353 research outputs found
Housing's role in a recovery
Housing tends to contribute significantly to an economic recovery.Housing - Finance ; Economic conditions
Should food be excluded from core CPI?
The greater a componentâs SNR, the more useful the component should be in forecasting headline CPI.Consumer price indexes ; Food prices
How accurate are forecasts in a recession?
Recessions ; Economic forecasting
Disagreement at the FOMC: the dissenting votes are just part of the story
Recently released data on economic forecasts made by voting and nonvoting members of the FOMC suggest that there is more disagreement than the voting record indicates.Federal Open Market Committee ; Monetary policy
Using stock market liquidity to forecast recessions
Market participants rebalance their portfolios in advance of a recession.Recessions ; Economic indicators
Uncertainty about when the Fed will raise interest rates
It's hard to make a firm prediction as to when the Fed will raise interest rates.Interest rates ; Monetary policy - United States
Tests of equal forecast accuracy for overlapping models
This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy when the models being compared are overlapping in the sense of Vuong (1989). Two models are overlapping when the true model contains just a subset of variables common to the larger sets of variables included in the competing forecasting models. We consider an out-of-sample version of the two-step testing procedure recommended by Vuong but also show that an exact one-step procedure is sometimes applicable. When the models are overlapping, we provide a simple-to-use fixed regressor wild bootstrap that can be used to conduct valid inference. Monte Carlo simulations generally support the theoretical results: the two-step procedure is conservative while the one-step procedure can be accurately sized when appropriate. We conclude with an empirical application comparing the predictive content of credit spreads to growth in real stock prices for forecasting U.S. real GDP growth.Forecasting
Real-time forecast averaging with ALFRED
This paper presents empirical evidence on the efficacy of forecast averaging using the ALFRED (ArchivaL Federal Reserve Economic Data) real-time database. We consider averages over a variety of bivariate vector autoregressive models. These models are distinguished from one another based on at least one of the following factors: (i) the choice of variables used as predictors, (ii) the number of lags, (iii) use of all available data or only data after the Great Moderation, (iv) the observation window used to estimate the model parameters and construct averaging weights, and (v) for the forecast horizons greater than one, the use of either iterated multistep or direct multistep methods. A variety of averaging methods are considered. The results indicate that the benefits of model averaging relative to Bayesian information criterion-based model selection are highly dependent on the class of models averaged The authors provide a novel decomposition of the forecast improvements that allows determination of the most (and least) helpful types of averaging methods and models averaged across.Economic forecasting ; Real-time data
Advances in forecast evaluation
This paper surveys recent developments in the evaluation of point forecasts. Taking Westâs (2006) survey as a starting point, we briefly cover the state of the literature as of the time of Westâs writing. We then focus on recent developments, including advancements in the evaluation of forecasts at the population level (based on true, unknown model coefficients), the evaluation of forecasts in the finite sample (based on estimated model coefficients), and the evaluation of conditional versus unconditional forecasts. We present original results in a few subject areas: the optimization of power in determining the split of a sample into in-sample and out-of-sample portions; whether the accuracy of inference in evaluation of multistep forecasts can be improved with the judicious choice of HAC estimator (it can); and the extension of Westâs (1996) theory results for population-level, unconditional forecast evaluation to the case of conditional forecast evaluation.Forecasting ; Time-series analysis
Forecasting of small macroeconomic VARs in the presence of instabilities
Small-scale VARs have come to be widely used in macroeconomics, for purposes ranging from forecasting output, prices, and interest rates to modeling expectations formation in theoretical models. However, a body of recent work suggests such VAR models may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. These methods include using different approaches to lag selection, observation windows for estimation, (over-) differencing, intercept correction, stochastically time--varying parameters, break dating, discounted least squares, Bayesian shrinkage, detrending of inflation and interest rates, and model averaging. Focusing on simple models of U.S. output, prices, and interest rates, this paper compares the effectiveness of such methods. Our goal is to identify those approaches that, in real time, yield the most accurate forecasts of these variables. We use forecasts from simple univariate time series models, the Survey of Professional Forecasters and the Federal Reserve Board's Greenbook as benchmarks.Economic forecasting ; Time-series analysis ; Real-time data
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