Dissecting Investment Strategies in the Cross Section and Time Series

2015 ◽  
Author(s):  
Jamil Baz ◽  
Nicolas M Granger ◽  
Campbell R. Harvey ◽  
Nicolas Le Roux ◽  
Sandy Rattray
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Investment Strategies ◽  
The Cross

scholarly journals Measuring creative capacity of Visegrad Four countries’ economies

Region Direct ◽  
2014 ◽  
Vol 7 (1) ◽  
pp. 77-104
Author(s):  
Martin Alexy ◽  
Marek Káčer
Keyword(s):  
Time Series ◽  
Czech Republic ◽  
Open Source ◽  
Cross Section ◽  
Empirical Results ◽  
The Cross ◽  
Creative Capacity

Abstract In this paper we study creative capacity of economies of Visegrad Four countries in the period 2000-2011. Creativity index is constructed based on the 3Ts concept of talent, technology and tolerance being the key components of the creativity. Creativity index is measured and calculated with both the cross-section and the time series dimensions. The paper provides index as an open source with the description of variables and their respective weights. Comparison of the creative capacity of economies is based on the empirical results of the Creativity index and its components. Czech Republic is the first and Hungary is the second in the ranking continuously during the examined period. Talent and technology areas are the main reasons for differences between the two leading countries and the rest.

scholarly journals Forecasting using cross-section average–augmented time series regressions

2020 ◽  
Author(s):  
Hande Karabiyik ◽  
Joakim Westerlund
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Regression Models ◽  
Common Factor ◽  
Principal Component ◽  
Small Sample ◽  
Interactive Effects ◽  
Cross Sectional ◽  
Second Development ◽  
The Cross

Summary There is a large and growing body of literature concerned with forecasting time series variables by the use of factor-augmented regression models. The workhorse of this literature is a two-step approach in which the factors are first estimated by applying the principal components method to a large panel of variables, and the forecast regression is then estimated, conditional on the first-step factor estimates. Another stream of research that has attracted much attention is concerned with the use of cross-section averages as common factor estimates in interactive effects panel regression models. The main justification for this second development is the simplicity and good performance of the cross-section averages when compared with estimated principal component factors. In view of this, it is quite surprising that no one has yet considered the use of cross-section averages for forecasting. Indeed, given the purpose to forecast the conditional mean, the use of the cross-sectional average to estimate the factors is only natural. The present paper can be seen as a reaction to this. The purpose is to investigate the asymptotic and small-sample properties of forecasts based on cross-section average–augmented regressions. In contrast to most existing studies, the investigation is carried out while allowing the number of factors to be unknown.

scholarly journals Unemployement and Labour Force Participation : The Canadian Experience

2005 ◽  
Vol 28 (1) ◽  
pp. 56-75
Author(s):  
Robert Swidinsky
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Labour Force ◽  
Labour Force Participation ◽  
Negative Response ◽  
Time Series Regression ◽  
Canadian Experience ◽  
Short Run ◽  
The Cross ◽  
Force Time

In an analysis of the short-run sensitivity of the Canadian labour force time series regression results appear inconclusive whereas cross-section regression results suggest a strong negative response to unemployment. Generally, the findings from the cross-section are comparable neither qualitatively nor quantitatively with those from the time series.

scholarly journals A SIMPLE EFFICIENT INSTRUMENTAL VARIABLE ESTIMATOR FOR PANEL AR(p) MODELS WHEN BOTHNANDTARE LARGE

2009 ◽  
Vol 25 (3) ◽  
pp. 873-890 ◽  
Author(s):  
Kazuhiko Hayakawa
Keyword(s):  
Time Series ◽  
Lower Bound ◽  
Asymptotic Distribution ◽  
Cross Section ◽  
Simulation Study ◽  
Instrumental Variable ◽  
Asymptotic Variance ◽  
The Cross ◽  
Instrumental Variable Estimator ◽  
Normally Distributed

In this paper, we show that for panel AR(p) models, an instrumental variable (IV) estimator with instruments deviated from past means has the same asymptotic distribution as the infeasible optimal IV estimator when bothNandT, the dimensions of the cross section and time series, are large. If we assume that the errors are normally distributed, the asymptotic variance of the proposed IV estimator is shown to attain the lower bound when bothNandTare large. A simulation study is conducted to assess the estimator.

The Determinants of Idiosyncratic Volatility

2017 ◽  
Vol 25 (4) ◽  
pp. 509-545
Author(s):  
Jaeuk Khil ◽  
Song Hee Kim ◽  
Eun Jung Lee
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Cross Section ◽  
Growth Rates ◽  
Idiosyncratic Volatility ◽  
Return Volatility ◽  
Firm Level ◽  
Cross Sectional ◽  
Asset Growth ◽  
The Cross

We investigate the cross-sectional and time-series determinants of idiosyncratic volatility in the Korean market. In particular, we focus on the empirical relation between firms’ asset growth rate and idiosyncratic stock return volatility. We find that, in the cross-section, companies with high idiosyncratic volatility tend to be small and highly leveraged, have high variance of ROE and Market to Book ratio, high turnover rate, and pay no dividends. Furthermore, firms with extreme (either high positive or negative) asset growth rates have high idiosyncratic return volatility than firms with moderate growth rates, suggesting the V-shaped relation between asset growth rate and idiosyncratic return volatility. We find that the V-shaped relation is robust even after controlling for other factors. In time-series, we find that firm-level idiosyncratic volatility is positively related to the dispersion of the cross-sectional asset growth rates. As a result, this study is contributed to show that the asset growth is the most important predictor of firm-level idiosyncratic return volatility in both the cross-section and the time-series in the Korean stock market. In addition, we show how the effect of risk factors varies with industries.

Common Ownership in America: 1980–2017

2021 ◽  
Vol 13 (3) ◽  
pp. 273-308
Author(s):  
Matthew Backus ◽  
Christopher Conlon ◽  
Michael Sinkinson
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Cash Flow ◽  
Historical Perspective ◽  
Common Ownership ◽  
Dramatic Rise ◽  
The Common ◽  
The Cross ◽  
Cash Flow Rights ◽  
And Control

We empirically assess the implications of the common ownership hypothesis from a historical perspective using the set of S&P 500 firms from 1980 to 2017. We show that the dramatic rise in common ownership in the time series is driven primarily by the rise of indexing and diversification and, in the cross section, by investor concentration, which the theory presumes to drive a wedge between cash flow rights and control. We also show that the theory predicts incentives for expropriation of undiversified shareholders via tunneling, even in the Berle and Means (1932) world of the widely held firm. (JEL D22, G32, G34, L21, L25)

On a model for a time series of cross-sections

1986 ◽  
Vol 23 (A) ◽  
pp. 113-125 ◽  
Author(s):  
P. M. Robinson
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Cross Sections ◽  
Standard Form ◽  
Cross Sectional ◽  
Aggregated Data ◽  
Section Data ◽  
Infinite Population ◽  
The Cross ◽  
Observation Times

Dynamic stationary models for mixed time series and cross-section data are studied. The models are of simple, standard form except that the unknown coefficients are not assumed constant over the cross-section; instead, each cross-sectional unit draws a parameter set from an infinite population. The models are framed in continuous time, which facilitates the handling of irregularly-spaced series, and observation times that vary over the cross-section, and covers also standard cases in which observations at the same regularly-spaced times are available for each unit. A variety of issues are considered, in particular stationarity and distributional questions, inference about the parameter distributions, and the behaviour of cross-sectionally aggregated data.

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