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Creator: Chari, V. V., Kehoe, Patrick J., and McGrattan, Ellen R. Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 364 Abstract:
The central finding of the recent structural vector autoregression (SVAR) literature with a differenced specification of hours is that technology shocks lead to a fall in hours. Researchers have used this finding to argue that real business cycle models are unpromising. We subject this SVAR specification to a natural economic test and show that when applied to data from a multiple-shock business cycle model, the procedure incorrectly concludes that the model could not have generated the data as long as demand shocks play a nontrivial role. We also test another popular specification, which uses the level of hours, and show that with nontrivial demand shocks, it cannot distinguish between real business cycle models and sticky price models. The crux of the problem for both SVAR specifications is that available data require a VAR with a small number of lags and such a VAR is a poor approximation to the model’s VAR.
Palavra-chave: Vector autoregressions, Real business cycle, Impulse response, and Technology shocks Sujeito: E32 - Business Fluctuations; Cycles, C51 - Model Construction and Estimation, E20 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy: General (includes Measurement and Data), E13 - General Aggregative Models: Neoclassical, E30 - Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data), E37 - Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications, and C32 - Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Creator: Doan, Thomas, Litterman, Robert B., and Sims, Christopher A. Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 093 Abstract:
This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. We apply the procedure to 10 macroeconomic variables and show that it produces more accurate out-of-sample forecasts than univariate equations do. Although cross-variable responses are damped by the prior, our estimates capture considerable interaction among the variables.
We provide unconditional forecasts as of 1982:12 and 1983:3. We also describe how a model such as this can be used to make conditional projections and analyze policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12.
While no automatic casual interpretations arise from models like ours, such models provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables. That information may help evaluate casual hypotheses without containing any such hypotheses.
Palavra-chave: Vector autoregressions, Macroeconomic modeling, Conditional projections, Forecasting, and Rayesian analysis
Creator: Schorfheide, Frank and Song, Dongho Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 701 Abstract:
This paper develops a vector autoregression (VAR) for macroeconomic time series which are observed at mixed frequencies – quarterly and monthly. The mixed-frequency VAR is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. Using a real-time data set, we generate and evaluate forecasts from the mixed-frequency VAR and compare them to forecasts from a VAR that is estimated based on data time-aggregated to quarterly frequency. We document how information that becomes available within the quarter improves the forecasts in real time.
Palavra-chave: Vector autoregressions, Real-time data, Bayesian methods, and Macroeconomic forecasting Sujeito: C11 - Bayesian Analysis: General, C32 - Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models, and C53 - Forecasting Models; Simulation Methods