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on Econometric Time Series |
| By: | Nikolaishvili, Giorgi (Wake Forest University, Economics Department) |
| Abstract: | I propose the pass-through impulse response function (PT-IRF) as a novel reduced-form empirical approach to measuring transmission channel dynamics. In essence, a PT-IRF quantifies the propagation of a shock through the Granger causality of a specified set of endogenous variables within a dynamical system. This approach has fewer informational requirements than alternative methods, such as structural parameter and empirical policy counterfactual exercises. A PT-IRF only requires the specification of a reduced-form VAR and identification of a shock of interest, bypassing the need to either build a structural model or identify multiple shocks. I demonstrate the flexibility of PT-IRFs by empirically analyzing the indirect dynamic transmission of oil price shocks to inflation and output via interest rates, as well as the indirect dynamic effect of monetary policy shocks on output via changes in credit supply. |
| Keywords: | Directed graph; dynamic propagation; Granger causality; vector autoregression |
| JEL: | C10 C32 C50 E52 |
| Date: | 2025–01–27 |
| URL: | https://d.repec.org/n?u=RePEc:ris:wfuewp:0121 |
| By: | Yoosoon Chang (Indiana University, Department of Economics); Ye Lu (School of Economics, University of Sydney); Joon Park (Indiana University, Department of Economics) |
| Abstract: | In this paper, we analyze regressions with observations collected at small time intervals over a long period of time. For the formal asymptotic analysis, we assume that samples are obtained from continuous time stochastic processes, and let the sampling interval δ shrink down to zero and the sample span T increase up to infinity. In this setup, we show that the standard Wald statistic diverges to infinity and the regression becomes spurious as long as δ → 0 sufficiently fast relative to T → ∞. Such a phenomenon is indeed what is frequently observedin practice for the type of regressions considered in the paper. In contrast, our asymptotic theory predicts that the spuriousness disappears if we use the robustversion of the Wald test with an appropriate long-run variance estimate. This is supported, strongly and unambiguously, by our empirical illustration using the regression of long-term on short-term interest rates. |
| Keywords: | high frequency regression, spurious regression, continuous-time model, asymptotics, long-run variance estimation |
| JEL: | C13 C22 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:inu:caeprp:2025001 |
| By: | Philippe Goulet Coulombe; Karin Klieber |
| Abstract: | The use of moving averages is pervasive in macroeconomic monitoring, particularly for tracking noisy series such as inflation. The choice of the look-back window is crucial. Too long of a moving average is not timely enough when faced with rapidly evolving economic conditions. Too narrow averages are noisy, limiting signal extraction capabilities. As is well known, this is a bias-variance trade-off. However, it is a time-varying one: the optimal size of the look-back window depends on current macroeconomic conditions. In this paper, we introduce a simple adaptive moving average estimator based on a Random Forest using as sole predictor a time trend. Then, we compare the narratives inferred from the new estimator to those derived from common alternatives across series such as headline inflation, core inflation, and real activity indicators. Notably, we find that this simple tool provides a different account of the post-pandemic inflation acceleration and subsequent deceleration. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.13222 |
| By: | Lutero, Giancarlo; Piovani, Alessandro |
| Abstract: | The past few years have been marked by the occurrence of many unexpected events that have had many social and economic repercussions, with the COVID-19 pandemic and rising tensions in energy commodity markets standing out above the others. This period of great uncertainty has also had a considerable effect on the production of official economic statistics, undermining the goodness and the predictive capacity of short-term stochastic models. In this condition of extreme unpredictability, there is a need for a strategy of monitoring and reviewing the seasonal adjustment models and anomalous observations, especially over the period 2020-2023. In this work several intervention strategies were defined and tested, focusing over series that manifested a distinct break in their dynamic. Temporary level shifts, included with their lagged versions, have proven to be a particularly useful tool. The outcomes reveal that the policies we considered are effective, and the TRAMO-SEATS procedure manages to be helpful in both ordinary and extraordinary conditions. The whole data analysis has been conducted with JDemetra+ that is a complete and flexible tool in performing several statistical estimates and tests. |
| Keywords: | Seasonal adjustment, structural breaks, outlier detection, intervention variables, JDemetra+ |
| JEL: | C51 C52 |
| Date: | 2025–01–16 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123354 |
| By: | Patrick Osatohanmwen (Free University of Bozen-Bolzano, Italy) |
| Abstract: | In many real-life processes, data with high positive skewness are very common. Moreover, these data tend to exhibit heterogeneous characteristics in such a manner that using one parametric univariate probability distribution becomes inadequate to model such data. When the heterogeneity of such data can be appropriately separated into two components: the main innovation component, where the bulk of data is centered, and the tail component which contains some few extreme observations, in such a way, and without a loss in generality, that the data possesses high skewness to the right, the use of hybrid models becomes very viable to model the data. In this paper, we propose a new two-component hybrid model which joins the half-normal distribution for the main innovation of a highly right-skewed data with the generalized Pareto distribution (GPD) for the observations in the data above a certain threshold. To enhance efficiency in the estimation of the parameters of the hybrid model, an unsupervised iterative algorithm (UIA) is adopted. An application of the hybrid model in modeling the absolute log returns of the S&P500 index and the intensity of rainfall which triggered some debris flow events in the South Tyrol region of Italy is carried out. |
| Keywords: | Estimation algorithm; Generalized Pareto distribution; Half-normal distribution; Hybrid model; S&P500. |
| JEL: | C02 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:bzn:wpaper:bemps108 |
| By: | Clément de Chaisemartin (Sciences Po Paris); Xavier D’Haultfoeuille (CREST-ENSAE) |
| Abstract: | Consider a parameter of interest, which can be consistently estimated under some conditions. Suppose also that we can at least partly test these conditions with specification tests. We consider the common practice of conducting inference on the parameter of interest conditional on not rejecting these tests. We show that if the tested conditions hold, conditional inference is valid, though possibly conservative. This holds generally, without imposing any assumption on the asymptotic dependence between the estimator of the parameter of interest and the specification test. |
| Date: | 2025–01–24 |
| URL: | https://d.repec.org/n?u=RePEc:crs:wpaper:2025-03 |