Econometrics is the application of statistical methods to economic data in order to give. Spurious regressions in econometrics sciencedirect. When p 0, and there is no persistence in the true expected return, the spur ious regression phenomenon is not a concern. Spurious regressions in econometrics essays in econometrics. Moreover in the presence of a long run component, spurious regressions arise. For example, kandel and stambaugh 1996 and fleming, kirby and ostdiek 2001 find that optimal portfolios respond by a substantial amount to small rsquares in standard models. We focus on the issue of spurious regression, related to the classic studies of yule 1926 and granger and newbold 1974. He has published research in the journal of economics and business, the journal of financial markets, the journal of finance, the financial analysts journal, the journal of investment management, has several book chapters, and has won the best investments paper award. That hypothesis is supported as strongly by x t as it is by s t only once in every 105,000 series.
Financial econometrics, mathematics and statistics. We confirm this with additional simulations, not reported in the tables, where we set p 0 and. In fact, the nonstationarity may be due to the presence of a unit root in both variables. The title indicates the terms spurious regression was known at least as early as. Giles department of economics university of victoria, b. Go to the next page of charts, and keep clicking next to get through all 30,000. We show that a combination of a noise and a long run component can explain the long run predictability puzzle pointed out in famafrench 1988. The effects of data snooping and spurious regression, journal of financial and quantitative analysis, 2007.
Spurious regressions springerlink skip to main content. Spurious regressions in financial economics 5 fraction of the variance in stock returns. Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of yule 1926 and. Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic. Spurious regression and data mining in conditional asset. Understanding spurious regression in financial economics understanding spurious regression in financial economics deng, ai 20140119 00. The odds for a spurious regressor become even longer when considering the second hypothesis, as we see from the second column of table 1. As was pointed out by granger and newbold 1974, the levels of many economic timeseries are integrated or nearly so, and if these data are used in a regression model then a high value for the coefficient of determination r 2 is likely to arise, even when the. Econometricians have known about type ii spurious regression since yule. Wayne ferson, john merrick 1987 nonstationarity and the stage of the business cycle effects in consumptionbased asset pricing relations, journal of financial economics 18, 127146.
Written by leading academics in the quantitative finance field, this book allows readers to implement the principles behind financial econometrics and statistics through realworld applications and problem sets. However, even a small rsquared can signal economically significant predictability. A simple solution of the spurious regression problem ideasrepec. The effects of data snooping and spurious regression, journal of financial and quantitative analysis, cambridge university.
The spurious regression phenomenon in least squares occurs for a wide range of data generating processes, such as driftless unit roots, unit roots with drift, long memory, trend and brokentrend stationarity. Asset pricing program even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of yule 1926 and granger and newbold. We find that spurious regression is a concern in regressions of stock returns on persistent lagged instruments, especially when the predictable component of returns is large. Newbold university of nottingham, nottingham ng7 2rd, england received may 1973, revised version received december 1973 1. He taught in britain, at the university of nottingham and in the united states, at the university of california, san diego. Canada abstract a spurious regression is one in which the timeseries variables are nonstationary and independent. Introduction it is very common to see reported in applied econometric literature time series.
Some important applications of spurious regressions in economics and finance, although this list is by no means exhaustive, include plosser et al. In this case, the usual statistical results for the linear regression model hold. Since type i spurious regression has not been pointed out in the literature, spurious regression of type ii is the only one usually discussed in econometrics. Inference for noisy long run component process munich. Islamic banking finance, monetary economics and econometrics. When looking at multiple time series, one can often be fooled by spurious correlations. Newbold, regressions in econometrics to take is that of the. Citeseerx document details isaac councill, lee giles, pradeep teregowda. National tsing hua university, department of quantitative finance, hsinchu city. It will appeal to a lessserved market of advanced students and scholars in finance, economics, accounting, and statistics. A wellknown case of a spurious relationship can be found in the timeseries literature, where a spurious regression is a regression that provides misleading statistical evidence of a linear relationship between independent nonstationary variables.
Understanding spurious regression in financial economics. Newboldsome comments on the evaluation of economic forecasts. Econometrics for financial and macroeconomic time series. Spurious regression with stationary time series msr economics. Deng 2005 recommends lag length fixed constants b in the interval. When is the next time something cool will happen in space. Wayne ferson, shmuel kandel, robert stambaugh 1987 tests of asset pricing with timevarying expected risk premiums and market betas, journal of finance. This paper studies the estimation of asset pricing model regressions with conditional alphas and betas, focusing on the joint effects of data snooping and spurious regression. Total downloads of all papers by sergei sarkissian. For the first threequarters of the 20th century the main workhorse of applied econometrics was the basic regression.
Newbold university of nottingham, nottingham ng7 zrd, england received may 1973, revised version received december 1973 1. If some or all of the variables in the regression are. In 2003, granger was awarded the nobel memorial prize in economic sciences, in recognition of. Asset pricing models with conditional betas and alphas. Spinoffs from this research range from unitroot tests to cointegration and errorcorrection models. This book provides an overview of results about spurious regression, pulled from disperse sources, and explains their implications. The second issue is data mining, as studied for stock returns by lo and mackinlay 1990, foster, smith and whaley 1997 and others. Indeed, spurious regressions have played a fundamental role in the building of modern time series econometrics and have revolutionized many of the procedures used in applied.
Or for something totally different, here is a pet project. However, formatting rules can vary widely between applications and fields of interest or study. Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of yule 1926 and granger and newbold 1974. Spurious regression problem dates back to yule 1926. Spurious regressions and cointegration karl whelan school of economics, ucd february 22, 2011 karl whelan ucd spurious regressions and cointegration february 22, 2011 1. Some important applications of spurious regressions in economics and finance, although this list is by no means exhaustive, include plosser. If the standard instruments employed in the literature arise as the result of a collective search through. Citeseerx spurious regressions in financial economics. Spurious regressions in financial economics 2 results will differ from those in the classical setting. Data mining for predictor variables interacts with spurious regression bias.
Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of yule 1926 and granger. Abstract even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock. Why do we sometimes get nonsense correlations between timeseries. An introductory economics textbook describes econometrics as allowing. The two effects reinforce each other, because more highly persistent series are more.
A simple solution of the spurious regression problem in. Lets see what is the problem, and how can we fix it. Ferson, sarkissian, and simin 2003a, b, 2009 noted that their finding of spurious regression in financial economics is well outside the classical setting of yule 1926 and granger and newbold 1974, in part because the dependent variable in the predictive regression, i. Book tomarket, dividend yield, and expected market returns. I am using morgan stanley ms symbol for illustration, precrisis time continue reading. Working papers journals software components books book chapters.
Spurious regressions in financial economics wayne e ferson. This is true even when the mea sured regressor is highly persistent. This is one of the most common mistakes people make in the world of statistics. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious suggested citation. This paper introduces a new approach to the modelling of a stationary long run component, which is an autoregressive process with near unit root and small sigma innovation. This work should prove useful to researchers in statistics, timeseries econometrics and applied economics.
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