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The gap between potential and actual output—the output gap—is a key variable for policymaking. This paper adapts the methodology developed in Blagrave and others (2015) to estimate the path of output gap in the U.S. economy. The results show that the output gap has considerably shrunk since the Great Recession, but still remains negative. While the results are more robust than other existing methodologies, there is still significant uncertainty surrounding the estimates.
The gap between potential and actual output—the output gap—is a key variable for policymaking. This paper adapts the methodology developed in Blagrave and others (2015) to estimate the path of output gap in the U.S. economy. The results show that the output gap has considerably shrunk since the Great Recession, but still remains negative. While the results are more robust than other existing methodologies, there is still significant uncertainty surrounding the estimates.
We apply a range of models to the U.K. data to obtain estimates of the output gap. A structural VAR with an appropriate identification strategy provides improved estimates of output gap with better real time properties and lower sensitivity to temporary shocks than the usual filtering techniques. It also produces smaller out-of-sample forecast errors for inflation. At the same time, however, our results suggest caution in basing policy decisions on output gap estimates.
This paper discusses several popular methods to estimate the ‘output gap’. It provides a unified, natural concept for the analysis, and demonstrates how to decompose the output gap into contributions of observed data on output, inflation, unemployment, and other variables. A simple bar-chart of contributing factors, in the case of multi-variable methods, sharpens the intuition behind the estimates and ultimately shows ‘what is in your output gap.’ The paper demonstrates how to interpret effects of data revisions and new data releases for output gap estimates (news effects) and how to obtain more insight into real-time properties of estimators.
Estimates of potential output and the neutral short-term interest rate play important roles in policy making. However, such estimates are associated with significant uncertainty and subject to significant revisions. This paper extends the structural multivariate filter methodology by adding a monetary policy block, which allows estimating the neutral rate of interest for the U.S. economy. The addition of the monetary policy block further improves the reliability of the structural multivariate filter.
This paper presents a new mixed frequency methodology to estimate output gaps and potential output on a quarterly basis. The methodology strongly relies on the production function method commonly agreed at the European level (D'Auria et. al., 2010) but it significantly improves it allowing to assess the impact of real time forecast for GDP and other underlying variables. This feature of the model is particularly welcome in the current Italian budgetary framework which has foreseen the introduction of the principle of a budget balance in structural terms in the Constitution.By allowing to measure output gap with a quarterly span on the basis of recent developments indicators, the methodology provides interesting hints on the cyclical position of the economy in real time to be used for deriving cyclically-adjusted fiscal aggregates.
Estimates of potential output are an important component of a structured forecasting and policy analysis system. Using information on capacity utilization, this paper extends the multivariate filter developed by Laxton and Tetlow (1992) and modified by Benes and others (2010), Blagrave and others (2015), and Alichi and others (2015). We show that, although still fairly uncertain, the real-time estimates from this approach are more accurate than estimates constructed from naïve univariate statistical filters. The paper presents illustrative estimates for the United States and discusses how the end-of-sample estimates can be improved with additional information.
This paper reviews a number of different methods that can be used to estimate potential output and the output gap. Measures of potential output and the output gap are useful to help identify the scope for sustainable noninflationary growth and to allow an assessment of the stance of macroeconomic policies. The paper then compares results from some of these methods to the case of Sweden, showing the range of estimates.
Abstract: This dissertation investigates the usage and estimation of the output gap. The wide use of the output gap as a variable in the monetary policy literature makes learning more about the output gap necessary. The biggest issue with the output gap is that although it is a straightforward theoretical concept it can not be observed directly, meaning it must be estimated. The economics literature currently estimates the output gap by three different methods. The first is direct detrending of the GDP data, the second is indirect estimation, and the third is the production function approach. This dissertation uses both the first and the third methods in order to produce an output gap estimate that is theoretically and econometrically attractive. We begin by investigating the long term trend in US real GDP directly from the GDP data using a new econometric technique, Adaptive Least Squares (ALS). ALS is a special case of the Kalman Filter that allows for a time varying parameter model to be estimated relatively easily. The estimated trend is then used to estimate the output gap. The results of our estimation suggest that GDP does not follow even a time-varying long term trend, so the output 'gap' as specified is illusory. Chapter 3 derives both an unemployment gap and a capacity utilization gap, using Adaptive Least Squares (ALS), and combines them to formulate our Factor Utilization Model. The use of both unemployment and capacity utilization allows us to consider the effects of both labor and capital under or over utilization, thus eliminating a potential substitution bias from the unemployment gap, and avoiding unit root problems from a univariate estimation of the output gap. Additionally the fact that the Factor Utilization Model can be estimated monthly allows for more frequent data availability. Our final chapter compares various estimates of the output gap including all of the estimates developed earlier. We group the output gap estimates into three broad categories, one-sided filters two-sided filters and real-time estimates. Two-sided filters use the entire history of the data in order to arrive at an estimate. This means that they are very useful for looking backwards at the economy to determine how things were, but they are of little use in saying what would, or should have been done in the past. One-sided filters only use the data from periods up to and including the period being estimated. This gives the estimate that would have been generated if the estimation was being done historically. The final group of estimates utilizes real-time data. This is the data as it was initially published before it was subsequently revised. We find that the GDP data and the Congressional Budget Office's estimate of the output gap are subject to large ex post revisions, but that the unemployment and capacity utilization data are not. This lends strength to our Factor Utilization Gap as our output gap proxy of choice.
The methodology used in this paper has three distinguishing features: the natural rate of unemployment and potential output are jointly estimated; estimation integrates wage and price data with “real” and structural data; and third, the methodology encompasses many of the methods found in the literature. The results indicate that potential output growth has recovered somewhat during the early 1980s, but remains below the rapid rates of increase in the late 1960s. The natural rate, after rising during the late 1960s and the 1970s, is found to have declined in the 1980s. The paper concludes with an assessment of medium-term prospects for potential output and-the natural rate.