Investment in Fixed Capital Stock: Testing for the Impact of Sectoral and Systemic Uncertainty*

This paper applies current theory recognizing the irreversibility of investment, in order to test for the impact of uncertainty on investment expenditure for a middle income country. The contribution of the paper is unique in two respects. First, it employs dynamic heterogeneous panel estimation techniques not previously applied to investment functions. Secondly, it explicitly tests for the impact of both sectoral and systemic uncertainty on investment expenditure. We find that both sectoral (as measured by output volatility) and systemic uncertainty (as measured by political instability) have a negative impact on investment rates in a middle income country context. Liquidity constraints and growth in total factor productivity are found to have no impact on investment, while trade liberalization has the impact predicted by Heckscher‐Ohlin trade theory. Finally, we find complementarity effects between physical capital and skilled human capital, suggesting that South African educational policies may have hampered investment in physical capital as well as the growth performance of the economy. Policy implications emphasize the importance of lowering uncertainty for investors, and the need for sound human capital investment.     

Forecasting with High‐Dimensional Panel VARs

This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time‐varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation‐free algorithm that relies on analytical approximations to the posterior. We use our methods to forecast inflation rates in the eurozone and show that these forecasts are superior to alternative methods for large vector autoregressions.     

LONG‐RUN RISKS IN THE TERM STRUCTURE OF INTEREST RATES: ESTIMATION

This paper estimates a model in which persistent fluctuations in expected consumption growth, expected inflation, and their time‐varying volatility determine asset price variation. The model features Epstein–Zin recursive preferences, which determine the market price of macro risk factors. Analysis of the US nominal term structure data from 1953 to 2006 shows that agents dislike high uncertainty and demand compensation for volatility risks. Also, the time variation of the term premium is driven by the compensation for inflation volatility risk, which is distinct from consumption volatility risk. The central role of inflation volatility risk in explaining the time‐varying term premium is consistent with other empirical evidence including survey data. In contrast, the existing long‐run risks literature emphasizes consumption volatility risk and ignores inflation‐specific time‐varying volatility. The estimation results of this paper suggest that inflation‐specific volatility risk is essential for fitting the time series of the US nominal term structure data. Copyright © 2012 John Wiley & Sons, Ltd.     

Global and Country‐Specific Output Growth Uncertainty and Macroeconomic Performance

We identify global and country‐specific measures of output growth uncertainty for a large OECD country sample by means of a dynamic factor model with stochastic volatility. We find evidence for major bouts of global uncertainty in the early 1970s and late 2000s, and a number of periods with elevated levels of either global or national uncertainty, particularly in the early 1980s, 1990s and 2000s. VAR impulse responses of national macroeconomic variables to our estimated measures of uncertainty reveal that global uncertainty is the major driver of macroeconomic performance in most countries, whereas the impact of national uncertainty is small and frequently insignificant. We also find that uncertainty is transmitted primarily through investment and trade flows rather than through consumption demand.     

Modelling multi‐period inflation uncertainty using a panel of density forecasts

This paper examines the determinants of inflation forecast uncertainty using a panel of density forecasts from the Survey of Professional Forecasters (SPF). Based on a dynamic heterogeneous panel data model, we find that the persistence in forecast uncertainty is much less than what the aggregate time series data would suggest. In addition, the strong link between past forecast errors and current forecast uncertainty, as often noted in the ARCH literature, is largely lost in a multi‐period context with varying forecast horizons. We propose a novel way of estimating ‘news’ and its variance using the Kullback‐Leibler information, and show that the latter is an important determinant of forecast uncertainty. Our evidence suggests a strong relationship of forecast uncertainty with level of inflation, but not with forecaster discord or with the volatility of a number of other macroeconomic indicators. Copyright © 2006 John Wiley & Sons, Ltd.     

The Hazard Rate of Foreign Direct Investment: A Structural Estimation of a Real‐option Model*

The hazard rate of investment is derived within a real‐option model, and its properties are analysed so as to directly study the relation between uncertainty and investment. Maximum likelihood estimates of the hazard are calculated using a sample of multinational enterprises (MNEs) that invested in Central and Eastern Europe over the period 1990–98. Employing a standard, non‐parametric specification of the hazard, our measure of uncertainty has a negative effect on investment, but the reduced‐form model is unable to control for nonlinearities in the relationship. The structural estimation of the option‐based hazard is instead able to account for the nonlinearities and exhibits a significant value of waiting, although the latter is independent of our measure of uncertainty. This finding supports the existence of alternative channels through which uncertainty can affect investment.     

Investment and Uncertainty: A Theory‐based Empirical Approach*

This paper provides empirical evidence on the dynamic effects of uncertainty on firm‐level capital accumulation. A novelty in this paper is that the firm‐level uncertainty indicator is motivated and derived from a theoretical model, the neoclassical investment model with time to build. This model also serves as the base for the empirical work, where an error‐correction approach is employed. I find a negative effect of uncertainty on capital accumulation, both in the short run and the long run. This outcome cannot be explained by the model alone. Instead, the results suggest that the predominant mechanism at work stems from irreversibility constraints.     

Crack spread hedging: accounting for time‐varying volatility spillovers in the energy futures markets

Crude oil, heating oil, and unleaded gasoline futures contracts are simultaneously analysed for their effectiveness in reducing price volatility for an energy trader. A conceptual model is developed for a trader hedging the ‘crack spread’. Various hedge ratio estimation techniques are compared to a Multivariate GARCH model that directly incorporates the time to maturity effect often found in futures markets. Modelling of the time‐variation in hedge ratios via the Multivariate GARCH methodology, and thus taking into account volatility spillovers between markets is shown to result in significant reductions in uncertainty even while accounting for trading costs. Copyright © 2002 John Wiley & Sons, Ltd.     

A multi‐level panel STAR model for US manufacturing sectors

We introduce a multi‐level smooth transition model for a panel of time series, which can be used to examine the presence of common nonlinear business cycle features across many variables. The model is positioned in between a fully pooled model, which imposes such common features, and a fully heterogeneous model, which allows for unrestricted nonlinearity. We introduce a second‐stage model linking the parameters that determine the timing of the switches between business cycle regimes to observable explanatory variables, thereby allowing for lead–lag relationships across panel members. We discuss representation, estimation by concentrated simulated maximum likelihood and inference. We illustrate our model using quarterly industrial production in 19 US manufacturing sectors, and document that there are subtle differences across sectors in leads and lags for switches between business cycle recessions and expansions. Copyright © 2005 John Wiley & Sons, Ltd.     

Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Empirical work in macroeconometrics has been mostly restricted to using vector autoregressions (VARs), even though there are strong theoretical reasons to consider general vector autoregressive moving averages (VARMAs). A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be largely dominated by VARs, particularly in terms of developing useful extensions. We address these computational challenges with a Bayesian approach. Specifically, we develop a Gibbs sampler for the basic VARMA, and demonstrate how it can be extended to models with time‐varying vector moving average (VMA) coefficients and stochastic volatility. We illustrate the methodology through a macroeconomic forecasting exercise. We show that in a class of models with stochastic volatility, VARMAs produce better density forecasts than VARs, particularly for short forecast horizons.     

The R&D Investment–Uncertainty Relationship: Do Strategic Rivalry and Firm Size Matter?

This paper uses a real options perspective to augment a standard research and development (R&D) investment model and implement a firm‐level empirical analysis to assess the practical significance of market uncertainty and its interactions with strategic rivalry and firm size. We use a measure of firm‐relevant market uncertainty along with panel data and find that firms invest less in current R&D as uncertainty about market returns increases. The effect of firm‐specific uncertainty on R&D investment is smaller in markets where strategic rivalry is likely to be more intense. Furthermore, holding access to financing constant, the effect of uncertainty on R&D investment is attenuated for large firms. Copyright © 2012 John Wiley & Sons, Ltd.     

Surprise,surprise – Measuring firm-level investment innovations

Using the IFO Investment Survey for the German manufacturing sector, we construct a forty-year panel of firm-level investment innovations (surprises). We document the cross-sectional and time-series properties of this panel, and our main findings are: the cross-sectional dispersion of investment surprises is countercyclical, as is their average within-firm time series volatility, and both are highly correlated. This justifies, in part, strategies in the literature to use cross-sectional moments for the calibration of heteroscedasticity. At the same time, the cross-sectional dispersion of investment is procyclical, suggesting a nonsmooth capital adjustment friction at the micro level. There is substantial dispersion in within-firm volatility, a feature consistent with a recent literature on information frictions at the firm-level. Finally, the aforementioned second moments of investment innovations are Granger-caused by recessions, but not vice versa, rendering simple exogenous uncertainty shocks less plausible as drivers of business cycles.     

Automated Model Selection in Finance: General‐to‐Specific Modelling of the Mean and Volatility Specifications*

General‐to‐Specific (GETS) modelling has witnessed major advances thanks to the automation of multi‐path GETS specification search. However, the estimation complexity associated with financial models constitutes an obstacle to automated multi‐path GETS modelling in finance. Making use of a recent result we provide and study simple but general and flexible methods that automate financial multi‐path GETS modelling. Starting from a general model where the mean specification can contain autoregressive terms and explanatory variables, and where the exponential volatility specification can include log‐ARCH terms, asymmetry terms, volatility proxies and other explanatory variables, the algorithm we propose returns parsimonious mean and volatility specifications.     

Mixed‐frequency models with moving‐average components

Temporal aggregation in general introduces a moving‐average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed‐frequency (MF) model. The MA component is generally neglected, likely to preserve the possibility of ordinary least squares estimation, but the consequences have never been properly studied in the MF context. In this paper we show, analytically, in Monte Carlo simulations and in a forecasting application on US macroeconomic variables, the relevance of considering the MA component in MF mixed‐data sampling (MIDAS) and unrestricted MIDAS models (MIDAS–autoregressive moving average (ARMA) and UMIDAS‐ARMA). Specifically, the simulation results indicate that the short‐term forecasting performance of MIDAS‐ARMA and UMIDAS‐ARMA are better than that of, respectively, MIDAS and UMIDAS. The empirical applications on nowcasting US gross domestic product (GDP) growth, investment growth, and GDP deflator inflation confirm this ranking. Moreover, in both simulation and empirical results, MIDAS‐ARMA is better than UMIDAS‐ARMA.     

Assessing the Transmission of Monetary Policy Using Time‐varying Parameter Dynamic Factor Models*

This article extends the current literature which questions the stability of the monetary transmission mechanism, by proposing a factor‐augmented vector autoregressive (VAR) model with time‐varying coefficients and stochastic volatility. The VAR coefficients and error covariances may change gradually in every period or be subject to abrupt breaks. The model is applied to 143 post‐World War II quarterly variables fully describing the US economy. I show that both endogenous and exogenous shocks to the US economy resulted in the high inflation volatility during the 1970s and early 1980s. The time‐varying factor augmented VAR produces impulse responses of inflation which significantly reduce the price puzzle. Impulse responses of other indicators of the economy show that the most notable changes in the transmission of unanticipated monetary policy shocks occurred for gross domestic product, investment, exchange rates and money.     

Common correlated effect cross‐sectional dependence corrections for nonlinear conditional mean panel models

This paper provides an approach to estimation and inference for nonlinear conditional mean panel data models, in the presence of cross‐sectional dependence. We modify Pesaran's (Econometrica, 2006, 74(4), 967–1012) common correlated effects correction to filter out the interactive unobserved multifactor structure. The estimation can be carried out using nonlinear least squares, by augmenting the set of explanatory variables with cross‐sectional averages of both linear and nonlinear terms. We propose pooled and mean group estimators, derive their asymptotic distributions, and show the consistency and asymptotic normality of the coefficients of the model. The features of the proposed estimators are investigated through extensive Monte Carlo experiments. We also present two empirical exercises. The first explores the nonlinear relationship between banks' capital ratios and riskiness. The second estimates the nonlinear effect of national savings on national investment in OECD countries depending on countries' openness.     

Realising the future: forecasting with high‐frequency‐based volatility (HEAVY) models

This paper studies in some detail a class of high‐frequency‐based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realised measures constructed from high‐frequency data. Our analysis identifies that the models have momentum and mean reversion effects, and that they adjust quickly to structural breaks in the level of the volatility process. We study how to estimate the models and how they perform through the credit crunch, comparing their fit to more traditional GARCH models. We analyse a model‐based bootstrap which allows us to estimate the entire predictive distribution of returns. We also provide an analysis of missing data in the context of these models. Copyright © 2010 John Wiley & Sons, Ltd.     

What are the macroeconomic effects of high‐frequency uncertainty shocks?

This paper evaluates the effects of high‐frequency uncertainty shocks on a set of low‐frequency macroeconomic variables representative of the US economy. Rather than estimating models at the same common low frequency, we use recently developed econometric models, which allow us to deal with data of different sampling frequencies. We find that credit and labor market variables react the most to uncertainty shocks in that they exhibit a prolonged negative response to such shocks. When looking at detailed investment subcategories, our estimates suggest that the most irreversible investment projects are the most affected by uncertainty shocks. We also find that the responses of macroeconomic variables to uncertainty shocks are relatively similar across single‐frequency and mixed‐frequency data models, suggesting that the temporal aggregation bias is not acute in this context.     

The impact of economic uncertainty on the decision of fertility: Evidence from Taiwan

Economic uncertainty has only recently begun to appear in research on the determinants of fertility. Therefore, the purpose of this study is to investigate how economic uncertainty affects the fertility rate in Taiwan. Official county-level panel data from 1998 to 2016 for 20 counties are utilized in DIFF-GMM and SYS-GMM models in dynamic panel regression estimation. The major finding of this study is that higher volatility of household disposable income will reduce the fertility rate. The empirical results support the proposition that economic uncertainty might be an important determinant of fertility decisions, explaining the decline in fertility in Taiwan.     

Global Engagement and Returns Volatility

This paper finds that a greater reliance on foreign market sales increases the volatility of firms’ stock returns, using high‐frequency data for publicly listed Japanese manufacturing firms over the period 2000–10. The two margins of global engagement we consider, namely, exports and sales via foreign affiliates (horizontal foreign direct investment), have both a positive and economically significant effect on firm‐level volatility. We find, however, that increasing the intensity of sales through foreign affiliates has a stronger effect on volatility than a similar change in export intensity. We also uncover evidence consistent with the notion that firms’ need to use external finance to cover the substantial costs involved in reaching foreign consumers can be an important channel through which firms’ participation in international markets increases their exposure to economic uncertainty.