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Working Paper
Assessing Macroeconomic Tail Risks in a Data-Rich Environment
We use a large set of economic and financial indicators to assess tail risks of the three macroeconomic variables: real GDP, unemployment, and inflation. When applied to U.S. data, we find evidence that a dense model using principal components (PC) as predictors might be misspecified by imposing the “common slope” assumption on the set of predictors across multiple quantiles. The common slope assumption ignores the heterogeneous informativeness of individual predictors on different quantiles. However, the parsimony of the PC-based approach improves the accuracy of out-of-sample forecasts ...
Working Paper
Financial Conditions and Economic Activity: Insights from Machine Learning
Machine learning (ML) techniques are used to construct a financial conditions index (FCI). The components of the ML-FCI are selected based on their ability to predict the unemployment rate one-year ahead. Three lessons for macroeconomics and variable selection/dimension reduction with large datasets emerge. First, variable transformations can drive results, emphasizing the need for transparency in selection of transformations and robustness to a range of reasonable choices. Second, there is strong evidence of nonlinearity in the relationship between financial variables and economic ...
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 ...
Working Paper
Quantifying Risks to Sovereign Market Access: Methods and Challenges
In this paper we use data from the euro area to study episodes when sovereigns lose market access. We construct a detailed dataset with potential indicators of market access tensions, and evaluate their ability to forecast episodes when market access is lost, using various econometric approaches. We find that factors associated with high market access tensions are not limited to financial markets, but also encompass developments in global demand, macroeconomic conditions and the fiscal stance. Using the top-performing indicators, we construct a number of market tension indices and use them as ...
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
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
This paper is concerned with the problem of variable selection when the marginal effects of signals on the target variable as well as the correlation of the covariates in the active set are allowed to vary over time, without committing to any particular model of parameter instabilities. It poses the issue of whether weighted or unweighted observations should be used at the variable selection stage in the presence of parameter instability, particularly when the number of potential covariates is large. Amongst the extant variable selection approaches, we focus on the One Covariate at a time ...
Working Paper
Countercyclical policy and the speed of recovery after recessions
We consider the effect of some policies intended to shorten recessions and accelerate recoveries. Our innovation is to analyze the duration of the recoveries of various U.S. states, which gives us a cross-section of both state- and national-level policies. Because we study multiple recessions for the same state and multiple states for the same recession, we can control for differences in the economic conditions preceding recessions and the causes of the recessions when evaluating various policies. We find that expansionary monetary policy at the national level helps to stimulate the exit of ...
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 ...