1. Azhar Iqbal, Director and Econometrician October 17, 2016
Macroeconomic Forecasting, Consensus and Individual Forecaster: A Real-Time Approach
Azhar Iqbal, Director and Econometrician
October 17, 2016

2. The Short-Term Forecasting The Fundamental Questions
Introduction
We use a modified Bayesian Vector Autoregressive (BVAR) approach to generate forecasts for these variables
The Short-Term Forecasting
Our study provides a real-time, real world, short-term macroeconomic (one month ahead) forecasting approach.
We compare our firm’s official real-time forecasts with the Bloomberg real-time consensus for 20 key macroeconomic variables.
Our study sheds light on five important areas of macroeconomic forecasting.
The Fundamental Questions
First, why do we care about short-term forecasting?
Second, why is an individual forecaster, who is better than a consensus, important?
Third, why is the release timing of the target (dependent) variable and predictors important?
Fourth, why are traditional forecast evaluation methods not enough?
Finally, why won’t the best model remain accurate forever?

3. Importance of Short-Term Forecasting: Surprises, Consensus and Individual Forecaster
Better forecasts of macroeconomic variables, prior to their announcements, would provide more opportunities to a firm to make a profit or reduce losses
A well-known fact is that scheduled macroeconomic variable announcements affect asset prices and volatility in the equity, bond and foreign exchange (FX) markets.
The empirical evidence of financial market responses to macroeconomic news announcements dates back to the 1980s.
Recently, Anderson et al. (2007) and Bartolini et al. (2008) found a strong financial market response to the macroeconomic variables release announcements.
Anderson et al. (2007) characterized the response of the U.S., German and British equity, bond and FX markets to real-time U.S. macroeconomic news announcements.
Therefore, accurate forecasts of key macroeconomic variables, prior to their announcements, will enhance profit opportunities to a firm.

4. Importance of Short-Term Forecasting: Surprises, Consensus and Individual Forecaster
Recent studies suggest that key macroeconomic variable release announcements affect asset prices; however, the effect is more significant when the actual news is different than market expectations
Individual Forecaster vs. Consensus
One major change, however, may have occurred during the last one and a half decades; the effect of macroeconomic news on the asset prices may have changed over time.
Rigobon-Sack (2008) and Bartolini et al. (2008) suggest key macroeconomic variable release announcements affect asset prices; however, the effect is more significant when the actual news is different than market expectations.
This conclusion has very important implications for real-time, short-term macroeconomic forecasting.
First, the importance of an individual forecaster, who is better than the market consensus, is increased since markets move significantly when the actual release is different than the market consensus.
Second, since markets move more when the actual release is different than market consensus, we should not use consensus forecast in betting on asset price movements as it would reduce opportunities to make money.

5. The Econometrics of Short-Term Forecasting
We employ a modified Bayesian Vector Autoregressive (BVAR) approach to generate our firm’s official forecasts
The Bayesian Vector Autoregressive Model
The performance of Litterman’s method is at least partially determined by the choice of several parameters popularly referred to as the “Minnesota Prior.”
Litterman (1986) was only able to implement a small number of possible parameter combinations due to limited and expensive computer power at that time.
With the programming flexibility and speed available with today’s advanced econometric software, such as SAS, we can run Litterman’s regression using many parameter combinations and find the “best” (in terms of RMSE) possible combination.
The above step will enhance the forecasting performance of the traditional BVAR model.
Since we forecast the difference form of a variable, that solves the non-stationary and cointegration problem.
In addition to the Alvarez-Ballabriga evidence in support of Sims’ views, in our opinion, it also depends on what the target variable is, especially in forecasting.

6. The Econometrics of Short-Term Forecasting
We suggest a better forecasting approach must have a smaller real-time RMSE as well as a higher real-time directional accuracy, on average, compared to the Bloomberg consensus
Forecast Evaluation
First, we set real-time, out-of-sample root mean square error (RMSE) as the forecast evaluation criterion for our firm’s official real-time forecast as well as the Bloomberg real-time consensus forecast for each of the 20 macroeconomic variables.
The real-time, out-of-sample RMSE is a good measure of forecast evaluation.
However, in the real world, and in the financial sector particularly, the direction of the release announcement is also important.
As Bartolini et al. (2008) suggest, the stronger-than-expected news announcement may increase interest rates, strengthen the dollar, and raise stock prices.
Therefore, it is essential to predict the direction of the actual release announcement to make money and/or reduce losses.

7. The Data and The Implementation Strategy
We forecast many macroeconomic and financial variables, and for this study, we selected the 20 major macroeconomic variables
The Data
In the real-world/financial sector, the level form of the many variables may not be relevant. Instead, a specific form of a particular variable is useful; e.g., a month-over-month (MoM) percent change in retail sales is more important than the level of the retail sales.
Therefore, it is vital to determine what form of the target variable is meaningful and what form we are going to forecast.
Once we determine the functional form of the target variable we will find the best predictors to forecast that variable.
The functional form of the predictors should be consistent with the functional form of the dependent variable.
The following is a real-world example:
The retail sales model (we predict a MoM percent change in the sales) with chain-store sales (a YoY percent change) has a simulated out-of-sample RMSE of $24,201 million.
On the other hand, if we use a MoM percent change of chain-store sales then the simulated out-of-sample RMSE drops significantly to $3,042 million.

8. The Data and The Implementation Strategy
We use a step-wise procedure, consisting of three steps, to choose the best model specification
Selection of the Best Model Specification
In the Wells Fargo Economics Group, we maintain a large data set of more than 500 variables.
After transformations, we create more than 1,500 variables as potential predictors.
In the first step, we start by taking the regression of the dependent variable against each of these 1,500 variables and retain those with significant predictive power.
We use SBC as the selection criterion in choosing these specifications. We selected ten variables from this step.
In the second step, we run a one-by-one Granger-causality test between the dependent variable against each of these 1,500 variables to come up with the top ten variables based on a Chi-squared test.
For the last step, we use a simulated out-of-sample RMSE as a statistical measure to find the final model specification.

9. The Data and The Implementation Strategy
The most important and the most neglected issue is the release timing of a dependent variable (as well as predictors), and this needs to be considered in the model specification process
Timing of the Release Announcement: Dependent Variable and Predictors
As we know, many macroeconomic variables release with a time lag, e.g., a one-month or two-month lag.
For instance, the ISM manufacturing index is released on the first business day of the month for the previous month, with a one-month lag; e.g., on June 1, 2016, the ISM manufacturing index was released for May 2016.
On the other hand, construction spending is released with a two-month lag, usually during the first week of the month; e.g., on June 1, 2016, construction spending data was released for April 2016.
In real-time, short-term macroeconomic forecasting, the most recent information about the predictors and dependent variable plays a crucial role to some extent a key to a successful forecast.
Another essential issue, and the most neglected at the same time, is the release timing of the dependent variable. For instance, ISM manufacturing, nonfarm payrolls, CPI, industrial production and personal income & spending data release with a one-month lag  but the precise release timing is different for all these variables.

10. Table 1: Net Change in Nonfarm Payrolls
The Results
Table 1: Net Change in Nonfarm Payrolls
Source: U.S. Department of Labor, Bloomberg LP and Wells Fargo Securities, LLC

11. Table 2A: Forecast Evaluation
The Results
Table 2A: Forecast Evaluation
Source: Bloomberg LP and Wells Fargo Securities, LLC

12. Table 2B: Forecast Evaluation
The Results
Table 2B: Forecast Evaluation
Source: Wells Fargo Securities, LLC

13. There is No Magic Bullet
Forecasting models are like sailing; they require constant adjustments to the financial and economic winds and currents
One-Model Specification Will Not Remain Accurate Forever
Usually, we try to find a best forecasting model, and once we finalize the so-called best model, we think that this is it.
However, based on our personal experience, one model specification will not remain the most accurate forever.
A Real-World Example:
Old employment model vs. Bloomberg consensus
New employment model vs. old employment model vs. Bloomberg consensus
Therefore, even a best model specification may need to be revised at some point in the future and hence, short-term forecasting is an evolving process.
Add-Factors
“Add-factors” are important.
Census, Weather, Cash for Clunkers, Tax Stimulus, etc…

14. Concluding Remarks
We compared our firm’s official real-time forecasts with the Bloomberg real-time consensus and concluded that our forecasts are more accurate than Bloomberg, on average, for key macroeconomic variables
Our study sheds light on five important areas of macroeconomic forecasting.
First, we do believe the macroeconomic variable release affects the financial market volatility; however, the effect is most significant when the actual release is different than the market expectation.
Second, the importance of an individual forecaster, who is better than consensus, is increased since the market moves more when the actual release is different than market consensus.
Third, in short-term forecasting, the actual release timing of the target variable (dependent variable), as well as the predictors, is very important and needs to be considered in model specification.
Fourth, using real-time, out-of-sample directional accuracy along with traditional forecast evaluation methods (RMSE) is a better approach.
Finally, one-model specification will not remain accurate forever.
Therefore, even a good forecasting model may need to be revised at some point in the future, making short-term forecasting an evolving process.

15. Wells Fargo Securities Economics Group
Diane Schumaker-Krieg …………………
Global Head of Research & Economics
Global Head of Research and Economics
Eric J. Viloria, Currency Strategist
Sarah House, Economist …………… …………
Michael A. Brown, Economist ……………… …
Jamie Feik, Economist
Economists
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John E. Silvia … …
Mark Vitner, Senior Economist……………....………. .
Jay H. Bryson, Global Economist …………………....……
Sam Bullard, Senior Economist
Nick Bennenbroek, Currency Strategist
Anika R. Khan, Senior Economist …
Eugenio J. Alemán, Senior Economist…
Azhar Iqbal, Econometrician………………… ……………
Tim Quinlan, Senior Economist …………… …………….
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Senior Economists
Erik Nelson, Currency Analyst
Misa Batcheller, Economic Analyst
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Julianne Causey, Economic Analyst
Administrative Assistants
Donna LaFleur, Executive Assistant.
Dawne Howes, Administrative Assistant
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