Thoughts on War and the Economic Outlook

It's been a difficult three weeks, given what's going on in Eastern Europe. I grew up under the threat of the BOMB. I was born the year of the Tet Offensive and the Prague Spring, and the first twenty years of my life was under the latent threat of nuclear holocaust. That is what makes Russia-Ukraine qualitatively different from all the other conflicts of the past 30 years, beyond the human tragedy involved, which we have seen in the Middle East and elsewhere. I'm worried and sad that it has come to this again.

More prosaically, war makes economic prognostication orders of magnitude more difficult, especially given the size and interconnections of both Russia and Ukraine in the global economy. It's not just oil & gas, but also wheat, corn, and a slew of other metals and minerals critical to global production. Ukraine supplies half the world's output of neon gas (of all things), which is critical to semiconductor manufacturing. An extended conflict will have severe ramifications on global prices and output. So this is likely to be a difficult year, with higher prices and more severe supply disruptions.

September 2015 IPI: Steady Recovery

Monday’s industrial production figures paint a picture of recovery (log annual and monthly changes; seasonally adjusted; 2000=100):

Manufacturing output has been steady for most of the year, but mining output (read: oil & gas) had been on a downtrend. More worrying to me was the sharp drop off in electricity generation – that pointed to underlying weakness in both business and consumer demand. Unless this was a dead cat bounce, September’s figures suggests whatever malaise hitting both sectors is now over.

Dec 2014 Industrial Production: Smokin’

Back in 2011, I remember the budget coming out and the complete disbelief at the government’s 2012 growth forecast. From economists to the man on the street, nobody thought growth would be anywhere near 5%, much less exceed it (this was right after the Greek bailout). Hell, I was wrong too – I thought 5% was achievable, but a little on the optimistic side. In the end, the economy hit 5.6% GDP growth for 2012, almost right in the middle of the government’s initial forecast.

Deja Vu.

July 2014 Industrial Production: A Bad Start To 3Q2014

Yikes (log annual and monthly changes; seasonally adjusted; 2000=100):

April 2014 Industrial Production: Steady As She Goes

The numbers aren't as spiffy as the out-of-this-world export growth numbers, but they’re pretty respectable (log annual and monthly changes; seasonally adjusted):

Industrial output rose by 4.0%, coming off last month’s 4.3% in log terms, with a pickup in mining helping to offset a pullback in manufacturing and electricity production.

March 2014 Industrial Production

The March numbers that came out on Monday didn’t show a full picture of health, but did confirm that 1Q2014 growth is going to be pretty decent (log annual and monthly changes; seasonally adjusted; 2000=100):

February 2014 Industrial Production

Last week’s IPI numbers gave me a headache – it’s part of another rebasing exercise, this time to 2010, which required a bit of work to splice – but things are looking up (log annual and monthly changes; seasonally adjusted; 2000=100):

December 2013 Industrial Production

Yesterday’s IPI report confirms the acceleration in economic growth at the end of last year (log annual and monthly changes; seasonally adjusted):

October 2013 Industrial Production

Industrial output appears to be moderating a little (log annual and monthly changes; seasonally adjusted):

The Basics Of Forecasting

I did a series of posts along these lines ages ago, but as an overall summary, the following post from David Giles covers much the same ground and then some (excerpt):

Forecasting from a Regression Model

There are several reasons why we estimate regression models, one of them being to generate forecasts of the dependent variable. I'm certainly not saying that this is the most important or the most interesting use of such models. Personally, I don't think this is the case.

So, why is this post about forecasting? Well, a few comments and questions that I've had from readers of this blog suggest to me that not all students of econometrics are completely clear about certain issues when it comes to using regression models for forecasting.

Let's see if we can clarify some terms that are used in this context, and in the process clear up any misunderstandings…

There are a couple of concepts in this post that I never got around to covering (cointegration and order of integration), and I prefer as a rule to use lags anyway, but this is a pretty good primer on the mechanics of forecasting.

September 2013 Industrial Production

The September numbers aren’t nearly as pretty as the improvement in external trade (log annual and monthly changes; seasonally adjusted; 2000=100):

August 2013 Industrial Production

Too good to last? The IPI has come back down to earth (log annual and monthly changes; seasonally adjusted):

July 2013 Industrial Production

Something is in the wind. US Purchasing Managers Indexes zoomed up in June, and lo and behold, Malaysia’s IPI is following suit (log annual and monthly changes; seasonally adjusted):

April 2013 Industrial Production: Green Shoots

After some fairly unenticing numbers in 1Q2013, industrial production in April showed some signs of life (log annual and monthly changes; seasonally adjusted):

A Question Of Sampling

I’m not an expert in sample design, but I think I’m qualified to comment on this (excerpt):

Opposition manipulated Umcedel study, claims prof

An academician has accused the opposition of manipulating the Universiti Malaya's Centre for Democracy and Elections' (Umcedel) finding that voters see opposition leader Anwar Ibrahim as more prime ministerial than incumbent Mohd Najib Abdul Razak.

Prof Zainal Kling (right), the Tun Abdul Ghafar Baba fellow at the Universiti Pendidikan Sultan Idris, said this in a national news agency Bernama report yesterday, rebutting the study's conclusion a day after it was announced to the press and academia...

Forecasting The 2020 High Income Threshold

This is going to be a technical post – feel free to skip to the end if you’re not comfortable with the math.

There’s apparently some question of why the ETP has a target of USD15,000 in per capita GNI by 2020, and how it was derived.

Scepticism over the target has always struck me as a little strange because of the uncertainty of forecasting over such a long horizon, and the desire for pseudo-precision involved in assuming such targets are meant to be operative. I mean – if you manage to get to USD14,999, does that mean we’ve failed? How about USD15,001? Would USD15,002 be significantly better?

February 2013 Industrial Production

I’ve been skipping a lot of data releases this month because work, and because of course with the elections a month away, there are more important topics to look at.

Nevertheless, the economy waits for no man, and this development is interesting enough to warrant a look (log annual and monthly changes; seasonally adjusted):

January 2013 Industrial Production

Yesterday’s industrial production numbers had some good, some bad (log annual and monthly changes; seasonally adjusted; 2000=100):

The good news is that in annual terms, industrial production is looking up a bit; the bad news is that the monthly numbers are flat to negative except in electricity production.

December 2012 Industrial Production

Next up from last week’s data releases, is December’s industrial production numbers. It’s only slightly better than the external trade data (log annual and monthly changes; seasonally adjusted):

Surveys And Opinions: Has No One Ever Heard Of Selection Bias?

I feel like banging my head against the wall (excerpt):

Study shows Barisan expected to win GE13

KUALA LUMPUR: Barisan Nasional is expected to win the 13th general election (GE13) which will be held this year, according to a study.

Titled “Study on Feedback of Undergraduate Voters”, it covered 3,000 respondents from Universiti Malaya (UM), Universiti Kebangsaan Malaysia (UKM) and Universiti Putra Malaysia (UPM).

The respondents involved undergraduates aged 21 and above from the Malay, Chinese and Indian communities, some of whom would be voting for the first time.