Can R software do piecewise spline regression?

To put it simply, hermite interpolation is approximated using a curve, and the highest number of times may be higher than triple.

Cubic spline interpolation is approximated by a continuous curve, with the highest degree being triplet.

Yes, the R language is very powerful in metering, do spline regression You can use the splines package and the lspline package, learn R econometrics together, welcome to contact by email.

Indicates that there is a certain trend.

Can you tell me the specific **?

1 Study**2 Collect data on Lensis and Monetary Policy and Premises Prudence 3 Build a model in eViews Personal experience should be done to call the ADF test, cointegration analysis, and Granger causality test.

Regression analysis. Study one random variable y against another (x) or set of (x1, x2,..., xk) methods for statistical analysis of dependencies of variables.

Issues to be aware of:

When applying regression**, you should first determine if there is a correlation between variables. If there is no correlation between the variables, applying the regression method to these variables will give wrong results. Attention should be paid to the correct application of regression analysis**

use qualitative analysis to determine the dependencies between phenomena;

Avoid arbitrary extrapolation of regression**;

apply appropriate data;

Fit. The so-called fitting refers to the value of a number of discrete functions of a function, by adjusting a number of pending coefficients f( 1, 2,... in the function, n), so that the function is least different from the known set of points (least squares sense).

If the function to be determined is linear, it is called linear fit or linear regression (mainly in statistics), otherwise it is called nonlinear fit or nonlinear regression. Expressions can also be piecewise functions, in which case this is called spline fitting.

The numerical statistics of a set of observations are consistent with the corresponding set of values. Figuratively speaking, fitting is to connect a series of points on a plane with a smooth curve. Because there are countless possibilities for this curve, there are various ways to fit it.

Fitted curves can generally be represented as functions. Depending on this function, there are different fitting names.

Polyfits polynomials can be used in MATLAB.

It's hard to say, if your data is relatively long and the lag order is not large, you can put in a little more endogenous variables. Generally, no more than 5.