clc, clear, close all


%######## Section to estimate other parameters with Bayesian estimation

% Upload the data for the Main model

data = xlsread('NewKoreanData.xlsx','MainModel','B4:K166'); %1980Q1-2020Q2

% billion won -> million won ->thousand won
raw.agg.rGDP         = data(:,1);
raw.agg.rConsumption = data(:,2);
raw.agg.rInvestment  = data(:,3);
raw.agg.rExport      = data(:,4); 
raw.agg.rImport      = data(:,5); 

raw.CAGDP     = data(:,6); % as in percent of GDP

raw.agg.population = data(:,7);
raw.px         = data(:, 8);
raw.pm         = data(:, 9);
raw.dollar     = data(:,10);

% Scale the data to per capita and ca to gdp percentage
raw.rGDP = raw.agg.rGDP./raw.agg.population;
raw.rConsumption = raw.agg.rConsumption./raw.agg.population;
raw.rInvestment = raw.agg.rInvestment./raw.agg.population;
raw.rExport = raw.agg.rExport./raw.agg.population;
raw.rImport = raw.agg.rImport./raw.agg.population;


% Attain the cyclical component of the QUARTERLY data with the HP filter
[raw.HPt_gdp, raw_rGDP] = hpfilter(log(raw.rGDP),1600); %in LOG form
[raw.HPt_c, raw_rConsumption] = hpfilter(log(raw.rConsumption),1600); %in LOG form
[raw.HPt_i, raw_rInvestment] = hpfilter(log(raw.rInvestment),1600); %in LOG form
[raw.HPt_x, raw_rExport] = hpfilter(log(raw.rExport),1600); %in LOG form
[raw.HPt_m, raw_rImport] = hpfilter(log(raw.rImport),1600); %in LOG form
[raw.HPt_px, raw_px] = hpfilter(log(raw.px),1600); %in LOG form
[raw.HPt_pm, raw_pm] = hpfilter(log(raw.pm),1600); %in LOG form
[raw.HPt_dollar, raw_dollar] = hpfilter(log(raw.dollar),1600); %in LOG form

[raw.HPt_cagdp, raw_CAGDP] = hpfilter(raw.CAGDP,1600); %in LEVEL form



save('dataestimation.mat','raw_rGDP','raw_rConsumption', 'raw_rInvestment', ...
       'raw_rExport','raw_rImport', 'raw_CAGDP', 'raw_px','raw_pm' ,'raw_dollar' ...
      );

%addpath \\userfs\hc1135\w2k\Desktop\dynare\4.5.6\matlab 
  
dynare chapter4_mainModel.mod