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on Econometric Time Series |
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Issue of 2025–06–16
25 papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | José Luis Montiel Olea; Mikkel Plagborg-Møller; Eric Qian; Christian K. Wolf |
| Abstract: | What should applied macroeconomists know about local projection (LP) and vector autoregression (VAR) impulse response estimators? The two methods share the same estimand, but in finite samples lie on opposite ends of a bias-variance trade-off. While the low bias of LPs comes at a quite steep variance cost, this cost must be paid to achieve robust uncertainty assessments. Hence, when the goal is to convey what can be learned about dynamic causal effects from the data, VARs should only be used with long lag lengths, ensuring equivalence with LP. For LP estimation, we provide guidance on selection of lag length and controls, bias correction, and confidence interval construction. |
| JEL: | C22 C32 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33871 |
| By: | Atsushi Inoue; Lutz Kilian |
| Abstract: | Some studies have expressed concern that the Gaussian-inverse Wishart-Haar prior typically employed in estimating sign-identified VAR models may be unintentionally informative about the implied prior for the structural impulse responses. We discuss how this prior may be reported and make explicit what impulse response priors a number of recently published studies specified, allowing the readers to decide whether they are comfortable with this prior. We discuss what features to look for in this prior in the absence of specific prior information about the responses, building on the notion of weakly informative priors in Gelman et al. (2013), and in the presence of such information. Our empirical examples illustrate that the Gaussian-inverse Wishart-Haar prior need not be unintentionally informative about the impulse responses. Moreover, even when it is, there are empirically verifiable conditions under which this fact becomes immaterial for the substantive conclusions. |
| Keywords: | Gaussian-inverse Wishart prior; Haar prior; impulse response; set indentification |
| JEL: | C22 C32 C52 E31 Q43 |
| Date: | 2025–05–09 |
| URL: | https://d.repec.org/n?u=RePEc:fip:feddwp:99955 |
| By: | Joshua C. C. Chan; Michael Pfarrhofer |
| Abstract: | We extend the standard VAR to jointly model the dynamics of binary, censored and continuous variables, and develop an efficient estimation approach that scales well to high-dimensional settings. In an out-of-sample forecasting exercise, we show that the proposed VARs forecast recessions and short-term interest rates well. We demonstrate the utility of the proposed framework using a wide rage of empirical applications, including conditional forecasting and a structural analysis that examines the dynamic effects of a financial shock on recession probabilities. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.01422 |
| By: | Lukas Berend; Jan Pr\"user |
| Abstract: | We propose a high-dimensional structural vector autoregression framework capable of accommodating a large number of linear inequality restrictions on impact impulse responses, structural shocks, and their element-wise products. Combining impact- and shock-inequality restrictions can be flexibly used to sharpen inference and to disentangle structurally interpretable shocks through sign and shock constraints. To estimate the model, we develop a highly efficient sampling algorithm that scales well with model dimension and the number of inequality restrictions on impact responses, as well as structural shocks. It remains computationally feasible even when existing algorithms may break down. To demonstrate the practical utility of our approach, we identify five structural shocks and examine the dynamic responses of thirty macroeconomic variables, highlighting the model's flexibility and feasibility in complex empirical settings. We provide empirical evidence that financial shocks are the most important driver of the dynamics of the business cycle. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.19244 |
| By: | Lajos Horvath; Gregory Rice; Yuqian Zhao |
| Abstract: | The problem of detecting change points in the regression parameters of a linear regression model with errors and covariates exhibiting heteroscedasticity is considered. Asymptotic results for weighted functionals of the cumulative sum (CUSUM) processes of model residuals are established when the model errors are weakly dependent and non-stationary, allowing for either abrupt or smooth changes in their variance. These theoretical results illuminate how to adapt standard change point test statistics for linear models to this setting. We studied such adapted change-point tests in simulation experiments, along with a finite sample adjustment to the proposed testing procedures. The results suggest that these methods perform well in practice for detecting multiple change points in the linear model parameters and controlling the Type I error rate in the presence of heteroscedasticity. We illustrate the use of these approaches in applications to test for instability in predictive regression models and explanatory asset pricing models. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.01296 |
| By: | Bahaa Aly, Tarek |
| Abstract: | This study presents a novel hybrid framework that integrated Long Short-Term Memory (LSTM) networks with Daubechies wavelet transforms to estimate Deep Impulse Response Functions (DIRF) for monthly macroeconomic time series, across five economies: Brazil, Egypt, Indonesia, United States, and the United Kingdom. Eight key variables, yield curve latent factors (LEVEL, SLOPE, CURVATURE), foreign exchange rates, equity indices, central bank policy rates, GDP growth rates, and inflation rates, were modeled using the proposed LSTM-Wavelet approach, and were compared against an ANN-Wavelet hybrid, and a traditional Vector Error Correction Model (VECM). The LSTM-Wavelet model achieved a superior overall median R2, outperforming the ANN-Wavelet and VECM. The approach excelled in capturing nonlinear dynamics and temporal dependencies for variables such as equity indices, policy rates, GDP, and inflation. Db4 was superior for capturing short and medium-term patterns in macroeconomic variables like GDP, EQUITY, and FX, cause its shorter filter and moderate smoothing excelled at isolating cyclical patterns in noisy, volatile data. Cumulative DIRFs revealed consistent cross variable dynamics e.g., yield curve shocks propagated to equity, FX, policy rates, GDP, and inflation, in line with economic theory. These findings underscored the hybrid model’s ability to capture non-linearity, multiscale interactions in macroeconomic data, offering valuable insights for forecasting and policy analysis. |
| Keywords: | Deep Impulse Response Function, Long Short-Term Memory, Daubechies Wavelet transform, Macroeconomics, nonlinearity, Forecasting |
| JEL: | C5 C53 C58 |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124905 |
| By: | Tommaso Proietti (CEIS & DEF, University of Rome "Tor Vergata"); Alessandro Giovannelli (University of L’Aquila) |
| Abstract: | The quantification of the interannual component of variability in climatological time series is essential for the assessment and prediction of the El Ni˜no - Southern Oscillation phenomenon. This is achieved by estimating the deviation of a climate variable (e.g., temperature, pressure, precipitation, or wind strength) from its normal conditions, defined by its baseline level and seasonal patterns. Climate normals are currently estimated by simple arithmetic averages calculated over the most recent 30-year period ending in a year divisible by 10. The suitability of the standard methodology has been questioned in the context of a changing climate, characterized by nonstationary conditions. The literature has focused on the choice of the bandwidth and the ability to account for trends induced by climate change. The paper contributes to the literature by proposing a regularized real time filter based on local trigonometric regression, optimizing the estimation bias-variance trade-off in the presence of climate change, and by introducing a class of seasonal kernels enhancing the localization of the estimates of climate normals. Application to sea surface temperature series in the Ni˜no 3.4 region and zonal and trade winds strength in the equatorial and tropical Pacific region, illustrates the relevance of our proposal. |
| Keywords: | Climate change; Seasonality; El Ni˜no - Southern Oscillation; Local Trigonometric Regression. |
| JEL: | C22 C32 C53 |
| Date: | 2025–06–04 |
| URL: | https://d.repec.org/n?u=RePEc:rtv:ceisrp:602 |
| By: | Yi Ding (Faculty of Business Administration, University of Macau); Xinghua Zheng (Department of ISOM, Hong Kong University of Science and Technology) |
| Abstract: | We study the estimation of scatter matrices in elliptical factor models with 2 + eth moment. For such heavy-tailed data, robust estimators like the Hubertype estimator in Fan et al. (2018) cannot achieve a sub-Gaussian convergence rate. In this paper, we develop an idiosyncratic-projected self-normalization method to remove the effect of the heavy-tailed scalar component and propose a robust estimator of the scatter matrix that achieves the sub-Gaussian rate under an ultra-high dimensional setting. Such a high convergence rate leads to superior performance in estimating high-dimensional global minimum variance portfolios. |
| Keywords: | High-dimension, elliptical model, factor model, scatter matrix, robust estimation |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:202529 |
| By: | Jonas E. Arias; Juan F. Rubio-Ram\'irez; Minchul Shin |
| Abstract: | We develop a new algorithm for inference based on SVARs identified with sign restrictions. The key insight of our algorithm is to break apart from the accept-reject tradition associated with sign-identified SVARs. We show that embedding an elliptical slice sampling within a Gibbs sampler approach can deliver dramatic gains in speed and turn previously infeasible applications into feasible ones. We provide a tractable example to illustrate the power of the elliptical slice sampling applied to sign-identified SVARs. We demonstrate the usefulness of our algorithm by applying it to a well-known small-SVAR model of the oil market featuring a tight identified set as well as to large SVAR model with more than 100 sign restrictions. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.23542 |
| By: | James Hebden; Fabian Winkler |
| Abstract: | We propose an efficient procedure to solve for policy counterfactuals in linear models with occasionally binding constraints in sequence space. Forecasts of the variables relevant for the policy problem, and their impulse responses to anticipated policy shocks, constitute sufficient information to construct valid counterfactuals. Knowledge of the structural model equations or filtering of structural shocks is not required. We solve for deterministic and stochastic paths under instrument rules as well as under optimal policy with commitment or subgame-perfect discretion. As an application, we compute counterfactuals of the U.S. economy after the pandemic shock of 2020 under several monetary policy regimes. |
| Keywords: | Sequence space; DSGE; Occasionally binding constraints; Optimal policy; Commitment; Discretion |
| JEL: | C61 C63 E52 |
| Date: | 2024–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:100035 |
| By: | Lukas Bauer |
| Abstract: | This paper provides comprehensive simulation results on the finite sample properties of the Diebold-Mariano (DM) test by Diebold and Mariano (1995) and the model confidence set (MCS) testing procedure by Hansen et al. (2011) applied to the asymmetric loss functions specific to financial tail risk forecasts, such as Value-at-Risk (VaR) and Expected Shortfall (ES). We focus on statistical loss functions that are strictly consistent in the sense of Gneiting (2011a). We find that the tests show little power against models that underestimate the tail risk at the most extreme quantile levels, while the finite sample properties generally improve with the quantile level and the out-of-sample size. For the small quantile levels and out-of-sample sizes of up to two years, we observe heavily skewed test statistics and non-negligible type III errors, which implies that researchers should be cautious about using standard normal or bootstrapped critical values. We demonstrate both empirically and theoretically how these unfavorable finite sample results relate to the asymmetric loss functions and the time varying volatility inherent in financial return data. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.23333 |
| By: | Christof Schmidhuber |
| Abstract: | The critical dynamics of conformal field theories on random surfaces is investigated beyond the dynamics of the overall area and the genus. It is found that the evolution of the order parameter in physical time is a multifractal random walk. Accordingly, the higher moments of time variations of the order parameter exhibit multifractal scaling. The series of Hurst exponents is computed and illustrated with the examples of the Ising-, 3-state-Potts-, and general minimal models on a random surface. Models are identified that can replicate the observed multifractal scaling in financial markets. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.23928 |
| By: | Carolina Nunes; Tiago Pinheiro |
| Abstract: | This paper estimates econometric models of default risk for individuals obtaining credit in Portugal using data from Banco de Portugal’s Credit Register. We estimate monthly default probabilities for mortgage and consumer loans over three, six, and twelve-month horizons. The models combine cross-sectional and time series components. The cross-sectional component captures default risk heterogeneity across individuals by relating default risk to loan and borrower characteristics. The time series component captures time variation in aggregate default risk by linking it with macroeconomic variables. Our findings indicate that the model’s performance in distinguishing between defaulting and non-defaulting borrowers is on par with or superior to existing literature. The results also show a close alignment between average default probabilities and actual default rates across various borrower characteristics and lending institutions. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ptu:wpaper:w202510 |
| By: | Mr. Geoffrey M Heenan; Karras Lui; Ian J Nield; Eucharist Muaulu; Viiaonaoperesi Reupena; Ivy Sabuga; Aiulu Tolovaa |
| Abstract: | This paper describes the recent work to strengthen the nowcasting capacity at the Central Bank of Samoa (CBS). It compiles available high-frequency datasets such as tourism receipts, agriculture market survey, remittances, among others, to nowcast real GDP in Samoa. Nowcasting enables the estimation of the present and near-term forecast. It employs standard nowcasting methods such as Bridge, Mixed Data Sampling (MIDAS), and Unrestricted MIDAS (U-MIDAS). All methods significantly outperform the naive forecasts. Our analysis show that forecast combination of the three methods minimizes the root mean squared error (RMSE) for both full and pre-COVID-19 samples, while U-MIDAS performs better during crises, particularly in identifying turning points during the COVID-19 pandemic. Strengthening nowcasting capacity is important for Samoa, where real GDP data release experiences up to a 90-day lag. |
| Keywords: | Forecasting; Nowcasting; Real GDP; Samoa |
| Date: | 2025–05–16 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/092 |
| By: | Guglielmo Maria Caporale; Luis Alberiko Gil-Alana |
| Abstract: | This paper examines persistence and nonlinearities in the US Federal Funds rate over the period from July 1954 to April 2025 by using fractional integration methods. More precisely, a general model including both deterministic and stochastic components is estimated under alternative assumptions concerning the error term (white noise and autocorrelation), and both linear and a nonlinear specification (the latter based on Chebyshev polynomials) are considered. The empirical results provide evidence of mean reversion but also of high persistence when allowing for autocorrelation in the errors. Moreover, they point towards significant nonlinearities in the stochastic behaviour of the series. Both are important properties of the Federal Funds rate, mainly reflecting underlying inflation persistence and policy shifts respectively. |
| Keywords: | US Federal Funds rate, fractional integration persistence, nonlinearities |
| JEL: | C22 E43 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11913 |
| By: | Hie Joo Ahn; Matteo Luciani |
| Abstract: | We disentangle price changes due to economy-wide shocks from those driven by idiosyncratic shocks by estimating a two-regime dynamic factor model with dynamic loadings on a new large dataset of finely disaggregated monthly personal consumption expenditures price inflation indexes for 1959-2023. We find that up to the mid-1990s and after the Covid pandemic, common shocks were the primary driver of US inflation dynamics and had long-lasting effects. In between, idiosyncratic shocks were the main driver, and common shocks had short-lived effects. |
| Keywords: | Core inflation; Dynamic factor model; Disaggregated consumer prices; Monetary policy |
| JEL: | C32 C43 C55 E31 E37 |
| Date: | 2024–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:100036 |
| By: | Md. Yeasin Rahat; Rajan Das Gupta; Nur Raisa Rahman; Sudipto Roy Pritom; Samiur Rahman Shakir; Md Imrul Hasan Showmick; Md. Jakir Hossen |
| Abstract: | The prediction of foreign exchange rates, such as the US Dollar (USD) to Bangladeshi Taka (BDT), plays a pivotal role in global financial markets, influencing trade, investments, and economic stability. This study leverages historical USD/BDT exchange rate data from 2018 to 2023, sourced from Yahoo Finance, to develop advanced machine learning models for accurate forecasting. A Long Short-Term Memory (LSTM) neural network is employed, achieving an exceptional accuracy of 99.449%, a Root Mean Square Error (RMSE) of 0.9858, and a test loss of 0.8523, significantly outperforming traditional methods like ARIMA (RMSE 1.342). Additionally, a Gradient Boosting Classifier (GBC) is applied for directional prediction, with backtesting on a $10, 000 initial capital revealing a 40.82% profitable trade rate, though resulting in a net loss of $20, 653.25 over 49 trades. The study analyzes historical trends, showing a decline in BDT/USD rates from 0.012 to 0.009, and incorporates normalized daily returns to capture volatility. These findings highlight the potential of deep learning in forex forecasting, offering traders and policymakers robust tools to mitigate risks. Future work could integrate sentiment analysis and real-time economic indicators to further enhance model adaptability in volatile markets. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.09851 |
| By: | Dominik Stempie\'n; Robert \'Slepaczuk |
| Abstract: | This research systematically develops and evaluates various hybrid modeling approaches by combining traditional econometric models (ARIMA and ARFIMA models) with machine learning and deep learning techniques (SVM, XGBoost, and LSTM models) to forecast financial time series. The empirical analysis is based on two distinct financial assets: the S&P 500 index and Bitcoin. By incorporating over two decades of daily data for the S&P 500 and almost ten years of Bitcoin data, the study provides a comprehensive evaluation of forecasting methodologies across different market conditions and periods of financial distress. Models' training and hyperparameter tuning procedure is performed using a novel three-fold dynamic cross-validation method. The applicability of applied models is evaluated using both forecast error metrics and trading performance indicators. The obtained findings indicate that the proper construction process of hybrid models plays a crucial role in developing profitable trading strategies, outperforming their individual components and the benchmark Buy&Hold strategy. The most effective hybrid model architecture was achieved by combining the econometric ARIMA model with either SVM or LSTM, under the assumption of a non-additive relationship between the linear and nonlinear components. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.19617 |
| By: | Masoud Ataei |
| Abstract: | This study evaluates the scale-dependent informational efficiency of stock markets using the Financial Chaos Index, a tensor-eigenvalue-based measure of realized volatility. Incorporating Granger causality and network-theoretic analysis across a range of economic, policy, and news-based uncertainty indices, we assess whether public information is efficiently incorporated into asset price fluctuations. Based on a 34-year time period from 1990 to 2023, at the daily frequency, the semi-strong form of the Efficient Market Hypothesis is rejected at the 1\% level of significance, indicating that asset price changes respond predictably to lagged news-based uncertainty. In contrast, at the monthly frequency, such predictive structure largely vanishes, supporting informational efficiency at coarser temporal resolutions. A structural analysis of the Granger causality network reveals that fiscal and monetary policy uncertainties act as core initiators of systemic volatility, while peripheral indices, such as those related to healthcare and consumer prices, serve as latent bridges that become activated under crisis conditions. These findings underscore the role of time-scale decomposition and structural asymmetries in diagnosing market inefficiencies and mapping the propagation of macro-financial uncertainty. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.01543 |
| By: | GUERRÓN QUINTANA, Pablo A.; JINNAI, Ryo; YAMAMOTO, Yohei |
| Abstract: | This chapter studies the relationship between asset price bubbles and macroeconomic fluctuations through both empirical analysis and theoretical modeling. We begin by applying the right-tailed unit root tests of Phillips et al. (2015a, b) to real stock and housing price indices in G-7 economies. These tests identify explosive dynamics in asset prices, and our findings show that such bubbly episodes frequently align with periods of economic expansion, suggesting a strong empirical link between asset booms and business cycle upswings. To investigate the mechanisms behind this co-movement, we modify the canonical bubble models of Tirole (1985) and Martin and Ventura (2012) by incorporating endogenous labor supply. However, in both cases, the emergence of a bubble fails to generate a robust macroeconomic expansion. Output and investment either decline or respond sluggishly, while labor hours fall in response to bubble formation. We then turn to the model of Guerron-Quintana et al. (2023), which embeds a variable capacity utilization mechanism into a dynamic general equilibrium framework. This amplification channel allows the model to produce simultaneous increases in output, consumption, investment, and labor during bubbly periods, consistent with empirical patterns. We also discuss the quantitative implementation challenges faced by this approach, highlighting the trade-offs involved in quantitatively modeling bubble-driven fluctuations. |
| Keywords: | asset price bubble, business cycles |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:hit:hituec:768 |
| By: | Boysen-Hogrefe, Jens |
| Abstract: | Analyzing US macro data via a structural vector-autoregressive model (SVAR), Deleidi and Mazzucato (2021) find strong positive spillovers from mission-oriented government spending on private research and development, as well as on overall economic activity (“crowding in”). Deleidi and Mazzucato apply the SVAR to first-differenced data despite the possibility of cointegration. The replication shows that the result hinges on the transformation of the data and the choice of the sample period. The time variation of the estimation results is substantial. When estimating the model with data starting after 1985, the results point to a temporary “crowding out” of private research and development spending. |
| Keywords: | Replication study, Mission oriented innovation policies, Fiscal multiplier, Sraffian supermultiplier |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ifwkie:318260 |
| By: | Dominik Stempie\'n; Janusz Gajda |
| Abstract: | We compare traditional approach of computing logarithmic returns with the fractional differencing method and its tempered extension as methods of data preparation before their usage in advanced machine learning models. Differencing parameters are estimated using multiple techniques. The empirical investigation is conducted on data from four major stock indices covering the most recent 10-year period. The set of explanatory variables is additionally extended with technical indicators. The effectiveness of the differencing methods is evaluated using both forecast error metrics and risk-adjusted return trading performance metrics. The findings suggest that fractional differentiation methods provide a suitable data transformation technique, improving the predictive model forecasting performance. Furthermore, the generated predictions appeared to be effective in constructing profitable trading strategies for both individual assets and a portfolio of stock indices. These results underline the importance of appropriate data transformation techniques in financial time series forecasting, supporting the application of memory-preserving techniques. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.19243 |
| By: | Yuhao Li |
| Abstract: | We propose new reproducing kernel-based tests for model checking in conditional moment restriction models. By regressing estimated residuals on kernel functions via kernel ridge regression (KRR), we obtain a coefficient function in a reproducing kernel Hilbert space (RKHS) that is zero if and only if the model is correctly specified. We introduce two classes of test statistics: (i) projection-based tests, using RKHS inner products to capture global deviations, and (ii) random location tests, evaluating the KRR estimator at randomly chosen covariate points to detect local departures. The tests are consistent against fixed alternatives and sensitive to local alternatives at the $n^{-1/2}$ rate. When nuisance parameters are estimated, Neyman orthogonality projections ensure valid inference without repeated estimation in bootstrap samples. The random location tests are interpretable and can visualize model misspecification. Simulations show strong power and size control, especially in higher dimensions, outperforming existing methods. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.01161 |
| By: | Tobias Adrian; Hongqi Chen; Max-Sebastian Dov\`i; Ji Hyung Lee |
| Abstract: | We analyse growth vulnerabilities in the US using quantile partial correlation regression, a selection-based machine-learning method that achieves model selection consistency under time series. We find that downside risk is primarily driven by financial, labour-market, and housing variables, with their importance changing over time. Decomposing downside risk into its individual components, we construct sector-specific indices that predict it, while controlling for information from other sectors, thereby isolating the downside risks emanating from each sector. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.00572 |
| By: | James H. Stock; Mark W. Watson |
| Abstract: | The COVID business cycle was unique. The recession was by far the deepest and shortest in the U.S. postwar record and the recovery was remarkably rapid. The cycle saw an unprecedented reallocation of employment and consumption away from in-person services towards goods that can be consumed at home and outdoors. This paper provides a simple empirical model that attributes these and other anomalies in real economic activity to a single unobserved shock. That shock is closely connected to COVID deaths, and diminishes in importance over the expansion, consistent with self-protective measures like masking, COVID fatigue, and eventually the availability of the vaccine. The COVID shock and anomalous COVID dynamics largely disappeared by late 2022. It appears that macrodynamics have returned to normal and that the structural shifts wrought by the pandemic have had limited effects on the underlying economic trends of key indicators, despite notable changes like the prevalence of remote work. The greatest macroeconomic legacy of the COVID business cycle has been on the national debt. |
| JEL: | E32 I00 I12 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33857 |