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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.
We analyze cyclical co-movement in credit, house prices, equity prices, and longterm interest rates across 17 advanced economies. Using a time-varying multi-level dynamic factor model and more than 130 years of data, we analyze the dynamics of co-movement at different levels of aggregation and compare recent developments to earlier episodes such as the early era of financial globalization from 1880 to 1913 and the Great Depression. We find that joint global dynamics across various financial quantities and prices as well as variable-specific global co-movements are important to explain fluctuations in the data. From a historical perspective, global co-movement in financial variables is not a new phenomenon, but its importance has increased for some variables since the 1980s. For equity prices, global cycles play currently a historically unprecedented role, explaining more than half of the fluctuations in the data. Global cycles in credit and housing have become much more pronounced and longer, but their importance in explaining dynamics has only increased for some economies including the US, the UK and Nordic European countries. We also include GDP in the analysis and find an increasing role for a global business cycle.
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.
In the aftermath of the global financial crisis, the state of macroeconomic modeling and the use of macroeconomic models in policy analysis has come under heavy criticism. Macroeconomists in academia and policy institutions have been blamed for relying too much on a particular class of macroeconomic models. This paper proposes a comparative approach to macroeconomic policy analysis that is open to competing modeling paradigms. Macroeconomic model comparison projects have helped produce some very influential insights such as the Taylor rule. However, they have been infrequent and costly, because they require the input of many teams of researchers and multiple meetings to obtain a limited set of comparative findings. This paper provides a new approach that enables individual researchers to conduct model comparisons easily, frequently, at low cost and on a large scale. Using this approach a model archive is built that includes many well-known empirically estimated models that may be used for quantitative analysis of monetary and fiscal stabilization policies. A computational platform is created that allows straightforward comparisons of models’ implications. Its application is illustrated by comparing different monetary and fiscal policies across selected models. Researchers can easily include new models in the data base and compare the effects of novel extensions to established benchmarks thereby fostering a comparative instead of insular approach to model development.
This paper investigates the accuracy and heterogeneity of output growth and inflation forecasts during the current and the four preceding NBER-dated U.S. recessions. We generate forecasts from six different models of the U.S. economy and compare them to professional forecasts from the Federal Reserve’s Greenbook and the Survey of Professional Forecasters (SPF). The model parameters and model forecasts are derived from historical data vintages so as to ensure comparability to historical forecasts by professionals. The mean model forecast comes surprisingly close to the mean SPF and Greenbook forecasts in terms of accuracy even though the models only make use of a small number of data series. Model forecasts compare particularly well to professional forecasts at a horizon of three to four quarters and during recoveries. The extent of forecast heterogeneity is similar for model and professional forecasts but varies substantially over time. Thus, forecast heterogeneity constitutes a potentially important source of economic fluctuations. While the particular reasons for diversity in professional forecasts are not observable, the diversity in model forecasts can be traced to different modeling assumptions, information sets and parameter estimates. JEL Classification: C53, D84, E31, E32, E37 Keywords: Forecasting, Business Cycles, Heterogeneous Beliefs, Forecast Distribution, Model Uncertainty, Bayesian Estimation
This paper explores the role of trade integration—or openness—for monetary policy transmission in a medium-scale New Keynesian model. Allowing for strategic complementarities in price-setting, we highlight a new dimension of the exchange rate channel by which monetary policy directly impacts domestic inflation. Although the strength of this effect increases with economic openness, it also requires that import prices respond to exchange rate changes. In this case domestic producers find it optimal to adjust their prices to exchange rate changes which alter the domestic currency price of their foreign competitors. We pin down key parameters of the model by matching impulse responses obtained from a vector autoregression on U.S. time series relative to an aggregate of industrialized countries. While we find evidence for strong complementarities, exchange rate pass-through is limited. Openness has therefore little bearing on monetary transmission in the estimated model.