Best ChatGPT prompts for Economists

Assume a dynamic ARIMA (p, d, q) model is used to forecast monthly GDP. Given the historical GDP dataset from 2000 to 2020, predict the GDP for the next 5 years and provide a 95% confidence interval for each prediction. Also, calculate the mean absolute percentage error (MAPE) and root mean squared percentage error (RMSPE) for the 5-year forecast period. Provide the forecast in a clear and concise table format. The dataset is as follows: - 2000: 10.5 - 2001: 11.1 - 2002: 11.7 - 2003: 12.3 - 2004: 13.0 - 2005: 13.7 - 2006: 14.3 - 2007: 15.0 - 2008: 15.7 - 2009: 16.2 - 2010: 16.6 - 2011: 17.0 - 2012: 17.4 - 2013: 17.7 - 2014: 18.2 - 2015: 18.5 - 2016: 19.1 - 2017: 19.5 - 2018: 19.8 - 2019: 20.3 - 2020: 20.7 Using Python's statsmodels library for the analysis. Consider seasonal factors and trends.

Using the monthly Consumer Price Index (CPI) dataset from 2000 to 2020, create a time-series analysis to identify the underlying patterns and seasonality in the data. Provide the following: (a) A clear and concise visual representation (e.g., line plot) of the raw data, (b) Decompose the time series into trend, seasonal, and residual components, and (c) Provide an interpretation of the results in the context of market trends. Also, consider using seasonal decomposition and statistical tests to identify any significant seasonal patterns.

Build an econometric model using linear regression to predict the future prices of crude oil based on the data from the last five years. Using an auto-regressive integrated moving average (ARIMA) model, create a forecast for the next 6 months and compare the two models' performance using metrics such as mean absolute error (MAE) and mean absolute percentage error (MAPE). The dataset is as follows: - Jan-2020: 60.7 - Dec-2020: 46.4 - Oct-2021: 81.1 - Nov-2021: 81.9 - Dec-2021: 81.4 - Jan-2022: 85.2 - Feb-2022: 86.1 - Mar-2022: 101.5 - Apr-2022: 104.1 - May-2022: 117.6 - Jun-2022: 122.9 - Jul-2022: 123.5 - Aug-2022: 105.4 - Sep-2022: 97.8 - Oct-2022: 89.2 - Nov-2022: 84.4 - Dec-2022: 81.7 - Jan-2023: 79.3 - Feb-2023: 77.5 - Mar-2023: 83.4 - Apr-2023: 84.8 - May-2023: 91.2. Using Python's Statsmodels library for the analysis.

For this scenario, analyze the dataset for US labor market indicators and use it to evaluate the effectiveness of the economic stimulus packages. Using historical labor data, compare the pre and post-stimulus periods and analyze the change in unemployment rates, new job postings, and participation rates before and after the stimulus packages. Assume the dataset is available for the time period of January 2010 to December 2014 and includes the monthly values for unemployment rates and new job postings. Also, include historical data on major economic indicators like GNI and inflation rates. The dataset will be provided. Calculate a set of key statistics, such as the mean, standard deviation, and the change from pre-stimulus to post-stimulus periods. Compare and contrast the labor market indicators between before and after the stimulus packages. Visualize the main findings using a set of bar charts and line plots.

Given a set of macroeconomic data, write a Python function to perform the following tasks: i) Load the dataset for inflation rates from 1990 to 2022. ii) Calculate and plot the moving averages of inflation rates over different time windows (3 months, 6 months, and 12 months). iii) Use linear regression to model the long-term inflation trends. iv) Create a visualization of the forecasted inflation rates over the next five years. The data is provided below: - 1990: 5.2 - 1991: 5.6 - 1992: 6.1 - 1993: 6.5 - 1994: 7.3 - 1995: 6.9 - 1996: 5.3 - 1997: 5.7 - 1998: 5.2 - 1999: 8.1 - 2000: 8.8 - 2001: 8.2 - 2002: 7.9 - 2003: 8.4 - 2004: 9.2 - 2005: 9.8 - 2006: 10.4 - 2007: 6.5 - 2008: 3.8 - 2009: 1.6 - 2010: 2.6 - 2011: 3.2 - 2012: 2.7 - 2013: 1.6 - 2014: 1.3 - 2015: 0.8 - 2016: 2.1 - 2017: 2.1 - 2018: 2.5 - 2019: 1.5 - 2020: 1.4 - 2021: 4.1 - 2022: 3.7