data preprocessing -凯发k8网页登录

format, plot, and transform time series data

apps

analyze and model econometric time series

create lag operator polynomial

	aggregate timetable data to daily periodicity
	aggregate timetable data to weekly periodicity
	aggregate timetable data to monthly periodicity
	aggregate timetable data to quarterly periodicity
	aggregate timetable data to semiannual periodicity
	aggregate timetable data to annual periodicity

add business calendar awareness to timetables

	convert prices to returns
	convert returns to prices
	create lagged time series data

	hodrick-prescott filter for trend and cyclical components
	baxter-king filter for trend and cyclical components
	christiano-fitzgerald filter for trend and cyclical components
	hamilton filter for trend and cyclical components

overlay recession bands on time series plot

	apply lag operator polynomial to filter time series
	determine stability of lag operator polynomial
	reflect lag operator polynomial coefficients around lag zero
	convert lag operator polynomial object to cell array
	determine if two `lagop` objects are same mathematical polynomial
	find lags associated with nonzero coefficients of `lagop` objects
	lag operator polynomial subtraction
	lag operator polynomial left division
	lag operator polynomial right division
	lag operator polynomial multiplication
	lag operator polynomial addition

prepare time series data at the matlab^® command line, and then import the set into econometric modeler.
import time series data from the matlab workspace or a mat-file into econometric modeler.
interactively plot univariate and multivariate time series data, then interpret and interact with the plots.
transform time series data interactively.
take a nonseasonal difference of a time series.
apply both nonseasonal and seasonal differencing using lag operator polynomial objects.
estimate long-term trend using a symmetric moving average function.
deseasonalize a time series using a stable seasonal filter.
apply seasonal filters to deseasonalize a time series.
estimate nonseasonal and seasonal trend components using parametric models.
use the hodrick-prescott filter to decompose a time series.
smooth the u.s. gdp by applying the one-sided and two-sided hodrick-prescott filters, and compare the resulting smoothed trends.
create lag operator polynomial objects.

understand model-selection techniques and econometrics toolbox™ features.
understand the definition, forms, and properties of stochastic processes.
analyze time series data using econometric modeler
interactively visualize and analyze univariate or multivariate time series data.
determine which data transformations are appropriate for your problem.
determine the characteristics of nonstationary processes.
learn about splitting time series into deterministic trend, seasonal, and irregular components.
some time series are decomposable into various trend components. to estimate a trend component without making parametric assumptions, you can consider using a filter.
you can use a seasonal filter (moving average) to estimate the seasonal component of a time series.
seasonal adjustment is the process of removing a nuisance periodic component. the result of a seasonal adjustment is a deseasonalized time series.
when you fit a time series model to data, lagged terms in the model require initialization, usually with observations at the beginning of the sample.