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Jel Classification:C33 

Working Paper
A Nowcasting Model for Canada: Do U.S. Variables Matter?

We propose a dynamic factor model for nowcasting the growth rate of quarterly real{{p}}Canadian gross domestic product. We show that the proposed model produces more accurate nowcasts than those produced by institutional forecasters, like the Bank of Canada, the The Organisation for Economic Co-operation and Development (OECD), and the survey collected by Bloomberg, which reflects the median forecast of market participants. We show that including U.S. data in a nowcasting model for Canada dramatically improves its predictive accuracy, mainly because of the absence of timely production data ...
Finance and Economics Discussion Series , Paper 2016-036

Working Paper
Pooled Bewley Estimator of Long-Run Relationships in Dynamic Heterogenous Panels

This paper, using the Bewley (1979) transformation of the autoregressive distributed lag model, proposes a pooled Bewley (PB) estimator of long-run coefficients for dynamic panels with heterogeneous short-run dynamics, in the same setting as the widely used Pooled Mean Group (PMG) estimator. The Bewley transform enables us to obtain an analytical closed form expression for the PB, which is not available when using the maximum likelihood approach. This lets us establish asymptotic normality of PB as n,T→∞ jointly, allowing for applications with n and T large and of the same order of ...
Globalization Institute Working Papers , Paper 409

Working Paper
FRED-SD: A Real-Time Database for State-Level Data with Forecasting Applications

We construct a real-time dataset (FRED-SD) with vintage data for the U.S. states that can be used to forecast both state-level and national-level variables. Our dataset includes approximately 28 variables per state, including labor market, production, and housing variables. We conduct two sets of real-time forecasting exercises. The first forecasts state-level labor-market variables using five different models and different levels of industrially-disaggregated data. The second forecasts a national-level variable exploiting the cross-section of state data. The state-forecasting experiments ...
Working Papers , Paper 2020-031

Working Paper
Large Vector Autoregressions with Stochastic Volatility and Flexible Priors

Recent research has shown that a reliable vector autoregressive model (VAR) for forecasting and structural analysis of macroeconomic data requires a large set of variables and modeling time variation in their volatilities. Yet, there are no papers jointly allowing for stochastic volatilities and large datasets, due to computational complexity. Moreover, homoskedastic VAR models for large datasets so far restrict substantially the allowed prior distributions on the parameters. In this paper we propose a new Bayesian estimation procedure for (possibly very large) VARs featuring time varying ...
Working Papers (Old Series) , Paper 1617

Working Paper
Revealing Cluster Structures Based on Mixed Sampling Frequencies

This paper proposes a new nonparametric mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel regression with mixed frequency data. The nonparametric MIDAS estimation method is more flexible and substantially simpler to implement than competing approaches. We show that the proposed clustering algorithm successfully recovers true membership in the cross-section, both in theory and in simulations, without requiring prior knowledge of the number of clusters. This methodology is applied to a mixed-frequency Okun's law model for state-level data in the U.S. and ...
Finance and Economics Discussion Series , Paper 2020-082

Working Paper
Simultaneous Spatial Panel Data Models with Common Shocks

I consider a simultaneous spatial panel data model, jointly modeling three effects: simultaneous effects, spatial effects and common shock effects. This joint modeling and consideration of cross-sectional heteroskedasticity result in a large number of incidental parameters. I propose two estimation approaches, a quasi-maximum likelihood (QML) method and an iterative generalized principal components (IGPC) method. I develop full inferential theories for the estimation approaches and study the trade-off between the model specifications and their respective asymptotic properties. I further ...
Supervisory Research and Analysis Working Papers , Paper RPA 17-3

Working Paper
The Factor Structure of Disagreement

We estimate a Bayesian three-dimensional dynamic factor model on the individual forecasts in the Survey of Professional Forecasters. The factors extract the most important dimensions along which disagreement comoves across variables. We interpret our results through a general semi-structural dispersed information model. The two most important factors in the data describe disagreement about aggregate supply and demand, respectively. Up until the Great Moderation, supply disagreement was dominant, while in recent decades and particularly during the Great Recession, demand disagreement was most ...
Finance and Economics Discussion Series , Paper 2021-046

Working Paper
Large panel data models with cross-sectional dependence: a survey

This paper provides an overview of the recent literature on estimation and inference in large panel data models with cross-sectional dependence. It reviews panel data models with strictly exogenous regressors as well as dynamic models with weakly exogenous regressors. The paper begins with a review of the concepts of weak and strong cross-sectional dependence, and discusses the exponent of cross-sectional dependence that characterizes the different degrees of cross-sectional dependence. It considers a number of alternative estimators for static and dynamic panel data models, distinguishing ...
Globalization Institute Working Papers , Paper 153

Journal Article
FRED-QD: A Quarterly Database for Macroeconomic Research

In this article, we present and describe FRED-QD, a large, quarterly frequency macroeconomic database that is currently available and regularly updated at https://research.stlouisfed.org/econ/mccracken/fred-databases/. The data provided are closely modeled to that used in Stock and Watson (2012a). As in our previous work on FRED-MD (McCracken and Ng, 2016), which is at a monthly frequency, our goal is simply to provide a publicly available source of macroeconomic "big data" that is updated in real time using the FRED® data service. We show that factors extracted from the FRED-QD dataset ...
Review , Volume 103 , Issue 1 , Pages 1-44

Working Paper
Revisiting the Great Ratios Hypothesis

The idea that certain economic variables are roughly constant in the long run is an old one. Kaldor described them as stylized facts, whereas Klein and Kosobud labelled them great ratios. While such ratios are widely adopted in theoretical models in economics as conditions for balanced growth, arbitrage or solvency, the empirical literature has tended to find little evidence for them. We argue that this outcome could be due to episodic failure of cointegration, possible two-way causality between the variables in the ratios and cross-country error dependence due to latent factors. We propose a ...
Globalization Institute Working Papers , Paper 415

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