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on Econometrics |
| By: | Nan Liu (Xiamen University); Yanbo Liu (Shandong University); Peter C.B. Phillips (Yale University, University of Auckland, Singapore Management University); Yajie Zhang (Singapore Management University) |
| Abstract: | Predictive regression models are often used to evaluate the predictive capability of economic fundamentals on bond and equity returns. Inferential procedures in these regressions typically employ parameter constancy or piecewise constancy in slope coefficients. Such formulations are prone to misspecification, more especially during periods of disturbance or evolution in prevailing economic and financial conditions, which can lead to size distortion and spurious evidence of predictability. To address these issues the present work proposes a semiparametric predictive regression model with mixed-root regressors and time-varying coefficients that allow for smooth evolution in the generating mechanism over time. For estimation and inference a novel variant of the self-generated instrument approach called Sieve-IVX is introduced, giving a robust approach to inference concerning time-varying predictability that is applicable irrespective of the degrees of persistence. Asymptotic theory of the Sieve-IVX approach is provided together with both pointwise and uniform inference procedures for testing predictability and model specification. Simulations show excellent performance characteristics of these statistics in finite samples. An empirical exercise is conducted to examine excess S&P 500 returns, \ applying Sieve-IVX regression coupled with pointwise and uniform tests to reveal evidence of time-varying patterns in the predictive capability of commonly used fundamental variables. |
| Date: | 2025–03–13 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2431 |
| By: | Jang, Tae-Seok; Sacht, Stephen |
| Abstract: | Contrary to claims in studies on financial economics, a sparse database often obscures the identification of parameters in macroeconomic models. These identification problems originate from the poorly defined mapping between a structural model and reduced-form parameters. Hence, researchers rely on prominent estimation methods, such as Bayesian approaches, which require sound knowledge of prior distributions on parameters. These approaches, however, are characterized by a flat likelihood and/or a posterior distribution driven mainly by prior information. To alleviate identification issues, we apply approximate Bayesian computation combined with the choice of specific moment conditions. This estimation approach not only allows for circumventing high dimensional likelihood functions but also avoids parameter identification problems given the use of a bootstrap method. Our estimation method is successfully applied to a hybrid version of the New Keynesian model. |
| Abstract: | Entgegen den Behauptungen in Studien zur Finanzökonomie erschwert eine spärliche Datenbasis oft die Identifizierung von Parametern in makroökonomischen Modellen. Diese Identifizierungsprobleme entstehen durch die schlecht definierte Abbildung zwischen einem strukturellen Modell und dessen Parametern in reduzierter Form. Daher greifen Forscher auf bekannte Methoden wie die Bayesianische Schätzung zurück, die eine fundierte Kenntnis der a-priori-Verteilungen der Parameter erfordert. Solche Ansätze zeichnen sich jedoch durch eine flache Likelihood-Verteilung und/oder eine, hauptsächlich durch a-priori-Informationen getriebene, posteriore Verteilung aus. Um Identifizierungsprobleme zu mildern, verwenden wir in diese Studie die Approximative Bayesianische Berechnungsmethode in Kombination mit der Wahl spezifischer Momente-Bedingungen. Dieser Schätzansatz ermöglicht nicht nur die Umgehung hochdimensionaler Likelihood-Funktionen, sondern vermeidet durch die Verwendung einer Bootstrap-Methode auch Probleme bei der Parameteridentifizierung. Unsere Schätzmethode wird erfolgreich auf eine hybride Version des Neukeynesianischen Modells angewendet. |
| Keywords: | Approximate Bayesian Computation, Identification, Moment Conditions, New-Keynesian model |
| JEL: | C11 C14 E12 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:hwwiwp:315485 |
| By: | Peter C.B. Phillips (Yale University, University of Auckland, Singapore Management University) |
| Abstract: | Cointegrating rank selection is studied in a function space reduced rank regression where the data are time series of cross section curves. A semiparametric approach to rank selection is employed using information criteria suitably modified to take account of the function space context, extending the linear cointegrating model to accommodate cross section data under general forms of dependence. A parametric formulation is employed analogous to recent work on cross section curve autoregression and cointegrating regression. Consistent cointegrating rank estimation is developed by the use of information criteria methods that are extended to the curve time series environment. The asymptotic theory involves two parameter Gaussian processes that generalize the standard limit processes involved in cointegrating regressions with conventional multiple time series. Simulations provide evidence of the effectiveness of consistent rank selection by the BIC criterion and the tendency of AIC to overestimate order as it does in standard lag order selection in autoregression as well as in reduced rank regression with multiple time series. |
| Date: | 2025–03–15 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2432 |
| By: | Simon Freyaldenhoven; Shikun Ke; Dingyi Li; Jose Luis Montiel Olea |
| Abstract: | What does the Fed talk about in its monetary policy discussions? We introduce a new statistical methodology to analyze text documents, and we use that methodology to recover the topics discussed during FOMC meetings. Topic models are a simple and popular tool for the statistical analysis of textual data. Their identification and estimation are typically enabled by assuming the existence of anchor words; that is, words that are exclusive to specific topics. In this paper we show that the existence of anchor words is statistically testable: There exists a hypothesis test with correct size that has nontrivial power. This means that the anchor-words assumption cannot be viewed simply as a convenient normalization. Central to our results is a simple characterization of when a column-stochastic matrix with known nonnegative rank admits a separable factorization. We test for the existence of anchor words in two different datasets derived from monetary policy discussions in the Federal Reserve and reject the null hypothesis that anchor words exist in one of them. |
| Keywords: | Anchor Words; Topic Models; Nonnegative Matrix Factorization; Hypothesis Testing |
| JEL: | C39 C55 |
| Date: | 2025–03–19 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:99851 |
| By: | Ye Chen (Capital University of Economics and Business); Peter C.B. Phillips (Yale University, University of Auckland, Singapore Management University); Shuping Shi (Macquarie University) |
| Abstract: | To safeguard economic and financial stability policymakers regularly take actions designed to increase resilience to systemic risks and curb speculative market behavior. To assess the effectiveness of such mitigation policies, we introduce a counterfactual approach tailored to accommodate the mildly explosive dynamics that occur during speculative bubbles. We derive asymptotics of the estimated treatment effect under a common factor structure that allows for explosive, I(1), and stationary factors, thereby having applicability to a wide range of prevailing economic conditions. An inferential procedure is proposed for the policy treatment effect that has asymptotic validity and demonstrates satisfactory finite sample performance. An empirical analysis examines the monetary policy of interest rate hikes implemented by the Reserve Bank of New Zealand, beginning in October 2021.This policy exerted a statistically significant cooling effect on all regional housing markets in New Zealand. Our findings show that this policy led to 20%-33% reductions in house prices in five out of six regions seven months after the enactment of the interest rate hike. |
| Date: | 2025–03–17 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2433 |
| By: | Jens H. E. Christensen; Daan Steenkamp |
| Abstract: | This paper introduces a novel arbitrage-free dynamic term structure model that jointly accounts for liquidity and credit risk premia in panels of bond prices. While liquidity risk is bond-specific, credit risk is common across bonds and follows a square-root process to ensure nonnegativity and econometric identification. A simulation study confirms the separate identification of liquidity and credit risk. We apply the model to South African government bond prices and document the existence of large and weakly correlated liquidity and credit risk premia. This underscores that liquidity and credit stresses are distinct risks to bond investors. |
| Date: | 2025–01–09 |
| URL: | https://d.repec.org/n?u=RePEc:rbz:wpaper:11074 |
| By: | Webel, Karsten |
| Abstract: | The COVID-19 outbreak in 2020 has fostered in many countries the development of new weekly economic indices for the timely tracking of pandemic-related turmoils and other forms of rapid economic changes. Such indices often utilise information from daily and weekly economic time series that normally exhibit complex forms of seasonal behaviour. The latter dynamics were initially removed with ad hoc or experimental methods due to the urgent need of instant results and hence the lack of time for inventing and approving more sophisticated alternatives. This, never- theless, has in turn inspired recent developments of seasonal adjustment methods tailored to the specifics of infra-monthly time series. Although sound theoretical descriptions of these tailored methods are already available, their performance has not been evaluated empirically in great detail so far. To fill this gap, we consider real-time data vintages of several infra-monthly economic time series for Germany and analyse the cross-vintage stability of holiday-related deterministic pretreatment effects as well as the revisions in various concurrent signal estimates obtained with experimental STL-based and selected elaborate methods, such as the extended ARIMA model-based and X-11 approaches. Our main findings are that the tai- lored methods tend to outperform the experimental ones in terms of computational speed, that the considered pretreatment routines yield generally stable parameter estimates across data vintages, and that the extended ARIMA model-based approach generates the smallest and least volatile revisions in many cases. |
| Keywords: | extended ARIMA model-based approach, extended X-11 approach, Google trends, JDemetra+, real-time analysis, signal extraction, stability analysis, STL approach |
| JEL: | C01 C02 C14 C22 C40 C50 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:315494 |
| By: | Hilde C. Bjornland; Jamie L. Cross; Jonas Holz |
| Abstract: | This paper examines how central banks respond to supply-side shocks and investigates the trade-offs they face in stabilizing inflation and output. To do so we develop a dual external instrument proxy structural vector autoregressive (SVAR) model to disentangle the macroeconomic effects of oil supply news and monetary policy shocks. Our identification strategy, which combines multiple external instruments with sign restrictions, enables a sharp distinction between structural shocks, allowing us to analyze their dynamic effects and construct policy counterfactuals for different central bank objectives. We find that both oil supply and monetary policy shocks significantly influence U.S. output and inflation. Moreover, while monetary policy can mitigate some of the output losses caused by oil price shocks, it cannot fully offset their inflationary effects. Finally, we estimate that the Federal Reserve’s historical response aligns closely with a policy that places twice as much weight on inflation stabilization than on output stabilization. |
| Keywords: | proxy-SVAR, monetary policy instrument, oil price instrument, counterfactual analysis, monetary policy trade-offs |
| JEL: | C32 E31 E43 Q41 Q43 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2025-19 |