Research

PUBLICATIONS

“Dating housing booms fueled by credit: A Markov switching approach”

Journal of Financial Stability, vol. 78, June 2025, 101412. LINK

National Bank of Slovakia, WP series, n. 9/2023. LINK

Abstract: This study aims to empirically identify the state of the US housing market. I do so by estimating a Markov switching model of housing prices, in which mortgage debt affects house prices nonlinearly and drives state transition probabilities. Second, I compute a state-contingent housing risk measure fed with the probability of being in each state. Finally, I show that such risk measure contains early warning information in a forecasting exercise to predict the charge-off rates of real estate residential loans and a financial stress index. The significance of this study is that it informs economic agents and policymakers about the state of the housing market mechanically.

“The effects of macro uncertainty shocks in the euro area: a FAVAR approach” (with Arne Gieseck)

Empirical Economics, vol. 68, pp. 2829-2872, 2025. LINK

National Bank of Slovakia, WP series, n. 6/2024. LINK

Abstract: This paper estimates the effects of uncertainty shocks on a large set of economic and financial variables in the euro area. For this purpose, we first build a large monthly macro dataset with euro area-wide data, which we summarize by principal components. Second, we estimate a heteroskedastic factor-augmented vector autoregressive (FAVAR) model using a survey-based measure of macroeconomic uncertainty and a large dataset. Third, we identify five shocks by employing a new identification scheme based on sign restrictions exploiting our large dataset, including uncertainty shocks, financial shocks, standard monetary policy shocks, aggregate demand shocks, and supply shocks. Fourth, we show more than one hundred impulse responses to an uncertainty shock. In this setup, we find that an uncertainty shock has a significantly negative effect on economic activity measures in the euro area, but has no significant effect on savings and inflation. Moreover, uncertainty shocks trigger a contractionary effect on several measures of financial stability. Finally, we discuss the results and possible policy implications.

“Short-term forecasting housing investment: An averaging approach from a CEE country”

Eastern European Economics, 2025. LINK

Latest version: LINK Media: Global Housing Watch

Abstract: This paper uses a model averaging tool to conditionally predict housing investment in Slovakia up to three quarters ahead. Motivated by the significant modeling uncertainty, I employ a forecast averaging approach that involves two steps. First, it estimates many vector error-correction models (VECM) using a battery of long- and short-run determinants. Second, it applies subset selection based on in-sample and pseudo out-of-sample criteria. This approach produces forecasts of housing investment that widely beat a battery of powerful benchmark models, including VAR, FAVAR, Ridge, and LASSO models. Overall, the results of this study add to the literature showing the good performance of model averaging tools and provide guidance to practitioners modeling and forecasting housing investment in Slovakia and other countries.

“Forecasting housing investment” (with Gabe De Bondt and Arne Gieseck)

Journal of Forecasting, 2023, vol. 42, issue 3, SI: Advances in Forecasting in Macro and Financial Markets, pp. 543-565. LINK

ECB, Working Paper Series, no. 2023/2807. LINK Presented at the ESCB WGF (Working Group on Forecasting).

Abstract: This study applies a model averaging approach to conditionally forecast housing investment in the largest Euro area countries and the Euro area. To account for substantial modeling uncertainty, it estimates a large and diverse number of vector error correction models using a wide set of long- and short-run determinants and applies subset selection based on in-sample and out-of-sample criteria. First, a pseudo out-of-sample forecast exercise shows that our model averaging approach consistently beats a battery of distinguished benchmark models, including BVARs, FAVARs, LASSO, and Ridge regressions. This evidences that model averaging provides more accurate forecasts also in the case of housing investment. Second, we find remarkable cross-country heterogeneity in the drivers of housing investment. Overall, these findings guide forecasters and modelers on improving housing investment models and policymakers on implementing country-specific housing market policies.

WORKING PAPERS

“Non-linear effects of standard monetary policy shocks on housing: Evidence from a CESEE country” (with Adriana Lojschová and Alicia Aguilar)

Forthcoming as Banque centrale du Luxembourg, BdE and NBS working papers. Part of the ESCB ChaMP Research Network. Presented at the 4th workstream 1 workshop at BdP, and at the 21st ESCB Emerging Markets workshop at the OeNB.

Latest version (August 2025): LINK

Abstract: This paper estimates the effects of standard monetary policy shocks on housing and other macro variables in Slovakia, a CESEE country. For that purpose, we use a nonlinear local projection model which uncovers asymmetries in these effects around three different dimensions: high versus low economic growth, interest rates and inflation. The main findings in this study are as follows. First, we often find no evidence of standard monetary policy eliciting a contractionary response in house prices or housing investment. Second, evidence is weakest during recessions and periods of low interest rates or low inflation. Third, these findings may be linked to the inability of monetary policy to trigger significant contractionary effects on household lending, which in turn may be linked to the effective lower bound on interest rates, the predominance of fixed-rate mortgages in Slovakia, or interaction between monetary and macroprudential policy. We also provide a discussion on the possible country characteristics that might drive these results and policy implications.

PAUSED

“Forecasting business investment: time series vs machine learning models”

Presentation (2024): LINK

Abstract: The aim of this study is to deploy a tool to conditionally forecast business investment in Slovakia, the sixth most volatile in the euro area. Motivated by significant modeling uncertainty, I propose a model averaging tool that estimates a large and diverse number of VAR, VECM, and machine learning models using a wide set of long- and short-run determinants, as well as soft indicators. After applying subset selection based on in-sample and pseudo-out-of-sample criteria up to three quarters ahead, only a relatively few time series models beat the naïve benchmark AR(1). Alternatively, only seven machine learning (ML) models outperform such benchmark, with an RMSE forecast gain of up to 12 pp., where the top ML model beats the top VECM model by a relatively mild margin, while using much more data.

“The effects of monetary policy shocks in the euro area”

Third PhD chapter. Latest version (2021): LINK

Abstract: This paper estimates the effects of monetary policy and central bank information shocks to subcomponents of GDP and other key macroeconomic variables in the euro area. Additionally, I evaluate whether such effects have changed in the last two decades. To perform such analysis I use an extended version of the SVAR model of Jarocinski and Karadi (2020) and a Proxy-SVAR model. The main findings in this study are as follows. First, purely monetary policy shocks have significantly negative effects on consumption, housing and business investment, having the largest impact on the latter. By contrast, the effects on prices are quite modest, consistent with the literature. Finally, our evidence suggest that the effects of purely monetary policy shocks have changed over time in the euro area. In particular, while during the 2000s the effects are the standard contractionary ones, during the 2010s it seems that the capacity of the ECB to affect economic variables such as business and housing investment and unemployment has been critically weakened.

“Differences in the impact of unconventional monetary policy between the FED and the ECB with a DSGE Model”

Master thesis at the Barcelona GSE. Latest version (2014): LINK

Abstract: The aim of this study is to investigate and identify the differences in the impact of the unconventional monetary policy that the European Central Bank and the Federal Reserve conducted during the Great Recession and also between different intensities in credit policy. To do this, I consider the Gertler and Karadi (2011) DSGE model with financial frictions, calibrate the model to mimic the Euro Area characteristics, and compare the outcome of the model for the two economies in terms of Impulse Responses. With these tools, I want to investigate three issues. First, I would want to know what would have happen in the Euro Area if the ECB had implemented a credit policy with the FED’s intensity. Second, I want to know if the ECB would have had to be more aggressive in terms of credit policy. And third, I want to investigate if it’s necessary to be close to the zero lower bound for the central bank to effectively implement such a credit policy. I show that the FED’s credit policy intensity fictionally conducted by the ECB is able to further reduce the spread and the decline in output, but not enough to sustain price stability. Thus, a more aggressive conventional and unconventional monetary policy seems to be welfare improving. Moreover, I don’t find association between low interest rates and effectiveness of credit policy, so there seems to be room for the investigation of unconventional monetary policy implementation in presence of financial sector disruptions at higher interest rates than levels close to the zero lower bound.