Factor extraction using Kalman filter and smoothing: This is not just another survey

Dynamic factor models have been the main “big data” tool used by empirical macroeconomists during the last 30 years. In this context, Kalman filter and smoothing (KFS) procedures can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity, and many other characteristics often observed in real systems of economic variables. The main contribution of this paper is to provide a comprehensive updated summary of the literature on latent common factors extracted using KFS procedures in the context of dynamic factor models, pointing out their potential limitations. Signal extraction and parameter estimation issues are separately analyzed. Identification issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases. This survey is relevant to researchers and practitioners interested not only in the theory of KFS procedures for factor extraction in dynamic factor models but also in their empirical application in macroeconomics and finance.     

What caused the rise of Airbnb? An examination of key macroeconomic factors

The social and economic implications of the Airbnb phenomenon have been the subject of much research. Yet, the academic literature on Airbnb is nascent. Specifically, the issue of whether major macroeconomic conditions affect the supply of Airbnb has not been investigated. To address this gap, we propose a conceptual model that explains the determinants of Airbnb supply and examine the extent to which major macroeconomic factors affect the supply of Airbnb. Specifically, we analyze the effects of hotel room rates (ADR), hotel demand, tourism demand, house prices, gross domestic product (GDP), wages and unemployment on the supply of Airbnb in 50 U.S. states. Results show that increases in hotel ADR, house prices, and GDP have contributed to an increase in the supply of Airbnb, whereas increases in unemployment rates and wages have adverse effects on Airbnb supply. Theoretical and policy implications are discussed within realms of macroeconomic theory.     

A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth

This paper estimates a three-frequency dynamic factor model for nowcasting the Canadian provincial gross domestic product (GDP). The Canadian provincial GDP at market prices is released by Statistics Canada annually with a significant lag (11 months). This necessitates a mixed-frequency approach that can process timely monthly data, the quarterly national accounts, and the annual target variable. The model is estimated on a wide set of provincial, national and international data. In a pseudo real-time exercise, we find that the model outperforms simple benchmarks and is competitive with more sophisticated mixed-frequency approaches (MIDAS models). We also find that variables from the Labour Force Survey are important predictors of real activity. This paper expands previous work that has documented the importance of foreign variables for nowcasting Canadian GDP. This paper finds that including national and foreign predictors is useful for Ontario, while worsening the nowcast performance for smaller provinces.     

Linking words in economic discourse: Implications for macroeconomic forecasts

This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature.     

Forecasting using heterogeneous panels with cross-sectional dependence

In this paper, we focus on forecasting methods that use heterogeneous panels in the presence of cross-sectional dependence in terms of both spatial error dependence and common factors. We propose two main approaches to estimating the factor structure: a residuals-based approach, and an approach that uses a panel of auxiliary variables to extract the factors. Small sample properties of the proposed methods are investigated through Monte Carlo simulations and applied to predict house price inflation in OECD countries.     

Performance sensitivity of non-executive compensation in China: The role of economic policy uncertainty

This paper examines the impact of economic policy uncertainty on non-executive employees from the perspective of pay-performance sensitivity (PPS). Economy-wide uncertainty can trigger adverse impacts for businesses, and in response enterprises may adjust employee pay to maintain their level of activity. Using firm-level data on A-share companies listed on the Shanghai and Shenzhen Stock Exchanges during 2003–2016, this paper finds that better-performing firms pay higher wages on average, which they adjust only during uncertain times. We also show that the impact of economic policy uncertainty on PPS is more pronounced in the context of labor-intensive, highly competitive industries and state-owned enterprises, because they tend to respond to uncertainty via wage adjustment. The evidence demonstrates that the pay-performance link is much weaker during uncertain times, when different subgroups react differently. However, our finding of a robust pay-performance relation holds, even with a range of firm-level controls and accounting for different levels of firm heterogeneity.     

Destabilizing role of futures markets on North American hard red spring wheat spot prices

The objective of the paper is to determine if the futures prices of hard red spring wheat (HRSW) have stabilizing or destabilizing impact on spot HRSW price in North America. Several important results emerge from thorough empirical analysis. First, both Granger causality tests and directed acyclic graph algorithms (DAGs) point to two-way causality between futures and spot HRSW prices and thus endogeneity in both prices formation. To the contrary, both procedures suggest that ending stocks are exogenous to spot and futures HRSW prices. Both vector error correction model and impulse response functions point to a large and long-lasting impact of a shock to futures price on spot price level. Finally, variance decomposition analysis indicates that futures prices are responsible for the bulk of spot price volatility in both short and long run. Our result is consistent with those of theoretical models suggesting that when production (supply side) is the dominant disturbance, spot price is destabilized in both the short and the long run by futures prices. An important implication of this research is the need for alternative market mechanisms or alternative farm policy measures that would mitigate price risk and ensure sustainable farming of American HRSW farmers.     

Forecasting the UK economy with a medium-scale Bayesian VAR

We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.     

Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals,financial stress and economic uncertainty

We develop models for examining possible predictors of growth of China's foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US–China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided.     

Professional forecasters and real-time forecasting with a DSGE model

This paper analyses the real-time forecasting performance of the New Keynesian DSGE model of Galí, Smets and Wouters (2012), estimated on euro area data. It investigates the extent to which the inclusion of forecasts of inflation, GDP growth and unemployment by professional forecasters improve the forecasting performance. We consider two approaches for conditioning on such information. Under the “noise” approach, the mean professional forecasts are assumed to be noisy indicators of the rational expectations forecasts implied by the DSGE model. Under the “news” approach, it is assumed that the forecasts reveal the presence of expected future structural shocks in line with those estimated in the past. The forecasts of the DSGE model are compared with those from a Bayesian VAR model, an AR(1) model, a sample mean and a random walk.