Consensus vs. Time‐series Forecasts of US 30‐year Home Mortgage Rates

Cogent Economics & Finance

2018

Self Cite

The cyclical variation behavior of the mortgage spread has motivated some studies to investigate its relationship to economic activity. Indeed, recent empirical findings indicate that the mortgage spread is a determinant/predictor of economic activity. We define the mortgage spread as the difference between the 30-year mortgage and 10-year Treasury rates and ask whether the Blue Chip (consensus) forecasts of these series are accurate for 1988-2015. Our findings indicate that the Blue Chip forecasts of both the 30-year mortgage and 10-year Treasury rates are biased, fail to outperform the random walk benchmark, and are directionally inaccurate. However, the Blue Chip forecasts of the mortgage spread are generally unbiased, outperform the random walk benchmark, and are directionally accurate-thus of value to a user. Given such evidence, a natural extension for future research is to explore whether the predictive information content of Blue Chip forecasts of the mortgage spread is a better predictor of output growth and whether it helps improve Blue Chip forecasts of output growth.

Section: Introductionsupporting

confidence: 83%

On accuracy of survey forecasts of US mortgage spread

Cogent Economics & Finance

2018

Self Cite

“…Despite the implications of the efficient market hypothesis, the literature contains studies that have proposed ways for producing accurate forecasts of interest rates. Baghestani (2008bBaghestani ( , 2010aBaghestani ( , 2017 shows that the predictive information content of both survey-based and model-based measures of expected inflation can help produce more accurate forecasts of the 10-year Treasury and the 30-year mortgage rate than the random walk benchmark. Guidolin and Timmermann (2009) propose a flexible forecast combination approach to generate accurate forecasts of the US short-term interest rates.…”

Section: Literature Reviewmentioning

confidence: 99%

Long-term interest rate predictability: Exploring the usefulness of survey forecasts of growth and inflation

Cogent Economics & Finance

2019

Self Cite

This study focuses on the consensus forecasts from the Survey of Professional Forecasters (SPF) for 1993-2017. These include the SPF forecasts of US 10-year Treasury rate (TBR), Moody's Aaa corporate bond rate (Aaa), CPI inflation, and real GDP growth. We show that both SPF and random walk forecasts of TBR and Aaa generally fail to be orthogonal to changes in SPF inflation (but not growth) forecasts. Such findings point to the potential usefulness of SPF inflation forecasts in improving the accuracy of SPF and random walk forecasts of TBR and Aaa. Further results indicate that changes in SPF inflation forecasts accurately predict directional change in both TBR and Aaa at longer forecast horizons for 2008-2017 (but not for 1993-2007). These latter results raise the question of whether long-term interest rates have become easier to predict, which deserves subsequent research.

“…This is especially true for exchange rates (Baghestani, 2009; Rossi, 2013), long‐term interest rates (Pesando, 1979; Reichenstein, 2006; Baghestani, 2008a) and stock prices (Fama, 1995). Existing evidence indicates that it is difficult for both model‐ and survey‐based forecasts of such indicators to outperform a simple random walk forecast (Meese and Rogoff, 1983; Frankel and Froot, 1987; Mitchell and Pearce, 2007; Baghestani, 2008b). A commonly cited reason is that today’s movement in these indicators efficiently reflects the expected future changes in relevant variables and thus makes it difficult for other types of forecasts to beat the random walk benchmark.…”

Section: Introductionmentioning

confidence: 99%

Gasoline price predictability: insights from consumer vehicle‐buying assessments

2021

OPEC Energy Review

Self Cite

This study employs the random walk forecasts of US retail gasoline prices for 2001–2020 in order to investigate the potential usefulness of consumer vehicle‐buying assessments for predicting gasoline prices. We show that the random walk forecasts are free of systematic bias and imply symmetric loss, but they do not fully contain the information on consumer vehicle‐buying assessments. For instance, the random walk forecast errors fail to be orthogonal to the difference of opinion of consumers about whether the next 12 months or so will be a good time or a bad time to buy a new vehicle, and to the difference of opinion of consumers about vehicle prices (but not interest rates). In addition, the random walk forecast errors fail to be orthogonal to the change in consumer opinion about gasoline prices, fuel efficiency and poor selections (quality). Such findings remain unchanged when we use the ‘weakly’ rational forecasts of gasoline prices instead of the random walk benchmark. Put together, our results provide new insights into the potential usefulness of consumer vehicle‐buying assessments for predicting gasoline prices. We recommend forecasters pay special attention to such assessments when forecasting retail gasoline prices, since they can potentially help produce forecasts that are superior to both the random walk and ‘weakly’ rational forecasts.