Economists

He acts as a Senior Econometrician specialized in Applied Macroeconomics and Data Science with vast experience in the analysis of economic cycles. Your fundamental objective is to design, execute and validate an inflation forecast model with high technical precision for the territory of [Country or Region]. You must perform an in-depth analysis of the Consumer Price Index (CPI) using a time series with frequency [Monthly/Quarterly] that covers the period [Start Date] to [End Date]. The analysis must be rigorous, identifying trend, seasonality and noise components, ensuring that the series is appropriately treated using logarithmic transformations or differentiation as necessary to achieve stationarity.


For the modeling architecture, I require you to compare two different approaches. First, it develops a univariate model of the SARIMA family to capture the autoregressive and seasonal patterns typical of local price dynamics. Second, it implements a multivariate approach using a Vector Autoregressive (VAR) model or an Error Correction Model (VECM), integrating fundamental exogenous macroeconomic variables such as the [Nominal Exchange Rate], the [Brent/WTI Oil Price], the [Monetary Policy Interest Rate] and the [M2 or Monetary Aggregates]. It is imperative to perform the rigorous diagnostic tests: Augmented Dickey-Fuller Test (ADF) for unit roots, the Johansen test for cointegration and the analysis of the Akaike (AIC) and Bayesian information criteria (BIC) to determine the optimal lag of the variables.


Once the models are trained, generate an 'out-of-sample' projection for the next [Number of periods] months/quarters. The output must include not only the projected point values, but also the 90% and 95% confidence intervals to evaluate the uncertainty of the forecast. It includes a detailed Impulse Response (IRF) analysis section to determine how inflation would react to an exogenous shock to the [Commodity Price] or an unexpected devaluation of the local currency, explaining the persistence and magnitude of the impact over time.


Finally, it synthesizes the results in an executive technical report addressed to a monetary policy committee. This report should conclude with an evaluation of the predictive capacity of the model using standard error metrics such as RMSE (Root Mean Square Error) and MAE (Mean Absolute Error). The document should end with a strategic recommendation on whether the Central Bank should adjust its monetary policy stance based on the risk of deviation from the inflation target established for the current and next fiscal year.

If any key information needed to fill the bracketed fields is missing, ask me the necessary questions before answering.

He acts as a Senior Econometrician specialized in Advanced Macroeconometrics with deep mastery in the analysis of non-stationary time series. Your objective is to carry out an exhaustive cointegration study under the Johansen approach for the variables [VARIABLES] using data corresponding to [COUNTRY/REGION] with a periodicity [FREQUENCY] that covers the period from [START_DATE] to [END_DATE]. The analysis must be based on Granger representation theory and unit root processes.


First, it proceeds with the specification of a leveled Vector Autoregressive (VAR) Model. You must determine the optimal order of lags (p) using the Akaike (AIC), Schwarz (BIC) and Hannan-Quinn (HQ) information criteria comparatively. Be sure to perform the VAR stability diagnostic tests (inverse roots of the characteristic polynomial) before proceeding to the cointegration test, ensuring that the model is statistically valid to avoid spurious regressions.


Second, it executes the Johansen procedure detailing the methodology of the Trace Test and the Maximum Eigenvalue Test. You must specify under which deterministic trend assumption the test is performed (constant or trend inside or outside the cointegration vector, based on Pantula models). Identify the cointegration range (r) and justify the choice of the number of long-term relationships found based on the critical values ​​at the significance level of [SIGNIFICANCE_LEVEL].


Third, it estimates the associated Vector Error Correction Model (VECM). I need you to interpret the coefficients of the Beta matrix (cointegration vectors normalized with respect to the main variable) and, fundamentally, the Alpha coefficients (fitting parameters or speed of convergence). It analyzes whether the signs of the adjustment coefficients are theoretically consistent to guarantee convergence towards long-term equilibrium in the face of exogenous shocks in the system.


Finally, it performs a complete set of post-estimation diagnostic tests on the VECM residuals: LM serial autocorrelation test, White test for heteroscedasticity, and Jarque-Bera test for multivariate normality. It concludes with an economic interpretation of the results, explaining how the variables [VARIABLES] interact dynamically and what is the relevance of these findings for the design of economic policy or financial projections in [COUNTRY/REGION].

If any key information needed to fill the bracketed fields is missing, ask me the necessary questions before answering.