Board of Governors of the Federal Reserve System
International Finance Discussion Papers
Number 1000, June 2010  Screen Reader
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Abstract:
Investmentspecific technology (IST) shocks are often interpreted as multifactor productivity (MFP) shocks in a separate investmentproducing sector. However, this interpretation is strictly valid only when some stringent conditions are satisfied. Some of these conditions are at odds with the data. Using a twosector model whose calibration is based on the U.S. InputOutput Tables, we consider the implications of relaxing several of these conditions. In particular, we show how the effects of IST shocks in a onesector model differ from those of MFP shocks to an investmentproducing sector of a twosector model. Importantly, with a menu of shocks drawn from recent empirical studies, MFP shocks induce a positive shortrun correlation between consumption and investment consistent with U.S. data, while IST shocks do not.
Keywords: DSGE models, multifactor productivity shocks, investmentspecific technology shocks
JEL classification: E13, E32
In postWWII U.S. data, the relative price of equipment investment has a downward trend and varies over the cycle. In a pair of highly influential papers, Greenwood, Hercowitz and Krusell (1997, 2000)hereafter GHKshowed how a modified onesector model can be used to analyze these regularities. They introduced an investmentspecific technology (IST) shock to distinguish equipment investment from other finaluse categories.^{1} In particular, when allocated to equipment investment, the single and undifferentiated good yields more or less installed capital depending on the level of IST, but when allocated to other uses it remains unchanged. Over the last decade, IST shocks have become a leading candidate explanation for postwar business cycle fluctuations.^{2} This paper is about the interpretation of IST shocks.
GHK point the way to an interpretation. They show that their onesector model is a special case of a model with two sectors, one that produces a good used only for equipment investment and another that produces a good used for both consumption and structures investment. Under certain conditions, an IST shock to equipment investment in their onesector model is equivalent for aggregate variables to a multifactor productivity (MFP) shock to equipment production in the twosector model. This "aggregate eqivalence" (AE) result provides a basis for interpreting the IST shock as an MFP shock.
It may come as no surprise that the GHK conditions for AE are quite restrictive and that some of them are clearly at odds with the data. They entail a production structure that differs significantly from the one implied by the U.S. InputOutput (IO) Tables. In addition, capital is perfectly mobile between sectors. Furthermore, there are no costs of adjusting investment.
We investigate the effects of reasonable departures from the GHK conditions for AE. We use a model with two production sectors calibrated to the U.S. IO Tables and other sectoral statistics.^{3} In this model, MFP increases in the machineryproducing sector have effects that are qualitatively different from IST increases in a onesector model, even though the models are calibrated to match the same aggregate features whenever possible. One important difference is that with MFP shocks, consumption is boosted at all horizons, while with IST shocks consumption is reduced initially.^{4}
Our twosector model has some similarities to the one posited by GHK to support their interpretation. Both models have two production sectors and the same three final goods (equipment investment, consumption, and structures investment).
However, we extend the GHK model in three ways. The first extension is that the outputs of both production sectors are used in "assembling"all three final goods. The two production sectors are the machinery () sector and its complement, the nonmachinery () sector. For example, equipment investment is assembled using machines from the sector and distribution services from the sector. Thus, the structure of our economy differs from that in GHK except in the limiting case of "complete specialization in assembly"in which output is used only in the assembly of equipment and output is used only in the assembly of consumption and structures. In this limiting case, the machinery sector could just as well be referred to as the equipment sector, as it is in GHK.
The other two extensions are additions of two types of real rigidities. First, as has become common in dynamic stochastic general equilibrium (DSGE) models, we allow for costs of changing investment.^{5} This extension enables us to consider conditions for equivalence under alternative specifications of these costs. Second, we allow for costs of adapting capital suitable for one sector for use in the other. This extension makes it possible for us to consider the case in which capital stocks are predetermined not only at the aggregate level but also at the sectoral level. Of course, others have considered this case, but to our knowledge none of them have explored the implications for the interpretation of IST shocks.
We derive conditions for AE in our extended model. These conditions can be divided into two distinct sets. Under one set of conditions there is twosector equivalence (TE): in a twosector model, IST shocks and sectoral MFP shocks are equivalent. The first condition is that there is "partial specialization"in assembly under which assembly of consumption and structures investment uses only nonmachinery output and assembly of equipment is CobbDouglas in both outputs. The limiting case of partial specialization is "complete specialization"in which equipment investment assembly uses only machinery output. This case is important because both GHK and the other DSGE literature that relates IST shocks to MFP shocks focus on it almost exclusively. The second condition is that investment adjustment costs are suitably specified as explained below. These conditions are sufficient for TE and are necessary for TE to first order.
Under a second set of conditions, aggregation is possible: a twosector model can be reduced to a onesector model for the determination of aggregate variables. First, there are no costs adapting capital used in one sector for use in the other. Second, the two sectoral production functions are identical up to a multiplicative productivity factor. The combined third and fourth conditions are that both depreciation rates and investment adjustmentcost functions are the same for all inherited stocks of a given type of capital.^{6} We can show that these conditions are sufficient for aggregation, that the third and fourth conditions are necessary, and that each of the first two conditions is necessary given the other.^{7}
Following the empirical validation for the importance of IST shocks provided by Fisher (2006) and Smets and Wouters (2007), a growing number of papers that attempt to estimate DSGE models have included IST shocks and found them to be a major driver of business cycle fluctuations. However, these studies struggle with the problem that if IST shocks are prominent, they cause the unconditional correlation between investment and consumption to be counterfactually negative. For example, Justiniano and Primiceri (2008) found that IST shocks are the most important drivers of business cycle fluctuations in U.S. output and hours. Using a onesector model, they show the comovement between investment and consumption to be positive in the data, but negative in the model. MFP shocks in the machinery sector, while sharing many features with IST shocks, have the potential to resolve this incongruence.
A good overview of the literature on comovement is provided by Christiano and Fitzgerald (1998). Recent contributions by Christiano et al. (2008) and Jaimovich and Rebelo (2009) point respectively to consumption habits in combination with investment adjustment costs and to departures from utility functions that are additively separable in consumption and leisure as mechanisms to generate comovement, even in the face of IST shocks. We abstract from consumption habits; although we allow for investment adjustment costs, our twosector model does not rely on such costs to generate comovement. Furthermore, at no point do we depart from preferences that are consistent with a balanced growth path.
In the final part of the paper we conduct a Monte Carlo experiment. We hypothesize that taking the twosector model as datagenerating process, the estimated onesector model would still imply negative comovement between consumption and investment. The results of the experiment support this hypothesis and confirm that the one and twosector models have dramatically different implications for the correlation of consumption and investment even for the small size of the estimation sample typically used in a macroeconometric context.
Our approach to the analysis of productivity changes is a combination of the growthaccounting approach based on industrial breakdownsin the style of Solow (1957) and Griliches and Jorgenson (1966) and the DSGE approach based on finaluse breakdowns. We use a closedeconomy model with a representative household and a production structure with three central features: two production sectors, three final goods, and two types of capital. We refer to "production sectors"rather than "industries"because the former terminology is more common in the literature on IST shocks.
We analyze productivity developments in two production sectors which we call the machinery () sector and the nonmachinery () sector. Both sectors comprise perfectly competitive firms. Consider the representative firm in sector (where in period . It hires labor () from households at a wage that is same for both sectors because labor is perfectly mobile between sectors. It also rents two types of capital from households: equipment capital ( ) and structures capital at rentals ( and ) that are sectorspecific because it is costly to reallocate capital. The firm minimizes the unit cost of producing a given number of physical units of its sector's output () subject to a sectorspecific CobbDouglas production function
(1) 
The factor shares for the two types of capital are and .
There is a multifactor productivity (MFP) shock which determines the efficiency units generated by physical machinery output ( . For example, for computers can be thought as the number of computers produced, and as the computing power generated by these computers. Accounting separately for physical and efficiency units facilitates comparison of MFP shocks with IST shocks.
Since it is competitive and there are constant returns to scale, the firm ends up selling at a price equal to unit cost. Let represent the factor cost of a unit of physical output ^{8}
We assume that the good is the numeraire, so . The factor cost of a physical unit of machinery is and the cost of an efficiency unit of machinery is so that
(2) 
There are three final goods: a consumption good () and two investment goods, one () used for gross investment in capital stocks and the other () used for gross investment in capital stocks. These goods are assembled by perfectly competitive final goods firms that use as inputs the outputs of the two production sectors, and these final goods are measured in efficiency units. When we find it expedient for the exposition, we us an upper bar to denote final goods measured in physical units.
The assembly function for is a constant elasticity of substitution (CES) function of the two consumption inputs, efficiency units of goods ( ) along with goods ():
(3) 
where and are the weights for and goods, and is the elasticity of substitution between and goods in the assembly of .
The assembly functions for and are CES functions of the two investment inputs, efficiency units of goods ( , ) along with goods ( , ):
(4) 
(5) 
where and are the weights given to and goods, and and are the elasticities of substitution between and goods.
The assembly firms minimize the unit cost of producing efficiency units of consumption, equipment, and structures.^{9} Because they are perfectly competitive, firms end up selling final goods at prices that are equal to these costs and that are indicated by , , and . We assume that the assembly functions for both and are intensive relative to the function for .
There is an investmentspecific technology (IST) shock which further enhances the efficiency of , equipment assembled using and inputs. The final total amount of equipment efficiency units is given by and the allin unit cost is so that
(6) 
For example, the expression and in the model are analogous to the measures of computer output and the price of computer output in the NIPA.
We sometimes refer to the case of "partial specialization"in assembly. Under partial specialization, the assembly functions for and depend only on the good:
(7) 
and the assembly function for total efficiency units of equipment investment, , is CobbDouglas:
(8) 
where incorporates the enhancements coming from as well as from and where represents equipment investment in "physical units" (number of computers). Therefore,
(9) 
where is the cost of a unit of and is the cost of a unit of
Partial specialization has the case of "complete specialization"as a limit. Under complete specialization, the assembly function for equipment investment depends only on the good and all machinery output is used for equipment investment so that
(10) 
The complete specialization case is important because, beginning with GHK, the literature that relates IST shocks to MFP shocks focuses almost exclusively on this case. For this reason, we assume complete specialization in our baseline case.
In period , the representative household supplies a fixed amount of labor and maximizes the intertemporal utility function
(11) 
where is a consumption preference shock. The household also chooses holdings of a single bond () denominated in the good (the numeraire good for the model). In addition, for each of the four inherited capital stocks ( , and ) the household decides how much to adapt to obtain the four capital stocks rented out for use in production ( , and ) as well as the fractions ( , and ) of investment of the two types ( or ) to be added to the four capital stocks. The distinction between capital inherited from the previous period, the stocks, and capital used in production, the stocks, allows us to nest in the same model the case in which capital is predetermined only at the aggregate level and the case in which capital is predetermined also at the sectoral level.
The household is subject to period budget constraints. In each period, factor income plus income from bonds held in the previous period must be at least enough to cover purchases of final goods (consumption goods and the two types of investment goods), as well as bonds:
(12) 
where , , , are the rental rates for the capital stocks used in production. The term is the gross return on bonds.
The household is subject to technological constraints when allocating capital. It inherits four capital stocks from the previous period. Inherited capital suited for one sector can be adapted for use in the other sector before being rented out, but only by incurring increasing marginal costs. For example, inherited equipment capital ( ) suited for the sector can be adapted for use in the sector ( . Therefore, the capital of type actually available for production in sector in period depends on how much has been adapted for production in that sector:
(13) 
Here, we restrict our attention to two special cases: the case in which capital can be adapted at no cost ( so that capital is predetermined only at the aggregate level, and the case in which the marginal cost of adapting capital becomes prohibitive ( so that capital is predetermined at the sectoral level as well.
The household is also subject to technological constraints when accumulating capital. The accumulation equations for structures capital are more straightforward, so we consider them first. Let represent the amount of capital available for production in sector in period without incurring any costs of adaptation:
(14) 
where is the proportion of total structures investment in period that is added to the structures capital suitable for sector in that period. has three components represented by the three terms on the right hand side of equation (14). The first is the amount of capital actually used in production in sector in period remaining after depreciation. The second is the amount of investment added to structures capital suitable for sector in period . The third represents the adjustment costs incurred if the investment in a given type of capital in period differs from that in period . It is important to note that while the IST shock does not enter the accumulation equations for structures capital by assumption, the MFP shock does enter through except in the case of complete specialization in assembly in which .
The accumulation equations for equipment capital are less straightforward because of the distinction between physical units and efficiency units. Let represent the amount of capital available for production in sector in period without incurring any costs of adaptation:
(15) 
where is the proportion of total equipment investment that is devoted to accumulation of structures capital suited for sector in period , and where the parameters and can take on the values of one or zero.^{10} Like has three components. The first components of and are completely analogous. The second component of is the amount of investment in equipment capital suited for sector measured in efficiency units. It reflects the increase in the efficiency of the machinery input resulting from the MFP shock which is imbedded in and the increase in efficiency resulting from the IST shock . The third component represents investment adjustment costs. If , then adjustment costs apply to efficiency units no matter whether or is the source of increased efficiency. We consider the implication of zero values for either or or both below.
It is instructive to consider the case of CobbDouglas assembly for equipment in which is given by
(16) 
where we have introduced the parameter and for ease of exposition. The first component of is the same as in the general case. The second component, investment in sector measured in efficiency units (computing power), can be expressed as the product of two terms, an efficiency enhancement term and investment measured in "physical units" (where is defined in Equation 8). For the third component, investment adjustment costs, there are two versions that are consistent in the sense that whenever and appear in the accumulation equations, they appear together in the same function ( ). First, if , then adjustment costs depend on efficiency units. Second, if then adjustment costs depend on physical units. In a third version where but , the two efficiency factors and do not always appear together in the same function. This last version is of interest because papers that attempt to capture the importance of IST shocks for the business cycle routinely incorporate investment adjustment costs that include some efficiency enhancements but not others.^{11} At least to us, it is not obvious how investment adjustment costs should be modeled.
The final household constraint is that for each type of investment good the proportions of the total amount added to the two capital stocks of the same type must sum to one:
Market clearing requires that the outputs of the production sectors must be used up in the assembly of final goods:
that labor demand equal labor supply,
(17) 
and that the bond be in zero net supply
(18) 
The conditions that firms' demands for and equal households' supplies are imposed implicitly by using the same symbol for both.
Under certain conditions, the aggregate effects of an MFP shock in the machinery sector of our model with two production sectors can be reproduced by an IST shock to equipment investment in a onesector model. In this sense, the two shocks display "aggregate equivalence (AE)"^{12} . Table 1 defines some additional aggregate variables, and Table 2 describes the onesector model.
In this section we interpret and extend the conditions for AE. We observe that the conditions can be divided into two distinct sets. Under one set, there is twosector equivalence; that is, IST shocks and MFP shocks are equivalent in a twosector model with different production functions in the two sectors. Under the other, aggregation is possible; that is, a twosector model can be reduced to a onesector model for the determination of aggregate variables. Previous discussions of AE assume that (using our terminology) assembly is completely specialized and that investment adjustment is costless. We extend the conditions for AE in two ways. First, we show that specialization in assembly of consumption and structures is necessary for AE but specialization of assembly of equipment is not. Second, we identify conditions under which there is AE when there are costs of adjusting investment.
This section also contains simulation results for a calibration that includes adjustment costs for investment and satisfies the extended conditions for AE. We use these results as a benchmark against which to compare results for calibrations that do not satisfy these conditions.
Here we state conditions for aggregate equivalence (AE) in our extended model.^{13}Throughout our discussion we maintain two standard assumptions. Production functions exhibit constant returns to scale, and adjustment costs are homogeneous of degree zero in current and lagged investment. Sketches of proofs of our assertions can be found in section A of the appendix. The conditions can be divided into two distinct sets denoted and . The conditions in set are sufficient for "twosector equivalence" (TE). By TE, we mean that in a model with two distinct production sectors and possibly with different production functions, an MFP shock () that raises output in the sector by a given percentage has the same sectoral and aggregate effects as a pair of IST shocks () that push up the effectiveness of equipment investment in both sectors by that given percentage. These conditions are also necessary for TE to firstorder. The set conditions are
A1. Assembly of both consumption and structures investment is specialized in nonmachinery output.^{14} Even though it is standard to assume specialization in assembly in DSGE models, in fact the outputs of several sectors are often used in the assembly goods for final uses. In particular, the finaluse equipment investment as it appears in the NIPA is a combination of machinery with transportation and distribution services.
A2. Assembly of equipment investment is a CobbDouglas function of machinery and nonmachinery outputs with a limiting case in which it is specialized in machinery output.
A3. If there are adjustment costs for equipment investment, MFP shocks and IST shocks enter the costs combined in the same function wherever they appear.
Greenwood, Hercowitz, and Krusell ( 1997) and Oulton (2007) assume that (using our terminology) assembly is completely specialized and that investment adjustment is costless. Under these assumptions, our conditions for TE are met, but the assumptions are unnecessarily restrictive.
The conditions in set B are sufficient for aggregation, that is, for the existence of a model with one production sector that yields the same values for aggregate variables as a model with distinct and production sectors:
B1. The production functions for and are identical up to a multiplicative factor.
B2. Inherited stocks of both equipment and structures capital are costlessly adaptable for renting out to either production sector.^{15}
B3. Depreciation rates for equipment stocks are identical for the and sectors. The same is true for structures stocks.
B4. Any investment adjustment costs for equipment are identical in the and sectors. The same is true for structures stocks.
If the conditions for TE (set A) and for aggregation (set B) are met, then there is AE whether or not investment adjustment costs are present.^{16} We have not found earlier statements of sufficient conditions for aggregation of capital accumulation equations when adjustment costs are present, our B4.
We can draw conclusions about the necessity of some of the conditions in Set B:
In fact, we conjecture, but have not yet shown, that all of the conditions in set B are necessary for aggregation.
Table 3 summarizes the parameter choices for the simulation that illustrates aggregate equivalence (AE) between IST and MFP shocks under our extended conditions for aggregation equivalence. To facilitate comparisons with previous work on IST shocks, we adhere to the parameter choices of Greenwood, Hercowitz, and Krusell (1997) whenever possible.^{17} Accordingly, the output share of equipment in both the and sectors is 17% and the share of structures is 13%. The parameters governing the assembly functions are set so that there is complete specialization: consumption and structures investment are assembled using inputs from the sector only, while equipment investment is assembled using inputs from the sector only.^{18} The depreciation rates for equipment and structures capital are 12.4% per quarter and 5.6% per quarter respectively. The discount factor is set at 0.99, consistent with an annualized real interest rate of 4%. The intertemporal substitution elasticity for consumption is taken to be 1.
There is one major departure from GHK: there are adjustment costs for investment in accord with recent common practice. The parameters governing adjustment costs for both types of investment ( and ) are set to 0.5. Adjustment costs are assumed to depend on efficiency units ( ).
Figures 1 and 2 show the effects of two distinct shocks in the baseline model. The solid lines relate to a permanent shock to , the level of investmentspecific technology. In this case, we could have cut off the model's sectoral details following Greenwood, Hercowitz, and Krusell (1997), and have simply obtained the aggregate responses from a canonical onesector RBC model augmented with an IST shock in the capital accumulation equation as described in Table 2. The dashed lines relate instead to a permanent MFP shock in the sector.
In all the figures presented, the sizes of the shocks are normalized so that aggregate output (in qualityadjusted units at constant prices) increases by 1 percent in the long run.^{19} For this calibration, the (qualityadjusted) relative price of equipment investment ( ) mirrors the path of the shocks, as shown in the bottom right panel of Figure 1.^{20}
As implied by the calibration, sector goods are used to assemble equipment investment only. The baseline calibration also implies that these shocks will produce equal effects on the aggregate variables as shown in Figure 1 since the requirements for AE between IST shocks and MFP shocks in the machinery sector (discussed above) are satisfied.
The capital accumulation process adds persistence to the effects of the shocks so that output takes a considerable number of quarters to approach its new steadystate level. The top two panels in the figure show the output response, but focus on different horizons so as to depict both the medium and longrun effects.
Both shocks make it possible to produce efficiency units of equipment investment with smaller amounts of factor inputs, regardless of which sector receives the investment. Taking account of investment adjustment costs has significant implications. Were it not for these costs, the substitution effect associated with the shocks would be so strong as to cause an immediate buildup of the equipment and structures capital stocks in the sector. Recall that under the conditions for aggregate equivalence capital is costlessly adaptable for use in different sectors. Therefore, labor and both kinds of capital inputs would be transferred immediately away from the sector and into the sector. Without investment adjustment costs, consumption would drop on impact, and then increase as higher production in the sector would push up the equipment capital stock in the sector. However, with quadratic adjustment costs in investment, it becomes costly to ramp up equipment investment, reducing the incentive to transfer factor inputs across sectors. Instead of spiking up, aggregate investment follows a hump shape. Accordingly, consumption declines more gradually.
The consumption share of output takes a long time to recover as shown in Figure 2. According to the baseline calibration, sector goods are the sole input in the assembly of consumption. First, sector output goes down, as factor inputs are moved to the sector that received the shock. Then, part of sector output is devoted to pushing up the sector's stock of structures.
The simulations in Figure 3 illustrate that the effects of an IST shock in a onesector model and those of an MFP shock in the machinery sector of a twosector model can differ substantially when there are departures from the conditions for aggregate equivalence summarized in Section 3.1.
The first comparison shown in Figure 3 involves the solid and dotted lines. As in Figure 1, the solid lines show the effects on aggregate variables of an IST (or a machinerysector MFP) shock when the conditions for aggregate equivalence are met. One of the conditions for aggregate equivalence in Section 3.1 is that adjustment costs depend on either efficiency units only (as is the case with the solid line) or physical units only. The dotted lines show the effects of a machinerysector MFP shock when all of the conditions for aggregate equivalence are met except that adjustment costs depend on a mixture of units as in some recent formulations.^{21} In particular, is set equal to 0 but , , and are left equal to 1. Specifying adjustment costs in this alternative way temporarily lowers the cost of adjustment relative to the specification that reflects only efficiency units. The difference is largest in the first period. The first comparison confirms that the specification of investment adjustment costs can, by itself, break aggregate equivalence.
The second comparison in Figure 3 involves the solid and dashed lines. The dashed lines show results for a machinerysector MFP shock under the alternative calibration reported in Table 2. The alternative calibration departs from the baseline calibration in three essential ways as described below. In order to highlight the importance of these departures, aggregate factor shares are kept the same as in the baseline calibration.
By setting capital stocks become predetermined in each sector as well as at the aggregate level.
Following Greenwood, Hercowitz, and Krusell (1997), the baseline calibration implies identical production functions across sectors. However, for the three factor inputs in the model, U.S. data imply different input intensities across the machinery and nonmachinery sectors (the and sectors in the model).
To differentiate the intensities of factor inputs across sectors, we used the following restrictions: a) while allowing variation across sectors, we kept the aggregate factor input intensities the same as in Greenwood, Hercowitz, and Krusell (1997); b) factor payments are equalized across sectors, making the factors' shares of sectoral output proportional to the sectoral stocks of capital (since production functions are CobbDouglas); c) factor input intensities are equal regardless of where the output of a sector is used.
We combined data for the net capital stock of private nonresidential fixed assets from the U.S. Bureau of Economic Analysis, with data from the InputOutput Bridge Table for Private Equipment and Software. The first data set contains data on the size of equipment and nonequipment capital stocks by sector. The second data set allowed us to ascertain the commodity composition of private equipment and software. Finally, we used BEA data to establish a sector's value added output. We focused on the year 2004, the latest available at the time of writing, but similar sectorspecific production functions would be implied by older vintages of data.
Our calculations show that the machineryproducing sector is less intensive in structures and labor than the aggregate economy, but more intensive in equipment capital. For the machinery sector, the share of structures is 11 percent, the labor share 46 percent, and the share of equipment capital the remaining 43 percent (thus, , , ). For the nonmachinery sector the share of structures is 13 percent, the share of labor 72 percent, and the share of equipment capital 15 percent.
The baseline calibration assumes complete specialization in the assembly of investment and consumption goods. Equipment investment is assembled using output from the sector only. In contrast, structures investment and consumption goods are assembled using output from the sector only. This complete specialization does not reflect the composition of final goods revealed in the InputOutput Bridge Tables that link final uses in the NIPA to sectors (industries) in the U.S. InputOutput Tables. For example, according to the data for 2004, wholesale and retail services (part of our nonmachinery sector) are important inputs not only for consumption but also for equipment investment, accounting for 15 percent of the total output of private equipment and software.^{22} Furthermore, electric and electronic products are used in the assembly of consumption, accounting for 4 percent of the total.^{23}
The model captures the commingling implied by the bridge tables through assembly functions that specify how inputs from the and sectors are combined to obtain consumption, structures investment, and equipment investment. In the alternative calibration used to generate the dashed lines in Figure 3, the share parameters for the assembly functions are set as follows: the shares for equipment investment are and the shares for consumption and structures investment are We assume that in each of the finalgood assembly functions the elasticity of substitution between inputs from the and sectors is 0.5 (i.e., ). This relatively low substitution elasticity seems appropriate given that the assembly functions capture the commingling of inputs as different as electronic equipment on one side and wholesale, retail, and transportation services on the other.
The size of the MFP shock hitting the sector shown in Figure 3 was chosen again to bring about a permanent 1 percent increase in aggregate output.
Some key differences between the IST and MFP shocks can be captured by decomposing the responses of consumption into substitution and wealth effects. The bottom left panels of the figure show the Hicksian decomposition laid out by King (1991) for general equilibrium models. For this decomposition the change in utility is computed in the following way:
(19) 
where a caret symbol denotes a variable in log deviation from its steady state. The wealth effect on consumption is given by the change in consumption that would yield the same change in utility as that generated by the shock, while the real interest rate is kept constant at its steady state value. Accordingly, the Euler equation for consumption for the model implies that the wealth effect on consumption is constant over time and equal to:
(20) 
The substitution effect is the path of consumption that would induce no change in utility in reaction to the interest rate changes induced by the shock. Accordingly, the substitution effect on consumption, , is the path of that solves the system:
(21)  
(22) 
where is expressed as the difference of the interest rate from its steady state value. Simple algebraic manipulations yield the result that , which allows one to solve for the full path of the substitution effect by combining knowledge of with Equation (22) above.
A common feature among changes implied by the alternative calibration is a reduction in the magnitude of the substitution effect on consumption associated with the MFP shock. With capital predetermined at the sectoral level, more of the factor inputs remain temporarily locked up in the sector, reducing the substitution effect associated with the MFP shock.^{24} This reduction dampens the response of consumption, as its composition is intensive in the output of the sector. Similarly, structures and equipment capital take longer to shift back and forth across sectors, making the response of aggregate investment more subdued.
Under the alternative calibration, with sectorspecific production functions, the making of sector goods used in equipment investment is more intensive in equipment capital relative to the aggregate. This feature contributes to the reduction in the substitution effect on consumption coming from the MFP shock relative to the IST shock. Accordingly, sector output and investment increase by less at first.
Finally, the incomplete specialization in the assembly of equipment investment not only reduces the magnitude of the substitution effect but also boosts the wealth effect. Relaxing the assumption of complete sectoral specialization implies that the MFP shock in the sector acquires a direct effect on consumption through the assembly function.
Altogether, the weaker substitution effect and stronger wealth effect lead to a uniform rise in consumption in reaction to the MFP shock (while consumption temporarily falls for the IST shock) and a corresponding reduction in the rise of investment relative to the effects of the IST shock. The cumulative effect of the departures from the baseline calibration is to generate qualitative differences between the responses to IST and MFP shocks as can be seen by comparing the dashed and dotted lines in Figure 3. While in the case of an IST shock in the onesector model, consumption falls initially, the response of the twosector model to the MFP shock in the machinery sector is such that consumption never falls. Similarly, aggregate investment shows protracted differences, with the response to the IST shock in the onesector model being persistently more pronounced than the response to the MFP shock in the twosector model.
Finally, this alternative calibration also causes a decoupling of the responses of the relative price of investment and the size of the MFP shock. As can be seen in the bottom right panel of Figure 3, the relative price of investment ceases to be the mirror image of the unitroot process for the MFP shock in the twosector model. The initial drop in the relative price of investment is not as pronounced as the longrun drop due to elevated demand for equipment investment. Under this scenario, using the relative price of investment to back out the size of IST shocks would be inappropriate.
While all of the departures from the baseline aggregate calibration are important in reversing the conditional correlation between consumption and investment implied by MFP shocks in the machinery sector, a key role is played by incomplete sectoral specialization in the production of final goods.^{25} Figure 4 compares again the effects of an IST shock in a onesector model with an MFP shock in a twosector model. The solid lines denoting the effects of the IST shock replicate what is also shown in Figure 1. The calibration used in constructing the effects of the MFP shock in the sector departs from the aggregate equivalence calibration summarized in Table 3 only insofar as it allows for incomplete sectoral specialization in the production of final goods, as described in Section 4.2. With the baseline calibration for investment adjustment costs, this change alone is sufficient to reverse the shortterm correlation between investment and consumption.
High adjustment costs for investment, by slowing adjustment, have the potential to dampen the negative correlation between consumption and investment following IST and sectorspecific MFP shocks. To investigate the importance of investment adjustment costs in preventing consumption from falling after a sectorspecific MFP shock, Figure 5 presents simulations that abstract from such costs.
The solid line shows again the effects of an IST shock in a onesector model as in Figure 1. We depart from the calibration described in Table 3 only insofar as we eliminate the investment adjustment costs by setting . As investment can now jump on impact, the negative correlation between consumption and investment becomes stronger.
The dashed lines show the effects of an MFP shock. The only departure from the calibration used in Figure 3 and summarized in Table 4 is again the elimination of investment adjustment costs. Even without investment adjustment costs, consumption never falls in reaction to an MFP shock in the equipmentproducing sector.
The dotted line in the figure reproduces the effects of the MFP shock shown in Figure 4 in that the calibration departs from the one shown in Table 3 by allowing for incomplete specialization. Furthermore, investment adjustment costs are turned off. The dotted line shows that consumption turns negative on impact. This simulation substantiates that incomplete specialization plays an important quantitative role in reducing the negative correlation between consumption and investment following shocks to the equipmentproducing sector. However, incomplete specialization alone cannot reverse the initial negative correlation between consumption and investment without adjustment costs. Furthermore, the simulation confirms that no single departure from the conditions for aggregate equivalenceby itselfcan account for the positive comovement between investment and consumption conditional on sectorspecific MFP shocks.
As illustrated above, in a onesector model a permanent increase in the level of the IST shock, would initially boost investment while compressing consumption. Conversely, in a twosector model with our alternative calibration, the corresponding increase in the MFP shock specific to the machinery sector, would imply positive conditional comovement between consumption and investment. Economywide MFP shocks imply positive conditional comovement of consumption and investment in both the onesector and twosector models. Thus, such MFP shocks, if important enough, could account for the unconditional positive correlation between consumption and investment observed for the United States.
We investigate this possibility using the empirical evidence provided by Fisher (2006), who separately identified economywide MFP shocks, productivity shocks specific to the machinery sector (equipment in Fisher's terminology), and other shocks. Fisher (2006) augmented the longrun identification scheme of Gali (1999) so as two distinguish between two kinds of productivity shocks. All productivity shocks are identified by the assumption that they are the only ones to affect the level of labor productivity in the long run. Technology shocks that are specific to the machinery sector are separated out from aggregate MFP shocks by the assumption that they are the only ones to affect the relative price of machinery in the long run. As noted by Fisher (2006), the sectorspecific productivity shocks could be interpreted either as IST shocks in a onesector model, or MFP shocks specific to the machinery sector in a twosector model such as ours.
We calibrate the relative importance of the two types of productivity shocks by matching the variance decomposition for output at business cycle frequencies estimated by Fisher (2006). Accordingly, overall MFP shocks account for 35 percent of the variation of output, and machineryspecific MFP (or IST shocks in the onesector model) the remaining 65 percent. When we match these statistics, the unconditional correlation between consumption and investment at businesscycle frequencies is 0.19 for the onesector model and 0.16 for the twosector model. Similar correlations obtain in the model for the unfiltered quarterly growth rates of consumption and investment: 0.17 for the onesector model and 0.17 for the twosector model. Using U.S. data, we estimate the level correlation between consumption and investment at 0.68 and the correlation between the growth rates at 0.20.^{26} The twosector model matches the sign of the level correlation and is strikingly close to the data for the correlation of the growth rates.
While the onesector model is not able to deliver a positive correlation between consumption and investment with plausibly calibrated machinerysector shocks and aggregate MFP shocks, it is still possible that the onesector misspecification bias might imply a different shock decomposition that could reproduce the correlations observed in the data. To investigate this possibility we performed a Monte Carlo exercise. In the exercise, the twosector model is the datagenerating process. The model was used to generate 100 replications of a sample containing 200 observations, the typical size of an estimation sample using aggregate timeseries data for the United States. The datagenerating process was calibrated to match Fisher's estimates for the relative importance of the two technology shocks. Even though the datagenerating process excluded consumption shocks, we considered them at the estimation stage as a way for the onesector model to reconcile the positive correlation between consumption and investment implied by datagenerating process. For each sample we used a maximum likelihood estimator to estimate the standard deviations for both the overall MFP shock and the IST (or sectorspecific MFP) shock as well as the standard deviation and AR(1) persistence parameter for the consumption shock. We performed the estimation for both the onesector model and the twosector model keeping all other parameters as set in tables 1 and 2 respectively. The levels of consumption and investment were the two observed variables.
Figure 6 shows the sampling distributions for the correlation between consumption and investment at business cycle frequencies implied by the Monte Carlo experiment. As is evident from the figure, even allowing for misspecification bias in the relative importance of the sectorspecific technology shock and the consumption shock, the onesector model still implies a negative correlation between consumption and investment. The distribution of correlations between consumption and investment for the twosector model serves for comparison. Interestingly, the correlations are tightly clustered around the pseudotrue value (denoted by the vertical line in the figure) implied by the datagenerating process. Such tight clustering confirms that the twosector model and onesector model have observationally different implications for the correlation between consumption and investment.^{27}
In postWWII U.S. data, the relative price of equipment investment trends downward and varies over the cycle. As a parsimonious way of allowing for these regularities, Greenwood, Hercowitz, and Krusell (1997) add an investmentspecific technology shock to an otherwise standard onesector model. Increases in this shock enhance the efficiency of the single homogeneous output when it is used for equipment investment but not otherwise. Greenwood, Hercowitz, and Krusell (2000) and several others provide empirical support for the view that IST shocks are important for explaining the cyclical behavior of aggregate variables.
The conventional interpretation of IST shocks is based on "aggregate equivalence"results. Under stringent conditions, IST shocks in a onesector model have the same effects on aggregate variables as MFP shocks in a twosector model. Revisiting these conditions, we extend them to take account of adjustment costs for investment and the sectoral composition of final goods.
We present impulse responses for two calibrations of our twosector model. One is a "baseline calibration"with adjustment costs for investment that satisfies our extended conditions for aggregate equivalence. The other is an "alternative calibration"with three important departures. The first is that capital stocks are predetermined at the sectoral level. The second and third departures reflect the inputoutput data: sectoral production functions have different factor intensities, and assembly functions reflect incomplete specialization. We compare the effects of a positive IST shock under the baseline calibration with those of a positive machinerysector MFP shock under the alternative calibration. There is a striking qualitative difference between the results: in the first several periods, investment rises and consumption falls with the IST shock, but investment and consumption both rise with the MFP shock. If investment adjustment costs are present, incomplete specialization in assembly is sufficient by itself to generate the difference in results.
In sum, our results have at least two important implications. First, a twosector model seems better suited than a onesector model for distinguishing empirically among alternative sources of economic fluctuations, even if the focus is on aggregate time series. Second, while IST shocks may be of interest in their own right, the structure of the U.S. economy seems to be such that interpreting them as standins for sectorspecific MFP shocks can be misleading.
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Table 1. Aggregation Equations


Table 2. OneSector Model Under Assumptions for Aggregate Equivalence
Utility maximization problem of households  

Utility maximization problem of households, subject to the constraints:  
Cost minimization problem of firms  
Cost minimization problem of firms, subject to the constraint  
Equilibrium Conditions 
All markets are assumed to be competitive. Recall that represents equipment investment in physical units. We left both shocks and in the description of the model to underscore their equivalence for aggregate variables.
Table 3a. Model Calibration for Baseline Experiment: Utility Function
Parameter  Determines 

Intertemporal consumption elast.  
Discount factor 
Table 3b. Model Calibration for Baseline Experiment: Depreciation Rates
Parameter  Determines 

Equipment capital  
Structures capital 
Table 3c. Model Calibration for Baseline Experiment: Adjustment Costs
Parameter  Determines 

M, N Equipment Capital  
= 0.5  M, N Equipment Investment 
M, N Equipment Investment  
M, N Structures Capital  
M, N Structures Investment 
Table 3d. Model Calibration for Baseline Experiment: M Goods Production
Parameter  Determines 

Labor share  
Structures share  
Equipment share 
Table 3e. Model Calibration for Baseline Experiment: N Goods Production
Parameter  Determines 

Labor share  
Structures share  
Equipment share 
Table 3f. Model Calibration for Baseline Experiment: Consumption Assembly
Parameter  Determines 

M goods intensity  
N goods intensity 
Table 3g. Model Calibration for Baseline Experiment: Assembly of Equipment Investment
Parameter  Determines 

M goods intensity  
N goods intensity 
Table 3h. Model Calibration for Baseline Experiment: Assembly of Structures Investment
Parameter  Determines 

M goods intensity  
N goods intensity 
Table 4a. Alternative Calibration: Changes Relative to Baseline: Adjustment Costs^{*}
Parameter  Determines 

M, N Equipment Capital  
M, N Structures Capital 
^{*}For ease of comparison with Table 3, this table only reports the parameters that vary from the baseline calibration.
Table 4b. Alternative Calibration: Changes Relative to Baseline: M Goods Production^{*}
Parameter  Determines 

Labor share  
Structures share  
Equipment share 
^{*}For ease of comparison with Table 3, this table only reports the parameters that vary from the baseline calibration.
Table 4c. Alternative Calibration: Changes Relative to Baseline: N Goods Production^{*}
Parameter  Determines 

Labor share  
Structures share  
Equipment share 
^{*}For ease of comparison with Table 3, this table only reports the parameters that vary from the baseline calibration.
Table 4d. Alternative Calibration: Changes Relative to Baseline: Consumption Assembly^{*}
Parameter  Determines 

M goods intensity  
Substitution elast. for M and N goods  
N goods intensity 
^{*}For ease of comparison with Table 3, this table only reports the parameters that vary from the baseline calibration.
Table 4e. Alternative Calibration: Changes Relative to Baseline: Assembly of Equipment Investment^{*}
Parameter  Determines 

M goods intensity  
Substitution elast. for M and N goods  
N goods intensity 
^{*}For ease of comparison with Table 3, this table only reports the parameters that vary from the baseline calibration.
Table 4f. Alternative Calibration: Changes Relative to Baseline: Assembly of Structures Investment^{*}
Parameter  Determines 

M goods intensity  
Substitution elast. for M and N goods  
N goods intensity 
^{*}For ease of comparison with Table 3, this table only reports the parameters that vary from the baseline calibration.
Figure 1. Equivalent IST and MFP Shocks Under Baseline Calibration
Figure 2. Equivalent IST and MFP Shocks Under Baseline Calibration (Sectoral Details)
Figure 3. Cummulative Effects of Departures from Baseline Calibration
Figure 4. IST Under Baseline Calibration and MFP Shocks With Incomplete Specialization in Assembly
Figure 5. Sensitiviety Analysis: No Investment Adjustment Costs
Figure 6. Probability Density Functions: Correlation Between Consumption and Investment at Business Cycle Frequencies
In this section of the appendix, we sketch proofs of some of the assertions in section 3.1
The equations of the model can be written in a form such that when the set A conditions are imposed and always enter together in the form . For example, Equation 16 repeated here for convenience
satisfies the set A conditions either when , or when . Conclude that for any change in there is an offsetting change in that leaves the equilibrium values of all variables unchanged.
Linearize the unrestricted equations of the model around a steady state. The combinations of shocks that yield equivalent outcomes are obtained by setting the changes for all the endogenous variables equal to zero for all periods. Consider an arbitrary sequence of changes in the MFP shock , Confirm that the zerochange equilibrium conditions can be satisfied only if terms in changes in and terms in changes in always appear together in the same linear combination. The necessity of condition  is established by noting that if  is not met, enters the assembly function for at least consumption or structures investment but does not enter either. That  and  are necessary is established by showing that a single linear combination of changes in and changes in would not satisfy some set of equations. For  the set comprises the equipment assembly function and the firstorder conditions for cost minimization in equipment assembly (not included in paper). For  the set comprises the equipment assembly function and the accumulation equations for equipment capital stocks.
As stated in the text, GHK and Oulten have shown that conditions B1 through B3 are sufficient for aggregation in models without investment adjustment costs. If condition B4 is imposed, sectoral capital stocks, investment flows, and capital accumulation equations can be aggregated to yield
(23) 
where and is equal to either zero or one.
It is evident that aggregation is not possible if depreciation rates and adjustment costs are not identical. For aggregation to be possible the production possibility curve (PPC) for the two production sectors must be a straight line. It is well known that in the standard twosector model if capital is costlessly adaptable the PPC is concave to the origin unless production functions are identical. Also, if production functions are identical, the PPC is concave to the origin unless capital is costlessly adaptable.
The discussion in the main body of the paper omitted to consider in isolation two departures from our baseline calibration. The effects of relaxing perfect capital mobility across sectors and of varying the factor intensities across sectors are illustrated below.
In Figure 7, the solid lines reproduce the responses to the IST shock from Figure 1. Instead, the dashed lines show the economy's response to an MFP shock in the sector when relaxing only the assumption of perfect capital mobility across sectors in every period. Perfect capital mobility, as argued before, is necessary to represent our twosector model as an aggregate onesector model. To produce the responses shown by the dashed lines, we set the parameters governing the capital adjustment costs and both equal to 100. This parametrization implies that sectoral capital allocations only move with a delay of one period. Thus capital stocks are not only predetermined at the aggregate level, but also at the sectoral level.
The size of the MFP shock hitting the sector was again chosen to bring about a permanent 1 percent increase in aggregate output. While the wealth effect on consumption is identical for the two shocks in Figure 7, the negative substitution effect is reduced in magnitude when the sectoral capital stocks are predetermined.
Figure 8 shows the responses to an IST shock in the aggregate model (replicating, for ease of comparison, what is also shown in figures 1 and 7), as well as the responses to an MFP shock in the machinery sector of a twosector model that allows for sectorspecific production functions (the only difference relative to the baseline calibration). Again, the magnitude of the MFP shock is chosen to match the 1 percent longrun increase in aggregate output for the IST shock.
The figure shows persistent differences in the responses of consumption and investment. As under the alternative calibration the making of sector goods used in equipment investment is more intensive in equipment capital relative to the aggregate, the substitution effect on consumption coming from the MFP shock is not as strong initially relative to the IST shock. Accordingly, sector output increases by less, at first. However, eventually more resources need to be devoted to the sector to maintain the larger stock of equipment capital implied by the alternative calibration, and the MFP shock in the investment sector leads to a larger longrun increase in equipment investment and a smaller longrun increase in consumption. Consequently, the wealth effect on consumption is smaller for the MFP shock than for the IST shock.
Figure 7. IST Under Baseline Calibration and MFP Shock With Capital Stocks Predetermined in Each Sector
Figure 8. IST Under Baseline Calibration and MFP Shocks With SectorSpecific Production Functions
** Affiliation and contact information: Luca Guerrieri, Federal Reserve Board, telephone (202) 452 2550, email [email protected]; Dale Henderson, Georgetown University, email [email protected]; Jinill Kim, Federal Reserve Board, telephone (202) 452 2981, email [email protected]. Return to text
*** The views expressed in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System. Return to text
1. Throughout this paper, we used the term "equipment"investment to refer to what is called "Equipment and Software"investment in the NIPA tables. Return to text
2. For example, see Greenwood, Hercowitz, and Krusell (2000), Fisher (2006), Smets and Wouters (2007), and Justiniano and Primiceri (2008). Return to text
3. One of the first papers to emphasize the importance of the inputoutput structure for the business cycle is Long and Plosser (1983). More recent contributions include Hornstein and Praschnik (1997) and Edge, Kiley, and Laforte (2008). Return to text
4. In related work, Swanson (2006) showed that MFP shocks at the sectoral level in a multisector model can lead to di�erent aggregate implications from those of MFP shocks in a onesector model. Return to text
5. Investment adjustment costs are not a part of the model developed by Greenwood, Hercowitz, and Krusell (1997), but are a common ingredient of models developed subsequently that also incorporate IST shocks. Return to text
6. There are two other standard assumptions. Production functions exhibit constant returns to scale, and investment adjustment costs are homogeneous of degree zero in current and lagged investment. As is well known, if the model economy is to have a balanced steadystate growth path, the production functions must be CobbDouglas. Return to text
7. We conjecture, but have not yet shown, that the four conditions are jointly necessary. Return to text
8. For example, is the multiplier in the Lagrangian expression ( ) used to the minimize costs of producing a given physical quantity :
where time subscripts have been omitted for simplicity. Return to text
9. For example, , is the multiplier in the Lagrangian expression ( ) used to the minimize costs of producing a given quantity :
where time subscripts have been omitted for simplicity. Return to text
10. For simplicity we assume that depreciation rates ( and ) and investment adjustmentcost parameters ( and ) may differ between types of capital but are the same across sectors of use. Return to text
11. See, for example, Smets and Wouters (2007) and Christiano, Motto, and Rostagno (2007). Return to text
12. Greenwood, Hercowitz, and Krusell (1997) and Greenwood, Hercowitz, and Krusell (2000) state sufficient conditions for AE in the case with CobbDouglas production functions, complete specialization in assembly, and no adjustment costs for investment. Oulton (2007) extends the GHK analysis to the case with general constant returns to scale (CRTS) production functions; Greenwood and Krusell (2007) provides further discussion. Return to text
13. We conjecture, but have not yet shown, that the four conditions are jointly necessary. Return to text
14. We conjecture, but have not yet shown, that the four conditions are jointly necessary. Return to text
15. Instead of assuming that is costlessly adaptable, Greenwood, Hercowitz, and Krusell (2000) assume that firms can move between sectors at will. Return to text
16. Greenwood, Hercowitz, and Krusell (2000) and Oulton (2007) have shown that in the absence of investment adjustment costs, conditions B1 through B3 are sufficient for aggregation. Return to text
17. For simplicity, we abstract from trend growth as well as capital and labor taxes, while Greenwood, Hercowitz, and Krusell (1997) incorporate them in their model. Return to text
18. The substitution elasticities between inputs in assembly become irrelevant under complete specialization. Return to text
19. In multisector models there are multiple ways of aggregating sectoral outputs depending, for instance, on which good is chosen as the numeraire. We focus on a measure of aggregate output that sums sectoral outputs at constant prices after adjusting for quality. This measure is defined as . This approach can be shown to be firstorder equivalent to a Tornqvist, chainweighted index. Return to text
20. Growth accounting exercises that exploit the tight shortrun relationship between the relative price of equipment investment and the size of technology shocks in constructing technology shock series might be intermingling technology shocks with demands shifts. For example, see Greenwood, Hercowitz, and Krusell (1997). Return to text
21. See, for example, Smets and Wouters (2007) and Christiano, Motto, and Rostagno (2007). Return to text
22. There are bridge tables for consumption as well as equipment and software investment but not for structures investment. We assume that the sectoral composition of structures investment is the same as that of consumption. Return to text
23. The machinery sector encompasses two components. The first component is the BEA definition of "Equipment and Software"Investment, after excluding the Transportation, Wholesale, and Retail Margins. The second component is the inputs from the following five BEA "industries"that are used in consumption: (334) Computer and Electronic Products; (335) Electrical equipment, appliances, and components; (513) Broadcasting and telecommunications; (514) Information and data processing services; and (5412OP) Miscellaneous professional, scientific and technical services. Return to text
24. Only with much higher input substitution elasticities would there be an incentive to shift so much labor to the sector as to lower the output of the sector. Return to text
25. Appendix B isolates the role of capital being predetermined at the sectoral level and of sectorspecific production functions in distancing the effects of MFP shocks in the machinery sector from those of IST shocks in a onesector model. Return to text
26. Both correlations reported were constructed with data from Table 1.1.6 of the National Income and Product Accounts for the period between 1947:q1 and 2009:q2. Return to text
27. Similar results apply for the sampling distribution of the correlation between the growth rate of consumption and investment. Return to text
This version is optimized for use by screen readers. Descriptions for all mathematical expressions are provided in LaTex format. A printable pdf version is available. Return to text