Tag: e

A Local Projections Approach to Difference-in-Differences Event Studies
A Neural Phillips Curve and a Deep Output Gap
agency
An Exploration of Trend-Cycle Decomposition Methodologies in Simulated Data
Autoregressive models
Ben Franklin's Autoencoder
Beveridge-Nelson Decomposition
Boosting: Why You Can Use the Hp Filter
Career success and career capital are cointegrated
Careers are a random walk
Change leads to insight far more often than insight leads to change
Correlation between prices and quantities reveals main market driver
Diffusion models
Don't detrend random walks with linear trends
Don't rely on inspiration to write
Dynamic covariate balancing: Estimating treatment effects over time with potential local projections
Dynamic factor model
Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics
Essays in Macroeconometrics
Exponentials drive asymmetry
FAQ: How do I extract the output gap?
Fat tails preclude ergodicity
First impression are high variance
Focus on the residuals
For the effectual reasoner, residuals are inputs rather than outputs
Forecast Error Variance Decompositions with Local Projections
Forecasting levels of log variables in vector autoregressions
Generalized Random Forests
Get leverage on the fixed cost of pain
Goals should be binomial
Good ideas are testable ideas
Good representations reduce sample complexity of downstream tasks
Granular IV
Granularity
Growth and Ideas
Hamilton filter
Highly likely events do not yield much information
How Big Is the Random Walk in GNP?
How Competitive is the Stock Market? Theory, Evidence from Portfolios, and Implications for the Rise of Passive Investing
How the Wealth Was Won: Factor Shares as Market Fundamentals
Ideas generate increasing returns because they are nonrivalrous
Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments
If you're not sure something is getting better, it's not
Impulse Response Estimation by Smooth Local Projections
In the long-run, all costs are variable
Inference in Heavy-Tailed Nonstationary Multivariate Time Series
Information is relative
Information is surprisal
Interaction generates non-normality
Invest in companies like you invest in your career
Iterative tinkering enhances effective IQ
Just do more
Labor relationships have debt-like features
Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I ( 1 ) cointegrated factors
Lasso Inference for High-Dimensional Time Series
Learned representations are more important than what you do with them
Linear Algebra Done Right
Linearly separable problems are easy to solve
Local projections
Local projections vs. VARs
Local Projections vs. VARs: Lessons From Thousands of DGPs
Look for high talent and high agency
Mathematical Foundations of Machine Learning
Maximize the entropy of your information sources
Maximize your output of testable ideas
Model Selection for Local Projections Instrumental Variable Methods - Empirical Application to Government Spending Multipliers.
Model-Free Impulse Responses
Moving average model
Network egress is a lock-in tactic for public cloud providers
Not all spurious correlation is random
Observe before you analyze
Origins of Stock Market Fluctuations
Perfect competition is not optimal under increasing returns to scale
Planning your time is like choosing to dance on beat
Pre-train your representations before learning judgement
Prediction is compression, compression is expression
Principal component analysis
Principal Component Analysis for Nonstationary Series
Principal components estimation and identification of static factors
Random walk
residuals
self-supervised learning
Short and Variable Lags
Sparse Trend Estimation
Subset selection for vector autoregressive processes using Lasso
Systematic shifts in scaling behavior based on organizational strategy in universities
Tariffs and Growth: Heterogeneous Eﬀects by Economic Structure
The Adaptive Lasso and Its Oracle Properties
The Bayesian Lasso
The Disappointing Recovery of Output after 2009
The Liquidity Channel of Fiscal Policy
Under fat tails, look for quick reasons to say yes
Under fat tails, outliers drive movements in the mean
Under fat tails, unbiasedness is overrated
Use TK liberally
variance
Vector autoregression
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Volatility is information
When in doubt, don't difference
When unskilled, complicate the game and add randomness
With the right representation, judgement is cheap

Explorer

Tag: e