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Working Paper
Voluntary and Mandatory Social Distancing: Evidence on COVID-19 Exposure Rates from Chinese Provinces and Selected Countries
This paper considers a modification of the standard Susceptible-Infected-Recovered (SIR) model of epidemics that allows for different degrees of compulsory as well as voluntary social distancing. It is shown that the fraction of the population that self-isolates varies with the perceived probability of contracting the disease. Implications of social distancing both on the epidemic and recession curves are investigated and their trade off is simulated under a number of different social distancing and economic participation scenarios. We show that mandating social distancing is very effective ...
Working Paper
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigates the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating model that includes all the signals and none of the noise variables. Properties of post selection regressions are also ...
Working Paper
Revisiting the Great Ratios Hypothesis
Kaldor called the constancy of certain ratios stylized facts, whereas Klein and Kosobud called them great ratios. While they often appear in theoretical models, the empirical literature finds little evidence for them, perhaps because the procedures used cannot deal with lack of cointegration, two-way causality and cross-country error dependence. We propose a new system pooled mean group estimator that can deal with these features. Monte Carlo results show it performs well compared with other estimators, and using it on a dataset over 150 years and 17 countries, we find support for five of the ...
Working Paper
A one-covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models
Model specification and selection are recurring themes in econometric analysis. Both topics become considerably more complicated in the case of large-dimensional data sets where the set of specification possibilities can become quite large. In the context of linear regression models, penalised regression has become the de facto benchmark technique used to trade off parsimony and fit when the number of possible covariates is large, often much larger than the number of available observations. However, issues such as the choice of a penalty function and tuning parameters associated with the use ...
Working Paper
A Counterfactual Economic Analysis of COVID-19 Using a Threshold Augmented Multi-Country Model
This paper develops a threshold-augmented dynamic multi-country model (TG-VAR) to quantify the macroeconomic effects of COVID-19. 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 in the case of 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 ...
Working Paper
Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks
This paper is concerned with the problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or exponential down-weighting. However, these studies start with a given model specification and do not consider the problem of variable selection. It is clear that, in the absence of breaks, researchers should weigh the observations equally at both the variable selection and forecasting stages. In this study, we investigate whether or not we should use ...
Working Paper
Aggregation in large dynamic panels
This paper investigates the problem of aggregation in the case of large linear dynamic panels, where each micro unit is potentially related to all other micro units, and where micro innovations are allowed to be cross sectionally dependent. Following Pesaran (2003), an optimal aggregate function is derived and used (i) to establish conditions under which Granger's (1980) conjecture regarding the long memory properties of aggregate variables from "a very large scale dynamic, econometric model" holds, and (ii) to show which distributional features of micro parameters can be identified from ...
Working Paper
Debt, inflation and growth robust estimation of long-run effects in dynamic panel data models
This paper investigates the long-run effects of public debt and inflation on economic growth. Our contribution is both theoretical and empirical. On the theoretical side, we develop a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in dynamic heterogeneous panel data models with cross-sectionally dependent errors. The relative merits of the CS-DL approach and other existing approaches in the literature are discussed and illustrated with small sample evidence obtained by means of Monte Carlo simulations. On the empirical side, using data on a ...
Working Paper
Pooled Bewley Estimator of Long-Run Relationships in Dynamic Heterogenous Panels
Using a transformation of the autoregressive distributed lag model due to Bewley, a novel pooled Bewley (PB) estimator of long-run coefficients for dynamic panels with heterogeneous short-run dynamics is proposed. The PB estimator is directly comparable to the widely used Pooled Mean Group (PMG) estimator, and is shown to be consistent and asymptotically normal. Monte Carlo simulations show good small sample performance of PB compared to the existing estimators in the literature, namely PMG, panel dynamic OLS (PDOLS) and panel fully-modified OLS (FMOLS). Application of two bias-correction ...
Working Paper
Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks
This paper is concerned with the problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows and exponential down-weighting. However, these studies start with a given model specification and do not consider the problem of variable selection, which is complicated by time variations in the effects of signal variables. In this study we investigate whether or not we should use weighted observations at the variable selection stage in the presence of ...