What is the best technique to employ as a robustness check with wavelet coherence to investigate the impact of uncertainty on the stock market ( weekly data)?
Here are a few techniques you can employ:
1. Bootstrap Analysis: Conduct a bootstrap analysis to assess the stability and significance of the wavelet coherence results. The bootstrap technique involves randomly resampling your data multiple times, estimating the wavelet coherence for each resampled dataset, and then constructing confidence intervals or p-values based on the distribution of the coherence estimates. This helps determine if the observed coherence values are statistically significant or if they could have occurred by chance.
2. Cross-Wavelet Analysis: Perform cross-wavelet analysis to examine the relationship between uncertainty and the stock market at different time scales. Cross-wavelet analysis allows you to identify time-varying associations between the two variables across different frequencies. By visualizing and analyzing the cross-wavelet coefficients, you can gain insights into how the relationship between uncertainty and the stock market may change over time.
3. Multiple Uncertainty Measures: Consider using multiple measures of uncertainty to investigate their impact on the stock market. This can include various indicators such as volatility indices (e.g., VIX), economic policy uncertainty indices (e.g., EPU index), or other relevant proxies for uncertainty. Analyzing the coherence between different measures of uncertainty and the stock market can provide a more comprehensive understanding of their relationship.
4. Robustness Across Different Time Windows: Explore the robustness of the wavelet coherence results by varying the time window size. Instead of using the entire time series, divide the data into multiple overlapping or non-overlapping sub-periods and estimate the wavelet coherence for each sub-period. This helps assess if the observed coherence is consistent across different time windows and strengthens the validity of your findings.
5. Control Variables: Consider including control variables in your analysis to account for potential confounding factors that may influence the relationship between uncertainty and the stock market. For example, macroeconomic variables like interest rates, GDP growth, or inflation rates can be included as control variables to mitigate the effects of broader economic factors.
Hope it helps
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