Empirical Bayesian Density Forecasting in Iowa and Shrinkage for the Monte Carlo Era

Lewis, Kurt F.; Whiteman, Charles H.

doi:10.1002/for.2316

Cited by 7 publications

(3 citation statements)

References 34 publications

(92 reference statements)

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“…The specification of a τ quantile maximizer provides a systematic definition of riskiness in terms of downside risk and upside chance (losses and gains) in a forecaster's asymmetric preference towards overprediction and underprediction. In an attempt to improve the forecast performance of predicting state tax revenues in Iowa, Lewis and Whiteman () provided the example that the Institute for Economic Research at the University of Iowa had used an asymmetric loss function that treated forecasted revenue shortfalls d =1,2,…,10 times as costly as equal‐sized surpluses.…”

Section: Introductionmentioning

confidence: 99%

Mincer–Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions

Güler

Zhang

2017

Journal of Forecasting

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Forecasts are pervasive in all areas of applications in business and daily life. Hence evaluating the accuracy of a forecast is important for both the generators and consumers of forecasts. There are two aspects in forecast evaluation: (a) measuring the accuracy of past forecasts using some summary statistics, and (b) testing the optimality properties of the forecasts through some diagnostic tests. On measuring the accuracy of a past forecast, this paper illustrates that the summary statistics used should match the loss function that was used to generate the forecast. If there is strong evidence that an asymmetric loss function has been used in the generation of a forecast, then a summary statistic that corresponds to that asymmetric loss function should be used in assessing the accuracy of the forecast instead of the popular root mean square error or mean absolute error. On testing the optimality of the forecasts, it is demonstrated how the quantile regressions set in the prediction–realization framework of Mincer and Zarnowitz (in J. Mincer (Ed.), Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance (pp. 14–20), 1969) can be used to recover the unknown parameter that controls the potentially asymmetric loss function used in generating the past forecasts. Finally, the prediction–realization framework is applied to the Federal Reserve's economic growth forecast and forecast sharing in a PC manufacturing supply chain. It is found that the Federal Reserve values overprediction approximately 1.5 times more costly than underprediction. It is also found that the PC manufacturer weighs positive forecast errors (under forecasts) about four times as costly as negative forecast errors (over forecasts).

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Section: Introductionmentioning

confidence: 99%

Mincer–Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions

Güler

Zhang

2017

Journal of Forecasting

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“…Cogley, Morozov, and Sargent (2005) used tilting to produce BVAR forecasts that conditioned on information in the Bank of England's forecast. More recently, Altavilla, Giacomini, and Ragusa (2013) used entropic tilting to combine survey-based forecasts of shortterm interest rates with yield curve forecasts from econometric models, and Lewis and Whiteman (2015) used tilting to improve forecasts of tax revenues in Iowa. These studies primarily focus on point forecasts-not only tilting based on point forecasts but also measuring performance in terms of point forecast accuracy.…”

Section: Introductionmentioning

confidence: 99%

Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts

Krüger

Clark

Ravazzolo

2017

Journal of Business & Economic Statistics

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This article shows entropic tilting to be a flexible and powerful tool for combining medium-term forecasts from BVARs with short-term forecasts from other sources (nowcasts from either surveys or other models). Tilting systematically improves the accuracy of both point and density forecasts, and tilting the BVAR forecasts based on nowcast means and variances yields slightly greater gains in density accuracy than does just tilting based on the nowcast means. Hence, entropic tilting can offer-more so for persistent variables than not-persistent variables-some benefits for accurately estimating the uncertainty of multistep forecasts that incorporate nowcast information.

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“…However, this prior has a drawback that it does not treat own lags and cross lags in the same fashion as the Minnesota prior (see, e.g., Geweke and Whiteman, 2006, for a detailed discussion). Another alternative is the "normal-diffuse" prior (e.g., Kadiyala and Karlsson, 1993;Lewis and Whiteman, 2006). It assumes independent Minnesota priors for coefficients in each equation and a diffuse prior for the variance-covariance matrix of disturbances.…”

Section: Bayesian Priorsmentioning

confidence: 99%

Bayesian multivariate predictions

Mao¹

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This work offers two strategies to raise the prediction accuracy of Vector Autoregressive (VAR) Models. The first strategy is to improve the Minnesota prior, which is frequently used for Bayesian VAR models. The improvement is achieved in two ways. First, the variance-covariance matrix of regression disturbances is treated as unknown and random to incorporate parameter uncertainty. Second, the prior variance-covariance matrix of regression coefficients is constructed as a function of the variance-covariance matrix of disturbances, in order to account for dependencies between different equations. Since different prior specifications unavoidably lead to different models, and forecasting capability of any such model is often limited, the second strategy is to build an optimal prediction pool of models by using the conventional log predictive score function. The effectiveness of the proposed strategies is examined for one-step-ahead, multi-4-step-ahead, and single-4-step-ahead predictions through two exercises. One exercise is predicting national output, inflation, and interest rate in the United States, and the other is predicting state tax revenue and personal income in Iowa. The empirical results indicate that a properly selected prior can improve the prediction performance of a BVAR model, and that a real-time optimal prediction pool can outperform a single best constituent model alone. Abstract Approved:Thesis Supervisor

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Empirical Bayesian Density Forecasting in Iowa and Shrinkage for the Monte Carlo Era

Cited by 7 publications

References 34 publications

Mincer–Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions

Mincer–Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions

Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts

Bayesian multivariate predictions

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