
on Econometric Time Series 
By:  Mikio Ito 
Abstract:  This article proposes an estimation method to detect breakpoints for linear time series models with their parameters that jump scarcely. Its basic idea owes the group LASSO (group least absolute shrinkage and selection operator). The method practically provides estimates of such timevarying parameters of the models. An example shows that our method can detect each structural breakpoint's date and magnitude. 
Date:  2022–02 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2202.02988&r= 
By:  Christis Katsouris 
Abstract:  We revisit classical asymptotics when testing for a structural break in linear regression models by obtaining the limit theory of residualbased and Waldtype processes. First, we establish the Brownian bridge limiting distribution of these test statistics. Second, we study the asymptotic behaviour of the partialsum processes in nonstationary (linear) time series regression models. Although, the particular comparisons of these two different modelling environments is done from the perspective of the partialsum processes, it emphasizes that the presence of nuisance parameters can change the asymptotic behaviour of the functionals under consideration. Simulation experiments verify size distortions when testing for a break in nonstationary time series regressions which indicates that the Brownian bridge limit cannot provide a suitable asymptotic approximation in this case. Further research is required to establish the cause of size distortions under the null hypothesis of parameter stability. 
Date:  2022–01 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2202.00141&r= 
By:  Pincheira, Pablo; Hardy, Nicolas 
Abstract:  In this paper, we propose a correlationbased test for the evaluation of two competing forecasts. Under the null hypothesis of equal correlations with the target variable, we derive the asymptotic distribution of our test using the Delta method. This null hypothesis is not necessarily equivalent to the null of equal Mean Squared Prediction Errors (MSPE). Specifically, it might be the case that the forecast displaying the lowest MSPE also exhibits the lowest correlation with the target variable: this is known as "The MSPE paradox" (Pincheira and Hardy; 2021). In this sense, our approach should be seen as complementary to traditional tests of equality in MSPE. Monte Carlo simulations indicate that our test has good size and power. Finally, we illustrate the use of our test in an empirical exercise in which we compare two different inflation forecasts for a sample of OECD economies. We find more rejections of the null of equal correlations than rejections of the null of equality in MSPE. 
Keywords:  Forecasting, timeseries, outofsample evaluation, mean squared prediction error, correlations. 
JEL:  C52 C53 E31 E37 F37 G17 
Date:  2022–02–16 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:112014&r= 
By:  José Luis Montiel Olea (Columbia University); Mikkel PlagborgMøller (Princeton University); Eric Qian (Princeton University) 
Abstract:  Two recent strands of the literature on Structural Vector Autoregressions (SVARs) use higher moments for identification. One of them exploits independence and nonGaussianity of the shocks; the other, stochastic volatility (heteroskedasticity). These approaches achieve point identification without imposing exclusion or sign restrictions. We review this work critically, and contrast its goals with the separate research program that has pushed for macroeconometrics to rely more heavily on credible economic restrictions and institutional knowledge, as is the standard in microeconometric policy evaluation. Identification based on higher moments imposes substantively stronger assumptions on the shock process than standard secondorder SVAR identification methods do. We recommend that these assumptions be tested in applied work. Even when the assumptions are not rejected, inference based on higher moments necessarily demands more from a finite sample than standard approaches do. Thus, in our view, weak identification issues should be given high priority by applied users. 
Keywords:  Structural Vector Autoregressions, macroeconometrics 
JEL:  C01 C10 
Date:  2021–08 
URL:  http://d.repec.org/n?u=RePEc:pri:econom:202124&r= 