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Author:Chudik, Alexander 

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 ...
Globalization Institute Working Papers , Paper 394

Working Paper
Size, openness, and macroeconomic interdependence

The curse of dimensionality, a problem associated with analyzing the interaction of a relatively large number of endogenous macroeconomic variables, is a prevailing issue in the open economy macro literature. The most common practice to mitigate this problem is to apply the so-called Small Open Economy Framework (SOEF). In this paper, we aim to review under which conditions the SOEF is a justifiable approximation and how severe the consequences of violation of key conditions might be. Thereby, we use a multicountry general equilibrium model as a laboratory. ; First, we derive the conditions ...
Globalization Institute Working Papers , Paper 103

Journal Article
Risk, uncertainty separately cloud global growth forecasting

Forecasts of global growth have historically been imprecise, punctuated by periods of optimism and pessimism. Inaccuracy in forecasting partly reflects quantifiable risks to the global outlook as well as economic uncertainty.
Economic Letter , Volume 11 , Issue 9 , Pages 1-4

Working Paper
Estimation of Impulse Response Functions When Shocks are Observed at a Higher Frequency than Outcome Variables

This paper proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions (IRFs) in a setup where (i) the shock of interest is observed, (ii) the impact variable of interest is observed at a lower frequency (as a temporally aggregated or sequentially sampled variable), (iii) the data-generating process (DGP) is given by a VAR model at the frequency of the shock, and (iv) the full set of relevant endogenous variables entering the DGP is unknown or unobserved. Consistency and asymptotic normality of the proposed MFDL estimators is established, and their small-sample ...
Globalization Institute Working Papers , Paper 356

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 ...
Globalization Institute Working Papers , Paper 394

Early Mandated Social Distancing Does Best to Control COVID–19 Spread

Voluntary social distancing and a lack of compliance with mandated polices have led to unnecessarily high infection rates and death tolls in a number of countries.
Dallas Fed Economics

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 ...
Globalization Institute Working Papers , Paper 162

Working Paper
Spatial considerations on the PPP debate

This paper studies the influence of aggregating across space when (i) testing the PPP theory or more generally pair-wise cointegration and (ii) evaluating the PPP puzzle. Our contribution is threefold: we show that aggregating foreign data and applying an ADF test may lead to erroneously reject the PPP hypothesis. We then show, on the basis of theoretical arguments as well as Monte Carlo experiments, that a sizable bias in the estimates of half-life deviations to PPP may be due to the effect of aggregation across space. We finally illustrate empirically the importance of spatial ...
Globalization Institute Working Papers , Paper 138

Discussion Paper
How the global perspective can help us identify structural shocks

This paper argues that global perspective can help us with the identification of structural shocks by utilizing the information on the signs of the responses of individual countries (cross section units). We demonstrate the main idea by means of Monte Carlo experiments and present an empirical application where we look at the effects of oil supply shocks on output and on global exchange rate constellation. Using a large-scale GVAR model of oil prices and the global economy, we find supply shocks tend to have a stronger impact on emerging economies' real output as compared with mature ...
Staff Papers , Issue Dec

Working Paper
Big data analytics: a new perspective

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 ...
Globalization Institute Working Papers , Paper 268

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