Dynamic quantile causal inference and forecasting

Ruzicka, Josef

Publication:
Dynamic quantile causal inference and forecasting

dc.contributor.advisor	Gonzalo, Jesús
dc.contributor.author	Ruzicka, Josef
dc.contributor.departamento	UC3M. Departamento de Economía	es
dc.contributor.funder	Ministerio de Educación, Cultura y Deporte (España)	es
dc.date.accessioned	2021-07-20T09:20:28Z
dc.date.available	2021-07-20T09:20:28Z
dc.date.issued	2021-04
dc.date.submitted	2021-06-28
dc.description.abstract	Standard impulse response functions measure the average effect of a shock on a response variable. However, different parts of the distribution of the response variable may react to the shock differently. The first chapter, “Quantile Structural Vector Autoregression”, introduces a framework to measure the dynamic causal effects of shocks on the entire distribution of response variables, not just on the mean. Various identification schemes are considered: shortrun and long-run restrictions, external instruments, and their combinations. Asymptotic distribution of the estimators is established. Simulations show our method is robust to heavy tails. Empirical applications reveal causal effects that cannot be captured by the standard approach. For example, the effect of oil price shock on GDP growth is statistically significant only in the left part of GDP growth distribution, so a spike in oil price may cause a recession, but there is no evidence that a drop in oil price may cause an expansion. Another application reveals that real activity shocks reduce stock market volatility. The second chapter, “Quantile Local Projections: Identification, Smooth Estimation, and Inference”, is devoted to an increasingly popular method to capture heterogeneity of impulse response functions, namely to local projections estimated by quantile regression. We study their identification by short-run restrictions, long-run restrictions, and external instruments. To overcome their excessive volatility, we introduce two novel estimators: Smooth Quantile Projections (SQP) and Smooth Quantile Projections with Instruments (SQPI). The SQPI inference is valid under weak instruments. We propose information criteria for optimal smoothing and apply the estimators to shocks in financial conditions and monetary policy. We demonstrate that financial conditions affect the entire distribution of future GDP growth and not just its lower part as previously thought. The third chapter, “Smooth Quantile Projections in a Data-Rich Environment”, modifies the estimator from the second chapter to construct distribution forecasting in a setting with potentially many variables. To this end we introduce a novel estimator, Smooth Quantile Projections with Lasso. The estimator involves two penalties, one controlling roughness of the forecasts over forecast horizons, while the other penalty selects the most informative set of predictors. We also introduce information criteria to guide the optimal choice of the two penalties and represent the problem as a linear program in standard form.	en
dc.description.degree	Programa de Doctorado en Economía por la Universidad Carlos III de Madrid	es
dc.description.responsability	Presidente: José Olmo Badenas.- Secretario: Victor Emilio Troster.- Vocal: Mario Alloza Frutos	es
dc.description.sponsorship	I gratefully acknowledge funding from the Ministerio de Educación, Cultura y Deporte through its grant Formación de Profesorado Universitario (FPU).	en
dc.identifier.uri	https://hdl.handle.net/10016/33112
dc.language.iso	eng	en
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España	*
dc.rights.accessRights	open access	es
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/	*
dc.subject.eciencia	Economía	es
dc.subject.other	Asymmetry	en
dc.subject.other	Causal inference	en
dc.subject.other	Impulse response	en
dc.subject.other	Instrumental variables	en
dc.subject.other	Quantile regression	en
dc.subject.other	Regularization	en
dc.subject.other	Shocks	en
dc.subject.other	Treatment effects	en
dc.subject.other	Vector autoregression	en
dc.title	Dynamic quantile causal inference and forecasting	en
dc.type	doctoral thesis	*
dspace.entity.type	Publication