Typed in google “Jokes on Forecasting”, it listed enough content to discourage doing forecasting.
But when typed “macroeconomic forecasting” far bigger number of useful links got populated. The volume itself was enough to avoid doing forecasting. It appeared the learning curve for doing forecasting meaningfully is steep, continuous, and tedious.
Therefore, it’s much easy to memorize the jokes those surfaced in the first search than doing forecasting seriously.
However that cannot be an ideal way out while working with corporate. A corporate economist need to predict the overall macroeconomic business environment in general and raw material prices, exchange rate, interest rate, etc. in specific.
It is widely admitted that the forecasting methodology is a hybrid procedure. It involves a combination of expert judgment and output from econometric models to arrive at a reasonably reliable forecast.
Judgmental calls in forecasting require comprehensive applicable knowledge in economics (especially macroeconomics) and regular scanning of macroeconomic business environment.
Secondly, capacity to develop econometric modeling is also essential. Thus the forecaster must have detail understanding about applied econometrics.
Furthermore, econometric modeling need data collection, data cleaning, data warehousing, process automation, find lag/lead relationship among the variables, etc. Powerful statistical software like R does all these almost flawlessly. Thus the forecaster should have decent command in writing codes in R.
Even after following a rigorous forecasting process, the forecast may deviate from the actual. It that cases the forecasting framework needs to be tuned whenever such deviation is reported. Moreover, if deviation is beyond certain level of acceptance the forecasting framework need to be overhauled completely.
These make forecasting a continuous and tedious assignment. However, the challenges can be overcome by dedication and commitment to generate reliable forecasts along with right tools in hand and clarity in mind.
The following resources may be useful in capacity development for doing macroeconomic business forecasting:-
But when typed “macroeconomic forecasting” far bigger number of useful links got populated. The volume itself was enough to avoid doing forecasting. It appeared the learning curve for doing forecasting meaningfully is steep, continuous, and tedious.
Therefore, it’s much easy to memorize the jokes those surfaced in the first search than doing forecasting seriously.
However that cannot be an ideal way out while working with corporate. A corporate economist need to predict the overall macroeconomic business environment in general and raw material prices, exchange rate, interest rate, etc. in specific.
It is widely admitted that the forecasting methodology is a hybrid procedure. It involves a combination of expert judgment and output from econometric models to arrive at a reasonably reliable forecast.
Judgmental calls in forecasting require comprehensive applicable knowledge in economics (especially macroeconomics) and regular scanning of macroeconomic business environment.
Secondly, capacity to develop econometric modeling is also essential. Thus the forecaster must have detail understanding about applied econometrics.
Furthermore, econometric modeling need data collection, data cleaning, data warehousing, process automation, find lag/lead relationship among the variables, etc. Powerful statistical software like R does all these almost flawlessly. Thus the forecaster should have decent command in writing codes in R.
Even after following a rigorous forecasting process, the forecast may deviate from the actual. It that cases the forecasting framework needs to be tuned whenever such deviation is reported. Moreover, if deviation is beyond certain level of acceptance the forecasting framework need to be overhauled completely.
These make forecasting a continuous and tedious assignment. However, the challenges can be overcome by dedication and commitment to generate reliable forecasts along with right tools in hand and clarity in mind.
The following resources may be useful in capacity development for doing macroeconomic business forecasting:-
- How to Choose the Right Forecasting Technique: An article published in 1970’s in Harvard Business Review. It is still useful in the world with flooded information and easy access to sophisticated forecasting tools.
- Forecasting: Principles and Practice: A free source text book on forecasting with R by Prof. Hyndman.
- Longterm Forecast: Forecasting methodology of EIU (The Economist Intelligence Unit)
- VAR forecasting methodology: A post in a discussion forum, giving steps to do forecasting using VAR in R.