Reproducibility Checks: Forecasting Climate Policy Uncertainty

• To generate "Table 2: Summary statistics of the training samples for the three CPU indices.", please refer to Table_02_Summary_Statistics.R.

• To generate "Table 3: Statistical characteristics of the training samples for the three CPU indices.", please refer to Table_03_Global_Characteristics.R.

• To view "Table 4: Time series of the training samples for the three CPU indices, together with their ACF and PACF plots.", please refer to Table_04_CPU_TS_ACF_PACF_Plots.R.

• To view "Figure 1: Global and US interest in the search terms (a) “Environmental Policy” and (b) “Carbon Credits” from January 2004 to June 2021. The series display similar temporal patterns, highlighting the close alignment between US and worldwide search behavior. Data sourced from Google Trends: https://trends.google.com/trends?geo=&hl=en-GB" and "Figure 2: Interest in the worldwide search terms “Climate Policy” and “Climate Risk” from January 2004 to June 2021, plotted alongside the primary US CPU index. The three series exhibit similar temporal patterns.", please refer to Figures_01_02_Google_Trend_Plot.R.

• To generate "Table 7: Evaluation of the BSTS-X_MG model's performance relative to baselines across all forecast horizons for the primary US CPU index using macro-financial variables and Google Trends indicators. The best and second-best results are highlighted.", please refer to Table_07_Evaluation_Comparison_PrimaryUS_MG.R. To check the forecasts across various algorithms, please refer to the following folders Dataset_Model_Forecasts and Dataset_Deep_Learning_Models_Forecasts. The performance evaluation summary can be found in Dataset_Model_Evaluation.

• To generate "Table 8: Evaluation of the BSTS-X_M model's performance relative to baselines across all forecast horizons for the primary US CPU index using macro-financial variables. The best and second-best results are highlighted.", please refer to Table_08_Evaluation_Comparison_PrimaryUS_M.R. To check the forecasts across various algorithms, please refer to the following folders Dataset_Model_Forecasts and Dataset_Deep_Learning_Models_Forecasts. The performance evaluation summary can be found in Dataset_Model_Evaluation.

• To view "Figure 3: Multiple comparisons with the best (MCB) plot for the models across the two sets of covariates and the three CPU indices. BSTS-X_MG-2.50, for instance, signifies that the average ranking of the BSTS-X_MG model, according to the RMSE metric, is 2.50. This same interpretation holds for other models.", please refer to Figure_03_MCB.R.

• To view "Figure 4: Murphy diagrams of BSTS-X_MG with baselines (BSTS-X_M (top) and ARIMA-X_MG (bottom)) for the 24-month-ahead forecasts for the (a) primary US, (b) alternate US, and (c) Global CPU indices. The parameter $\theta$ represents the shape parameter as defined in Eqn.(2). Lower scores indicate better performance.", please refer to Figure_04_Murphy_Diagram.R.

• To view "Figure 5: Impulse response functions generated via the local projections method, quantifying the CPU index's dynamic reactions to one-standard-deviation innovations in macroeconomic and financial cycle variables. The reaction is represented by the solid black line, and the 95% confidence intervals are represented by the gray shaded regions. The dashed black line displays the zero line. The sample period runs from April 1987 to June 2021.", please refer to Figure_05_Impulse_Response_Functions.R.

• To generate "Table 9: Evaluation of BSTS-X_MG models with time-invariant and time-varying regression coefficients (BSTS-X^TI and BSTS-X^TV, respectively) across all forecast horizons and CPU indices using macro-financial variables and Google Trends indicators. The best results are highlighted.", please refer to Table_09_Ablation_Study.R. The performance evaluation summary can be found in Dataset_Model_Evaluation.

• To view "Figure 6: Inclusion probabilities of variables selected by the BSTS model over a 24-month-ahead forecast horizon. The plot reflects the relative importance of each variable in contributing to the model’s predictive accuracy, with higher probabilities indicating greater explanatory relevance.", please refer to Figure_06_Important_Features_Plot.R.

• To view "Figure 7: Visualization of the ground truth (red), fitted values and forecasts of the BSTS-X_MG model (green), forecasts of ARIMA-X_M (orange), and the credible intervals generated by BSTS (yellow shaded) for the 6-month holdout period of the (a) primary US, (b) alternate US, and (c) Global CPU series. The plot on the left displays the training data along with the fitted values of BSTS-X_MG. The plot on the right presents the semi-long-term forecasts shown as both point estimates and credible intervals.", please refer to Figure_07_Credible_Intervals_6.R.

• To view "Figure A.8: Wavelet coherence plots showing time-frequency co-movement between the primary US CPU index and (a) macroeconomic and financial cycle variables and (b) Google Trends indicators. Color intensity represents the strength of association, with red indicating strong co-movement and blue weak co-movement. Arrows indicate phase relations, showing the lead-lag direction and whether series move in the same or opposite direction.", please refer to Figure_08_WCA.R.

• To generate "Table 10: Summary of statistical screening results for the macroeconomic and financial cycle variables." and "Table 11: Summary of statistical screening results for Google Trends indicators.", please refer to Table_10_11_Statistical_Screening.R.

• To generate "Table 12: Evaluation of the BSTS-X_MG model's performance relative to baselines across all forecast horizons for the alternate US CPU index using macro-financial variables and Google Trends indicators. The best and second-best results are highlighted.", please refer to Table_12_Evaluation_Comparison_AlternateUS_MG.R. To check the forecasts across various algorithms, please refer to the following folders Dataset_Model_Forecasts and Dataset_Deep_Learning_Models_Forecasts. The performance evaluation summary can be found in Dataset_Model_Evaluation.

• To generate "Table 13: Evaluation of the BSTS-X_M model's performance relative to baselines across all forecast horizons for the alternate US CPU index using macro-financial variables. The best and second-best results are highlighted.", please refer to Table_13_Evaluation_Comparison_AlternateUS_M.R. To check the forecasts across various algorithms, please refer to the following folders Dataset_Model_Forecasts and Dataset_Deep_Learning_Models_Forecasts. The performance evaluation summary can be found in Dataset_Model_Evaluation.

• To generate "Table 14: Evaluation of the BSTS-X_MG model's performance relative to baselines across all forecast horizons for the Global CPU index using macro-financial variables and Google Trends indicators. The best and second-best results are highlighted.", please refer to Table_14_Evaluation_Comparison_Global_MG.R. To check the forecasts across various algorithms, please refer to the following folders Dataset_Model_Forecasts and Dataset_Deep_Learning_Models_Forecasts. The performance evaluation summary can be found in Dataset_Model_Evaluation.

• To generate "Table 15: Evaluation of the BSTS-X_M model's performance relative to baselines across all forecast horizons for the Global CPU index using macro-financial variables. The best and second-best results are highlighted.", please refer to Table_15_Evaluation_Comparison_Global_M.R. To check the forecasts across various algorithms, please refer to the following folders Dataset_Model_Forecasts and Dataset_Deep_Learning_Models_Forecasts. The performance evaluation summary can be found in Dataset_Model_Evaluation.