Improved Dividend Estimation from Intraday Quotes

Liquid financial markets, such as the options market of the S&P 500 index, create vast amounts of data every day, i.e., so-called intraday data. However, this highly granular data is often reduced to single-time when used to estimate financial quantities. This under-utilization of the data may reduce the quality of the estimates. In this paper, we study the impacts on estimation quality when using intraday data to estimate dividends. The methodology is based on earlier linear regression (ordinary least squares) estimates, which have been adapted to intraday data. Further, the method is also generalized in two aspects. First, the dividends are expressed as present values of future dividends rather than dividend yields. Second, to account for heteroscedasticity, the estimation methodology was formulated as a weighted least squares, where the weights are determined from the market data. This method is compared with a traditional method on out-of-sample S&P 500 European options market data. The results show that estimations based on intraday data have, with statistical significance, a higher quality than the corresponding single-times estimates. Additionally, the two generalizations of the methodology are shown to improve the estimation quality further.

The effects of absent fathers on adolescent criminal activity: an economic approach

Simple ordinary least squares estimates indicate that absent fathers boost probabilities of adolescent criminal behavior by 16–38%, but those numbers likely are biased by unobserved heterogeneity. This paper first presents an economic model explaining that unobserved heterogeneity. Then turning to empirics, fixed effects, which attempt to address that bias, suggest that absent fathers reduce certain types of adolescent crime, while lagged-dependent variable models suggest the opposite. Those conflicting conclusions are resolved by an approach that combines those two estimators using an orthogonal reparameterization approach, with model parameters calculated using a Bayesian algorithm. The main finding is that absent fathers do not appear to directly affect adolescent criminal activity. Rather, families with absent fathers possess traits that appear to correlate with increased adolescent criminal behaviors.

Relationship between rice farming and polygenic scores potentially linked to agriculture in China

Following domestication in the lower Yangtze River valley 9400 years ago, rice farming spread throughout China and changed lifestyle patterns among Neolithic populations. Here, we report evidence that the advent of rice domestication and cultivation may have shaped humans not only culturally but also genetically. Leveraging recent findings from molecular genetics, we construct a number of polygenic scores (PGSs) of behavioural traits and examine their associations with rice cultivation based on a sample of 4101 individuals recently collected from mainland China. A total of nine polygenic traits and genotypes are investigated in this study, including PGSs of height, body mass index, depression, time discounting, reproduction, educational attainment, risk preference, ADH1B rs1229984 and ALDH2 rs671. Two-stage least-squares estimates of the county-level percentage of cultivated land devoted to paddy rice on the PGS of age at first birth ( b = −0.029, p = 0.021) and ALDH2 rs671 ( b = 0.182, p < 0.001) are both statistically significant and robust to a wide range of potential confounds and alternative explanations. These findings imply that rice farming may influence human evolution in relatively recent human history.

Double Differencing by Demeaning: Applications to Hypocenter Location and Wavespeed Tomography

ABSTRACT Double differencing of body-wave arrival times has proved to be a useful technique for increasing the resolution of earthquake locations and elastic wavespeed images, primarily because (1) differences in arrival times often can be determined with much greater precision than absolute onset times and (2) differencing reduces the effects of unknown, unmodeled, or otherwise unconstrained variables on the arrival times, at least to the extent that those effects are common to the observations in question. A disadvantage of double differencing is that the system of linearized equations that must be iteratively solved generally is much larger than the undifferenced set of equations, in terms of both the number of rows and the number of nonzero elements. In this article, a procedure based on demeaning subsets of the system of equations for hypocenters and wavespeeds that preserves the advantages of double differencing is described; it is significantly more efficient for both wavespeed-only tomography and joint hypocenter location-wavespeed tomography. Tests suggest that such demeaning is more efficient than double differencing for hypocenter location as well, despite double-differencing kernels having fewer nonzeros. When these subsets of the demeaned system are appropriately scaled and simplified estimates of observational uncertainty are used, the least-squares estimate of the perturbations to hypocenters and wavespeeds from demeaning are identical to those obtained by double differencing. This equivalence breaks down in the case of general, observation-specific weighting, but tests suggest that the resulting differences in least-squares estimates are likely to be inconsequential. Hence, demeaning offers clear advantages in efficiency and tractability over double differencing, particularly for wavespeed tomography.

INFLUENCE OF MATRIX ERRORS ON PARAMETER ESTIMATES BY THE LEAST SQUARES METHOD

The paper investigates the effect of distorsions of the linear model matrix on the statistical characteristics of the least squares estimates.

A SARIMA and Adjusted SARIMA Models in a Seasonal Nonstationary Time Series; Evidence of Enugu Monthly Rainfall

The paper compares SARIMA and adjusted SARIMA(ASARIMA) in a regular stationary series where the underlying variable is seasonally nonstationary. Adopting empirical rainfall data and Box-Jenkins iterative algorithm that calculates least squares estimates, Out of 11 sub-classes of SARIMA and 7 sub-classes of ASARIMA models, AIC chose ASARIMA(2,1,1)12 over all sub-classes of SARIMA(p,0,q)x(P,1,Q)12 identified. Diagnostic test indicates absence of autocorrelation up to the 48th lag. The forecast values generated by the fitted model are closely related to the actual values. Hence, ASARIMA can be recommended for regular stationary time series with seasonal characteristics and where parameter redundancy and large sum of square errors are penalized.

Supply of immigrant entrepreneurs and native entrepreneurship

Using nationally representative data from the United States, the author estimates the causal impact of immigrant entrepreneurship on entrepreneurial propensities of natives. The author draws data from the Annual Social and Economic Supplement of the Current Population Survey and uses within-state variation in supply of immigrant entrepreneurs for identification. To address concerns of endogeneity in the supply of immigrant entrepreneurs, the author takes advantage of a quasi-experiment provided by the State Children's Health Insurance Program. While the Ordinary Least Squares estimates indicate a positive effect, the Two Stage Least Squares estimates suggest that, on average, there is no significant effect of immigrant entrepreneurs on native entrepreneurship. Moreover, there is no net effect on subgroups of natives separated by skill level. There is also some evidence that immigrant entrepreneurs may “crowd-in” Blacks into certain types of self-employment. These results are in contrast to the significant negative impact suggested by the previous literature.

A Note on the Asymptotic Normality Theory of the Least Squares Estimates in Multivariate HAR-RV Models

In this work, multivariate heterogeneous autoregressive-realized volatility (HAR-RV) models are discussed with their least squares estimations. We consider multivariate HAR models of order p with q multiple assets to explore the relationships between two or more assets’ volatility. The strictly stationary solution of the HAR(p,q) model is investigated as well as the asymptotic normality theories of the least squares estimates are established in the cases of i.i.d. and correlated errors. In addition, an exponentially weighted multivariate HAR model with a common decay rate on the coefficients is discussed together with the common rate estimation. A Monte Carlo simulation is conducted to validate the estimations: sample mean and standard error of the estimates as well as empirical coverage and average length of confidence intervals are calculated. Lastly, real data of volatility of Gold spot price and S&P index are applied to the model and it is shown that the bivariate HAR model fitted by selected optimal lags and estimated coefficients is well matched with the volatility of the financial data.

Cross-Validation for the Uncertain Chapman-Richards Growth Model with Imprecise Observations

Regression analysis estimates the relationships among variables which has been widely used in growth curves, and cross-validation as a model selection method assesses the generalization ability of regression models. Classical methods assume that the observation values of variables are precise numbers while in many cases data are imprecisely collected. So this paper explores the Chapman-Richards growth model which is one of the widely used growth models with imprecise observations under the framework of uncertainty theory. The least squares estimates of unknown parameters in this model are given. Moreover, cross-validation with imprecise observations is proposed. Furthermore, estimates of the expected value and variance of the uncertain error using residuals are given. In addition, ways to predict the value of response variable with new observed values of predictor variables are discussed. Finally, a numerical example illustrates our approach.