by University of Southampton, Department of Economics in Southampton .
Written in English
|Series||Progress Paper -- M.4|
Abstract. Serial correlation and serial dependence have been central to time series econometrics. The existence of serial correlation complicates statistical inference of econometric models; and in time series analysis, inference of serial correlation, or more generally, serial dependence, is crucial to characterize the dynamics of time series by: 5. Get this from a library! An econometric model of serial correlation and illiquidity in hedge fund returns. [Mila Getmansky; Andrew W Lo; Igor Makarov; National Bureau of Economic Research.] -- Abstract: The returns to hedge funds and other alternative investments are often highly serially correlated in sharp contrast to the returns of more traditional investment vehicles such as long-only. Published: Getmansky, Mila, Andrew W. Lo and Igor Makarov. "An Econometric Model Of Serial Correlation And Illiquidity In Hedge Fund Returns," Journal of Financial Economics, , v74(3,Dec), citation courtesy of. Users who downloaded this paper also downloaded* these. Serial correlation and serial dependence have been central to time series econometrics. The existence of serial correlation complicates statistical inference of econometric models; and in time series analysis, inference of serial correlation, or more generally, serial dependence, is crucial to characterize the dynamics of time series processes.
In this paper the testing and estimation problems are discussed in the case of serial correlation. Various models are particular cases of the general framework considered: the nonlinear simultaneous equations models, the probit models, the tobit models, the disequilibrium models, the frontier models, etc. Department of Economics University of Wisconsin-Madison Ap Outline 1 Heteroskedasticity 2 Relaxing the Serial Correlation Assumption AR Models MA Models Using ARMA Models Newey West Standard Errors 3 Panel Data. In this set of lecture notes we will learn about heteroskedasticity and serial correlation. However the principal disadvantage of Python in econometrics is the lack of documentation and examples. For this reason, I wrote a book called Practical Econometrics with Python (You can check the first chapter and index as sample on amazon), that try to link the theory with practical examples. It moves from basic themes like OLS or GLS to. I do get serial correlation and cross-sectional dependence when I run the model using EVIEWS 8. I would like to know if there is a way to overcome this. I appreciate your comments on this.
serial correlation is induced, the common theme and underlying driver is illiquidity exposure. In this paper, we develop an explicit econometric model of smoothed returns and derive its implications for common performance statistics such as the mean, standard deviation, and Sharpe ratio. The prospect of spurious serial correlation and biased sample moments in reported returns is not new. Such effects are available in the literature on ‘‘nonsynchronous trading’’, which refers to security prices recorded at different In this paper,we develop anexplicit econometric model of smoothed returns and. Time Series Data and Serial Correlation. GDP is commonly defined as the value of goods and services produced over a given time period. The data set is provided by the authors and can be downloaded provides quarterly data on U.S. real (i.e. inflation adjusted) GDP from to Download PDF: Sorry, we are unable to provide the full text but you may find it at the following location(s): (external link).