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The paper illustrates based on an example the importance of consistency between the empirical measurement and the concept of variables in estimated macroeconomic models. Since standard New Keynesian models do not account for demographic trends and sectoral shifts, the authors proposes adjusting hours worked per capita used to estimate such models accordingly to enhance the consistency between the data and the model. Without this adjustment, low frequency shifts in hours lead to unreasonable trends in the output gap, caused by the close link between hours and the output gap in such models.
The retirement wave of baby boomers, for example, lowers U.S. aggregate hours per capita, which leads to erroneous permanently negative output gap estimates following the Great Recession. After correcting hours for changes in the age composition, the estimated output gap closes gradually instead following the years after the Great Recession.
We propose a simple modification of the time series filter by Hamilton (2018) that yields reliable and economically meaningful real-time output gap estimates. The original filter relies on 8-quarter-ahead forecast errors of a simple autoregression of log real GDP. While this approach yields a cyclical component of GDP that is hardly revised with new incoming data due to the one-sided filtering approach, it does not cover typical business cycle frequencies evenly, but short business cycles are muted and medium length business cycles are amplified. Further, the estimated trend is as volatile as GDP itself and can thus hardly be interpreted as potential GDP. A simple modification that is based on the mean of 4- to 12-quarter-ahead forecast errors shares the favorable real-time properties of the Hamilton filter, but leads to a much better coverage of typical business cycle frequencies and a smooth estimated trend. Based on output growth and inflation forecasts and a comparison to revised output gap estimates from policy institutions, we find that real-time output gaps based on the modified Hamilton filter are economically much more meaningful measures of the business cycle than those based on other simple statistical trend-cycle decomposition techniques such as the HP or the Bandpass filter.
This paper investigates the accuracy of point and density forecasts of four DSGE models for inflation, output growth and the federal funds rate. Model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. Point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods. Despite their common underlying New Keynesian modeling philosophy, forecasts of different DSGE models turn out to be quite distinct. Weighted forecasts are more precise than forecasts from individual models. The accuracy of a simple average of DSGE model forecasts is comparable to Greenbook projections for medium term horizons. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts.
This paper investigates the accuracy of forecasts from four DSGE models for inflation, output growth and the federal funds rate using a real-time dataset synchronized with the Fed’s Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as accurate as the Greenbook projections for output growth and the federal funds rate. Only for inflation the model forecasts are dominated by the Greenbook projections. A comparison with forecasts from Bayesian VARs shows that the economic structure of the DSGE models which is useful for the interpretation of forecasts does not lower the accuracy of forecasts. Combining forecasts of several DSGE models increases precision in comparison to individual model forecasts. Comparing density forecasts with the actual distribution of observations shows that DSGE models overestimate uncertainty around point forecasts.
We examine both the degree and the structural stability of inflation persistence at different quantiles of the conditional inflation distribution. Previous research focused exclusively on persistence at the conditional mean of the inflation rate. As economic theory provides reasons for inflation persistence to differ across conditional quantiles, this is a potentially severe constraint. Conventional studies of inflation persistence cannot identify changes in persistence at selected quantiles that leave persistence at the median of the distribution unchanged. Based on post-war US data we indeed find robust evidence for a structural break in persistence at all quantiles of the inflation process in the early 1980s. While prior to the 1980s inflation was not mean reverting, quantile autoregression based unit root tests suggest that since the end of the Volcker disinflation the unit root can be rejected at every quantile of the conditional inflation distribution.
Recently, we evaluated a fiscal consolidation strategy for the United States that would bring the government budget into balance by gradually reducing government spending relative to GDP to the ratio that prevailed prior to the crisis (Cogan et al, JEDC 2013). Specifically, we published an analysis of the macroeconomic consequences of the 2013 Budget Resolution that was passed by the U.S. House of Representatives in March 2012. In this note, we provide an update of our research that evaluates this year’s budget reform proposal that is to be discussed and voted on in the House of Representative in March 2013. Contrary to the views voiced by critics of fiscal consolidation, we show that such a reduction in government purchases and transfer payments can increase GDP immediately and permanently relative to a policy without spending restraint. Our research makes use of a modern structural model of the economy that incorporates the long-standing essential features of economics: opportunity costs, efficiency, foresight and incentives. GDP rises because households take into account that spending restraint helps avoid future increases in tax rates. Lower taxes imply less distorted incentives for work, investment and production relative to a scenario without fiscal consolidation and lead to higher growth.
In the aftermath of the global financial crisis and great recession, many countries face substantial deficits and growing debts. In the United States, federal government outlays as a ratio to GDP rose substantially from about 19.5 percent before the crisis to over 24 percent after the crisis. In this paper we consider a fiscal consolidation strategy that brings the budget to balance by gradually reducing this spending ratio over time to the level that prevailed prior to the crisis. A crucial issue is the impact of such a consolidation strategy on the economy. We use structural macroeconomic models to estimate this impact focussing primarily on a dynamic stochastic general equilibrium model with price and wage rigidities and adjustment costs. We separate out the impact of reductions in government purchases and transfers, and we allow for a reduction in both distortionary taxes and government debt relative to the baseline of no consolidation. According to the model simulations GDP rises in the short run upon announcement and implementation of this fiscal consolidation strategy and remains higher than the baseline in the long run. We explore the role of the mix of expenditure cuts and tax reductions as well as gradualism in achieving this policy outcome. Finally, we conduct sensitivity studies regarding the type of model used and its parameterization.
During the 1970s, industrial countries, including the US and continental Europa, experienced a combination of slow productivity growth and high unemplyoment. Subsequent research has shown that the standard model of unemployment actually gives counterfactual predictions. Motivated by the observation that the 1970s were also characterized by high and rising inflation, Tesfaselassie and Wolters examine the effect of growth on unemployment in the presence of nominal price rigidity.
The authors demonstrate that the effect of growth on unemployment may be positive or negative. Faster growth leads to lower unemployment if the rate of inflation is high enough. There is a threshold level of inflation below which faster growth leads to higher unemployment and above which faster growth leads to lower unemployment. The threshold level in turn depends on labor market characteristics, such as hiring efficiency, the job destruction rate, workers' relative bargaining power and the opportunity cost of work.
This contribution draws on two recent publications in which the macroeconomic model data base (www.macromodelbase.com) is employed for model comparisons. The comparative approach is used to base policy analysis on a systematic evaluation of the different implications that a certain economic policy can have when submitted to different modeling approaches. In this manner, policy recommendations are more robust to modeling uncertainty. By extending the comparative approach to forecasting, the authors investigate the accuracy of different forecasting models and obtain more reliable mean forecasts.