scholarly journals Machine Replacement and the Business Cycle: Lumps and Bumps

1999 ◽  
Vol 89 (4) ◽  
pp. 921-946 ◽  
Author(s):  
Russell Cooper ◽  
John Haltiwanger ◽  
Laura Power
Keyword(s):  
Capital Stock ◽  
Cross Sectional ◽  
Lumpy Investment ◽  
Level Data ◽  
Investment Dynamics ◽  
Aggregate Investment ◽  
The Cross ◽  
The Business Cycle ◽  
Plant Level ◽  
Machine Replacement

This paper explores investment fluctuations due to discrete changes in a plant's capital stock. The resulting aggregate investment dynamics are surprisingly rich, reflecting the interaction between a replacement cycle, the cross-sectional distribution of the age of the capital stock, and an aggregate shock. Using plant-level data, lumpy investment is procyclical and more likely for older capital. Further, the predicted path of aggregate investment that neglects vintage effects tracks actual aggregate investment reasonably well. However, ignoring fluctuations in the cross-sectional distribution of investment vintages can yield predictable nontrivial errors in forecasting changes in aggregate investment. (JEL E22, E32)

scholarly journals Plant-Level Adjustment and Aggregate Investment Dynamics

1995 ◽  
Vol 1995 (2) ◽  
pp. 1 ◽  
Author(s):  
Ricardo J. Caballero ◽  
Eduardo M. R. A. Engel ◽  
John C. Haltiwanger ◽  
Michael Woodford ◽  
Robert E. Hall
Keyword(s):  
Investment Dynamics ◽  
Aggregate Investment ◽  
Plant Level

scholarly journals Man-Bites-Dog Business Cycles

2014 ◽  
Vol 104 (8) ◽  
pp. 2320-2367 ◽  
Author(s):  
Kristoffer P. Nimark
Keyword(s):  
Business Cycles ◽  
Business Cycle ◽  
Large Change ◽  
Cycle Model ◽  
Cross Sectional ◽  
Macroeconomic Volatility ◽  
Positive Correlation ◽  
The Cross ◽  
Business Cycle Model ◽  
The Business Cycle

The newsworthiness of an event is partly determined by how unusual it is and this paper investigates the business cycle implications of this fact. Signals that are more likely to be observed after unusual events may increase both uncertainty and disagreement among agents. In a simple business cycle model, such signals can explain why we observe (i) occasional large changes in macroeconomic aggregate variables without a correspondingly large change in underlying fundamentals, (ii) persistent periods of high macroeconomic volatility and, (iii) a positive correlation between absolute changes in macrovariables and the cross-sectional dispersion of survey expectations. (JEL D81, D82, D84, E23, E31, E32)

scholarly journals Aggregate Implications of Lumpy Investment: New Evidence and a DSGE Model

2013 ◽  
Vol 5 (4) ◽  
pp. 29-67 ◽  
Author(s):  
Rüdiger Bachmann ◽  
Ricardo J. Caballero ◽  
Eduardo M. R. A. Engel
Keyword(s):  
Business Cycle ◽  
Time Variation ◽  
Specific Time ◽  
Dsge Model ◽  
Lumpy Investment ◽  
Macroeconomic Analysis ◽  
Capital Adjustment ◽  
Aggregate Investment ◽  
The Business Cycle ◽  
New Evidence

The sensitivity of US aggregate investment to shocks is procyclical. The response upon impact increases by approximately 50 percent from the trough to the peak of the business cycle. This feature of the data follows naturally from a DSGE model with lumpy microeconomic capital adjustment. Beyond explaining this specific time variation, our model and evidence provide a counterexample to the claim that microeconomic investment lumpiness is inconsequential for macroeconomic analysis. (JEL E13, E22, E32)

scholarly journals Price and Investment Dynamics: Theory and Plant-Level Data

2009 ◽  
Vol 41 (5) ◽  
pp. 907-934 ◽  
Author(s):  
MAGNUS LUNDIN ◽  
NILS GOTTFRIES ◽  
CHARLOTTE BUCHT ◽  
TOMAS LINDSTRÖM
Keyword(s):  
Level Data ◽  
Investment Dynamics ◽  
Plant Level

On the cross-sectional shape of the cellulose crystallites in the tunicate Halocynthia papillosa

1990 ◽  
Vol 48 (3) ◽  
pp. 566-567
Author(s):  
J.-F. Revol ◽  
Y. Van Daele ◽  
F. Gaill
Keyword(s):  
Contrast Imaging ◽  
Animal Kingdom ◽  
Bright Field ◽  
Field Mode ◽  
Cross Sectional ◽  
Ultrathin Sections ◽  
The Cross ◽  
Sectional Shape ◽  
Valonia Ventricosa ◽  
C Nmr

The only form of cellulose which could unequivocally be ascribed to the animal kingdom is the tunicin that occurs in the tests of the tunicates. Recently, high-resolution solid-state l3C NMR revealed that tunicin belongs to the Iβ form of cellulose as opposed to the Iα form found in Valonia and bacterial celluloses. The high perfection of the tunicin crystallites led us to study its crosssectional shape and to compare it with the shape of those in Valonia ventricosa (V.v.), the goal being to relate the cross-section of cellulose crystallites with the two allomorphs Iα and Iβ.In the present work the source of tunicin was the test of the ascidian Halocvnthia papillosa (H.p.). Diffraction contrast imaging in the bright field mode was applied on ultrathin sections of the V.v. cell wall and H.p. test with cellulose crystallites perpendicular to the plane of the sections. The electron microscope, a Philips 400T, was operated at 120 kV in a low intensity beam condition.

A Technique for Measuring the Cross-Sectional Axes of the Longissimus Dorsi Muscle and Fat Depth in Lambs1,2

1960 ◽  
Vol 19 (3) ◽  
pp. 803-809
Author(s):  
D. J. Matthews ◽  
R. A. Merkel ◽  
J. D. Wheat ◽  
R. F. Cox
Keyword(s):  
Longissimus Dorsi ◽  
Cross Sectional ◽  
Longissimus Dorsi Muscle ◽  
Dorsi Muscle ◽  
The Cross

Automated Diagonal Slice and View Solution for 3D Device Structure Analysis

2018 ◽  
Author(s):  
Sang Hoon Lee ◽  
Jeff Blackwood ◽  
Stacey Stone ◽  
Michael Schmidt ◽  
Mark Williamson ◽  
...  
Keyword(s):  
Cross Section ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Image Data ◽  
Planar Surface ◽  
Device Structure ◽  
Cross Sectional ◽  
Device Structures ◽  
The Cross ◽  
Planar Analysis

Abstract The cross-sectional and planar analysis of current generation 3D device structures can be analyzed using a single Focused Ion Beam (FIB) mill. This is achieved using a diagonal milling technique that exposes a multilayer planar surface as well as the cross-section. this provides image data allowing for an efficient method to monitor the fabrication process and find device design errors. This process saves tremendous sample-to-data time, decreasing it from days to hours while still providing precise defect and structure data.

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