Time Series Regression Methods
Non-parametric regression with a latent time series
... several methods for estimation of the unknown quantities. All our methods are based on minimizing sample sums of squared ...linear regression paradigm because of its many advantages, Fan and Gijbels ...
29
Regression Models for Binary Time Series with Gaps
... Abstract Time series of discrete random variables present unique statistical challenges due to serial correlation and uneven sampling ...While regression models for a series of counts are well ...
34
Time series forecasting by principal covariate regression.
... with time series forecasting in the presence of a large number of ...forecast methods with many predictors consist of two steps, where the large set of predictors is first summarized by means of a ...
38
Variance Estimation in Time Series Regression Models
... estimation methods such as GLS or MLE procedures which often require inversion of large ...linear regression model with AR(p) disturbance and a wide range of trending independent variables, and that OLS ...
9
Time series regression studies in environmental epidemiology.
... Time series regression studies have been widely used in environ- mental epidemiology, notably in investigating the short-term asso- ciations between exposures such as air pollution, weather variables ...
10
Variance Estimation in Time Series Regression Models
... estimation methods such as GLS or MLE procedures which often require inversion of large ...linear regression model with AR(p) disturbance and a wide range of trending independent variables, and that OLS ...
9
Bayesian Methods in Nonlinear Time Series
... a regression model context, and Chan and Tong [12], in the TAR model context, propose to make the transition between regimes ...linear time series ...
32
Time series data mining methods
... to time series data mining ...to time series forecasting, including multilayer perceptron, Bayesian neural networks, radial basis functions, Kernel regression, k-nearest neighbor re- ...
68
Time-Series Cross-Section Methods
... Cross-validation is useful for finding whether one (or a small number) of units should be excluded from the regression. We may also suspect that different units follow different regimes. Thus the older West ...
18
A nonparametric regression cross spectrum for multivariate time series
... physiological series considered above is a typical ...accurate methods than the algorithm proposed here can be devised, will be worth pursuing in future ...
42
Estimation of a nonparametric regression spectrum for multivariate time series
... nonparametric regression spec- trum is particularily useful when the observed series have strong deterministic ...the regression spectrum based on the ...the time domain was considered in ...
31
Efficient robust estimation of time-series regression models
... robust methods, or the necessity to choose auxiliary tuning parameters without rigorous ...existing methods, 2S- LWS has an asymptotic distribution independent of the initial robust estimation, is ...
13
Bayesian Methods for Functional and Time Series Data
... 3.4.2 Time-Varying Parameter Models: The Fama-French Asset Pricing Model Asset pricing models commonly feature highly structured factor models to par- simoniously model the co-movement of stock ...a time ...
181
Nonlinear time series: semiparametric and nonparametric methods
... semiparametric methods and mod- els have been proposed and studied extensively in the economics and statistics ...dent time series ...semiparametric methods and models may be ap- plied to ...
245
Advanced classification and time-series methods in marketing..
... Three regression models are subsequently estimated, with as dependent variable the dynamic correlation in, respectively, the short, medium and long-run frequency band, and as explanatory variables the various ...
188
Methods for the estimation of missing values in time series
... scalar time series to the vector cases. The series is assumed to be generated by a possibly partially nonstationary and noninvertible vector autoregressive moving average ...the series to be ...
205
Machine Learning Methods with Time Series Dependence
... There are two principal advantages of training the Markov model in a discrim- inative fashion. First, as mentioned above, it is easier to estimate a k-dimensional probability vector than k p-variate probability measures. ...
231
Regression with Stationary Time Series
... trended series as in Figure 2-1 is ...the series in the same direction over ...two series have in common is that the (independent) shocks to both series are highly persistent, yet Granger and ...
16
Methods of Time Series
... This is the simple method of studying trend. In this method the given time series data are plotted on graph paper by taking time on X-axis and the other variable on Y-axis. The graph obtained will ...
27
Generalized linear time series regression
... Example 1. We use German labour market data, from a 2% random sample of employees subject to social security, grouped by year and age for the time period 1980–2004. Fitzenberger et al. (2001) and Fitzenberger & ...
9