pwc-archive/papers-with-abstracts huggingface dataset:
The Transmission of Monetary Policy via Common Cycles in the Euro Area
Authors: Lukas Berend, Jan Prüser
Date: 2024-10-08 00:00:00

We use a FAVAR model with proxy variables and sign restrictions to investigate the role of the euro area's common output and inflation cycles in the transmission of monetary policy shocks. Our findings indicate that common cycles explain most of the variation in output and inflation across member countries. However, Southern European economies exhibit a notable divergence from these cycles in the aftermath of the financial crisis. Building on this evidence, we demonstrate that monetary policy is homogeneously propagated to member countries via the common cycles. In contrast, country-specific transmission channels lead to heterogeneous country responses to monetary policy shocks. Consequently, our empirical results suggest that the divergent effects of ECB monetary policy are attributable to heterogeneous country-specific exposures to financial markets, rather than to dis-synchronized economies within the euro area.
$\texttt{rdid}$ and $\texttt{rdidstag}$: Stata commands for robust difference-in-differences
Authors: Kyunghoon Ban, Désiré Kédagni
Date: 2024-10-07 00:00:00

This article provides a Stata package for the implementation of the robust difference-in-differences (RDID) method developed in Ban and K\'edagni (2023). It contains three main commands: $\texttt{rdid}$, $\texttt{rdid_dy}$, $\texttt{rdidstag}$, which we describe in the introduction and the main text. We illustrate these commands through simulations and empirical examples.
Large datasets for the Euro Area and its member countries and the dynamic effects of the common monetary policy
Authors: Matteo Barigozzi, Claudio Lissona, Lorenzo Tonni
Date: 2024-10-07 00:00:00
Tasks: Time Series

We present and describe a new publicly available large dataset which encompasses quarterly and monthly macroeconomic time series for both the Euro Area (EA) as a whole and its ten primary member countries. The dataset, which is called EA-MD-QD, includes more than 800 time series and spans the period from January 2000 to the latest available month. Since January 2024 EA-MD-QD is updated on a monthly basis and constantly revised, making it an essential resource for conducting policy analysis related to economic outcomes in the EA. To illustrate the usefulness of EA-MD-QD, we study the country specific Impulse Responses of the EA wide monetary policy shock by means of the Common Component VAR plus either Instrumental Variables or Sign Restrictions identification schemes. The results reveal asymmetries in the transmission of the monetary policy shock across countries, particularly between core and peripheral countries. Additionally, we find comovements across Euro Area countries' business cycles to be driven mostly by real variables, compared to nominal ones.
Democratizing Strategic Planning in Master-Planned Communities
Authors: Christopher K. Allsup, Irene S. Gabashvili
Date: 2024-10-07 00:00:00
Tasks: Sensitivity

This paper introduces a strategic planning tool for master-planned communities designed specifically to quantify residents' subjective preferences about large investments in amenities and infrastructure projects. Drawing on data obtained from brief online surveys, the tool ranks alternative plans by considering the aggregate anticipated utilization of each proposed amenity and cost sensitivity to it (or risk sensitivity for infrastructure plans). In addition, the tool estimates the percentage of households that favor the preferred plan and predicts whether residents would actually be willing to fund the project. The mathematical underpinnings of the tool are borrowed from utility theory, incorporating exponential functions to model diminishing marginal returns on quality, cost, and risk mitigation.
A Structural Approach to Growth-at-Risk
Authors: Robert Wojciechowski
Date: 2024-10-06 00:00:00

We identify the structural impulse responses of quantiles of the outcome variable to a shock. Our estimation strategy explicitly distinguishes treatment from control variables, allowing us to model responses of unconditional quantiles while using controls for identification. Disentangling the effect of adding control variables on identification versus interpretation brings our structural quantile impulse responses conceptually closer to structural mean impulse responses. Applying our methodology to study the impact of financial shocks on lower quantiles of output growth confirms that financial shocks have an outsized effect on growth-at-risk, but the magnitude of our estimates is more extreme than in previous studies.
Inference in High-Dimensional Linear Projections: Multi-Horizon Granger Causality and Network Connectedness
Authors: Eugene Dettaa, EnDong Wang
Date: 2024-10-06 00:00:00

This paper presents a Wald test for multi-horizon Granger causality within a high-dimensional sparse Vector Autoregression (VAR) framework. The null hypothesis focuses on the causal coefficients of interest in a local projection (LP) at a given horizon. Nevertheless, the post-double-selection method on LP may not be applicable in this context, as a sparse VAR model does not necessarily imply a sparse LP for horizon h>1. To validate the proposed test, we develop two types of de-biased estimators for the causal coefficients of interest, both relying on first-step machine learning estimators of the VAR slope parameters. The first estimator is derived from the Least Squares method, while the second is obtained through a two-stage approach that offers potential efficiency gains. We further derive heteroskedasticity- and autocorrelation-consistent (HAC) inference for each estimator. Additionally, we propose a robust inference method for the two-stage estimator, eliminating the need to correct for serial correlation in the projection residuals. Monte Carlo simulations show that the two-stage estimator with robust inference outperforms the Least Squares method in terms of the Wald test size, particularly for longer projection horizons. We apply our methodology to analyze the interconnectedness of policy-related economic uncertainty among a large set of countries in both the short and long run. Specifically, we construct a causal network to visualize how economic uncertainty spreads across countries over time. Our empirical findings reveal, among other insights, that in the short run (1 and 3 months), the U.S. influences China, while in the long run (9 and 12 months), China influences the U.S. Identifying these connections can help anticipate a country's potential vulnerabilities and propose proactive solutions to mitigate the transmission of economic uncertainty.
A new GARCH model with a deterministic time-varying intercept
Authors: Niklas Ahlgren, Alexander Back, Timo Teräsvirta
Date: 2024-10-04 00:00:00
Tasks: Time Series

It is common for long financial time series to exhibit gradual change in the unconditional volatility. We propose a new model that captures this type of nonstationarity in a parsimonious way. The model augments the volatility equation of a standard GARCH model by a deterministic time-varying intercept. It captures structural change that slowly affects the amplitude of a time series while keeping the short-run dynamics constant. We parameterize the intercept as a linear combination of logistic transition functions. We show that the model can be derived from a multiplicative decomposition of volatility and preserves the financial motivation of variance decomposition. We use the theory of locally stationary processes to show that the quasi maximum likelihood estimator (QMLE) of the parameters of the model is consistent and asymptotically normally distributed. We examine the quality of the asymptotic approximation in a small simulation study. An empirical application to Oracle Corporation stock returns demonstrates the usefulness of the model. We find that the persistence implied by the GARCH parameter estimates is reduced by including a time-varying intercept in the volatility equation.
Partially Identified Heterogeneous Treatment Effect with Selection: An Application to Gender Gaps
Authors: Xiaolin Sun, Xueyan Zhao, D. S. Poskitt
Date: 2024-10-02 00:00:00
Tasks: Selection bias

This paper addresses the sample selection model within the context of the gender gap problem, where even random treatment assignment is affected by selection bias. By offering a robust alternative free from distributional or specification assumptions, we bound the treatment effect under the sample selection model with an exclusion restriction, an assumption whose validity is tested in the literature. This exclusion restriction allows for further segmentation of the population into distinct types based on observed and unobserved characteristics. For each type, we derive the proportions and bound the gender gap accordingly. Notably, trends in type proportions and gender gap bounds reveal an increasing proportion of always-working individuals over time, alongside variations in bounds, including a general decline across time and consistently higher bounds for those in high-potential wage groups. Further analysis, considering additional assumptions, highlights persistent gender gaps for some types, while other types exhibit differing or inconclusive trends. This underscores the necessity of separating individuals by type to understand the heterogeneous nature of the gender gap.
New Tests of Equal Forecast Accuracy for Factor-Augmented Regressions with Weaker Loadings
Authors: Luca Margaritella, Ovidijus Stauskas
Date: 2024-09-30 00:00:00
Tasks: regression

We provide the theoretical foundation for the recently proposed tests of equal forecast accuracy and encompassing by Pitarakis (2023a) and Pitarakis (2023b), when the competing forecast specification is that of a factor-augmented regression model, whose loadings are allowed to be homogeneously/heterogeneously weak. This should be of interest for practitioners, as at the moment there is no theory available to justify the use of these simple and powerful tests in such context.
Forecasting short-term inflation in Argentina with Random Forest Models
Authors: Federico Daniel Forte
Date: 2024-10-02 00:00:00

This paper examines the performance of Random Forest models in forecasting short-term monthly inflation in Argentina, based on a database of monthly indicators since 1962. It is found that these models achieve forecast accuracy that is statistically comparable to the consensus of market analysts' expectations surveyed by the Central Bank of Argentina (BCRA) and to traditional econometric models. One advantage of Random Forest models is that, as they are non-parametric, they allow for the exploration of nonlinear effects in the predictive power of certain macroeconomic variables on inflation. Among other findings, the relative importance of the exchange rate gap in forecasting inflation increases when the gap between the parallel and official exchange rates exceeds 60%. The predictive power of the exchange rate on inflation rises when the BCRA's net international reserves are negative or close to zero (specifically, below USD 2 billion). The relative importance of inflation inertia and the nominal interest rate in forecasting the following month's inflation increases when the nominal levels of inflation and/or interest rates rise.
A Nonparametric Test of Heterogeneous Treatment Effects under Interference
Authors: Julius Owusu
Date: 2024-10-01 00:00:00

Statistical inference of heterogeneous treatment effects (HTEs) across predefined subgroups is challenging when units interact because treatment effects may vary by pre-treatment variables, post-treatment exposure variables (that measure the exposure to other units' treatment statuses), or both. Thus, the conventional HTEs testing procedures may be invalid under interference. In this paper, I develop statistical methods to infer HTEs and disentangle the drivers of treatment effects heterogeneity in populations where units interact. Specifically, I incorporate clustered interference into the potential outcomes model and propose kernel-based test statistics for the null hypotheses of (i) no HTEs by treatment assignment (or post-treatment exposure variables) for all pre-treatment variables values and (ii) no HTEs by pre-treatment variables for all treatment assignment vectors. I recommend a multiple-testing algorithm to disentangle the source of heterogeneity in treatment effects. I prove the asymptotic properties of the proposed test statistics. Finally, I illustrate the application of the test procedures in an empirical setting using an experimental data set from a Chinese weather insurance program.
Valid Inference on Functions of Causal Effects in the Absence of Microdata
Authors: Vedant Vohra
Date: 2024-09-30 00:00:00
Tasks: valid

Economists are often interested in functions of multiple causal effects, a leading example of which is evaluating a policy's cost-effectiveness. The benefits and costs might be captured by multiple causal effects and aggregated into a scalar measure of cost-effectiveness. Oftentimes, the microdata underlying these estimates is inaccessible; only published estimates and their corresponding standard errors are available. We provide a method to conduct inference on non-linear functions of causal effects when the only information available is the point estimates and their standard errors. We apply our method to inference for the Marginal Value of Public Funds (MVPF) of government policies.
Synthetic Difference in Differences for Repeated Cross-Sectional Data
Authors: Yoann Morin
Date: 2024-09-30 00:00:00

The synthetic difference-in-differences method provides an efficient method to estimate a causal effect with a latent factor model. However, it relies on the use of panel data. This paper presents an adaptation of the synthetic difference-in-differences method for repeated cross-sectional data. The treatment is considered to be at the group level so that it is possible to aggregate data by group to compute the two types of synthetic difference-in-differences weights on these aggregated data. Then, I develop and compute a third type of weight that accounts for the different number of observations in each cross-section. Simulation results show that the performance of the synthetic difference-in-differences estimator is improved when using the third type of weights on repeated cross-sectional data.
Factors in Fashion: Factor Analysis towards the Mode
Authors: Zhe Sun, Yundong Tu
Date: 2024-09-28 00:00:00
Tasks: Dimensionality Reduction, Model Selection

The modal factor model represents a new factor model for dimension reduction in high dimensional panel data. Unlike the approximate factor model that targets for the mean factors, it captures factors that influence the conditional mode of the distribution of the observables. Statistical inference is developed with the aid of mode estimation, where the modal factors and the loadings are estimated through maximizing a kernel-type objective function. An easy-to-implement alternating maximization algorithm is designed to obtain the estimators numerically. Two model selection criteria are further proposed to determine the number of factors. The asymptotic properties of the proposed estimators are established under some regularity conditions. Simulations demonstrate the nice finite sample performance of our proposed estimators, even in the presence of heavy-tailed and asymmetric idiosyncratic error distributions. Finally, the application to inflation forecasting illustrates the practical merits of modal factors.
Large Bayesian Tensor VARs with Stochastic Volatility
Authors: Joshua C. C. Chan, Yaling Qi
Date: 2024-09-24 00:00:00

We consider Bayesian tensor vector autoregressions (TVARs) in which the VAR coefficients are arranged as a three-dimensional array or tensor, and this coefficient tensor is parameterized using a low-rank CP decomposition. We develop a family of TVARs using a general stochastic volatility specification, which includes a wide variety of commonly-used multivariate stochastic volatility and COVID-19 outlier-augmented models. In a forecasting exercise involving 40 US quarterly variables, we show that these TVARs outperform the standard Bayesian VAR with the Minnesota prior. The results also suggest that the parsimonious common stochastic volatility model tends to forecast better than the more flexible Cholesky stochastic volatility model.
Non-linear dependence and Granger causality: A vine copula approach
Authors: Roberto Fuentes-Martínez, Irene Crimaldi, Armando Rungi
Date: 2024-09-23 00:00:00

Inspired by Jang et al. (2022), we propose a Granger causality-in-the-mean test for bivariate $k-$Markov stationary processes based on a recently introduced class of non-linear models, i.e., vine copula models. By means of a simulation study, we show that the proposed test improves on the statistical properties of the original test in Jang et al. (2022), and also of other previous methods, constituting an excellent tool for testing Granger causality in the presence of non-linear dependence structures. Finally, we apply our test to study the pairwise relationships between energy consumption, GDP and investment in the U.S. and, notably, we find that Granger-causality runs two ways between GDP and energy consumption.
A simple but powerful tail index regression
Authors: João Nicolau, Paulo M. M. Rodrigues
Date: 2024-09-20 00:00:00
Tasks: regression

This paper introduces a flexible framework for the estimation of the conditional tail index of heavy tailed distributions. In this framework, the tail index is computed from an auxiliary linear regression model that facilitates estimation and inference based on established econometric methods, such as ordinary least squares (OLS), least absolute deviations, or M-estimation. We show theoretically and via simulations that OLS provides interesting results. Our Monte Carlo results highlight the adequate finite sample properties of the OLS tail index estimator computed from the proposed new framework and contrast its behavior to that of tail index estimates obtained by maximum likelihood estimation of exponential regression models, which is one of the approaches currently in use in the literature. An empirical analysis of the impact of determinants of the conditional left- and right-tail indexes of commodities' return distributions highlights the empirical relevance of our proposed approach. The novel framework's flexibility allows for extensions and generalizations in various directions, empowering researchers and practitioners to straightforwardly explore a wide range of research questions.
Dynamic tail risk forecasting: what do realized skewness and kurtosis add?
Authors: Giampiero Gallo, Ostap Okhrin, Giuseppe Storti
Date: 2024-09-20 00:00:00
Tasks: Diagnostic

This paper compares the accuracy of tail risk forecasts with a focus on including realized skewness and kurtosis in "additive" and "multiplicative" models. Utilizing a panel of 960 US stocks, we conduct diagnostic tests, employ scoring functions, and implement rolling window forecasting to evaluate the performance of Value at Risk (VaR) and Expected Shortfall (ES) forecasts. Additionally, we examine the impact of the window length on forecast accuracy. We propose model specifications that incorporate realized skewness and kurtosis for enhanced precision. Our findings provide insights into the importance of considering skewness and kurtosis in tail risk modeling, contributing to the existing literature and offering practical implications for risk practitioners and researchers.
Testing for equal predictive accuracy with strong dependence
Authors: Laura Coroneo, Fabrizio Iacone
Date: 2024-09-19 00:00:00

We analyse the properties of the Diebold and Mariano (1995) test in the presence of autocorrelation in the loss differential. We show that the power of the Diebold and Mariano (1995) test decreases as the dependence increases, making it more difficult to obtain statistically significant evidence of superior predictive ability against less accurate benchmarks. We also find that, after a certain threshold, the test has no power and the correct null hypothesis is spuriously rejected. Taken together, these results caution to seriously consider the dependence properties of the loss differential before the application of the Diebold and Mariano (1995) test.
Parameters on the boundary in predictive regression
Authors: Giuseppe Cavaliere, Iliyan Georgiev, Edoardo Zanelli
Date: 2024-09-19 00:00:00
Tasks: regression

We consider bootstrap inference in predictive (or Granger-causality) regressions when the parameter of interest may lie on the boundary of the parameter space, here defined by means of a smooth inequality constraint. For instance, this situation occurs when the definition of the parameter space allows for the cases of either no predictability or sign-restricted predictability. We show that in this context constrained estimation gives rise to bootstrap statistics whose limit distribution is, in general, random, and thus distinct from the limit null distribution of the original statistics of interest. This is due to both (i) the possible location of the true parameter vector on the boundary of the parameter space, and (ii) the possible non-stationarity of the posited predicting (resp. Granger-causing) variable. We discuss a modification of the standard fixed-regressor wild bootstrap scheme where the bootstrap parameter space is shifted by a data-dependent function in order to eliminate the portion of limiting bootstrap randomness attributable to the boundary, and prove validity of the associated bootstrap inference under non-stationarity of the predicting variable as the only remaining source of limiting bootstrap randomness. Our approach, which is initially presented in a simple location model, has bearing on inference in parameter-on-the-boundary situations beyond the predictive regression problem.
Robust Bond Risk Premia Predictability Test in the Quantiles
Authors: Xiaosai Liao, Xinjue Li, Qingliang Fan
Date: 2024-09-19 00:00:00
Tasks: quantile regression

Different from existing literature on testing the macro-spanning hypothesis of bond risk premia, which only considers mean regressions, this paper investigates whether the yield curve represented by CP factor (Cochrane and Piazzesi, 2005) contains all available information about future bond returns in a predictive quantile regression with many other macroeconomic variables. In this study, we introduce the Trend in Debt Holding (TDH) as a novel predictor, testing it alongside established macro indicators such as Trend Inflation (TI) (Cieslak and Povala, 2015), and macro factors from Ludvigson and Ng (2009). A significant challenge in this study is the invalidity of traditional quantile model inference approaches, given the high persistence of many macro variables involved. Furthermore, the existing methods addressing this issue do not perform well in the marginal test with many highly persistent predictors. Thus, we suggest a robust inference approach, whose size and power performance are shown to be better than existing tests. Using data from 1980-2022, the macro-spanning hypothesis is strongly supported at center quantiles by the empirical finding that the CP factor has predictive power while all other macro variables have negligible predictive power in this case. On the other hand, the evidence against the macro-spanning hypothesis is found at tail quantiles, in which TDH has predictive power at right tail quantiles while TI has predictive power at both tails quantiles. Finally, we show the performance of in-sample and out-of-sample predictions implemented by the proposed method are better than existing methods.