Acts of Solidarity: Crossing and Reiterating Israeli–Palestinian Frontiers

In academic and public discourse on the Zionist–Palestinian conflict, there still prevails a ‘methodological nationalism’ based on a separatist imagination overshadowing the existence and role of Israeli–Palestinian forms of communality and solidarity. This article analyses micro‐political practices that cross existing frontiers, both within Israel and between the occupied Palestinian territories and Israel. Through recent conceptualizations of ‘acts’, I read these ethnographic episodes in their intentional and performative dimension. What is the role of these ‘acts’? What are their effects, both on participants and the wider public? Through two interconnected cases, different functions of acts are explored. The first case relates to encounters between Israelis and Palestinians in the embattled city of Hebron in the occupied Palestinian territories; the second investigates moments during a Gandhi‐inspired peace march at the ‘internal’ frontier of the Israeli Negev desert. The ethnographic perspective reveals what lies behind and beneath the acts, going beyond the conflict's obvious structures of power. Acts function primarily as a valve of catharsis for the participants themselves, both overcoming and reproducing hegemonic discursive elements of the conflict. Paradoxically, acts of solidarity are often crucial in shaping public knowledge about the conflict in more sectarian terms.     

Experiences of Urban Militarism: Spatial Stigma,Ruins and Everyday Life

A key question in urban sociology is how people interpret the urban environment. At a time when cities are increasingly militarized, this question is particularly important for understanding how militarism impacts urban life. However, urban sociologists have not addressed how people experience militarized environments. This article turns to this question by considering the case of Lydda‐Lod, an Israeli city that has been demographically and physically transformed by war, displacement and securitization. Drawing on Wacquant's sociology of spatial stigma and adding insights from works on emotions in (post‐)conflict cities, I examine how poor Palestinians think and feel about the surveilled districts where they live within the city's broader landscape of ruins. I show how the Israeli military, security and policing agencies have collectively produced spatial stigmatization of these districts. I discuss how Palestinians respond to this spatial stigma by attaching a sense of worthlessness to their districts. However, this reproduction of spatial stigma is punctuated by expressions of care for the built environment and by a desire to revalorize collective Palestinian life in the city. I conclude by discussing how a perspective on militarized cities focused on everyday responses to militarism and attentive to marginalities enriches urban sociology and urban studies more generally.     

Evaluating New‐Keynesian Models of a Small Open Economy*

We suggest a strategy to evaluate members of a class of New‐Keynesian models of a small open economy. As an example, we estimate a modified version of the model in Svensson [Journal of International Economics (2000) Vol. 50, pp. 155–183] and compare its impulse response and variance decomposition functions with those a structural vector autoregression (VAR) model. The focus is on responses to foreign rather than to domestic shocks, which facilitates identification. Some results are that US shocks account for large shares of the variance of Canadian variables, that little of this influence is due to real exchange rate movements, and that Canadian monetary policy is not adequately described by a Taylor rule.     

The Early Millennium Slowdown: Replicating the Peersman (2005) Results

This paper undertakes both a narrow and wide replication of the constant coefficients vector autoregression (VAR) identified with sign restrictions considered by Peersman (Journal of Applied Econometrics 2005; 20 (2): 185–207. His results for the US are robust to an increase in the sample period from 2002:Q2 to 2014:Q2, but the extension to time‐varying parameters highlights the importance of testing the robustness of results against time variation. In particular, there are differences across models regarding the role of individual shocks during the 2001 US slowdown. Copyright © 2015 John Wiley & Sons, Ltd.     

EVALUATING REAL‐TIME VAR FORECASTS WITH AN INFORMATIVE DEMOCRATIC PRIOR

This paper proposes Bayesian forecasting in a vector autoregression using a democratic prior. This prior is chosen to match the predictions of survey respondents. In particular, the unconditional mean for each series in the vector autoregression is centered around long‐horizon survey forecasts. Heavy shrinkage toward the democratic prior is found to give good real‐time predictions of a range of macroeconomic variables, as these survey projections are good at quickly capturing endpoint shifts. Copyright © 2012 John Wiley & Sons, Ltd.     

Searching for the Causal Structure of a Vector Autoregression*

We provide an accessible introduction to graph‐theoretic methods for causal analysis. Building on the work of Swanson and Granger (Journal of the American Statistical Association, Vol. 92, pp. 357–367, 1997), and generalizing to a larger class of models, we show how to apply graph‐theoretic methods to selecting the causal order for a structural vector autoregression (SVAR). We evaluate the PC (causal search) algorithm in a Monte Carlo study. The PC algorithm uses tests of conditional independence to select among the possible causal orders – or at least to reduce the admissible causal orders to a narrow equivalence class. Our findings suggest that graph‐theoretic methods may prove to be a useful tool in the analysis of SVARs.     

Structural VARs and noninvertible macroeconomic models

We resume the line of research pioneered by C. A. Sims and Zha (Macroeconomic Dynamics, 2006, 10, 231–272) and make two novel contributions. First, we provide a formal treatment of partial fundamentalness—that is, the idea that a structural vector autoregression (VAR) can recover, either exactly or with good approximation, a single shock or a subset of shocks, even when the underlying model is nonfundamental. In particular, we extend the measure of partial fundamentalness proposed by Sims and Zha to the finite‐order case and study the implications of partial fundamentalness for impulse‐response and variance‐decomposition analysis. Second, we present an application where we validate a theory of news shocks and find it to be in line with the empirical evidence.     

Finite Sample Correction Factors for Panel Cointegration Tests*

In this paper we present finite T mean and variance correction factors and corresponding response surface regressions for the panel cointegration tests presented in Pedroni (1999, 2004) , Westerlund (2005) , Larsson et al. (2001) and Breitung (2005) . For the single equation tests, we consider up to 12 regressors and for the system tests vector autoregression dimensions up to 12 variables. All commonly used specifications for the deterministic components are considered. The sample sizes considered are T ∈ {10,20,30,40,50,60,70,80,90,100,200,500}.     

An Introduction to Best Empirical Models when the Parameter Space is Infinite Dimensional*

Ploberger and Phillips (Econometrica, Vol. 71, pp. 627–673, 2003) proved a result that provides a bound on how close a fitted empirical model can get to the true model when the model is represented by a parameterized probability measure on a finite dimensional parameter space. The present note extends that result to cases where the parameter space is infinite dimensional. The results have implications for model choice in infinite dimensional problems and highlight some of the difficulties, including technical difficulties, presented by models of infinite dimension. Some implications for forecasting are considered and some applications are given, including the empirically relevant case of vector autoregression (VAR) models of infinite order.     

Econometric Methods for Modelling Systems With a Mixture of i(1) and i(0) Variables

This paper considers structural models with both I(1) and I(0) variables. The structural shocks associated with either set of variables could be permanent or transitory. We classify the shocks as (P1,P0) and (T1,T0), where P/T distinguishes permanent/transitory, while 1/0 means they are attached to structural equations with either I(1) or I(0) variables as their ‘dependent’ variable. We show that P0 shocks can affect cointegration analysis and provide a formula to compute the permanent component if they are present. Finally, we reformulate a well‐known empirical structural vector autoregression showing the impact of P0 shocks when there are just long‐run parametric and sign restrictions. Copyright © 2015 John Wiley & Sons, Ltd.     

Identifying the true nature of price discovery and cross-market informational flow in the investment grade CDS and equity markets

We examine the comparative efficiency of systematic investment grade credit default swap (CDS) and equity markets using a time-varying coefficient vector autoregression. This modeling framework enables a view of cross-market informational flow along each point in the time-period under investigation by taking into account parameter instability. We obtain smoothing estimates of parameters capturing such flow between CDS and equity markets using daily data from 2004 to 2019, and measure the strength of flow via relative predictive gains. In contrast to prior studies, we find a two-way interactive effect in which certain types of information are captured more efficiently in prices by each market. We also find that the time-varying coefficient vector autoregression results in superior forecasting gains relative to models not accounting for price discovery. These results have implications for systematic investors, arbitrageurs and stakeholders who monitor systematic markets for their informational content.     

Miller and Modigliani,Predictive Return Regressions and Cointegration*

This paper investigates the use of alternative measures of dividend yields to predict US aggregate stock returns. Following Miller and Modigliani [Journal of Business (1961), Vol. 34, pp. 411–433] we construct a cashflow yield that includes both dividend and non‐dividend cashflows to shareholders. Using a data set covering the course of the 20th century, we show in a cointegrating vector autoregression framework that this measure has strong and stable predictive power for returns. The weak predictive power of standard measures of the dividend yield is explained by the strong rejection of the implied cointegrating and causality restrictions on the impact of non‐dividend cashflows.     

Combining Time Variation and Mixed Frequencies: an Analysis of Government Spending Multipliers in Italy

In this paper, we propose a time‐varying parameter vector autoregression (VAR) model with stochastic volatility which allows for estimation on data sampled at different frequencies. Our contribution is twofold. First, we extend the methodology developed by Cogley and Sargent (Drifts and volatilities: monetary policies and outcomes in the post WWII U.S. Review of Economic Studies 2005; 8 : 262–302) and Primiceri (Time varying structural vector autoregressions and monetary policy. Review of Economic Studies 2005; 72 : 821–852) to a mixed‐frequency setting. In particular, our approach allows for the inclusion of two different categories of variables (high‐frequency and low‐frequency) into the same time‐varying model. Second, we use this model to study the macroeconomic effects of government spending shocks in Italy over the 1988:Q4–2013:Q3 period. Italy—as well as most other euro area economies—is characterized by short quarterly time series for fiscal variables, whereas annual data are generally available for a longer sample before 1999. Our results show that the proposed time‐varying mixed‐frequency model improves on the performance of a simple linear interpolation model in generating the true path of the missing observations. Second, our empirical analysis suggests that government spending shocks tend to have positive effects on output in Italy. The fiscal multiplier, which is maximized at the 1‐year horizon, follows a U‐shape over the sample considered: it peaks at around 1.5 at the beginning of the sample; it then stabilizes between 0.8 and 0.9 from the mid 1990s to the late 2000s, before rising again to above unity during the recent crisis. Copyright © 2015 John Wiley & Sons, Ltd.     

Fractional cointegration and price discovery in Canadian commodities

Using as a unifying theme commodities important to the Canadian economy, recently developed tools are applied to studying price discovery in the spot and futures markets. For each commodity the fractionally cointegrated vector autoregression (FCVAR) model of Johansen and Neilsen is estimated and tested against the special case of the conventional cointegrated vector autoregression (CVAR). These models characterize the fundamental value of a commodity as the common stochastic trend shared by its cointegrated spot and futures prices, and so price discovery can be analyzed using the permanent-transitory decomposition of Gonzalo and Granger. Model forecasts are evaluated and compared using a distributional result due to Clark and West. The generalization to fractional cointegration is found to be statistically significant. However the economic significance of this generalization—in terms of forecast accuracy and the profitability of mean–variance dynamic trading strategies—is more fragile than may have been appreciated.     

Two are better than one: Volatility forecasting using multiplicative component GARCH-MIDAS models

We examine the properties and forecast performance of multiplicative volatility specifications that belong to the class of generalized autoregressive conditional heteroskedasticity–mixed-data sampling (GARCH-MIDAS) models suggested in Engle, Ghysels, and Sohn (Review of Economics and Statistics, 2013, 95, 776–797). In those models volatility is decomposed into a short-term GARCH component and a long-term component that is driven by an explanatory variable. We derive the kurtosis of returns, the autocorrelation function of squared returns, and the R² of a Mincer–Zarnowitz regression and evaluate the QMLE and forecast performance of these models in a Monte Carlo simulation. For S&P 500 data, we compare the forecast performance of GARCH-MIDAS models with a wide range of competitor models such as HAR (heterogeneous autoregression), realized GARCH, HEAVY (high-frequency-based volatility) and Markov-switching GARCH. Our results show that the GARCH-MIDAS based on housing starts as an explanatory variable significantly outperforms all competitor models at forecast horizons of 2 and 3 months ahead.     

Leaning into the Wind: A Structural VAR Investigation of UK Monetary Policy*

This paper adapts Uhlig's [Journal of Monetary Economics (2005) forthcoming] sign restriction identification methodology to investigate the effects of UK monetary policy using a structural vector autoregression (VAR). It shows that shocks which can reasonably be described as monetary policy shocks have played only a small role in the total variation of UK monetary and macroeconomic variables. Most of the variation in UK monetary variables has been due to their systematic reaction to other macroeconomic shocks, namely non‐monetary aggregate demand, aggregate supply, and oil price shocks. We also find, without imposing any long run identifying restrictions, that aggregate supply shocks have permanent effects on output.     

Blood Sports and Cherry Pie

Abstract . The results are reported of empirically testing two hypotheses relating to violence in a professional team sport: one, that hockey fans have a taste for violence (bockey is a “blood sport”) so that, in general, game attendance and violence in the National Hockey League are positively related; and two, more specifically, that the more extreme degrees of violence are positively associated with American, not Canadian, attendance. The data are game by game data for the 1983/84 season, violence is measured by various categories of penalty minutes (minors, majors, misconducts), and the model is a system of two reduced form equations. The results confirm that there is a significant and positive relationship between aggregate measures of violence (total penalty minutes) and attendance for games played in both American and Canadian cities; and there is a significant positive relationship between the more extreme forms of violence (proxied by majors and misconducts) and attendance only in American cities     

Rare shocks vs. non-linearities: What drives extreme events in the economy? Some empirical evidence

A small-scale vector autoregression (VAR) is used to shed some light on the roles of extreme shocks and non-linearities during stress events observed in the economy. The model focuses on the link between credit/financial markets and the real economy and is estimated on US quarterly data for the period 1984–2013. Extreme shocks are accounted for by assuming t-distributed reduced-form shocks. Non-linearity is allowed by the possibility of regime switch in the shock propagation mechanism. Strong evidence for fat tails in error distributions is found. Moreover, the results suggest that accounting for extreme shocks rather than explicit modeling of non-linearity contributes to the explanatory power of the model. Finally, it is shown that the accuracy of density forecasts improves if non-linearities and shock distributions with fat tails are considered.     

Forecasting crude oil prices with DSGE models

In this study, we conducted an oil prices forecasting competition among a set of structural models, including vector autoregression and dynamic stochastic general equilibrium (DSGE) models. Our results highlight two principles. First, forecasts should exploit the fact that real oil prices are mean reverting over long horizons. Second, models should not replicate the high volatility of the oil prices observed in samples. By following these principles, we show that an oil sector DSGE model performs much better at real oil price forecasting than random walk or vector autoregression.