each to the Indian real GDP growth, suggesting that the dynamics of transmission mechanism is time-varying. Our methodology on the use of forecast data can help policy makers of especially developing countries with frequent economic crises and data limitations to adjust their policy targets in real time.

How Far Ahead Can We Forecast? Evidence From Cross-country Surveys

Using monthly GDP forecasts from Consensus Economics Inc. for 18 developed countries reported over 24 different forecast horizons during 1989-2004, we find that the survey forecasts do not have much value when the horizon goes beyond 18 months. Using two alternative approaches to measure the flow of new information in fixed-target survey forecasts, we found that the biggest improvement in forecasting performance comes when the forecast horizon is around 14 months. The dynamics of information accumulation over forecast horizons can provide both the forecasters and their clients with an important clue in their selection of the timing and frequency in the use of forecasting services. The limits to forecasting that these private market forecasters exhibit are indicative of the current state of macroeconomic foresight.

How quickly do forecasters incorporate news? Evidence from cross-country surveys

Using forecasts from Consensus Economics Inc., we provide evidence on the efficiency of real GDP growth forecasts by testing whether forecast revisions are uncorrelated. As the forecast data used are multi-dimensional—18 countries, 24 monthly forecasts for the current and the following year and 16 target years—the panel estimation takes into account the complex structure of the variance–covariance matrix due to propagation of shocks across countries and economic linkages among them. Efficiency is rejected for all 18 countries: forecast revisions show a high degree of serial correlation. We then develop a framework for characterizing the nature of the inefficiency in forecasts. For a smaller set of countries, the G-7, we estimate a VAR model on forecast revisions. The degree of inefficiency, as mananifested in the serial correlation of forecast revisions, tends to be smaller in forecasts of the USA than in forecasts for European countries. Our framework also shows that one of the sources of the inefficiency in a country’s forecasts is resistance to utilizing foreign news. Thus the quality of forecasts for many of these countries can be significantly improved if forecasters pay more attention to news originating from outside their respective countries. This is particularly the case for Canadian and French forecasts, which would gain by paying greater attention than they do to news from the USA and Germany, respectively.Consensus economics; forecast inefficiency; GMM; VAR; panel data

On aggregation bias in fixed-event forecast efficiency tests

This note shows that problems due to aggregation in fixed-event forecast efficiency tests are not as severe as they are in unbiasedness tests. We also show that first lags of consensus revisions should be avoided in the tests.Aggregation bias; fixed-event; weak-efficiency