Yash P. Mehra (1999) - A Forward-Looking Monetary Policy Reaction Function, Richmond Fed

The Federal Reserve’s reaction function, which summarizes how the Federal Reserve (Fed) alters monetary policy in response to economic developments, plays an important role in macroeconomic and policy analyses. It can be helpful in predicting actual policy actions, thereby serving as a benchmark for assessing the current stance and the future direction of monetary policy. Also, in macro models, the reaction function is central in evaluating Fed policy and determining effects of other macro policies or economic shocks, implying macroeconomic performance may itself depend upon the conduct of monetary policy. Consequently, there is considerable interest in identifying the nature of actual policy pursued by the Fed and determining whether the estimated reaction function fostered or hindered macroeconomic stability.1

José R. Sánchez-Fung (2002) - Estimating a Monetary Policy Reaction Function for The Dominican Republic, Discussion Paper 02/01 University of Kent

This paper specifies and estimates a hybrid monetary policy base reaction function for the Dominican Republic (DR). The estimated reactions suggest that the Central Bank has been biased towards targeting the gap between the
parallel and official exchange rates, apparently doing so in a more systematic fashion after the mid 1980s. Remarkably, these findings are in line with the Central Bank’s long-standing endorsement of a multiple exchange rate regime, and could imply a process of learning, given the monetary authorities’ preferences.

And some small snippets; Wikipedia and the Apelia Economics Blog.  

Robert Fildesa and Herman Stekler (2002) - The State of Macroeconomic Forecasting, Journal of Macroeconomics Volume 24, Issue 4, December 2002, Pages 435-468

Macroeconomic forecasts are used extensively in industry and government. The historical accuracy of US and UK forecasts are examined in the light of different approaches to evaluating macroforecasts. Issues discussed include the comparative accuracy of macroeconometric models compared to their time series alternatives, whether the forecasting record has improved over time, the rationality of macroeconomic forecasts and how a forecasting service should be chosen. The role of judgement in producing the forecasts is also considered where the evidence unequivocally favors such interventions. Finally the use of macroeconomic forecasts and their effectiveness is discussed. The conclusion drawn is that researchers have paid too little attention to the issue of improving the forecasting accuracy record. Finally, areas where improvements are most likely to be found are discussed.

Scott Schuh (2001) - An Evaluation of Recent Macroeconomic Forecast Errors, Boston Fed

Despite a significant decline in the pace of economic growth in the second half of 2000, macroeconomic forecasters underpredicted real GDP growth and overpredicted the unemployment rate by a significant amount, for the fifth consecutive year. On average, real GDP forecasts were about 2 percentage points below the actual data for the
1996--2000 period, and unemployment rate forecasts about 0.5 percentage point above. On a more positive note, forecasters ended their chronic overprediction of inflation during much of this period. Nevertheless, surprisingly
large and persistent errors in recent forecasts of GDP, inflation, and unemployment have perplexed macroeconomists and policymakers for quite some time, and they merit closer examination.1

Paul Ormerod (1997) - The Impossibility of Accurate Macro-Economic Forecasting, Economic Affairs Volume 17 Issue 1 Page 44-49, March 1997

The macro-economic short-term forecasting record in the West over the past thirty years is very poor. Modern non-linear signal processing techniques can be used to show that such inaccuracy is a deep and inherent property of the data themselves. The forecasting record simply cannot be improved. Much economic policy still focuses on short-term intervention based on short-term forecasts. But such efforts are futile because forecasts of sufficient accuracy over time cannot be made.

X. Diebold, Francis (1997) The Past, Present, and Future of Macroeconomic Forecasting Journal of Economic Perspectives 12, 175-192.

Broadly defined, macroeconomic forecasting is alive and well. Nonstructural forecasting, which is based largely on reduced-form correlations, has always been well and continues to improve. Structural forecasting, which aligns itself with economic theory and hence rises and falls with theory, receded following the decline of Keynesian theory. In recent years, however, powerful new dynamic stochastic general equilibrium theory has been developed, and structural macroeconomic forecasting is poised for resurgence.