scholarly journals The Substitution Elasticity, Factor Shares, Long-Run Growth, and the Low-Frequency Panel Model

2016 ◽  
Author(s):  
Robert S. Chirinko ◽  
Debdulal Mallick
Keyword(s):  
Low Frequency ◽  
Panel Model ◽  
Long Run ◽  
Factor Shares ◽  
Substitution Elasticity

Tradeoff Theory and Leverage Dynamics of High-Frequency Debt Issuers*

2020 ◽  
Author(s):  
B Espen Eckbo ◽  
Michael Kisser
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Debt Financing ◽  
Trade Off ◽  
Speed Of Adjustment ◽  
Industrial Companies ◽  
Long Run ◽  
Speed Up ◽  
Target Leverage ◽  
Leverage Dynamics

Abstract We test whether high-frequency net-debt issuers (HFIs)—public industrial companies with relatively low issuance costs and high debt-financing benefits—manage leverage toward long-run targets. Our answer is they do not: (1) the leverage–profitability correlation is negative even in quarters with leverage rebalancing; (2) the speed-of-adjustment to target leverage deviations is no higher for HFIs than for low-frequency net-debt issuers; and (3) under-leveraged HFIs do not speed up rebalancing activity in significant investment periods. Thus, even in the subset of firms most likely to follow dynamic trade-off theory, the theory does not appear to hold.

Application of a Hybrid Boussinesq-Panel Model for Motion Predictions of a Moored Sevan-Floater in Finite Water Depth

2017 ◽  
Author(s):  
Jikun You ◽  
Einar Bernt Glomnes
Keyword(s):  
Water Depth ◽  
Low Frequency ◽  
Boussinesq Equations ◽  
Wave Force ◽  
Irregular Waves ◽  
Wave Forces ◽  
Rankine Source ◽  
Panel Model ◽  
Finite Water Depth ◽  
Incident Waves

This paper presents the applications of an efficient hybrid time-domain simulation model for predicting moored Sevan-floater motions in irregular waves and finite water depth. The irregular incident waves are modeled by the extended Boussinesq equations, which can capture wave-wave interactions and the low-frequency long waves accurately in finite and shallow water depth. By imposing the incident wave kinematics on the surface of the floater, a panel model based on Rankine source method is applied for the calculation of wave forces and corresponding floater motions. The contributions from low-frequency components in incident waves as well as their diffraction effects are included in the wave force calculations. Validation of the irregular waves simulated by the present numerical model are performed against experimental data. Then, the simulated moored floater motions are compared with model test results and results based on Newman’s approximation. The general good agreements with experimental results demonstrate the present model can be used as an alternative for this problem while Newman’s approximation shows non-conservative results.

scholarly journals From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)

2019 ◽  
Vol 23 (4) ◽  
pp. 651-687 ◽  
Author(s):  
Michael J. Zyphur ◽  
Paul D. Allison ◽  
Louis Tay ◽  
Manuel C. Voelkle ◽  
Kristopher J. Preacher ◽  
...  
Keyword(s):  
Causal Inference ◽  
Structural Equation ◽  
National Income ◽  
Well Being ◽  
Equation Modeling ◽  
Data Sets ◽  
Subjective Well Being ◽  
Panel Model ◽  
Long Run ◽  
Short Run

This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs . We conclude with a discussion of issues surrounding causal inference.

scholarly journals The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment

2018 ◽  
Vol 108 (6) ◽  
pp. 1488-1542 ◽  
Author(s):  
Daron Acemoglu ◽  
Pascual Restrepo
Keyword(s):  
Capital Accumulation ◽  
New Technologies ◽  
Balanced Growth ◽  
Long Run ◽  
Rental Rate ◽  
Static Version ◽  
Balanced Growth Path ◽  
The Creation ◽  
The Cost ◽  
Factor Shares

We examine the concerns that new technologies will render labor redundant in a framework in which tasks previously performed by labor can be automated and new versions of existing tasks, in which labor has a comparative advantage, can be created. In a static version where capital is fixed and technology is exogenous, automation reduces employment and the labor share, and may even reduce wages, while the creation of new tasks has the opposite effects. Our full model endogenizes capital accumulation and the direction of research toward automation and the creation of new tasks. If the long-run rental rate of capital relative to the wage is sufficiently low, the long-run equilibrium involves automation of all tasks. Otherwise, there exists a stable balanced growth path in which the two types of innovations go hand-in-hand. Stability is a consequence of the fact that automation reduces the cost of producing using labor, and thus discourages further automation and encourages the creation of new tasks. In an extension with heterogeneous skills, we show that inequality increases during transitions driven both by faster automation and the introduction of new tasks, and characterize the conditions under which inequality stabilizes in the long run. (JEL D63, E22, E23, E24, J24, O33, O41)

scholarly journals Uncertainty in forecasts of long-run economic growth

2018 ◽  
Vol 115 (21) ◽  
pp. 5409-5414 ◽  
Author(s):  
P. Christensen ◽  
K. Gillingham ◽  
W. Nordhaus
Keyword(s):  
Economic Growth ◽  
Growth Rates ◽  
Social Insurance ◽  
Low Frequency ◽  
The United States ◽  
Long Run ◽  
Per Capita ◽  
Econometric Approach ◽  
Economic Growth Rates ◽  
Global Growth

Forecasts of long-run economic growth are critical inputs into policy decisions being made today on the economy and the environment. Despite its importance, there is a sparse literature on long-run forecasts of economic growth and the uncertainty in such forecasts. This study presents comprehensive probabilistic long-run projections of global and regional per-capita economic growth rates, comparing estimates from an expert survey and a low-frequency econometric approach. Our primary results suggest a median 2010–2100 global growth rate in per-capita gross domestic product of 2.1% per year, with a standard deviation (SD) of 1.1 percentage points, indicating substantially higher uncertainty than is implied in existing forecasts. The larger range of growth rates implies a greater likelihood of extreme climate change outcomes than is currently assumed and has important implications for social insurance programs in the United States.

scholarly journals The Substitution Elasticity, Factor Shares, and the Low-Frequency Panel Model

2017 ◽  
Vol 9 (4) ◽  
pp. 225-253 ◽  
Author(s):  
Robert S. Chirinko ◽  
Debdulal Mallick
Keyword(s):  
Low Frequency ◽  
Estimation Methods ◽  
Function Estimation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Long Run ◽  
New Strategy ◽  
Low Pass ◽  
Secular Decline ◽  
Production Function Estimation

The value of the elasticity of substitution between labor and capital (σ) is a crucial assumption in understanding the secular decline in the labor share of income. This paper develops and implements a new strategy for estimating this crucial parameter by combining a low-pass filter with panel data to identify the low-frequency/long-run relations appropriate to production function estimation. Standard estimation methods, which do not filter out transitory variation, generate downwardly biased estimates of 40 percent to 70 percent relative to the benchmark value. Despite correcting for this bias, our preferred estimate of 0.40 is substantially below the Cobb-Douglas assumption of σ = 1. (JEL C51, E22, E24, E25, O41)

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