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Mamma mia! Revealing hidden heterogeneity by PCA-biplot : MPC puzzle for Italy's elderly poor
(2023)
I investigate consumption patterns in Italy and use a PCA-biplot to discover a consumption puzzle for the elderly poor. Data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS) indicate that Italian poor old-aged households boast lower levels of the marginal propensity to consume (MPC) than suggested by the dominant consumption models. A customized regression analysis exhibits group differences with richer peers to be only half as large as prescribed by a traditional linear regression model. This analysis has benefited from a visualization technique for high-dimensional matrices related to the unsupervised machine learning literature. I demonstrate that PCA-biplots are a useful tool to reveal hidden relations and to help researchers to formulate simple research questions. The method is presented in detail and suggestions on incorporating it in the econometric modeling pipeline are given.
We investigate consumption patterns in Europe with supervised machine learning methods and reveal differences in age and wealth impact across countries. Using data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS), we assess how age and (liquid) wealth affect the marginal propensity to consume (MPC) in the Netherlands, Germany, France, and Italy. Our regression analysis takes the specification by Christelis et al. (2019) as a starting point. Decision trees are used to suggest alternative variable splits to create categorical variables for customized regression specifications. The results suggest an impact of differing wealth distributions and retirement systems across the studied Eurozone members and are relevant to European policy makers due to joint Eurozone monetary policy and increasing supranational fiscal authority of the EU. The analysis is further substantiated by a supervised machine learning analysis using a random forest and XGBoost algorithm.
Optimal monetary policy studies typically rely on a single structural model and identification of model-specific rules that minimize the unconditional volatilities of inflation and real activity. In their proposed approach, the authors take a large set of structural models and look for the model-robust rules that minimize the volatilities at those frequencies that policymakers are most interested in stabilizing. Compared to the status quo approach, their results suggest that policymakers should be more restrained in their inflation responses when their aim is to stabilize inflation and output growth at specific frequencies. Additional caution is called for due to model uncertainty.
This paper examines how the implementation of a new dark order - Midpoint Extended Life Order on NASDAQ - impacts financial markets stability in terms of occurrences of mini-flash crashes in individual securities. We use high-frequency order book data and apply panel regression analysis to estimate the effect of M-ELO trading on market stability and liquidity provision. The results suggest a predominance of a speed bump effect of M-ELO rather than a darkness effect. We find that the introduction of M-ELO increases market stability by reducing the average number of mini-flash crashes, but its impact on market quality is mixed.
I have assessed changes in the monetary policy stance in the euro area since its inception by applying a Bayesian time-varying parameter framework in conjunction with the Hamiltonian Monte Carlo algorithm. I find that the estimated policy response has varied considerably over time. Most of the results suggest that the response weakened after the onset of the financial crisis and while quantitative measures were still in place, although there are also indications that the weakening of the response to the expected inflation gap may have been less pronounced. I also find that the policy response has become more forceful over the course of the recent sharp rise in inflation. Furthermore, it is essential to model the stochastic volatility relating to deviations from the policy rule as it materially influences the results.
This paper presents and compares Bernoulli iterative approaches for solving linear DSGE models. The methods are compared using nearly 100 different models from the Macroeconomic Model Data Base (MMB) and different parameterizations of the monetary policy rule in the medium-scale New Keynesian model of Smets and Wouters (2007) iteratively. I find that Bernoulli methods compare favorably in solving DSGE models to the QZ, providing similar accuracy as measured by the forward error of the solution at a comparable computation burden. The method can guarantee convergence to a particular, e.g., unique stable, solution and can be combined with other iterative methods, such as the Newton method, lending themselves especially to refining solutions.
Unconventional green
(2023)
We analyze the effects of the PEPP (Pandemic Emergency Purchase Programme), the temporary quantitative easing implemented by the ECB immediately after the burst of the Covid-19 pandemic. We show that the differences in aim, size and flexibility with respect to the traditional Corporate Sector Purchase Programme (CSPP) were able to significantly involve, in addition to the directly targeted bonds, also the green bond segment. Via a standard difference- in-differences model we estimate that the yield on green bonds declined by more than 20 basis points after the PEPP. In order to take into account also the differences attributable to the eligibility to the programme, we employ a triple difference estimator. Bonds that at the same time were green and eligible benefitted of an additional premium of 39 basis points.
Fabo, Janˇcokov ́a, Kempf, and P ́astor (2021) show that papers written by central bank researchers find quantitative easing (QE) to be more effective than papers written by academics. Weale and Wieladek (2022) show that a subset of these results lose statistical significance when OLS regressions are replaced by regressions that downweight outliers. We examine those outliers and find no reason to downweight them. Most of them represent estimates from influential central bank papers published in respectable academic journals. For example, among the five papers finding the largest peak effect of QE on output, all five are published in high-quality journals (Journal of Monetary Economics, Journal of Money, Credit and Banking, and Applied Economics Letters), and their average number of citations is well over 200. Moreover, we show that these papers have supported policy communication by the world’s leading central banks and shaped the public perception of the effectiveness of QE. New evidence based on quantile regressions further supports the results in Fabo et al. (2021).
Industry concentration and markups in the US have been rising over the last 3-4 decades. However, the causes remain largely unknown. This paper uses machine learning on regulatory documents to construct a novel dataset on compliance costs to examine the effect of regulations on market power. The dataset is comprehensive and consists of all significant regulations at the 6-digit NAICS level from 1970-2018. We find that regulatory costs have increased by $1 trillion during this period. We document that an increase in regulatory costs results in lower (higher) sales, employment, markups, and profitability for small (large) firms. Regulation driven increase in concentration is associated with lower elasticity of entry with respect to Tobin's Q, lower productivity and investment after the late 1990s. We estimate that increased regulations can explain 31-37% of the rise in market power. Finally, we uncover the political economy of rulemaking. While large firms are opposed to regulations in general, they push for the passage of regulations that have an adverse impact on small firms.
Output gap revisions can be large even after many years. Real-time reliability tests might therefore be sensitive to the choice of the final output gap vintage that the real-time estimates are compared to. This is the case for the Federal Reserve’s output gap. When accounting for revisions in response to the global financial crisis in the final output gap, the improvement in real-time reliability since the mid-1990s is much smaller than found by Edge and Rudd (Review of Economics and Statistics, 2016, 98(4), 785-791). The negative bias of real-time estimates from the 1980s has disappeared, but the size of revisions continues to be as large as the output gap itself.
The authors systematically analyse how the realtime reliability assessment is affected through varying the final output gap vintage. They find that the largest changes are caused by output gap revisions after recessions. Economists revise their models in response to such events, leading to economically important revisions not only for the most recent years, but reaching back up to two decades. This might improve the understanding of past business cycle dynamics, but decreases the reliability of real-time output gaps ex post.