New-tenant rent inflation rose sharply during the COVID-19 pandemic, subsequently falling. Concomitantly, consumer price index (CPI) tenant rent, which measures rent increases for both new and continuing renters, rose more gradually and, after falling somewhat, has remained elevated. To illustrate why CPI rent inflation has remained elevated, we combine a measure of new-tenant rents and annual renter mobility rates to create a simulated CPI tenant rent inflation measure. We use this simulation to define a “rent gap” that represents the difference between actual CPI tenant rent inflation and rent inflation we would observe if every tenant experienced new-tenant rent inflation. This gap has declined since hitting its peak at the end of 2022 but remains high, implying that existing rents for continuing renters may still be notably below new-tenant rent levels and that rent inflation may remain elevated. However, the future path remains uncertain because it depends on future mobility rates, future passthrough rates, and future new-tenant rent inflation.

Annual Inflation Rate (%)–United Kingdom.

The Inflation Management Services market is experiencing robust growth, driven by increasing global inflation and the need for businesses to mitigate its impact on profitability and long-term sustainability. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% between 2025 and 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors, including rising energy prices, supply chain disruptions, and increased government intervention to control inflation. Businesses across various sectors, particularly those in manufacturing, retail, and finance, are actively seeking sophisticated strategies to forecast and manage inflation effectively. The increasing adoption of advanced analytics, predictive modeling, and AI-powered solutions further enhances the market's growth trajectory. Consulting firms like McKinsey & Company, Bain & Company, and Deloitte are playing a significant role in providing these services, leveraging their expertise in economic forecasting, financial modeling, and risk management. The market is segmented by service type (e.g., forecasting, hedging, pricing strategies), industry vertical, and geography. Regional growth is expected to be strongest in North America and Europe, driven by high inflation rates and a strong emphasis on corporate financial planning in these regions. While the market presents significant opportunities, challenges such as data scarcity and the complexity of accurately predicting inflation remain. The effectiveness of inflation management services is also contingent on external factors such as government policies and unexpected global events. Despite these constraints, the consistent need to protect profitability and shareholder value, along with the advancement of analytical tools, positions the inflation management services market for continued growth in the foreseeable future. The competitive landscape is characterized by a mix of large consulting firms offering comprehensive solutions and specialized firms focusing on niche areas within inflation management. The market will likely see further consolidation as firms strive to offer end-to-end solutions and expand their geographical reach.

The database consists of a survey of NASA Interplanetary Missions (NIMs) launched between May 1989 and October 2023. Mission data includes system-level attributes in support of advanced system-level modeling. The attributes include elements of the program, schedule, cost, and technical design. Cost silos include three inflation formats: Then$ representing realized expenses in the Year of recording, NASA’s New Start Inflation Index (NSII) adjusted expenses, and Personal Consumption Expenditures Price Index (PCEPI) adjusted expenses. Inflated values are adjusted to fiscal Year 2022 equivalent dollars with an index of 1.00 applied forward to adjust to 2023 and 2024 equivalent expenditures. The NIM database serves as a reference for future research and cost analytics in NASA's system-level analysis of the NIM., , # NASA interplanetary mission (NIM) dataset

Dataset DOI: 10.5061/dryad.fj6q5745z

Description of the data and file structure

The database consists of a survey of NASA Interplanetary Missions (NIMs) launched between May 1989 and October 2023. Mission data includes system-level attributes in support of advanced system-level modeling. The attributes include elements of the program, schedule, cost, and technical design. Cost silos include three inflation formats: Then$ representing realized expenses in the Year of recording, NASA’s New Start Inflation Index (NSII) adjusted expenses, and Personal Consumption Expenditures Price Index (PCEPI) adjusted expenses. Inflated values are adjusted to fiscal Year 2022 equivalent dollars with an index of 1.00 applied forward to adjust to 2023 and 2024 equivalent expenditures. The NIM database serves as a reference for future research and cost analytics in NASA's system-level analysis of the Interplanetary Mission.

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Monthly inflation in the United States indicates non-normality in the form of either occasional big shocks or marked changes in the level of the series. We develop a univariate state space model with symmetric stable shocks for this series. The non-Gaussian model is estimated by the Sorenson-Alspach filtering algorithm. Even after removing conditional heteroscedasticity, normality is rejected in favour of a stable distribution with exponent 1·83. Our model can be used for forecasting future inflation, and to simulate historical inflation forecasts conditional on the history of inflation. Relative to the Gaussian model, the stable model accounts for outliers and level shifts better, provides tighter estimates of trend inflation, and gives more realistic assessment of uncertainty during confusing episodes.

This package contains all the code and data necessary to replicate the empirical analyses and dynamic model simulations presented in "Inflation Risk and the Finance-Growth Nexus" by Alexandre Corhay and Jincheng Tong, including scripts for data collection, processing, estimation, and model-based simulations.

Data collection, along with hydraulic and fluvial egg transport modeling, were completed along a 70.9-mile reach of the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam. Data were collected during two surveys: October 27–November 4, 2016, and June 26–29, 2017. Water-quality data collected in this reach included surface measurements and vertical profiles of water temperature, specific conductance, pH, dissolved oxygen, turbidity, relative chlorophyll, and relative phycocyanin. Streamflow and velocity data were collected simultaneously with the water-quality data at cross sections and along longitudinal lines (corresponding to the water-quality surface measurements) and at selected stationary locations (corresponding to the water-quality vertical profiles). The data were collected to understand variability of flow and water-quality conditions relative to simulated reaches of the Ohio River and to aid in identifying parts of the reach that may provide conditions favorable to spawning and recruitment habitat for bighead carp (Hypophthalmichthys nobilis). A copy of an existing hydraulic model of the Ohio River was obtained from the National Weather Service and used to simulate hydraulic conditions for four different streamflows. Streamflows used for the simulations were selected to represent a range of conditions from a high-streamflow event to a seasonal dry-weather event. Outputs from the hydraulic model were used as input to the Fluvial Egg Drift Simulator (FluEgg) along with a range of five water temperatures observed in water-quality data and four potential spawning locations to simulate the extents and quantile positions of developing bighead carp, from egg hatching to the gas bladder inflation stage, under each scenario. A total of 80 simulations were run. Results from the FluEgg scenarios (which include only the hydraulic influences on survival that result from settling, irrespective of mortality from other physical factors such as excess turbulence, or biological factors such as fertilization failure, predation or starvation) indicate that the majority of the eggs will hatch, about half will die, and a quarter of the surviving larvae will reach the gas bladder inflation stage within the modeled reach. The overall average percentage of embryos surviving to the gas bladder inflation stage was 13.1 percent. Individual simulations have embryo survival percentages as high as 49.1 percent. The highest embryo survival percentages occurred for eggs spawned at a streamflow of 38,100 cubic feet per second and water temperatures of 24°C to 30°C. Conversely, embryo survival percentages were lowest for the lowest and highest streamflows regardless of water temperature or spawn location. Under low water temperature, high-streamflow conditions, some of the eggs did not hatch nor did the larvae reach the gas bladder inflation stage until passing beyond the downstream model domain. While the final quantile positions of the eggs and larvae beyond the downstream model domain are unknown, the outcomes still provide useful information.

Additional file 1. Additional tables and code. Additional tables obtained from the simulation study and R code to reproduce the analyses.

This directory contains an index.html file with links to the run
directories and idl plotting routines with secondary data for the
other figures for the paper "Simulating relic gravitational waves from
inflationary magnetogenesis" by Axel Brandenburg and Ramkishor Sharma. If
anything turns out to be incomplete, please email brandenb@nordita.org.
See also the notes.pdf file in the paper directory with information about
the importance of the f'/f term in the expression for the E field.

Growth curve modeling (GCM) has been one of the most popular statistical methods to examine participants’ growth trajectories using longitudinal data. In spite of the popularity of GCM, little attention has been paid to the possible influence of time-specific errors, which influence all participants at each timepoint. In this article, we demonstrate that the failure to take into account such time-specific errors in GCM produces considerable inflation of type-1 error rates in statistical tests of fixed effects (e.g., coefficients for the linear and quadratic terms). We propose a GCM that appropriately incorporates time-specific errors using mixed-effects models to address the problem. We also provide an applied example to illustrate that GCM with and without time-specific errors would lead to different substantive conclusions about the true growth trajectories. Comparisons with other models in longitudinal data analysis and potential issues of model misspecification are discussed.

This dataset contains synthetic data representing key economic indicators and housing market trends in the UK from 2002 to 2023. The dataset includes quarterly data points for the following variables:

Date: Quarterly timestamps from Q1 2002 to Q4 2023. Housing Cost Index: An index representing the general trend in UK housing prices over time. The values are generated to simulate a typical upward trend observed in real estate markets. Interest Rate (%): The Bank of England's base interest rate, represented as a percentage. The values range from 0.5% to 6%, reflecting typical interest rate fluctuations. Inflation Rate (%): The Consumer Price Index (CPI) values, represented as a percentage, ranging from 1% to 5%, simulating typical inflation trends. Employment Levels (000s): The number of employed individuals in the UK, represented in thousands. The data simulates employment levels ranging from 25 million to 35 million. Growth in Wage (%): The average wage growth rate per quarter, represented as a percentage, ranging from 2% to 7%. GDP Growth Rate (%): The quarterly growth rate of the UK's Gross Domestic Product (GDP), represented as a percentage, with values ranging from -2% to 5%, simulating economic growth and contraction periods. This dataset can be used for educational purposes, including time series analysis, regression modeling, and economic research. Please note that the data is synthetic and not derived from actual historical records. It aims to replicate realistic patterns and trends observed in the UK economy and housing market during the specified period.

UVA/riboflavin corneal cross-linking (CXL) is a common used approach to treat progressive keratoconus. This study aims to investigate the alteration of corneal stiffness following CXL by mimicking the inflation of the eye under the in vivo loading conditions. Seven paired porcine eye globes were involved in the inflation test to examine the corneal behaviour. Cornea-only model was constructed using the finite element method, without considering the deformation contribution from sclera and limbus. Inverse analysis was conducted to calibrate the non-linear material behaviours in order to reproduce the inflation test. The corneal stress and strain values were then extracted from the finite element models and tangent modulus was calculated under stress level at 0.03 MPa. UVA/riboflavin cross-linked corneas displayed a significant increase in the material stiffness. At the IOP of 27.25 mmHg, the average displacements of corneal apex were 307 ± 65 μm and 437 ± 63 μm (p = 0.02) in CXL and PBS corneas, respectively. Comparisons performed on tangent modulus ratios at a stress of 0.03 MPa, the tangent modulus measured in the corneas treated with the CXL was 2.48 ± 0.69, with a 43±24% increase comparing to its PBS control. The data supported that corneal material properties can be well-described using this inflation methods following CXL. The inflation test is valuable for investigating the mechanical response of the intact human cornea within physiological IOP ranges, providing benchmarks against which the numerical developments can be translated to clinic.

Methods Fresh porcine eyes were obtained from a local abattoir (Morphets, Tan house farm, Widnes) and tested within 6-9 hours after death. Soft muscular tissue was removed with surgical scissors. The superior direction was marked and the eye globe was placed in a customized compartment for accurate needle insertion through the posterior pole. The internal eye components were removed through the posterior pole using a 14G needle. The needle was then lightly glued around the posterior pole and the intra-ocular cavity was washed with 5 to 6 ml PBS (Sigma, Dorset, United Kingdom). The outer surface of the globe was continually kept hydrated by applying PBS every 2-5 minutes. Random speckles were applied on the globe by lightly spraying a waterproof and fast drying black paint to facilitate deformation tracking in post-analysis. The prepared specimen was then placed into a custom-designed eye chamber filled with PBS, and transferred onto the inflation rig.

The inflation test rig provides full-field observation of ocular response to uniform intraocular pressure (IOP) changes. The physical test equipment is fully bespoke having been designed and built in-house. The equipment features closed loop control software written in LabVIEW (version 10.0.1, RRID:SCR_014325) to regulate IOP while collecting real-time data by triggering cameras to take pictures of the globe. The obtained images are used for measurement of deformation across the globe. The specimen was clamped in a horizontally placed eye chamber with high precision real-time laser (LK-2001, Keyence, UK) pointing towards the apical displacement. An array of six high resolution digital cameras (18.0 megapixels, 550D, Canon, Tokyo, Japan) surrounding the eye chamber and a pressure adjusting tank was placed vertically to inflate the eye while taking synchronous images. The camera setup allows an angle of 25° within each pair and an angle of 120° between each set.

A custom-built LabVIEW software was used to tightly control the pressure. The experiments started by 3 pre-conditioning cycles. The pre-conditioning cycles were to ensure the eye was sitting comfortably on the needle, and the tissue behavior was repeatable (15). An initial pressure of 2.5 mmHg was used to balance the external pressure applied by PBS in the pressure chamber, and was therefore considered a zero-pressure point for the inflation test. Specimens were loaded to a maximum internal load at a medium rate of 0.55 mmHg/s for each cycle. During each cycle the eye was allowed to relax for a period of 2 minutes which was obtained experimentally to allow tissue to fully recover to its relaxation state. The behavior of specimen in the final loading cycle was used for post-analysis.

After the experiment was completed, the eye was removed from the test rig and dissected into anterior and posterior parts. Eight meridian profiles of discrete thickness measurements were selected. The thickness at each desired point on each meridian line was determined using an in-house developed Thickness Measurement Device (TMD) (LTA-HS, Newport, Oxfordshire, UK) which was developed by the Biomechanical Engineering group to measure the thickness of biological tissue. A vertical measurement probe was located at a height of about 30 mm above the centre point of the support. The probe moved down with a controlled velocity until it reached the surface of the tissue. By precisely knowing the original distance between the initial position of probe and the surface of support, the measured value was recorded as the thickness of the tissue.

To decrease the geometrical complexity and understand the effect of CXL treatment on corneas where the application of interest is, we built up a corneal-only model by excluding the sclera part from a whole globe model. In this corneal model, the orphan mesh of geometry was constructed with Abaqus 6.13 (Dassault Systèmes Simulia Corp., Rhode Island, USA) using bespoke software. The 2592 elements with 8611 total nodes adopted the hybrid and quadratic type with triangular cross-section (C3D15H), which were arranged in 12 rings across the cornea surface and 3 layers through the thickness. Corneal apex was restrained against displacement in X- and Y-directions, whereas limbus was restrained in the X-, Y-, and Z-direction. The intraocular pressure was distributed on the posterior surface of the cornea. The apical displacement of the entire cornea was extracted by the displacement of corneal apex minus the average displacement of limbus in the anterior-posterior direction.

The image profiles obtained were analyzed using a 2D DIC method named Particle Image Velocimetry (PIV) to obtain deformations on the surface of the eye (Figure 3) (21, 22). PIV compares an un-deformed and deformed image pairs of specimen surface which was speckled to present the local displacements within the selected subsets. Three discrete locations including corneal apex and limbus were measured from each camera. As only cornea was considered in the study, the cornea deformation was calculated by subtracting the average displacement of limbus in the anterior-posterior direction from the displacement of corneal apex.

An in-house built software that uses Particle Swarm Optimization (PSO) as an optimization strategy was developed in Matlab (RRID:SCR_001622) to conduct the inverse analysis optimization due to its success in the engineering applications. PSO evaluates the fitness of the apical displacement between simulation and experiment and iterates over the different values of material parameters to decrease the error until the best fitness appears. The material constitutive model chosen to demonstrate the material behavior of ocular tissue during loading was Ogden model, utilized in a number of previous studies on soft tissues.

The Ogden material model order one relies on two parameters of μ (shear modulus) and α (strain hardening exponent) to define the non-linear material behavior. The use of first order material model (N=1) reduced the complexity of optimization and thus the computational cost as a result of less variables. The values of material parameters α and μ represented the output of the inverse modelling process that resulted in the highest fitness of simulation against inflation experiment. The design optimization process adjusts the value of μ and α within the constitutive model while setting a wide lower and upper boundary range (lower boundary = [0.005, 50]; upper boundary = [0.2, 200]). The error limit of RMS was set as 10%, which terminated the optimization once the error is lower than the limit. With these parameters, stress and strain could then be extracted from the numerical modelling results. The uniaxial-mode stress was calculated through obtained μ and α in Table 2, based on the previously described method and then tangent modulus was calculated numerically from the gradient of the resulting stress-strain curve.

This page contains results from 304 Fluvial Egg Drift Simulator (FluEgg; version 4.1.1) simulations of invasive carp eggs and larvae in the Maumee River, Ohio, under unsteady flow conditions. FluEgg models the drift and dispersion of eggs and larvae in fluvial environments. The eggs develop, changing in size and density, and eventually hatch into larvae. The simulations end when the larvae reach the gas bladder inflation stage or when the set duration of the simulation is exceeded (whichever comes first). FluEgg requires the user to provide hydraulic data to drive the drift model. The hydraulic inputs for these FluEgg simulations were generated using a one-dimensional unsteady hydraulic model of the Maumee River (see the other child items of this data release for more information about the hydraulic model) for four unsteady flow periods in which grass carp eggs or larvae were collected on the Maumee River: July 11-14, 2017, June 11-14, 2018, June 22-27, 2018, and May 28-30, 2019. The upstream end of the model domain (0.0 river kilometers) is located 280 meters downstream from Independence Dam near Defiance, Ohio, and the downstream end of the model domain is the mouth of the Maumee River at Lake Erie near NOAA tidal gage 9063085 (95.6 river kilometers). In FluEgg, the hydraulic conditions at the downstream end of the model domain extend infinitely downstream to allow eggs and larvae to drift beyond the model domain. Therefore, any drift distances greater than 95.6 kilometers should be excluded from further analysis of these data. FluEgg simulations were first run in reverse using the reverse time particle tracking algorithm (RTPT) in FluEgg using the time, location, and developmental stage of 73 captured grass carp eggs and larvae as input. Accounting for replicates, a total of 28 FluEgg simulations were run in reverse for a single invasive carp species (grass carp). Because RTPT simulations result in distributions of potential spawning areas, a series of 276 iterative forward FluEgg simulations were run to further refine the likely grass carp spawning area for the 28 groups of eggs/larvae. Each simulation included 10,000 grass carp eggs, which were assumed to have been spawned at the water surface and at the midpoint of the channel. This page includes: --MaumeeRiver_unsteady_fluegg_reverse_sim_list.csv: comma-separated values (csv) file listing the simulation parameters used for 28 unsteady FluEgg RTPT simulations (reverse) --MaumeeRiver_unsteady_fluegg_forward_sim_list.csv: comma-separated values (csv) file listing the simulation parameters used for 276 unsteady FluEgg simulations (forward) --MaumeeRiver_centerline.KML: KML file of the Maumee River centerline that represents the model domain --MaumeeRiver_unsteady_fluegg_reverse_output.zip: ZIP file containing Hierarchical Data Format 5 (HDF5) results files from 28 reverse FluEgg simulations with the naming convention Maumee_RTPT_RunX_10Kgc_TIMESTEPs.h5, where RunX is the run number (1 to 28) and TIMESTEPs is the simulation timestep in seconds. Each HDF5 file has a corresponding set of simulation parameters given in MaumeeRiver_unsteady_fluegg_reverse_sim_list.csv. --MaumeeRiver_unsteady_fluegg_forward_output.zip: ZIP file containing Hierarchical Data Format 5 (HDF5) results files from 276 forward FluEgg simulations with the naming convention Maumee_FRunIteration_10Kgc_TIMESTEPs.h5, where FRunIteration is the forward simulation identifier (run number and iteration; F1a, F1b, F1c) and TIMESTEPs is the simulation timestep in seconds. Each HDF5 file has a corresponding set of simulation parameters given in MaumeeRiver_unsteady_fluegg_forward_sim_list.csv.

Means of EM Estimates, MADE (within parentheses), and computational efforts (CPU time) for TVZIP-INARCH (2) models where zero-inflation is driven by an exogenous variable.

This research investigates the role of dimples in enhancing the aerodynamic characteristics of a Blended-Wing-Body (BWB) airframe. Numerical simulations, grounded in Computational Fluid Dynamics (CFD), were utilized to model turbulent airflow and assess the aerodynamic forces acting on the wing structure. The k-ω Shear-Stress Transport (SST) turbulence model was applied to effectively solve the governing equations. The impact of four dimple indentation depths (d/Dd = 0.025, 0.05, 0.075, and 0.1) at six specific locations on either the suction or pressure sides of the BWB wing surface was investigated. Simulations were performed at Mach 0.15 and Mach 0.6, treating the flow as incompressible and compressible, respectively, to capture variations in aerodynamic behavior. The evaluation involved analyzing the drag coefficient (CD), lift coefficient (CL), and lift-to-drag (L/D) ratio. The results reveal that, under optimal conditions, a dimpled BWB surface can achieve a reduction in CD by as much as 4.09% relative to a non-modified surface, without negatively impacting lift. This improvement is primarily due to the dimples’ capacity to maintain attached flow and postpone flow separation. Implementing dimples on the BWB wing surface as a passive flow control method has proven effective in enhancing the aerodynamic efficiency of lifting surfaces.

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The military simulation and virtual training market is expected to grow at a CAGR of 5% during the forecast period. Cost-effective virtual training, drivers.2, and drivers.3 are some of the significant factors fueling military simulation and virtual training market growth.

Cost-effective virtual training

To estimate the market size of military simulation and virtual training market, Technavio has tracked the recent trends and developments in the industry. The market size has been developed in terms of value by considering the following factors Vendor sales: It excludes all freelance tutoring services and discounts and rebates Revenues: Taken in local currencies, if not available in US dollars, for each country and vendor and then converted to US dollars using the yearly average currency exchange rate of 2019, the base year. This implies that the figures reflect industry trends, not distorted by fluctuations in international exchange rates. Exclusions: The report does not consider the effect of inflation and price fluctuation over the forecast period Currency: Unless explicitly mentioned, all revenues are represented in US dollars The market sizing has been built and validated using multiple demand-side and supply-side approaches for a detailed understanding of the military simulation and virtual training market. The specific market sizing approaches used for evaluating the military simulation and virtual training market are: Top down: Validated the market based on the contribution of military simulation and virtual training to the overall global education market Bottom up: Validated the market on the basis of the revenue and/or volume or market share of key education services companies providing military simulation and virtual training Combination: Using a combination of more than one approach described above and integrating the results in the data model Within the above-mentioned market sizing models, analysts have made assumptions and estimates listed below: Total enrollment/demand Average price of product/course/solution/subscription fees New installations For this report, we have also used the following macro data in modeling the market size for 2019 Population GDP growth Government spending on education Disposable income Based on the above data models, Technavio has estimated the total value for global military simulation and virtual training market as $12,545.33 billion in 2019.

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The military simulation and virtual training market is expected to grow at a CAGR of 5% during the forecast period. Cost-effective virtual training, drivers.2, and drivers.3 are some of the significant factors fueling military simulation and virtual training market growth.

Cost-effective virtual training

To estimate the market size of military simulation and virtual training market, Technavio has tracked the recent trends and developments in the industry. The market size has been developed in terms of value by considering the following factors Vendor sales: It excludes all freelance tutoring services and discounts and rebates Revenues: Taken in local currencies, if not available in US dollars, for each country and vendor and then converted to US dollars using the yearly average currency exchange rate of 2019, the base year. This implies that the figures reflect industry trends, not distorted by fluctuations in international exchange rates. Exclusions: The report does not consider the effect of inflation and price fluctuation over the forecast period Currency: Unless explicitly mentioned, all revenues are represented in US dollars The market sizing has been built and validated using multiple demand-side and supply-side approaches for a detailed understanding of the military simulation and virtual training market. The specific market sizing approaches used for evaluating the military simulation and virtual training market are: Top down: Validated the market based on the contribution of military simulation and virtual training to the overall global education market Bottom up: Validated the market on the basis of the revenue and/or volume or market share of key education services companies providing military simulation and virtual training Combination: Using a combination of more than one approach described above and integrating the results in the data model Within the above-mentioned market sizing models, analysts have made assumptions and estimates listed below: Total enrollment/demand Average price of product/course/solution/subscription fees New installations For this report, we have also used the following macro data in modeling the market size for 2019 Population GDP growth Government spending on education Disposable income Based on the above data models, Technavio has estimated the total value for global military simulation and virtual training market as $12,545.33 billion in 2019.

Série de taux annuels d’inflation (IPC) fournie par l’INSEE pour les années 2000 à 2024.

Summary: Quarterly time series (starting in 1959Q4) of estimates of macroeconomic stars and output gap. These estimates of stars and other model objects were developed using a semi-structural model to jointly estimate “stars” — long-run levels of output (its growth rate), the unemployment rate, the real interest rate, productivity growth, price inflation, and wage inflation. It features links between survey expectations and stars, time-variation in macroeconomic relationships, and stochastic volatility. Survey data help discipline stars’ estimates and have been crucial in estimating a high-dimensional model since the pandemic. The model has desirable real-time properties, competitive forecasting performance, and superior fit to the data compared to variants without the empirical features mentioned above. The paper that developed the model is available from the Working Paper Series of the Federal Reserve Bank of Cleveland - A Unified Framework to Estimate Macroeconomic Stars. For the historical real-time archives: https://github.com/zamansaeed/macrostars/Citation:To learn more about the data and the model, see:Zaman, Saeed. 2024. "A Unified Framework to Estimate Macroeconomic Stars." Working Paper No. 21-23R2. Federal Reserve Bank of Cleveland. https://doi.org/10.26509/frbc-wp-202123r2.JEL CodesC5, E4, E31, E24, O4File Description:Each vintage includes the posterior mean, 68% and 90% Credible Intervals for:U-star: long-run level of unemployment rateR-star: long-run real rate of interestPi-star: long-run level of price inflationP-star: long-run level of productivity growthW-star: long-run level of nominal wage inflationG-star: growth rate of potential outputOutput Gap: cyclical assessment of the US economy Persistence in price inflation (gap)Persistence in nominal wage inflation (gap)Slope of the price Phillips CurveSlope of the wage Phillips CurveShort-run passthrough from prices to wagesWedge: between W-star and (P-star + Pi-star)D: the catch all component in R-star equationStochastic volatility price inflation gapStochastic volatility nominal wage inflation gapStochastic volatility labor productivity gapStochastic volatility interest rate gapStochastic volatility output gapStochastic volatility UR gapDisclaimer:These data are updated by the authors and are not an official product of the Federal Reserve Bank of Cleveland.Latest Estimates of Stars (and the output gap):-- based on US data through 2025Q2In bold is the (posterior) Mean estimate and in parentheses 68% coverage Interval:U-star (long-run level of unemployment rate): 4.4% (4.0% to 4.8%)R-star (long-run real rate of interest): 1.5% (0.8% to 2.2%)Pi-star (long-run level of price inflation): 2.2% (1.7% to 2.6%)P-star (long-run level of productivity growth): 1.7% (1.1% to 2.2%)W-star (long-run level of nominal wage inflation): 3.6% (3.2% to 4.0%)G-star (growth rate of potential output): 2.7% (2.5% to 3.0%)Output Gap (cyclical assessment of the US economy): +0.3% (-0.6% to +1.2%)

We demonstrate that the disruption index (CD) recently applied to publication and patent citation networks by Park et al. (Nature, 2023) systematically decreases over time due to secular growth in research and patent production, following two distinct mechanisms unrelated to innovation – the first structural and the second behavioral. The structural explanation follows from ‘citation inflation’ (CI) (Petersen et al., Research Policy, 2018), an inextricable feature of real citation networks. One driver of CI is the ever-increasing length of reference lists, which causes the CD index to systematically decrease. The behavioral explanation reflects shifts in scholarly citation practice (e.g. self-citation) that increase the rate of triadic closure in citation networks and confounds efforts to measure disruptive innovation using CD. Combined, these two mechanisms render CD unsuitable for cross-temporal analysis, and call into question the interpretations provided by Park et al. Methods Enclosed data accompany the following publications:

Alexander M. Petersen, Felber Arroyave, Fabio Pammolli (2025). The disruption index suffers from citation inflation: re-analysis of temporal CD trend and relationship with team size reveal discrepancies. J. Informetrics 19, 101605 (2025). DOI:10.1016/j.joi.2024.101605

Alexander M. Petersen, Felber Arroyave, Fabio Pammolli (2024). The disruption index is biased by citation inflation. Quantitative Science Studies 5, 936-953 (2024). DOI:10.1162/qss_a_00333

To summarize, enclosed are two types of data: 1) Empirical publication-level data accompanied by code (do-files) for running multi-variate regressions in STATA 2) Raw network data produced for 6 citation network scenarios. For each scenario, we include 4 synthetic networks each, for a total of 24 citation networks. Each citation network is comprised of 125270 nodes that were systematically added in cohorts, therefore representing a null model for evolving citation networks, and thereby useful for benchmarking existing and new bibliometric measures. These data were generated using a synthetic citation network model developed and reported in: Pan, R. K., Petersen, A. M., Pammolli, F. & Fortunato, S. The memory of science: Inflation, myopia, and the knowledge network. Journal of Informetrics 12, 656–678 (2018).