Frederic Mishkin: Estimating Potential Output

This is a nice summary of how economists measure the economy's maximum sustainable level of output.

The term "maximum sustainable level of output" is a better description of what we are trying to measure than the more common terms such "potential output" or "full employment." Let me try to illustrate with an example since it is sometimes confusing when economists talk about overheated economies producing more than their potential. How can an economy produce more than its maximum output level? For a graduate student, over the course of an entire quarter, there is a certain maximum sustainable level of effort. It might be, say, 14 hours of class and study per day on average. That is "full employment" or "potential output." But in the short-run it's possible to exceed  that level of output. Right before a test students can work 20+ hours a day, more than full employment, but such a level of output is not sustainable over the longer run. People need a minimum level of sleep, time to eat, etc. So, potential output for students is the level of effort that is sustainable day after day after day, not the most that can be accomplished in a given 24 hour time period.

A business can do the same thing. If it has 10% of its trucks off the road for maintenance at any given time (i.e. "sleeping"), it can keep those on the road when demand is really high to deliver a little extra, keep workers overtime, run the production lines 24 hours a day without maintenance, etc. But that kind of effort, though possible in short bursts, is not sustainable over the longer haul (with the existing level of resources). Trucks and production lines have to be taken down for maintenance every so often or there will be big problems down the road, people won't work long days continuously, etc. Here, too, when we talk about potential output we don't mean how much the economy can produce in the short-run when it's overheated (sort of like just before an exam), but rather what it can do on a sustainable basis over time with a given quantity of inputs.

More formally, then, we can define potential output as the level of output the economy would produce if labor and all other resources are fully and efficiently employed, where full employment means the maximum sustainable level of activity.

But how do we actually measure potential output? Here's Frederic Mishkin with the details on three different approaches. It's not easy:

Estimating Potential Output, by Governor Frederic S. Mishkin, Federal Reserve: This conference focuses on measurement issues, and in my remarks I want to focus on one of the most important measurement issues that we at the Federal Reserve and other central banks face:  How do we determine whether the economy is operating above or below its maximum sustainable level?  That is, how do we estimate the path of potential output?¹

The Federal Reserve operates under a dual mandate to achieve both price stability and maximum sustainable employment.  In that context, it is natural to think of potential output as the level of output that is consistent with the maximum sustainable level of employment:  That is, it is the level of output at which demand and supply in the aggregate economy are balanced so that, all else being equal, inflation tends to gravitate to its long-run expected value.

The combination of the dual mandate and this definition suggests two reasons that estimating the path of potential output is so central to the conduct of monetary policy.  First, to evaluate whether our policies will help achieve the maximum sustainable employment objective of the dual mandate, we need know the level of future output that would be consistent with that objective.  Second, the level of output relative to potential output, which is referred to as the output gap, plays an important role in the inflation process.  When the actual level of output is above potential output--so that the output gap is positive--labor and product markets are excessively tight; then, if things such as expected inflation and temporary supply factors are held constant, inflation will tend to rise.  Conversely, when the output gap is negative and labor and product markets are slack, inflation will tend to fall.  Estimates of the future path of potential output are therefore needed to assess whether the projected path of output that is implied by current monetary policy will lead inflation to move in a direction that is consistent with price stability.

Because estimates of potential output are an important part of central bankers' toolkits, the Federal Reserve and other central banks devote considerable resources to getting the best measures of potential output possible.  In this talk, I want to explore something that Bismarck warned us we shouldn't want to examine:  "what goes into the sausage"--or in this case, what goes into central bankers' thinking about how to estimate potential output.

Broadly speaking, there are three basic approaches to estimating potential output: (1) aggregate approaches; (2) production function, or growth-accounting, approaches; and (3) the newest kid on the block, dynamic stochastic general equilibrium (DSGE) approaches.  Let's look at each of these in turn, with the major focus on the production function approach, one to which we at the Federal Reserve currently pay a lot of attention.

Aggregate Approaches
Aggregate approaches to estimating potential output can be thought of as top down approaches because they look at relationships involving aggregate variables and use them to derive measures of potential output. For example, one way of estimating potential output is to assume that if a change in employment or output is sustainable, then it is likely to be permanent. This assumption suggests using univariate statistical methods to identify the permanent component of changes in output, which could then be viewed as a reasonable measure of potential output. Examples of such an approach include the work of Beveridge and Nelson (1981) and Clark (1987). Although univariate approaches to measuring potential output have the advantage of simplicity and can provide a feel for what potential output might be, they suffer from several disadvantages. First, they require a variety of statistical assumptions about which economic theory provides little guidance--for example, about the correlation between permanent and transitory components or whether the permanent component should be modeled as a random walk. Perfectly sensible alternative assumptions can lead to very different estimates of potential output. Second, these purely statistical approaches do not tell us whether this measure of the permanent component of output movements provides information about the most important aspect of potential output from a central banker's perspective--namely, its association with a stable rate of inflation.

In measuring potential output, we therefore need to bring in some economics. One potentially valuable economic relationship we can use is the "natural rate" version of the Phillips curve, which followed from the seminal research of Nobel prize winners Milton Friedman (1968) and Edmund (Ned) Phelps (1967). Friedman and Phelps demonstrated that there should not be a long-run tradeoff between inflation and unemployment and that the economy will gravitate to some natural rate of unemployment in the long run no matter what the rate of inflation is. In other words, the long-run Phillips curve is vertical, and attempts to lower unemployment below the natural rate will only result in higher inflation.

According to the natural-rate hypothesis, there is a natural rate of unemployment--also more commonly referred to as the NAIRU (non-accelerating inflation rate of unemployment)--at which inflation tends to gravitate to its long-run expected value.² A natural rate of output--that is, potential output--corresponds to the NAIRU. The difference between actual and potential output, the output gap, tells us whether inflation will tend to move up or down, holding things like inflation expectations, energy prices, import prices, and so forth constant. The natural-rate hypothesis thus suggests that potential output can be estimated from a multivariate approach in which potential output is an unobserved component in the relationship between inflation and the output gap. Kuttner (1994) provides a good example of this approach.³

An alternative approach involves deriving the NAIRU directly from estimates of Phillips curves and then using Okun's law--which relates the output gap to the unemployment gap (the actual unemployment rate minus the NAIRU)--to estimate potential output.⁴ These multivariate approaches are reasonably simple and make intuitive sense, but they also have serious drawbacks. First, they require that the specification of the Phillips curve is correct. For example, the model needs to correctly characterize the relationship between the unemployment rate gap and inflation dynamics while taking into account how inflation expectations are formed, and it should not leave out any other variables that have an impact on inflation.⁵ Indeed, many economists criticize the Phillips curve, with some even declaring it dead.⁶

Second, using Okun's law to derive potential output requires an appropriate specification for the dynamics of the relationship between output and unemployment gaps. However, cyclical fluctuations in productivity and labor supply can complicate this relationship. Moreover, Okun's law can be thrown well off course during periods of unanticipated structural change in the economy, such as the early 1970s, when U.S. productivity growth slowed. As a result of these influences, Okun's law has not always been a reliable guide to the relationship between the unemployment gap and the output gap and thus has not always been the most useful guide for estimating potential output.⁷ Finally, even if the Phillips curve and the Okun's law relationships are specified correctly--a big if--the statistical uncertainty about the estimates of the NAIRU, and therefore also about potential output, is very large--certainly larger than a policymaker would like. For example, the estimates of Staiger, Stock, and Watson (1997a and b) of the 95 percent confidence interval for the NAIRU were as much as 3 percentage points wide. Thus estimates of the NAIRU, in isolation, provide policymakers with little real-time insight for assessing the effect of labor markets on inflation pressures.

Production Function (Growth-accounting) Approaches
Because of the shortcomings of the aggregate approaches described above, some researchers estimate potential output using a production function approach that generates an estimate of potential from the underlying factors of production. This approach is sometimes referred to as "growth accounting" because, after the log of a production function is differentiated, output growth can be expressed as a weighted average of the growth of factor inputs--that is, capital services and labor input (hours worked and labor quality)--and a residual--multifactor (also called total factor) productivity growth. The NAIRU concept still plays an important role in this approach because it helps to determine the level of potential output.

A major advantage of the growth-accounting approach is that it focuses on the various factors that drive growth in potential output, rather than simply on the historical behavior of output growth or on the historical relationship between output and labor inputs as in Okun's law. The disaggregated nature of the growth-accounting approach means that more data can be used to estimate potential output. These additional data are likely to be particularly valuable when the economy is undergoing major structural changes--for example, the productivity slowdown starting around the early 1970s, the surge and subsequent slowdown in population growth from the baby-boom generation's entry and (now) exit from the labor force, the remarkable upsurge in labor participation of females in the 1970s and 1980s, and the pickup in productivity growth starting in the second half of the 1990s. Given these advantages, it is not surprising that the growth-accounting framework is widely used to estimate the path of potential output, both by central banks such as the Federal Reserve and by academic researchers.

In its simplest formulation, the growth-accounting framework characterizes output growth as the sum of the growth rate of raw labor hours and the growth rate of output per hour, that is, labor productivity.⁸ In turn, the growth rate of labor hours is described as the sum of population growth (civilian non-institutional population aged sixteen and older), the rate of change of the labor force participation rate, the rate of change in the employment rate, and the rate of change in the number of hours in the average workweek. Labor productivity is decomposed into the contributions of capital deepening (the marginal product of capital--typically estimated as capital's share of income--times the growth rate of capital services per hour), changes in labor quality, and the growth rate of multifactor productivity.

To obtain estimates of potential output growth, researchers can use economic analysis to estimate the individual components above that go into the growth-accounting framework. Much research has been conducted along these lines in the Federal Reserve System and elsewhere. For example, Aaronson and others (2006) examine what variables influence individual decisions to participate in the labor market (birth cohort, age, sex, number and age of children, and so forth). They then use this information together with the relative size of different cohorts to show that the aging of the baby-boom cohort can explain much of the decline in labor force participation since 2000 and why the participation rate is likely to continue to decline further in the future. Given slower projected population growth and what appears to be a downward trend in the number of hours in the average workweek, these results suggest that potential output growth will be slower than it otherwise would have been. Of course, such projections are subject to uncertainty, and economists hold a range of views about the prospects for future labor force growth. One important unknown is whether increases in longevity and better health will boost the labor force participation rates of older individuals more than currently embedded in these projections.

Similarly, economists at the Fed have been at the forefront of research on why labor productivity growth ratcheted upward in the second half of the 1990s and continued at a surprisingly robust pace over the first half of this decade. Oliner, Sichel, and Stiroh (2007), for example, find that the IT (information technology) sector has been a key element in the higher productivity growth that we have been experiencing since the mid-1990s.⁹ But they also find that the sources of growth in the second half of the 1990s were quite different than in the period after 2000. In the 1995-2000 period, labor productivity growth was driven by substantial gains in multifactor productivity in the tech sector, which, in turn, led to sharp reductions in the prices of high-tech equipment and stimulated investment in this type of capital in other sectors of the economy. Since 2000, high productivity growth has apparently been driven importantly by industry restructuring in response to pressures on profits (the firms that saw the sharpest drops in profits were those that had the largest gains in labor productivity) and by a reallocation of material and labor inputs across industries. Their estimate for the current trend labor productivity growth is centered around 2-1/4 percent, but they find large uncertainty around this estimate, with a 95 percent confidence interval that ranges from 1-1/4 percent to 3-1/4 percent. Roberts (2001) estimates even larger uncertainty around structural labor productivity growth.

Despite the advantages of the growth-accounting framework, it still presents some difficulties. For one, there is, as I just highlighted, a large degree of uncertainty surrounding the estimates of the components that go into the growth-accounting formulas. In addition, the growth-accounting framework requires a substantial amount of data, some of which are not especially reliable. For example, it is especially difficult to measure the growth rate of capital services, and the Bureau of Labor Statistics releases its initial estimates with a lag of at least one year. In addition, the bureau's two measures of employment, one derived from a survey of firms and the other from a survey of households, often provide very different pictures of what is happening in the labor market. Also data for capital services, labor composition, and multifactor productivity are not readily available for all sectors of the U.S. economy.

Because it is so difficult to reliably estimate potential output using either the aggregate or the growth-accounting approach, it should come as no surprise that we at the Federal Reserve use a lot of judgment in constructing our estimates of potential output. In particular, we see judgment as playing three important roles in our procedures. First, it enables us to take account of the effects of structural changes in the economy that cannot be modeled directly. Second, it allows us to deal with model misspecifications that cannot be corrected. Third, we can use judgment to correct for measurement errors or inconsistencies in economic data. For example, we judgmentally adjust model-based estimates of the NAIRU to account for movements in the unemployment rate unrelated to changes in labor market slack. Of these, the most important has been the shift in the demographic composition of the labor force, driven largely by the entrance and subsequent maturation of the baby-boom generation. But economists have pointed to a number of other factors that would influence the NAIRU as well.

Another example relates to the way in which we estimate the trend growth rate of multifactor productivity. In particular, estimates of trend multi-factor productivity growth tend to be sensitive to the choice of modeling strategy, a problem that became particularly apparent during the "jobless recovery" of the early 1990s, when the normal relationship between output growth and employment growth appeared to break down. Statistical filtering models like those described by Roberts (2001) tended in real time to attribute much of the weak employment growth to an acceleration in trend productivity. Later on, however, after employment recovered, it became clear that the unusual behavior of employment during that period was explained better as a temporary reluctance by firms to hire than as a step-up in the rate of trend productivity growth. In a similar vein, it may at times make sense to down weight more-recent estimates of the data used in filtering exercises to account for the possibility of future revisions to the data.

Finally, it can often be useful to look at Okun's law as a check on the estimates of potential output derived from the growth-accounting approach. Although, as I noted above, one should not expect Okun's law to hold from quarter to quarter, the relationship is relatively robust over longer periods, and a persistent deviation in the unemployment rate from that predicted by Okun's law might call into question the estimated trends in one or more of the components in potential output.

Dynamic Stochastic General Equilibrium Approaches
The real business cycle literature, which started with the work of Nobel Prize winners Finn Kydland and Edward Prescott (1982), features optimizing agents and emphasizes the role of technology shocks in explaining both economic growth and business cycles. Dynamic stochastic general equilibrium (DSGE) models contain many features of the earlier real business cycle literature, but, because they allow for rigidities and imperfections in markets, they are often referred to as New Keynesian models. The New Keynesian DSGE models provide more-realistic, yet still theoretically elegant, representations of the economy, and their development has been an exciting area of research in macroeconomics in recent years.

New Keynesian DSGE models provide a somewhat different, but complementary, perspective on the definition of potential output than the one I outlined at the beginning of this speech. In particular, we might think of potential output as the level of output that an economy could attain if the inefficiencies resulting from nominal wage and price rigidities were removed--that is, if wages and prices were fully flexible.¹⁰ The definition of potential output as a flexible price equilibrium has much in common with the more conventional definition I discussed earlier because over time prices (and wages) do gravitate toward their equilibrium levels. As a result, the DSGE definition accords with the idea that potential output is the level of output at which inflation tends neither to rise nor fall.

That said, the DSGE view of potential output also has important differences with the earlier approaches to estimating potential output. Although research on using DSGE models to estimate potential output is in its infancy and so should be read cautiously, papers such as Neiss and Nelson (2005) and Edge, Kiley, and Laforte (2007) are finding that the properties of potential output and output gap fluctuations can be quite different from conventional measures. For example, in many DSGE models, potential output can undergo swings over the business cycle, a result that should not be surprising considering that the early real business cycle models viewed the business cycle as being primarily an efficient response to shocks to the economy. In addition, fiscal policy shocks, changes in households' preferences with regard to saving and consumption, changes in preferences about leisure that affect labor supply, and terms-of-trade shocks can all cause potential output to fluctuate. In contrast, growth-accounting approaches to estimating potential output generally assume that such shocks have no important effects on potential output at business-cycle frequencies. As a consequence, their estimates typically have smaller fluctuations than measures of potential output derived from DSGE models, and thus the output gaps in the current generation of DSGE models tend to be less variable than conventional measures and can be quite different for particular periods.

Although the research on DSGE models is promising, measures of potential output and the output gap from these models are controversial. The DSGE measures of potential output are far more model dependent than more-conventional measures because they depend on the estimated parameters of the model and on the model's estimates of the structural shocks hitting the economy. As a result, DSGE models with different characterizations of the economy's underlying structure can produce substantially different estimates of potential output. This is apparent, for example, in the large differences between the potential output measures in the DSGE models of Neiss and Nelson (2005) from those in Edge, Kiley, and Laforte (2007). Moreover, DSGE models often require strong assumptions to identify the shocks to potential output from model equation residuals. The finding that these models imply smaller and less persistent output gaps than traditional models may simply reflect the fact that inefficiencies other than price rigidities, such as real wage rigidities, are important for output fluctuations.¹¹ As a result, some policymakers have been quite critical of the implication of DSGE models that a substantial fraction of business-cycle fluctuations are efficient and so do not require a response from monetary policymakers.¹²

Implications for Policy
Now that we have looked inside the sausage of estimating potential output, I hope you have not lost your appetite for thinking about what these measurement issues mean for monetary policy. As I indicated earlier, considerable uncertainty surrounds the measures of potential output derived from any of the approaches I have discussed. In addition, there is also what economists call Knightian uncertainty (named after the famous University of Chicago economist Frank Knight)--the fact that we are not even sure of the appropriate modeling approach to measure potential output. Adding even more to the uncertainty of potential output measures are (1) that the observable data do not always correspond to the data we would like to have to produce measures of potential output and (2) that initial estimates of observable data can subsequently be revised substantially, resulting in a very different picture of what is happening to potential output and the output gap. Orphanides (2001) points out that output gaps were grossly mismeasured in the 1970s, in part because the initially published data did not reflect the true state of the economy.¹³

Where does the high uncertainty about actual and potential output leave us at central banks? Does it mean that we should abandon our focus on potential output and output gaps in making decisions on monetary policy? I think not. For better or worse, we cannot escape the need for information on output gaps so that we can forecast the future path of inflation and evaluate the current setting of our monetary policy instruments. However, we also need to recognize that because measures of potential output and output gaps are so uncertain, we must always be aware that they might be providing misleading signals as to the future course of inflation and the appropriateness of the stance of policy. In assessing whether there is slack in the economy, we at central banks look not only at our estimates of output gaps but also at a wide range of indicators drawn from the labor, product, and financial markets to provide us with a perspective on the balance of supply and demand in the economy. Most important, the substantial uncertainty in our measures of potential output implies that we need to be cautious about taking on board the implications of our current estimates of the output gap. For example, if inflation is moving in a different direction than the output gap would suggest, then we should take seriously the possibility that our output gap measure is not providing us with reliable information.

The bottom line is that we must never take our eye off of the inflation ball. Good policymaking requires that we acknowledge what we are unsure about, and this requirement applies particularly to measures of potential output.

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References

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Altig, David, Terry Fitzgerald, and Peter Rupert (1997). "Okun's Law Revisited: Should We Worry about Low Unemployment?" Economic Commentary, Federal Reserve Bank of Cleveland (May).

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Estrella, Arturo, and Frederic S. Mishkin (1999). "Rethinking the Role of NAIRU in Monetary Policy: Implications of Model Formulation and Uncertainty," in John B. Taylor, ed., Monetary Policy Rules. Chicago: University of Chicago Press, pp. 405-30.

Fallick, Bruce, Charles A. Fleischman, and Jonathan Pingle (2006). "How the Graying of the Baby Boom Affects the U.S. Labor Market," in The Economic Outlook for 2007: Papers Presented at the Fifty-Third Annual Conference on the Economic Outlook. Ann Arbor: University of Michigan, pp. 102-18.

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Oliner, Stephen D., and Daniel E. Sichel (2000). "The Resurgence of Growth in the late 1990s: Is Information Technology the Story?" Journal of Economic Perspectives, vol. 14 (Fall), pp. 3-22.

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Orphanides, Athanasios, Richard D. Porter, David Reifschneider, Robert Tetlow, and Frederico Finan (2000). "Errors in the Measurement of the Output Gap and the Design of Monetary Policy," Journal of Economics and Business, vol. 52, (January-April), pp. 117-41.

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Staiger, Douglas, James H. Stock, and Mark W. Watson (1997b). "The NAIRU, Unemployment, and Monetary Policy," Journal of Economic Perspectives, vol. 11 (Winter), pp. 33-49.

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Footnotes

1. I want to thank Andrew Figura, Charles Fleischman, John Roberts, and William Wascher for their helpful comments and assistance on this speech. Return to text

2. The term "NAIRU" comes from a paper by Nobel prize winner Franco Modigliani and Lucas Papademos, now the vice president of the European Central Bank (Modigliani and Papademos, 1978). Although NAIRU and the natural rate of unemployment are frequently used interchangeably, there is a subtle distinction between the two. The natural rate of unemployment is the rate at which inflation would tend to gravitate to its long-run expected value, while NAIRU, as originally defined, is the unemployment rate at which inflation will have no tendency to move up or down. Depending on what shocks are hitting the economy, the NAIRU could deviate from the natural rate of unemployment (for example, see Estrella and Mishkin, 1999). Because the NAIRU terminology is more common than the natural rate of unemployment terminology, I am using the NAIRU terminology even though I think that the natural rate of unemployment terminology is more accurate. Return to text

3. Other examples of the multivariate approach include Apel and Jansson (1999), Cochrane (1994), and Dupasquier, Guay and St-Amant (1999). Return to text

4. Okun's law was originally specified as the relationship between real GDP growth and changes in the unemployment rate (see Okun, 1962). Return to text

5. See Gordon (1982 and many subsequent papers) for a description of the "triangle" model of inflation. The three sides of the triangle are inflation inertia (captured by the lags of inflation), excess demand (measured by the unemployment rate gap or GDP gap), and supply shocks--such as the relative prices of imports, food, and energy. Following George Perry's (1970) early work, it is common to adjust the NAIRU for changes in the composition of the labor force. Fallick, Fleischman, and Pingle (2006) estimate that shifts in the demographic composition of the labor force can explain a decline in the unemployment rate of nearly 1 percentage point between 1977 and 2006. Return to text

6. See, for example, Atkeson and Ohanian (2001). Return to text

7. See, for example, Altig, Fitzgerald, and Rupert (1997), Rudebusch (2000), and Groshen and Potter (2003). Return to text

8. Because official series for productivity for all sectors of the economy are not available, the growth-accounting framework most often focuses on the private nonfarm business (NFB) sector, which in the United States accounts for more than three-quarters of total output. Productivity for the overall economy is then derived from estimates for the nonfarm business sector and then cruder estimates for the other sectors (government, farm, housing services, and households and institutions) generally based on the univariate and multivariate statistical approaches described earlier. Return to text

9. See also Oliner and Sichel (2000, 2002), Corrado and Slifman (1999), and Jorgenson, Ho, and Stiroh (2004), among others. Return to text

10. See, for example, Goodfriend and King (1997) or Woodford (2003). Return to text

11. For example, Blanchard and Gali (2007) have proposed a model with real wage rigidities that, if incorporated into a DSGE model, would likely show output gap estimates that are more similar to traditional gaps. Return to text

12. See, for example, Bean (2005). Return to text

13. See also Orphanides and others, (2000) and Orphanides and van Norden (2002). Return to text

Comments

[cm]

Wow, before I read the long article, I dispute the claim that "14 daily hours of class and study" can be sustained, or are even realistic. When you figure 8 hours of sleep (and nobody please give me how you can do with less) and a not too generous 1+1 hour of personal activities between bed and door, the rest of the day is certainly not filled with only class and study, but enough slack where (part of) the mind can rest, even when this slack cannot be used "productively" for other substantial activities.

Likewise, the idea that somebody can work a fully productive 40 hours per week (as opposed to being 40 hours in the office or on the floor and working perhaps 30 hours tops) and sustain this for decades without mental burnout or physical breakdown is rather questionable -- "all work and no play makes Jack a dull boy".

[calmo]

Flippant me skips (Mark says "nice", but I cannot do it people. Twas the thunderous "Dynamic Stochastic General Equilibrium Approaches" and the references rivaling the phonebook.) Mishkin to nudge cm.

Remember those dogs who were spouting ad nauseum "24/7" during the late 90s? Well, the mental horsepower during that period was undetectable.
Of course there are genuine instances of this phenomena of working 7 days a week, but it gets little mention and of that, one would think that the extraterrestials had landed...those illegal aliens, who do not rest at the weekend. [Sometimes the reflective coating on the economists' windows designed to reflect the heat is applied on both sides and the ground outside mysteriously becomes those infamous "externalities".]
I could be inspired to continue for atleast another 5 minutes here...accomplishing units of productivity (excuse me while I amuse myself over the MBA talk of "accomplishment units") not seen by mere mortals taking hours, days even, to achieve the same results.
I am reminded of Hume who finishes his Human Nature by the time he was 21...not from slugging away at it "24/7" or 14hr/day, but by bein inspired...and so far cm is doin it for me better than Mish.

[Mark Thoma]

Thanks - I hate getting zero comments on a post. Guess it wasn't dynamic enough.

[calmo]

Shoot Mark, don't stoop to my standards.
I'm warning you.
Seriously, I can get to Mishkin and any lack of dynamics, but in my own time (this bein the famous weekend an all).
I shall return to my non-flippant self at a later date to dispense my greatest (ok, possibly even serious) attention to the deadly (possibly deadly serious) Frederic Mishkin.

[2slugbaits]

Mark,

I'm still try to square what Mishkin wrote here with what he said in your post from a month ago regarding inflation being solely a monetary phenomenon. Here it sounds like Mishkin is conceding that overheating the economy can generate inflation; but in the piece from a month ago he seemed to be arguing that neither fiscal shocks nor supply shocks could create inflation, only monetary shocks. Good post, I'm just trying to sort it out.

[Mark Thoma]

The inflation occurs during the adjustment back to the long-run equilibrium, but after that it stabilizes.

I.e., the inflation he is talking about is a short-run, temporary type that occurs with the adjustment process. When the economy is overheated (y>y*), inflation lowers aggregate demand (quantity demanded for the micro types) until y=y* again, but once at y* the inflation pressure subsides.

[2slugbaits]

Okay, but then it sounds like he is using "inflation" in two different senses. In the article from a month ago Mishkin distinguished between "inflation" and a change in the price level. If I'm understanding this later post it sounds like here he is using "inflation" in a sloppier sense and he actually means changes in price level. I bring this up because the earlier article from Mishkin went to great pains to distinguish between inflation and changes in price level and here he seems to be ignoring that distinction.

[Mark Thoma]

Yes, as I noted in the original post, we are not careful about making the distinction.

The reason is that from a policy perspective you may be able to help with both short-run and long-run inflation problems, so using one term is a convenience when talking about policy options.

I've been meaning to do another post about this - to say, okay, now that we understand the distinction between long-run systemic inflation and short-run price adjustments back to the long-run equilibrium, what should the Fed do? How should it respond to each, etc.

[Outside the Box]

Demographic induced slowing of the potential growth rate. This may partly explain recent corporate emphasis on investing overseas rather than in the US. Slowing population growth, coupled with an aging population, limit potential domestic profit opportunities. Historically, older people have tended to work fewer hours, and spent less. Total US GDP is likely to continue to shrink as a percentage of total world GDP.

[Andrew C]

It is a pity Professor Mishkin was not more precise about the link between the output gap and price changes, for this is one of the most curious parts about modern monetary economics. There are at least three main responses to a temporary positive "output gap": prices rise in response to the pressure on activity levels, but subsequently fall when activity levels return to normal(so the expected future price change is negative); prices rise, and then stay at the same level when activity returns to normal (so the expected future price rise is zero); or prices rise, and then keep on rising at the same rate (so the future inflation rate is equal to the rate of increase in prices when activity was high.) Mishkin doesn't indicate which of these three responses he thinks normally represents the economy. It seems that individual commodity markets are represented by the first, as prices rise then fall in response to high activity levels. (When activity levels are low, prices fall by only a little as goods can be stored.) But most central banks seem to be believe the nominal economy is essentially unstable in the sense that a temporary rise in activity levels will lead to a permanent increase in inflation expectations unless some contervailing action is taken. This fear that price changes have a destabilising effect on inflation expectations appears to be the main reason that central banks are largely reluctant to allow prices to change in response to demand shocks. Fortunately, disaggregated commodity markets have fewer fears of this type, with spot and future prices often seen in backwardation in response to temporary supply or demand shocks.

Perhaps central banking would be easier if there were futures markets on CPI contracts and central bank governers could jawbone expectations down in a measurable way whenever the output gap was temporarily high. On second thoughts, maybe that is what the TIPS markets are implicitly providing.

[riverbed application acceleration]

John sums up telepresence from a network perspective, "Telepresence is an interactive real- time application, which means it is delay sensitive, loss sensitive and jitter sensitive. This sounds familiar: it is just like VoIP, with the one difference being that it has huge bandwidth requirements." It's that last part that makes things more difficult. No form of QoS can allocate bandwidth that doesn't exist and it doesn't have provisions to force the application to downscale the experience based on realtime metrics. ...