In my previous blog post I outlined a four-equation model to be used for real-world macroeconomic forecasting. In today’s post I will look a bit closer at one of these equations – or rather I will discuss how we can think about forecasting the supply side of the economy.

So we basically want to understand how and why the (short-run) aggregate supply (AS) curve shifts in our AS/AD framework. I will discuss the different kinds of supply shocks we can think of and then I will discuss how we can use financial market data to forecast the impact of such shocks on aggregate supply.

But first let us think about how most real-world macroeconomic forecasters think about supply shocks. The theoretical economist might be surprised to learn that there basically is no supply side in the “mental models” most forecasters are using.

Rather most real-world forecasters think of supply shocks as a kind of demand shock. Lets for example think of an increase in the price of oil. In an AS/AD framework an oil price hike would shift the AS curve (for an oil importing country) to the left causing real GDP growth to drop and inflation to increase. This is basically a story of higher production costs leading companies to reduce output.

However, in the thinking of real-world forecasters an oil price hike is something which increase inflation and as a direct consequence causes real disposable incomes to drop. As real disposable income drops so does private consumption growth and given that most of these real-world forecasters are doing what I have termed national accounting economics a drop in private consumption automatically leads real GDP to drop (remember Y=C+I+G+X-M).

In reality most real-world forecasters therefore assume two things. 1) Prices are not only sticky, but fixed and 2) the monetary policy regime is a fixed exchange rate regime meaning there will be no monetary offset to what effectively is an velocity-shock. Therefore “translating” what real-world forecasters are doing into a AS/AD framework when thinking about oil prices is to think of an oil price increase as a drop in aggregate demand and at the same time assume that the AS is flat. Hence, in the real-world forecaster’s model an oil price increase shifts the AD curve to the left as illustrated below.

We see that this causes real GDP growth to drop, while leaving inflation unchanged (due to the flat AS curve). The real-world forecaster obviously doesn’t think an oil price hike will not cause inflation to increase. However, she will model that in a separate (mostly) unrelated model.

In fact as a general rule the real-world forecaster will think of the determination of real GDP as 100% demand side driven and at the same time think of inflation as 100% supply side driven. In that sense I don’t think I am wrong when claiming that to most real-world forecasters inflation is never a monetary phenomena, but rather completely driven by supply side/cost factors.

Hence, she will probably use inflation models that mostly rely on developments in wages, oil prices, food prices and exchange rates, while probably also assuming some “mean-reversion” in the sense that over 2 or 3 years inflation will converge towards a given inflation target.

This kind of faulty thinking is also what leads some economists to believe that an earthquake or war is great for the economy. What they see is the effect on aggregate demand, while completely ignoring the supply side part of the story.

What I here will argue is that we should stop thinking about supply shocks as quasi-demand shocks. A supply shock is not the AD curve moving along the AS curve, but rather the AS curve shifting left or right and moving along the AD curve.

Different types of AS shocks

To be able to practically do forecasting of the impact of supply shocks we need first to think of what kind of supply shocks we have. To do this I think this rudimentary production function is useful:

(1) Y= A*f(K, L, O)

Here Y is the level of real GDP, while K, L and O are capital, labour and raw materials (such as oil). A is what we call the Total Factor Productivity (TFP) – or said in another way how clever we are at using the three productions factors in production.

Hence, we can basically think of shifts in the AS curve as shocks to one or more of these four variables (A, K, L or O). These shocks can be permanent of temporary.

Getting practical with supply shocks

Doing real-world macroeconomic forecasting often is about dealing with practical matters rather than with theoretical issues (that is why most real-world forecasters over time turn into pretty bad theoretical economists). What I am trying achieve here is how we can maintain a sober theoretical approach to forecasting while at the same time keeping things practical.

Furthermore, it should be noted that a lot of “forecasting” is not really forecasting in the sense of forecasting the future. Rather it is about “forecasting” how for example quarterly GDP data for the present quarter will come in when they are published at a later stage. So what we really are looking for are real-time indicators. Here I believe the financial markets are extremely useful.

Furthermore, financial markets are also giving us insight into the future impact on real GDP growth and inflation of shocks.

But lets get practical and that also means that I here only will focus on two of the four different types shocks outlined above – capital shocks and raw material shocks. I focus on these two primarily because I think those are the most important and common supply shocks that we empirically are facing.

Cost of capital shocks

Often real-world forecasters think of the cost of capital (the “interest rate”) as something which is determined by monetary policy and something, which is impacting private consumption and investments and hence “aggregate demand” (in the most vulgar Keynesian form).

However, this is not what I think of here. Rather to me the cost of capital is something that determines investments and hence the capital stock in the economy. That is a supply factor.

Let me illustrate what is going on in Russia at the moment. We are seeing massive capital outflows from Russia. The real-world forecaster probably sees this as a negative shock to demand. However, that in my view is not what it is. (The capital outflows, however, can have an indirect impact on aggregate demand if it causes the central bank to tighten monetary policy).

Rather what we are seeing is a drop in the supply of capital available in the Russian economy. Capital is simply becoming less easily available and hence more expensive.

The case of Russia today also provides us with a clue to what kind of financial market indicators we can use to identify shocks to the cost of capital.

I would particularly focus on three different measures of the cost of capital.

First, what is the stock market telling us? If the Russian stock market drops relative to earnings or relative to stock markets in the rest of the world then that is telling us the cost of capital is increasing in Russia.

Second, we can also look at the price of ensuring against a Russian government default – a so-called Credit Default Swap. This is also an indirect measure of the cost of capital in Russia.

Third and finally we can also look at the bond yield spread for Russian bonds – either government or corporate bonds – versus similar bond yields in the US or the eurozone.

Again in practical terms I think it would be useful to create an index of these different measures of the cost of capital to get a single “number” for the shocks to the cost of capital in a given country. I will try to do that in a later post when I try to estimate (or simulate) an AS curve for one or more countries.

Raw material costs – it is mostly about oil prices

Shocks to oil prices seem to be the most common shock to aggregate supply across countries in the world. Furthermore, the correlation between oil prices and other commodity prices historically has been fairly high so in my view for the practically oriented macroeconomic forecaster the most easy way to capture shocks to raw material costs is simply to look at the price of oil in local currency.

Hence, in reality there will be a quite high correlation between the oil price and most other commodity prices. Therefore, for practical purposes using the oil price measured in local currency will be entirely enough to capture most of the raw material shocks to most economies.

Putting it all together

In my previous post I outlined a simple AS curve, which I think would make both economic and econometric sense for most economies in the world.

(1) y = a0 + a1*n + SS

Where y is real GDP growth (%q/q), n is nominal GDP growth and SS is a supply shock. a0 and a1 are coefficients.

Given the discussion above we can now identify SS by thinking of SS as either cost of capital shocks or shocks to the oil price. There are certainly other supply shocks out there, but for practical purposes I think that these two supply side factors “capture” the large majority of of supply side shocks in most real-world economies.

This gives us an expanded AS curve:

(1)’ y = a0 + a1*n + a2*CoC + a3*OP + a4*Ygap(t-1)

Where CoC is an index for the cost of capital and OP is the oil price in local currency (de-trended). Note that I here also have included the output gap (Ygap) lagged one quarter to take into account the equilibrating process towards “full employment” in the economy. a2, a3 and a4 are coefficients.

Next step – lets try to estimate the model

I will try to do that in my next blog post, but my readers are obviously very welcome to do the econometrics on their own on whatever country they want.

PS I use the term “real-world macroeconomic forecasters” above as a generalisation. Some might argue that this is far too much of a generalisation and that the world of forecasting has changed. That might be it and I know a lot of central bankers are terribly proud of their DSGE models, but if they look themselves in the mirror they will soon acknowledge the fact most arguments made by central bank officials are not based on DSGE thinking, but rather on national accounting economics.

Most economists who have worked as macro economists for governments, central bank or commercial banks will sooner or later be engaged in doing macroeconomic forecasting.

Oddly enough most economists are not really educated to do forecasting. What we learn in university is mostly theoretical. We learn about different economic schools and models (Keynesian, New Keynesian, RBC, monetarist etc). And maybe we learn about econometric methods to estimate different “models”, but that is very far from the daily realities of real-world macroeconomics forecasters.

The fact is that the challenges that real-world macroeconomic forecasters are facing are of a completely different type than the theoretical models that we learn in university.

Just think about the present discussion about how the weather has influenced US macroeconomic data recently. Or think about the discussion that we constantly have about the quality of Chinese macroeconomic data – can we really trust the data produced by a communist government or any government? Think about the problems of seasonal adjustments. Are seasonal patterns really constant over time? And we could go on about such technical issues. Something we never heard about in the university.

This are the kind of challenges those of us who do macroeconomic forecasting deal with on a daily basis. It is not very sexy, but it is a very large part of real-world macroeconomic forecasting.

I believe that these unsexy challenges actually are among the main reasons why so many macroeconomic forecasters become quite ignorant about theoretical issues and why they tend to practise a quite vulgar form of “Keynesianism” – or what I have earlier termed national accounting economics.

That basically means that most real-world forecasters start out with the familiar national account identity – Y=C+I+G+X-M. And then they simply make “sub-forecasts” for private consumption growth (C), investment growth (I), public expenditures (G), exports (X) and imports (M) and then add up all these elements to a forecast for real GDP growth (Y). This would often (always!) include some fiddling with the data so to ensure a nice “profile” for the forecast path for real GDP as most forecasters prefer that the real GDP growth returns to trend growth within 2 or 3 years.

What is notable about this form of forecasting is that it ignores a lot of factors. Most important in my view is that the monetary policy rule/regime is totally ignored. To the extent any monetary policy is thought about it is mostly considerations about the impact of interest rates on private consumption and investments. Second, supply shocks are mostly reduced to a quasi-demand side story in the sense that for example an increase in the oil price is something, which reduces real disposable incomes rather than something, which is increasing production costs. There is no direct relationship between the production and demand side of the economy and inflation and inflation forecasts are mostly made in other “parallel” models, that are not directly related to the rest of the modeling.

An alternative approach – applied AS/AD forecasting and modelling

I am no saint and I have also for years used a similar approach when I have been involved in macroeconomic forecasting – both working for government and in the financial sector. In many ways I have for years lived in a parallel universe or rather one universe where I was thinking in a certain way (as a Market Monetarist) and another universe where I was doing macroeconomic forecasting (national accounting style).

I had the same problem as everybody else – I had to overcome a lot of practical problems. That led me to continue to do forecasting in a way I fundamentally found unsatisfactory. I am still not fully satisfied with the way I do macroeconomic forecasting in my day-job, but I think I am a lot closer to getting it right now than before. It has been a nearly 20 years journey.

In this post – and more to come – I will try to sketch an alternative approach to do real-world macroeconomic forecasting. Not because I necessarily think it will produce more accurate forecasts (we can’t forecast shocks!), but because I think the approach I will outline here will make a lot more sense economically speaking.

My starting point is a simple AS/AD framework in the spirit of Cowen and Tabarrak. We start with a familiar graph.

In the AS/AD framework we basically have only two macroeconomic variables – inflation (p) and real GDP (y). Of course from this we also get nominal GDP (n=p+y).

I think we from this can estimate fairly simple AS and AD curves that will be easy and simple to use in real-world macroeconomic forecasting and analysis.

A four-equation model

In later blog posts I will get into more details with my overall framework, but for now we will start out with a four-equation model.

We start with the AS curve. The important thing is to differentiate between when we “move along” the AS curve and when the AS curve shifts left or right.

It follows from economic theory that the (short-term) AS curve is upward sloping. This means that there is a positive correlation between on the one hand real GDP growth (y) and inflation (p) and therefore also nominal GDP growth. We can use this insight to write a simple equation for our AS curve:

(1) y = a0 + a1*n + SS

Where y is for example the quarterly real GDP growth rate, while n is nominal GDP growth. a0 and a1 are coefficients. SS is a shift variable that will capture exogenous supply shocks (shifts in the AS curve). In a slightly more advanced model we could also include the (real) output gap in model – so real GDP growth would be higher as long as the output gap is negative. That would mean that the output gap would gradually close even if nominal GDP (aggregate demand did not change). That would account for the long-run AS being vertical.

And variations of that equation are easy to estimate and my experience is that there is a very good fit to such models for most countries I have tried to apply this type of model to. The clever reader will realise that this is basically a simple form of a Phillips curve.

In a later post I will return to discuss the AS curve in more detail and I will particularly discuss how to use financial market information to forecast the impact of supply shocks on real GDP growth and to discuss different forms of supply shocks.

In the spirit of Cowen and Tabarrak our AD curve is basically the equation of exchange (in growth rates):

(2) n = m + v

Where n is nominal GDP growth, m is the money supply (for example M2) growth rate, while v is the growth rate of money-velocity.

We can think of different ways to model both m and v. That is our two next equations.

Equation (3) is an equation for the central bank’s policy rule. We assume that the central bank directly controls the money supply. This is of course not necessarily a realistic assumption and in an alternative formulation we could write (2) as n=k*b*v, where k is the money multiplier and b is the money base. However, for simplicity we will start with the simple version here.

We can obviously think of all kind of policy rules – inflation targeting, nominal GDP targeting or some kind of exchange rate targeting. This is the general form of our policy rule:

(3) m = f(TARGETS) + PS

m is the policy instrument, the money supply, while TARGETS is whatever policy target(s) we can think of and PS is discretionary changes in monetary policy (policy shocks).

Whether we want to estimate or “simulate” (3) will depend on whether or not there has been any changes (structural breaks) in the overall monetary policy regime or not.

Finally we have an equation for the development in money-velocity (v). This is basically a model for the development in money demand. There is a huge literature on this, but overall I think it is useful to apply a fairly eclectic approach to modelling v:

(4) v=f(V0, V1,…, VN) + VS

Where velocity is determined as a function of a number of variables – for example the yield curve or the exchange rate etc. VS is a velocity shock, which we should think of as unpredictable shocks to money-velocity – policy induced or not.

Overall, I think this framework can easily be applied to most economies in the world and a major advantage is that we can get down to the basics by just looking at three macroeconomic variables: (the growth rates of) Real GDP, Nominal GDP (or the GDP deflator) and the money supply.

Based on these variables and equations we can basically decompose the actual development in any economy between on the one hand supply shocks and on the other hand demand shocks (monetary policy shocks and velocity shocks).

How will we do forecasting with this model?

Operationally we could think of a set-up where we keep (the growth of) money-velocity (v) constant in the forecasting period. Furthermore, we would assume that our three shocks (supply, monetary policy and velocity) are zero as we by definition cannot forecast shocks. However, we could also think of these shocks as “forecastable” in the very short-term (within a quarter). I will in later posts return to this topic – how to use financial variables as an approximation of the expected impact of shocks to aggregate demand and supply.

If we want to apply our model to actual forecasting we first see that the monetary policy rule is the rule that “closes” the model. The central bank is assumed to set the money supply growth rate to hit a given monetary policy target – for example a NGDP target.

So if we for example have a given money supply growth rate at our starting point and NGDP growth is at a rate slower than the targeted growth rate and we are assuming that the central bank will try to hit the target NGDP growth rate in 2 or 3 years then we get a “growth path” for the money supply. We could think of this growth being implemented by the central bank or by the market (for example through a Scott Sumner style NGDP futures market).

This will give us a forecast for the growth rateof  not only the money supply, but also for real GDP and inflation (the GDP deflator). Obviously we can build more or less sophisticated models for private consumption, investments, unemployment, the trade balance etc. to expand the model, but the overall framework would be the same.

What is important is that we with this four-equation set-up can both get an understanding of what shocks are driving the business cycle in real-time and we can at the same time use the model to forecast RGDP growth and inflation (and obviously NGDP growth). And most important – this will not only be empirically useful, but it will also – and this is much more important – allow us to make economically meaningful forecasts.

In the coming weeks I will try to go more into detail with a discussion of the individual equations in the model – including how we can use financial market data to improve macroeconomic forecasting.

And then hopefully at a point I will be able to present a model for a “random” country to show that this set-up actually has a practical application.

PS Returning to the US weather and the macroeconomic development in the US I believe that the framework presented above should make it fairly easy to decompose present US growth to better understand whether we are now seeing a negative demand shock (monetary tightening) or just a short-term negative supply shock (bad weather).

A couple of days ago Scott Sumner had an interesting post on the theme “It’s policy regime that needs fixing”.

In his post Scott made an interesting observation:

“Good policy is rules-based, but first you must use discretion to decide the optimal rule. But we never actually do pick a rule, and hence the frustration of us rules proponents, we are always seeing the wrong debate take place.”

That reminded me about something I wrote back in May 2012. It is very similar to what Scott is saying. This is from my post “The discretionary decision to introduce rules”:

“At the core of Market Monetarists thinking is that monetary policy should be conducted within a clearly rule based framework. However, as Market Monetarists we are facing a dilemma. The rules or rather quasi-rules that are presently being followed by the major central banks in the world are in our view the wrong rules. We are advocating NGDP level targeting, while most of the major central banks in the world are instead inflation targeters.

So we have a problem. We believe strongly that monetary policy should be based on rules rather than on discretion. But to change the wrong rules (inflation targeting) to the right rules (NGDP targeting) you need to make a discretionary decision. There is no way around this, but it is not unproblematic.”

Scott’s as well as my point is that we need to get away from discussing the day-to-day policy actions of central banks. It for example does not make much sense to discuss whether the Federal Reserve should “taper” or not when it is unclear what monetary policy target the Fed is trying to hit.

Hence, we can really only understand whether monetary policy is too tight or too easy if we know what target the central bank is trying to achieve. Obviously we can assume that the Fed has a given nominal target and then argue that given that target it is right or wrong to taper presently.

Focusing on discussing monetary policy rules will also do a lot to unite Free Market economists who during the Great Recession have been split over the issue of how best to respond to the Great Recession. The point is exactly that central bankers should not be fire-fighters who makes discretionary decision to save the world from fires they often themselves have started. Optimally we should get rid of central bankers altogether and let rules and markets determine monetary conditions.

What we should be debating is what monetary policy rules best ensure nominal stability. I personally continue to think that NGDP level targeting is the “best” rule in terms of achieving nominal stability and the smallest level of distortion of relative prices, but there might be pragmatic reasons why other rules might be preferable for certain countries.

PS I am struggling a bit with a writer’s block at the moment. The key reason is that it so far has been an extremely busy year for me as there seems to be a constant flow of bad news for Emerging Markets. Lately of course the Ukrainian-Russian conflict has been taking a lot of my time and mental energy. I must admit that I am deeply worried about events in Eastern Europe and I find myself making historical comparisons that I don’t like to think about. Frustrations over bad central bankers is one thing – it is a completely another thing to worry about men with guns and bombs. Maybe in fact there is a relationship between the two things…anyway I would welcome any suggestions for topics regarding monetary issues that my readers would like to read about.

PPS If you want to understand what I worry about in relationship to the Ukraine-Russia situation you should read this book. Hopefully I am not overly hysterical in my thinking…

March 5th is my birthday (yes, I am turning 43 today)

In 1953 Stalin died on this date. Last year on this day Hugo Chavez died. This year my nephew’s hamster died.

And in 1946 on this date Churchill made this important speech, which unfortunately these days seems more important than at anytime since 1989.

Update: Also on this date – today – Liz Wahl did this. Bravo Liz.

As geo-political tensions continue to increase and all eyes are on Ukraine I have been very busy analysing and talking about the impact on the markets and the global economy.

This is from an interview I did with Radio Free Europe today:

Lars Christensen, head of emerging markets analysis at Danske Bank in Copenhagen, says a big reason for the fall is foreign investors’ perceptions of Russia are changing as the Ukrainian crisis deepens.

“There is a fear among investors that Russia is moving away from the West. Whether or not that should be called a new Cold war is controversial but, at least, investor sentiment is influenced by the fact that we are seeing a cooling down of relations between East and West. And obviously in such an environment you would see less foreign direct investment into Russia,” Christensen says.

…But the pressure of the Ukraine crisis could make it even harder now for Russia to decide where to stop the ruble’s slide.

Christensen notes that the farther the ruble falls, the more expensive it becomes for the central bank to protect it.

“We saw in 2008, in connection with the Georgian conflict and the financial crisis, that the ruble came under significant pressure and that when the Russian central bank at that time intervened heavily and spent $200 billion to defend the ruble and failed that it had very significant costs for the Russian economy in terms of high interest rates and significantly lower growth,” Christensen said.

“So, I think the Russian central bank is aware of those risks and is therefore likely to allow the ruble to continue to depreciate but will from time to time step in and try to curb that sell-off.”

…But if the Ukraine crisis is not solved, much greater economic troubles for Russia — and for the West — could lie ahead.

As Moscow maintains a threatening posture toward Ukraine and the West responds with warnings of possible sanctions, the exchanges sound increasingly like echoes of the Cold War.

And any real slide back toward Cold War risks weakening the infrastructure of trade agreements that today underpins much of the global economy.

“If we were to move to a new Cold War-style scenario, then we would see more fundamental negative impacts that would mean higher defense spending, a less open, global economy, and trade barriers coming up between East and West,” Christensen says.

“I think we often forget how beneficial the end of the Cold War has been for the global economy and it would be terrible from a global economic perspective to see us moving in the other direction.”

I wish I had a more positive message to talk about, but unfortunately geo-political risks in Europe overshadows everything else at the moment.

I just found a reference to this sensational new Working Paper – “Stock Picking Skills of SEC Employees” – by Shivaram Rajgopal and Roger M. White. This is the abstract:

“We use a new data set obtained via a Freedom of Information Act request to investigate the trading strategies of the employees of the Securities and Exchange Commission (SEC). We find that a hedge portfolio that goes long on SEC employees’ buys and short on SEC employees’ sells earns positive and economically significant abnormal returns of (i) about 4% per year for all securities in general; and (ii) about 8.5% in U.S. common stocks in particular. The abnormal returns stem not from the buys but from the sale of stock ahead of a decline in stock prices. We find that at least some of these SEC employee trading profits are information based, as they tend to divest (i) in the run-up to SEC enforcement actions; and (ii) in the interim period between a corporate insider’s paper-based filing of the sale of restricted stock with the SEC and the appearance of the electronic record of such sale online on EDGAR. These results raise questions about potential rent seeking activities of the regulator’s employees.”

What can I say – or rather what should I say other than WAUW!

PS You might want to listen to Elvis Costello’s “Watching The Detectives” (HT Dave O)