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  • "A Counterfactual Economic Analysis of Covid-19 Using a Threshold Augmented Multi-Country Model", by Alexander Chudik, Kamiar Mohaddes, M. Hashem Pesaran, Mehdi Raissi, and Alessandro Rebucci, forthcoming in Journal of International Money and Finance, May 2021.

    Abstract: This paper develops a threshold-augmented dynamic multi-country model (TG-VAR) to quantify the macroeconomic effects of the Covid-19 pandemic. We show that there exist threshold effects in the relationship between output growth and excess global volatility at individual country levels in a significant majority of advanced economies and several emerging markets. We then estimate a more general multi-country model augmented with these threshold effects as well as long term interest rates, oil prices, exchange rates and equity returns to perform counterfactual analyses. We distinguish common global factors from trade-related spillovers, and identify the Covid-19 shock using GDP growth projection revisions of the IMF in 2020Q1. We account for sample uncertainty by bootstrapping the multi-country model estimated over four decades of quarterly observations. Our results show that, without policy support, the Covid-19 pandemic would cause a significant and long-lasting fall in world output, with outcomes that are quite heterogenous across countries and regions. While the impact on China and other emerging Asian economies are estimated to be less severe, the United Kingdom, and several other advanced economies may experience deeper and longer-lasting effects. Non-Asian emerging markets stand out for their vulnerability. We show that no country is immune to the economic fallout of the pandemic because of global interconnections as evidenced by the case of Sweden. We also find that long-term interest rates could temporarily fall below their pre-Covid-19 lows in core advanced economies, but this does not seem to be the case in emerging markets.
    JEL Classifications: C32, E44, F44
    Key Words: Threshold-augmented Global VAR (TGVAR), international business cycle, Covid-19, global volatility, threshold effects.
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  • "An Augmented Anderson-Hsiao Estimator for Dynamic Short-T Panels", by Alexander Chudik and M. Hashem Pesaran, forthcoming in Econometric Reviews, CESifo WP no. 6688.

    Abstract: This paper introduces the idea of self-instrumenting endogenous regressors in settings when the correlation between these regressors and the errors can be derived and used to bias-correct the moment conditions. The resulting bias-corrected moment conditions are less likely to be subject to the weak instrument problem and can be used on their own or in conjunction with other available moment conditions to obtain more efficient estimators. This approach can be applied to estimation of a variety of models such as spatial and dynamic panel data models. This paper focuses on the latter, and proposes a new estimator for short T dynamic panels by augmenting Anderson and Hsiao (AAH) estimator with bias-corrected quadratic moment conditions in first differences which substantially improve the small sample performance of the AH estimator without sacrificing on the generality of its underlying assumptions regarding the fixed effects, initial values, and heteroskedasticity of error terms. Using Monte Carlo experiments it is shown that AAH estimator represents a substantial improvement over the AH estimator and more importantly it performs well even when compared to Arellano and Bond and Blundell and Bond (BB) estimators that are based on more restrictive assumptions, and continues to have satisfactory performance in cases where the standard GMM estimators are inconsistent. Finally, to decide between AAH and BB estimators we also propose a Hausman type test which is shown to work well when T is small and n sufficiently large.
    JEL Classifications: C12, C13, C23
    Key Words: Short-T Dynamic Panels, GMM, Bias-Corrected Moment Conditions, BMM, Self-Instrumenting, Nonlinear Moment Conditions, Panel VARs, Hausman Test, Monte Carlo Evidence.
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  • "Regional Heterogeneity and U.S. Presidential Elections: Real-Time 2020 Forecasts and Evaluation", by Rashad Ahmed and M. Hashem Pesaran, forthcoming in International Journal of Forecasting, June 2021.

    Abstract: This paper exploits cross-sectional variation at the level of U.S. counties to generate real-time forecasts for the 2020 U.S. presidential election. The forecasting models are trained on data covering the period 2000-2016, using high-dimensional variable selection techniques. Our county-based approach contrasts the literature that focuses on national and state level data but uses longer time periods to train their models. The paper reports forecasts of popular and electoral college vote outcomes and provides a detailed ex post evaluation of the forecasts released in real time prior to the election. It is shown that all of these forecasts outperform autoregressive benchmarks, with a pooled national model using One-Covariate-at-a-time-Multiple-Testing (OCMT) variable selection significantly outperforming all models in forecasting both the U.S. mainland national vote share and electoral college outcomes (forecasting 236 electoral votes for the Republican party compared to 232 realized). This paper also shows that key determinants of voting outcomes at the county level include incumbency effects, unemployment, poverty, educational attainment, house price changes, and international competitiveness. The results are also supportive of myopic voting: economic fluctuations realized a few months before the election tend to be more powerful predictors of voting outcomes than their long-horizon analogues.
    JEL Classifications: C53, C55, D72
    Key Words: Real-time Forecasts, Popular and Electoral College Votes, Simultaneity, High Dimensional Forecasting Models, Lasso, One Covariate at a time Multiple Testing, OCMT.
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