Spurious Regression Bias Research Articles

Is the Presidential Premium Spurious

A hotly debated question in finance is whether the higher stock returns under Democratic presidencies relative to Republican presidencies represent abnormal return, risk premium, or mere statistical fluke. This paper investigates whether this presidential premium is due to spurious-regression bias, data mining, or economic policy uncertainty. Decomposing the presidential premium into expected and unexpected components, we find that over two-thirds of the premium is unexpected, which is inconsistent with the spurious regression bias explanation. The presidential premium is not explained by data mining given that it persists in the post-publication period, and remains robust even if we purge returns of their covariation with economic policy uncertainty.

Is the presidential premium spurious?

Abstract A hotly debated question in finance is whether the higher stock returns under Democratic presidencies relative to Republican presidencies represent abnormal return, risk premium, or mere statistical fluke. This paper investigates whether this presidential premium is due to spurious-regression bias, data mining, or economic policy uncertainty. Decomposing the presidential premium into expected and unexpected components, we find that over two-thirds of the premium is unexpected, which is inconsistent with the spurious regression bias explanation. The presidential premium is not explained by data mining given that it persists in the post-publication period, and remains robust even if we purge returns of their covariation with economic policy uncertainty.

ELASTICITY OF SUBSTITUTION AND TECHNICAL PROGRESS: IS THERE A MISSPECIFICATION PROBLEM?

In previous work, we estimated a dynamic model of the Italian economy, showing that its weakness in the past two decades is mainly due to the slowdown in total factor productivity growth. In those models, two parameters play a key role: technological progress and the elasticity of substitution. Recent estimates of those parameters are affected, in our opinion, by a specification problem: technological parameters are inherently long-run but their estimates are based on short-run data. Looking deeply into the estimation procedure, we show that the misspecification issue present in the estimates gives rise to a spurious regression bias (high R2, low DW), because the standard approach does not incorporate frictions and rigidities. Our modeling strategy takes account of them. Although we cannot in general say that our framework gets rid of the serial correlation problem, the statistics for our model do show that residuals are not serially correlated.

P-083 – Recueil d'urine chez moins de 1 mois par technique de stimulation

We examine whether military regimes harm stock market performance by investigating stock returns in ten emerging markets under military and civilian rule. We find no evidence of military regimes having a significantly negative impact on stock returns. In the case of Thailand and Pakistan, we find a significant positive military return premium. These returns cannot be explained by economic cycles, stock market cycles, or returns volatility. Our findings are robust to worldwide stock market movements, tests for spurious regression bias and randomization-bootstrap tests. Our results contradict the common view that military rule has a negative impact on stock market performance.

Military regimes and stock market performance

We examine whether military regimes harm stock market performance by investigating stock returns in ten emerging markets under military and civilian rule. We find no evidence of military regimes having a significantly negative impact on stock returns. In the case of Thailand and Pakistan, we find a significant positive military return premium. These returns cannot be explained by economic cycles, stock market cycles, or returns volatility. Our findings are robust to worldwide stock market movements, tests for spurious regression bias and randomization-bootstrap tests. Our results contradict the common view that military rule has a negative impact on stock market performance.

The long of it: Odds that investor sentiment spuriously predicts anomaly returns

Extremely long odds accompany the chance that spurious-regression bias accounts for investor sentiment׳s observed role in stock-return anomalies. We replace investor sentiment with a simulated persistent series in regressions reported by Stambaugh, Yu, and Yuan (2012), who find higher long-short anomaly profits following high sentiment, due entirely to the short leg. Among 200 million simulated regressors, we find none that support those conclusions as strongly as investor sentiment. The key is consistency across anomalies. Obtaining just the predicted signs for the regression coefficients across the 11 anomalies examined in the above study occurs only once for every 43 simulated regressors.

Understanding Spurious Regression in Financial Economics

A new asymptotic framework is used to provide finite sample approximations for various statistics in the spurious return predictive regression analyzed by Ferson, Sarkissian, and Simin (2003a). Our theory explains all the findings of Ferson, Sarkissian, and Simin (2003a) and confirms the theoretical possibility of a spurious regression bias. The theory developed in the article has important implications with respect to existing inferential theories in predictive regressions. We also propose a simple diagnostic test to detect potential spurious regression bias in empirical analysis. The test is applied to four variants of the SP500 monthly stock returns and the six Fama-French benchmark portfolio monthly returns.

The Long of It: Odds That Investor Sentiment Spuriously Predicts Anomaly Returns

Extremely long odds accompany the chance that spurious-regression bias accounts for investor sentiment's observed role in stock-return anomalies. We replace investor sentiment with a simulated persistent series in regressions reported by Stambaugh, Yu and Yuan (2012), who find higher long-short anomaly profits following high sentiment, due entirely to the short leg. Among 200 million simulated regressors, we find none that support those conclusions as strongly as investor sentiment. The key is consistency across anomalies. Obtaining just the predicted signs for the regression coefficients across the 11 anomalies examined in the above study occurs only once for every 43 simulated regressors.

Spurious Regressions of Stationary AR(p) Processes with Structural Breaks

When a pair of independent series is highly persistent, there is a spurious regression bias in a regression between these series, closely related to the classic studies of Granger and Newbold (1974). Although this is well known to occur with independent I(1) processes, this paper provides theoretical and numerical evidence that the phenomenon of spurious regression also arises in regressions between stationary AR(p) processes with structural breaks, which occur at different points in time, in the means and the trends. The intuition behind this is that structural breaks can increase the persistence levels in the processes (e.g., Granger and Hyung (2004)), which then leads to spurious regressions. These phenomena occur for general distributions and serial dependence of the innovation terms.

Spurious Regressions of Stationary AR(p) Processes with Structural Breaks

When a pair of independent series are highly persistent, there is a spurious regression bias in a regression between these series, closely related to the classic studies of Granger and Newbold [1974]. Although this is well known to occur with independent I(1) processes, this paper provides theoretical and numerical evidences that the phenomenon of spurious regression also arises in regressions between stationary AR(p) processes with structural breaks in the means and the trends. An intuition behind this is that structural breaks can increase the persistence levels in the processes (see, e.g., Granger and Hyung [2004]), which then leads to spurious regressions. These phenomena occur for general distributions and serial dependence of the innovation terms.

Relative Spurious Regression Bias in Business-Cycle Models of Stock Momentum

We investigate whether persistent macroeconomic variables can be reliably shown to predict momentum profits. We find that when predictor variables are persistent and the predictive relationship is estimated over a period that overlaps the momentum portfolio formation period, predicted returns will be positively correlated with past returns. This occurs even when returns are constructed to be serially uncorrelated and independent of the predictors. This means there will be a positive predicted return to momentum portfolios and the macroeconomic variables will appear to explain momentum profits, even when stock returns are unrelated to the macroeconomic variables. We show that the apparent ability of the business-cycle model in Chordia and Shivakumar (2002) to explain intermediate-term momentum profits comes from this effect, which we term a relative spurious regression bias. We demonstrate that results similar to those of Chordia and Shivakumar obtain when we replace their macroeconomic variables with random variables constructed to be persistent but independent of stock returns. Once the parameter estimation period is chosen to be prior to the portfolio formation period, the predictive power of the macroeconomic variables disappears and momentum is no longer explained by the business cycle model.

Spurious Regressions in Financial Economics?

ABSTRACTEven 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). Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious.

Spurious Regressions in Financial Economics?

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). Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious.

Productivity, private and public capital, and real wage in the US

This paper examines the relationship between real wages and productivity in the United States from 1948–90, by including in the measure of productivity the impact of public capital as well as private capital. The results confirm recent studies that have indicated that when the private and public capital stock is controlled for, real wage is countercyclical, and validate diminishing returns to labour, positive returns to public capital and a procyclical effect of capacity utilization on real wage. Further, the issue of spurious regression bias that arises when variables are nonstationary was addressed, and long run relationships were then estimated. Results establish a long-run relationship between productivity, real wage, the employment to capital ratio, and public to private capital ratio. Using the statistical estimates herein, if the public capital stock had remained at the historical 1948–65 ratio, rather than declining, productivity would have been between 2.4 and 2.9 percentage points higher and real wages would have been between 2 to 2.8 percentage points higher, ceteris paribus.