The Race To Predict India’s Economic Data
My eldest son took up athletics a year ago. All the kids in his group are between six and nine years old, so the training is pretty playful – most of the time. But when they have to race against each other, the competition heats up. Not just between the kids, but among the passionate parents urging them on from the side-line.
On Your Marks…
Forecasting economic indicators often resembles a race as well. Each quarter, between 45 and 50 economists who participate in the Bloomberg survey lace up their running shoes to see who comes first, second and third. In other words: who is the most accurate? Every economist uses their own forecasting techniques. Some just take a guess at GDP growth by using a crystal ball, while others talk to clients or use economic models.
At RaboResearch, we use two model approaches. Of course, we have eyes and ears on the ground in Mumbai and elsewhere in India who keep in contact on a weekly basis. If there are important developments which are not properly accounted for by the models, we always take the liberty to override our model outcomes based on ‘expert opinion’.
First, for medium-term purposes we use a model based on seasonally-trended extrapolation techniques, which we combine with effects of policy measures announced by the government. In this approach, the external sector is viewed within a closed accounting setting using a global econometric trade model.
Our second modelling approach is new. This year, we have been developing nowcasting models which we use to override short-term forecast. The basic principle of ‘nowcasting’ (a contraction of the words now and forecasting) is the exploitation of data at higher frequencies to get an early estimate of the target variable before the actual print is released.
There are two very good reasons for taking a closer look at the potential of nowcasting in India.
- First, our nowcasting models accurately predicted India’s recent slump in economic activity.
- Second, the models could make a useful contribution to the discussion about the accuracy of India’s GDP statistics.
Recently, the former economic top advisor to the government Arvind Subramanian raised doubts about the reliability of India’s national accounts data, stating that annual GDP growth between 2011 and 2017 was only 4.5 percent on average, compared to official numbers of 7 percent. We can use our nowcasting models to see if this claim can be validated.
To return to our running theme, let’s introduce our nowcasting models to you as three athletes in competition.
The goal of the three athletes (our three models) is to finish first (i.e. to accurately predict GDP growth). To test their fitness, we use ‘out-of-sample testing’. This means that instead of estimating the models for the entire data sample, we exclude the last GDP realisations to test their prediction accuracy. Put more simply: we blindfold our three contenders for the final part of the track and see how close they come to the finish line. Now let’s meet our three competitors.
- Mr Traditional: Mr Traditional has a long track record of winning races. He does not believe in modern approaches to running, preferring to rely on proven concepts that helped him win in the past. For instance, a traditional training scheme (i.e. vehicle sales), paying his sport club contribution (i.e. tax revenues), a good night’s sleep (i.e. electricity production), an oatmeal breakfast (i.e. oil consumption) and the running shoes he bought in the 1980s (i.e. inflation).
- Ms Modern: Ms Modern has not been running competitively as long as Mr Traditional has, so she lacks some experience. She uses not only the traditional proven concepts, but also the more advanced tools and services now available in the sport to crank up her performance. For instance, high altitude training camps overseas (i.e. vehicle exports), data analytics to monitor her blood pressure and stamina, and advice from a sports nutritionist (i.e. services sector purchasing managers’ index).
- Mr BVAR: Mr Bayesian Vector Autoregressive (let’s call him Mr BVAR for short) is the odd one out, as he is completely agnostic. He is only interested in winning and couldn’t care less how he achieves it. So, it’s impossible to trace exactly what factors contribute to his success.
Now let’s see how our three contenders are performing, beginning with Mr Traditional.
(For a complete overview of our nowcasting models, the variables used per model, the estimation technique and results, and the out-of-sample forecast tests, we refer to our economic report Nowcasting the Indian economy.) After a strong start in the period before and during the Global Financial Crisis, his performance weakened significantly after 2015. This finding is more or less in line with the observations by Subramanian, who also registers a breakdown of his models when applied to more recent years. Especially on the last part of the track, where contenders have to wear a blindfold, Mr Traditional runs all over the place, spinning in circles rather than heading straight for the finish line.
One reason for Mr Traditional’s poor performance after 2015 might be that the economic structure of emerging markets, such as India, generally transforms at a rapid pace, the services sector and financial markets have become much more important for the economy.
This might also explain the breakdown of Subramanian’s models, since they are also based on relatively ‘traditional’ determinants of economic growth, such as credit, electricity and trade.
Ms Modern does use several services available to athletes, so let’s check if she is able to outperform Mr Traditional.
Ms Modern performs much better than Mr Traditional over the entire race. Even when she has to put on the blindfold, she manages to follow the running track, but in her enthusiasm, she overshoots the finishing line and ends up in the long-jump sand pit.
In contrast to this slight overshoot, Mr BVAR cuts corners during the race and ultimately grinds to a halt right before the finish line. Given that Ms Modern overshoots and Mr BVAR undershoots the finish line, we ultimately get the best results when both athletes join forces and run together.
Keeping The Sport Clean
Nowcasting modelling definitely has its upsides. We are able to use these techniques to accurately gauge economic activity in India, especially when we combine the two models that take proper account of India’s changing economic structure.
From this perspective, we do not find indications that the official GDP data are overestimated.
Our models do not show a significant break in the series post-2011 period and they arrive at an estimated average annual growth rate of 7 percent in the period 2011-2019.
Of course, testing the reliability of India’s national accounts data requires other types of analyses, but we believe we have shown that nowcasting can be useful in this discussion as well.
Going forward, we expect an ongoing debate about the quality of India’s statistics, which will intensify now and again. Since solid and reliable statistics and forecasts are the cornerstone of global investment decisions, it is even more important that the new Modi government takes action to remove any lingering concerns. For instance, by installing an independent committee with statistical experts from all over the world to review the quality of the national accounts data. Just as the reputation of sports, such as athletics and cycling, can be damaged by doping allegations, the discussion about India’s statistical credibility could dent India’s popularity as an international investment destination.
Hugo Erken is Head - International Economics at RaboResearch Global Economics & Markets.
The views expressed here are those of the author, and do not necessarily represent the views of BloombergQuint or its Editorial team.