Tara M. Sinclair

Directional forecasts of GDP and inflation: a joint evaluation with an application to Federal Reserve predictions

Many studies have undertaken separate analyses of the Feds forecasts of real Gross Domestic Product (GDP) growth and inflation. This article presents a method for jointly evaluating the direction of change predictions of these variables. We conclude that some of the inflation forecasts, examined separately, were not valuable. However, the joint pattern of GDP and inflation projections was generally in accord with the economys movements. ‘… directional forecasting … is now an increasingly popular metric for forecasting performance….’ (Pesaran and Timmermann, 2004, 414)

Asymmetry in the Business Cycle: Friedman's Plucking Model with Correlated Innovations

This paper develops an unobserved components model for U.S. real GDP that allows for both asymmetric transitory movements and correlation between the permanent and transitory innovations. The asymmetry is modeled using Markov-switching in the transitory component in the spirit of Kim and Nelsons (1999) version of Friedmans plucking model. The findings suggest that ignoring the correlation between permanent and transitory movements underestimates the role of permanent movements, whereas ignoring asymmetry in the transitory component underestimates the role of temporary movements in U.S. real GDP. These results imply that both permanent movements and asymmetric transitory shocks are important for explaining post-war output fluctuations in the U.S. and for explaining the recession that began in 2007 in particular.

Can the Fed Predict the State of the Economy

Recent research has documented that the Federal Reserve produces systematic errors in forecasting inflation, real GDP growth, and the unemployment rate, even though these forecasts are unbiased. We show that these systematic errors reveal that the Fed is “surprised” by real and inflationary cycles. Using a modified Mincer-Zarnowitz regression, we show that the Fed knows the state of the economy for the current quarter, but cannot predict it one quarter ahead.

Output Fluctuations in the G-7: An Unobserved Components Approach

This paper proposes a multivariate unobserved components model to simultaneously decompose the real GDP for each of the G-7 countries into their respective trend and cycle components. In contrast to previous literature, our model allows for explicit correlation between all the contemporaneous trend and cycle shocks. We find that all the G-7 countries have highly variable stochastic permanent components for output, even once we allow for structural breaks. We also find that common restrictions on the correlations between trend and cycle shocks are rejected by the data. In particular, we find that correlations across permanent and transitory shocks are important both within and across countries.

Testing Stationarity with Unobserved Components Models

In the aftermath of the global financial crisis, competing measures of the trend in macroeconomic variables such as US real GDP have featured prominently in policy debates. A key question is whether the large shocks to macroeconomic variables will have permanent effects—i.e., in econometric terms, do the data contain stochastic trends? Unobserved components models provide a convenient way to estimate stochastic trends for time series data, with their existence typically motivated by stationarity tests that allow for at most a deterministic trend under the null hypothesis. However, given the small sample sizes available for most macroeconomic variables, standard Lagrange multiplier tests of stationarity will perform poorly when the data are highly persistent. To address this problem, we propose the use of a likelihood ratio test of stationarity based directly on the unobserved components models used in estimation of stochastic trends. We demonstrate that a bootstrap version of this test has far better small-sample properties for empirically-relevant data generating processes than bootstrap versions of the standard Lagrange multiplier tests. An application to US real GDP produces stronger support for the presence of large permanent shocks when using the likelihood ratio test as compared to the standard tests.

Mongolia: Measuring the Output Gap

This paper compares the output gap estimates for Mongolia based on a number of different methods. Special attention is paid to the substantial role of mining in the Mongolian economy. We find that a Blanchard and Quah-type joint model of output and inflation provides a more robust estimate of the output gap for Mongolia than the traditional statistical decompositions.

EVALUATING FORECASTS OF A VECTOR OF VARIABLES: A GERMAN FORECASTING COMPETITION

In this paper we present an evaluation of forecasts of a vector of variables of the German economy made by different institutions. Our method permits one to evaluate the forecasts for each year and then if one is interested to combine the years. We use our method to determine an overall winner for a forecasting competition across twenty-five different institutions for a single time period using a vector of eight key economic variables. Typically forecasting competitions are judged on a variable-by-variable basis, but our methodology allows us to determine how each competitor performed overall. We find that the Bundesbank was the overall winner for 2013.

Do Fed Forecast Errors Matter

There is a large literature evaluating the forecasts of the Federal Reserve by testing their rationality and measuring the size of their forecast errors. There is also a substantial literature and debate on the impact of the Fed’s monetary policy on the economy. We know little, however about the impact of the Fed’s forecast errors on economic outcomes. This paper constructs a measure of a forecast error shock for the Federal Reserve based on the assumption that the Fed follows a forward-looking Taylor rule. Given the effort the Fed puts towards producing forecasts that do not have an endogenous error component, we treat the Fed’s forecast errors as a shock, analogous to a monetary policy shock. Our shock, however, is different in that it is completely unintended by the monetary authority rather than simply unanticipated by the public. We follow Romer and Romer (2004) and investigate the effect of the forecast error shock on output and price movements. Our results suggest that although the absolute magnitude of the forecast error shock is large, the impact of the shock on the macroeconomy is quite small. This finding is robust across a range of different specifications. The maximum impact suggests a decline of less than 0.3 percent of real GDP and less than 0.4 percent of GDP deflator in response to a 100 basis point contractionary forecast error shock.

The Failure of Forecasts in the Great Recession

Macroeconomic forecasts are used to make decisions by everyone from business leaders to government policymakers. But the forecasts they rely on are most flawed just when they are most needed. The authors present an analysis of the accuracy of forecasts, focusing specifically on the latest recession in the United States. It is fair to conclude that economists completely failed to anticipate the fall in GDP and employment.