parameterEstimates.m

function results = parameterEstimates(logReturnsTable)
    % Estimate parameters for TVP-VAR model using MCMC algorithm
    % logReturnsTable: table containing log returns data
    
    % Extract numeric data (excluding 'Date' or non-numeric columns)
    numericCols = varfun(@isnumeric, logReturnsTable, 'OutputFormat', 'uniform');
    numericTable = logReturnsTable(:, numericCols);

    % Initialize storage for results
    numParams = size(numericTable, 2);
    results = table('Size', [numParams, 7], ...
        'VariableTypes', repmat({'double'}, 1, 7), ...
        'VariableNames', {'Mean', 'Stdev', 'CI_Lower', 'CI_Upper', 'Geweke', 'Inefficiency', 'Parameter'});

    % Loop through each numeric column of the table
    for i = 1:numParams
        data = numericTable{:, i};
        
        % Ensure no missing or NaN values
        data = data(~isnan(data));

        % Perform MCMC estimation
        [meanVal, stdevVal, ci, gewekeStat, ineffFactor] = mcmcEstimation(data);
        
        % Store results
        results.Mean(i) = meanVal;
        results.Stdev(i) = stdevVal;
        results.CI_Lower(i) = ci(1);
        results.CI_Upper(i) = ci(2);
        results.Geweke(i) = gewekeStat;
        results.Inefficiency(i) = ineffFactor;
        results.Parameter(i) = i; % Parameter index
    end
end
function [meanVal, stdevVal, ci, gewekeStat, ineffFactor] = mcmcEstimation(data)
    % Perform MCMC estimation (replace with actual MCMC logic)
    % Ensure `data` is a valid numeric array
    if isempty(data) || ~isnumeric(data)
        error('Data must be a non-empty numeric array.');
    end
    
    meanVal = mean(data);
    stdevVal = std(data);
    ci = quantile(data, [0.025, 0.975]); % 95% credible intervals
    gewekeStat = rand(); % Placeholder for Geweke diagnostic
    ineffFactor = randi([5, 100]); % Placeholder for inefficiency factor
end