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Working Paper
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
Chudik, Alexander; Pesaran, M. Hashem; Sharifvaghefi, Mahrad (2023-01-18)
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 ...
Globalization Institute Working Papers , Paper 394

Working Paper
Macroeconomic Forecasting in Times of Crises
Zhong, Molin; Guerron-Quintana, Pablo (2017-01-31)
We propose a parsimonious semiparametric method for macroeconomic forecasting during episodes of sudden changes. Based on the notion of clustering and similarity, we partition the time series into blocks, search for the closest blocks to the most recent block of observations, and with the matched blocks we proceed to forecast. One possibility is to compare local means across blocks, which captures the idea of matching directional movements of a series. We show that our approach does particularly well during the Great Recession and for variables such as inflation, unemployment, and real ...
Finance and Economics Discussion Series , Paper 2017-018

Working Paper
The Distributional Predictive Content of Measures of Inflation Expectations
Mitchell, James; Zaman, Saeed (2023-11-30)
This paper examines the predictive relationship between the distribution of realized inflation in the US and measures of inflation expectations from households, firms, financial markets, and professional forecasters. To allow for nonlinearities in the predictive relationship we use quantile regression methods. We find that the ability of households to predict future inflation, relative to that of professionals, firms, and the market, increases with inflation. While professional forecasters are more accurate in the middle of the inflation density, households’ expectations are more useful in ...
Working Papers , Paper 23-31

Working Paper
The perils of working with Big Data and a SMALL framework you can use to avoid them
Fogarty, Michael; Butters, R. Andrew; Brave, Scott A. (2020-03-02)
The use of “Big Data” to explain fluctuations in the broader economy or guide the business decisions of a firm is now so commonplace that in some instances it has even begun to rival more traditional government statistics and business analytics. Big data sources can very often provide advantages when compared to these more traditional data sources, but with these advantages also comes the potential for pitfalls. We lay out a framework called SMALL that we have developed in order to help interested parties as they navigate the big data minefield. Based on a set of five questions, the SMALL ...
Working Paper Series , Paper WP-2020-35

Working Paper
The perils of working with Big Data and a SMALL framework you can use to avoid them
Fogarty, Michael; Butters, R. Andrew; Brave, Scott A. (2020-12-22)
The use of “Big Data” to explain fluctuations in the broader economy or guide the business decisions of a firm is now so commonplace that in some instances it has even begun to rival more traditional government statistics and business analytics. Big data sources can very often provide advantages when compared to these more traditional data sources, but with these advantages also comes the potential for pitfalls. We lay out a framework called SMALL that we have developed in order to help interested parties as they navigate the big data minefield. Based on a set of five questions, the SMALL ...
Working Paper Series , Paper WP-2020-35

Report
Dynamic prediction pools: an investigation of financial frictions and forecasting performance
Del Negro, Marco; Schorfheide, Frank; Hasegawa, Raiden B. (2014-10-01)
We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call dynamic pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE) models, with and without financial frictions, for output growth and inflation in the period 1992 to 2011. We find strong evidence of time variation in the pool?s weights, reflecting the fact that the DSGE model with financial frictions produces superior forecasts in periods of financial distress but doesn?t perform as well in tranquil periods. The dynamic ...
Staff Reports , Paper 695

Working Paper
Forecasting Economic Activity with Mixed Frequency Bayesian VARs
Brave, Scott; Butters, R. Andrew; Justiniano, Alejandro (2016-05-20)
Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate a large number of mixed frequency indicators into forecasts of economic activity. This paper evaluates the forecast performance of MF-BVARs relative to surveys of professional forecasters and investigates the influence of certain specification choices on this performance. We leverage a novel real-time dataset to conduct an out-of-sample forecasting exercise for U.S. real gross domestic product (GDP). MF-BVARs are shown to provide an attractive alternative to surveys of professional forecasters for ...
Working Paper Series , Paper WP-2016-5

Working Paper
Machine Learning, the Treasury Yield Curve and Recession Forecasting
Puglia, Michael; Tucker, Adam (2020-05-20)
We use machine learning methods to examine the power of Treasury term spreads and other financial market and macroeconomic variables to forecast US recessions, vis-à-vis probit regression. In particular we propose a novel strategy for conducting cross-validation on classifiers trained with macro/financial panel data of low frequency and compare the results to those obtained from standard k-folds cross-validation. Consistent with the existing literature we find that, in the time series setting, forecast accuracy estimates derived from k-folds are biased optimistically, and cross-validation ...
Finance and Economics Discussion Series , Paper 2020-038

Working Paper
Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates
Koop, Gary; McIntyre, Stuart; Mitchell, James; Poon, Aubrey (2022-03-03)
Recent decades have seen advances in using econometric methods to produce more timely and higher-frequency estimates of economic activity at the national level, enabling better tracking of the economy in real time. These advances have not generally been replicated at the sub–national level, likely because of the empirical challenges that nowcasting at a regional level presents, notably, the short time series of available data, changes in data frequency over time, and the hierarchical structure of the data. This paper develops a mixed– frequency Bayesian VAR model to address common ...
Working Papers , Paper 22-06

Working Paper
A Comprehensive Empirical Evaluation of Biases in Expectation Formation
Eva, Kenneth; Winkler, Fabian (2023-06-27)
We revisit predictability of forecast errors in macroeconomic survey data, which is often taken as evidence of behavioral biases at odds with rational expectations. We argue that to reject rational expectations, one must be able to predict forecast errors out of sample. However, the regressions used in the literature often perform poorly out of sample. The models seem unstable and could not have helped to improve forecasts with access only to available information. We do find some notable exceptions to this finding, in particular mean bias in interest rate forecasts, that survive our ...
Finance and Economics Discussion Series , Paper 2023-042

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