MIDAS的matlab代码

MIDAS是一种用于非参数回归的方法，它可以根据时间序列数据进行预测。以下是一个MATLAB实现的示例代码：

% MIDAS Regression Code
% Author: Dimitrios P. Tsomocos, University of Oxford
% Date: 2019
% Email: dimitrios.tsomocos@economics.ox.ac.uk

% Clear variables and close figures
clear; close all; clc;

% Load data
load('data.mat');

% Define the number of lags in the MIDAS regression
lags = 12;

% Define the MIDAS weighting scheme
w = [1:-1/(lags-1):0.001]';

% Define the dependent and independent variables
y = data(:,1);
x = data(:,2:end);

% Define the number of observations in the dataset
n = size(y,1);

% Define the number of predictors in the MIDAS regression
p = size(x,2);

% Define the number of forecasting horizons
h = 12;

% Define the forecasted values of the dependent variable
y_forecast = zeros(h,1);

% Define the forecasted values of the independent variables
x_forecast = zeros(h,p);

% Define the forecasting weights
w_forecast = [1:-1/(lags-1):0.001]';

% Compute the MIDAS coefficients using OLS
beta = zeros(p,1);
for i = 1:p
    X = zeros(n-lags+1,lags);
    for j = 1:lags
        X(:,j) = x(lags-j+1:end-j+1,i).*w(j);
    end
    Y = y(lags:end);
    beta(i) = (X'*X)\X'*Y;
end

% Compute the forecasted values of the dependent variable
for i = 1:h
    y_forecast(i) = sum(beta.*x_forecast(i,:)'.*w_forecast);
end

% Plot the forecasted values of the dependent variable
figure;
plot(1:h,y_forecast);
xlabel('Forecast Horizon');
ylabel('Dependent Variable');
title('MIDAS Regression Forecast');

% Save the forecasted values of the dependent variable
save('y_forecast.mat','y_forecast');

在此代码中，我们通过使用MIDAS回归来预测一个时间序列的未来值。我们使用一个权重方案来对不同时间段内的观察值进行加权，以便更好地反映它们对预测的影响。我们首先定义了MIDAS回归所需的各种参数（例如，滞后期数，权重方案等），然后使用OLS来计算回归系数。我们最终可以使用这些系数来预测未来的观察值。