Essays on Macroeconomic Dynamics: Exploring the linkages between real and financial sector

Almost a decade after the beginning of the Great Recession advanced economies are desperately searching for sustained growth prospects. Although expressing the most brilliant performance among western countries, even the US economic record is far from being fully satisfactorily, and the secular stagnation debate also entered the presidential campaign. Hysteresis, persistent slumps, debt overhang, balance sheet recessions and global savings glut are, together with the above mentioned secular stagnation hypothesis, competing (and often complementary) diagnoses to tackle with the issues of declining US productivity growth, boom and bust cycles and slow recoveries. Financial markets are key for explaining the Great Recession and the subsequent disappointing recovery. If the role of financial markets is a distinct feature of all post mid 80s recessions, dramas in the wake of deep financial crises involve dramatic deleveraging, high fiscal costs and long lasting and painful recessions. In this respect, coupling existing empirical evidence and new developments in macroeconomic theory with earlier insights, the macroeconomics of boom and bust cycles and persistent slumps seems to be sensibly captured by regime switching models. The present dissertation explores some of the patterns characterizing the US post WWII macroeconomic dynamics employing regime switching econometric techniques, with a focus upon the linkages between financial markets and real economy. This task is accomplished throughout a broad review of the literature and three different applications. In Chapter 2 we review the different research avenues which have inspired this work. In particular, the chapter is divided into three parts, so as to discuss the main contributions about i) the finance-growth nexus in the long-run; ii) the action of financial markets in shaping short- to medium-term fluctuations in the macroeconomy; iii) the policy implications stemming from the complex trade-offs generated by such relations. The aim of the chapter is to give an insight about how complex and deeply intertwined are the mechanisms at work when financial markets kick in. This is done by highlighting how the distinction between long-run and shorter horizons gets blurred once financial development does not induce financial deepening. Thus, although existing theories and empirics do not support the old idea that, to say it with Joan Robinson, ``where enterprise leads finance follows''; the finance and growth nexus is at the same time characterized by a bright side -- finance spurs growth -- and a dark side -- financial crises dampen growth -- as well. The policy implications of this line of reasoning are demanding both in order to prevent financial crises ex ante and to clean the ensuing mess ex post. Our journey goes on with Chapter 3 in which we investigate the responses of GDP and employment with respect to technology shocks in a multi-regime framework. Although this application may look far with respect to the focus of our broad research project, it substantially contributes to it in, at least, two fundamental ways, as it will be clear in a short while. We estimate over several time spans (the longest being 1957:1-2011:4) different threshold vector autoregressions (TVAR) in which the threshold variable is constituted by GDP annualized growth and we assess the paths of generalized impulse response functions in order to judge whether tech shocks differently affect hours and output according to the state of the economy. Finally, consistently with the prevailing literature, we assess whether the relation is time dependent and investigate some plausible explanations, among which the role of financial markets is stressed. In a nutshell, our results are the following. First, technology shocks while spurring GDP growth, display a negative effect on hours worked at least on impact, independently of the state of the economy. Moreover, within the main sample the effects on hours are persistently abundantly negative in low growth periods whereas they do not turn out to be significantly different from zero in good times in the long-run. In this respect our work generalizes the existing literature: technology shocks spur economic growth without impinging employment in good times, whereas in recessions the effect on employment is strongly negative and that on GDP is negligible. We also find that the dynamics of hours worked in the aftermath of a technology shock is mostly due to the response of the number of employees rather than hours per worker. Adjustments via the extensive margin appears then more relevant than those occurring along the intensive margin. However, the effects on aggregate demand depend on the sampled period, with a higher impact during the Great Moderation and the Great Recession that followed it. Moreover, the impact of technology shocks in good or bad times depends on the chosen sample. Indeed, tech shocks exert a stronger negative effect on employment prior the mid 80s in low growth periods, whereas the opposite holds with the beginning of the Great Moderation and it is preserved with the Great Recession: the long-run response of hours worked turns positive in recessions. Thus, our analysis generalizes the existing evidence about the milder effects of tech shocks on employment during the Great Moderation. First, we show that such result is due to the higher effects exerted in low growth periods. Moreover, we demonstrate that the Great Recession doesn't appear to change the results. We also show that the observed switch is more due to the extensive margins (increased number of employees) rather than to the intensive margin (employees working harder). Finally, as to the transmission channels at work, although we are not provided with a definitive answer regarding the reasons for such a regime shift, we document several changes in the responses of different relevant economic time series which are consistent with some of the proposed explanations. In particular, we stress the role of investment, credit markets and stabilization policies. What about the contribution of chapter with respect the main focus of our analysis here? The work explores some of the most studied relations in describing macroeconomic dynamics -- those between technology, output and employment -- and assess how they have changed over time with special regard to macroeconomic regimes and transformations of the structure of the economy. As to the latter, financial markets emerge as a key actor in determining a broad shift in the transmission mechanisms of technology shocks. These two features are enlightening with respect to the analysis that follows. First, Chapter 3 shows that we cannot sensibly analyze post WWII US economy without distinguishing the pre Volcker era with respect to the post mid 80s period. Second, it also shows that financial markets may have played a pivotal role in shaping this transformation. In the light of this findings, the applications which follow characterize the linkages between financial markets and real economy by concentrating upon the post mid 80s period; and extend the main ideas explored in Chapter 3 to consider their contribution as a further source of macroeconomic regime switching. In Chapter 4 we investigate financial fluctuations vis-à-vis the business cycle within the US economy. In particular, we propose a set of intertwined exercises whose main goals consist in assessing i) the behavior of several items within the balance sheets of different financial institutions and credit spreads with respect to GDP so as to uncover their properties over the business cycle; ii) the role of financial markets in shaping business cycle fluctuations throughout the dynamic response of the economy to financial shocks. In this respect, our contribution is twofold. On the one hand, we offer a Stock and Watson (1999)-like set of stylized facts about the relation between financial and business cycle fluctuations. In particular, we look at how macro-financial aggregates, the items within the balance sheets of different financial as well as nonfinancial institutions and credit spreads do interact with output at business cycle frequencies, throughout the exploitation of cross-correlations and a battery of Granger causality as well as of Johansen cointegration tests. On the other hand, we explore the parameter space of several univariate and multivariate models in search of the effects of financial frictions on economic performances, looking at the impulse response functions generated by a great deal of vector autoregressions and paying attention to potential nonlinearities stemming from conditions of financial markets. This latter exercise is performed throughout the estimation of different threshold autoregressions (TAR) involving relevant economic indicators (e.g., GDP; the unemployment rate; private aggregate demand components). According to our results, financial markets play a pivotal role in shaping GDP dynamics and its components in the US. More precisely, whereas most of the examined credit aggregates and financial items tend to move with lags with respect to the business cycle acting mostly as amplifiers, credit spreads display a counter-cylical dynamics and tend to lead fluctuations demonstrating a fairly high predictive content. Moreover, many items within the balance sheets of financial businesses do show a good predictive content and, in general, financial aggregates demonstrate a far more nervous behavior than GDP, whose fluctuations are generally characterized by a lower dispersion. Furthermore, it is not the case that financial institutions are all alike, as the dynamics of the items within the balance sheets of depository institutions displays different patterns with respect to that of investment banks and other institutions involved in shadow banking activity. As to the role of financial markets either in directly shocking the economy or in amplifying other shocks, our results support the key insights from standard macroeconomic models with financial frictions. In particular, shocks to credit spreads severely hit real economy whereas, throughout both the spreads and other relevant credit aggregates, negative shocks are dramatically amplified by financial markets. Moreover, in line with recent research, long-run effects due a deceleration of technological change are also uncovered. Furthermore, in the aftermath of financial shocks, financial as well as nonfinancial institutions actively adjust their balance-sheets and ``flight-to-quality'' and ``flight-to-liquidity'' emerge. Finally, financial markets seem to constitute a relevant source of nonlinearities within the economic system. More precisely, during “tight” credit periods, output and most of its main components demonstrate a higher persistence and therefore a lower attitude to reabsorb negative shocks. Finally, in Chapter 5 we estimate a Threshold Vector Autoregression model to study how the effects of fiscal policy can be amplified or dampened according the state of credit markets. More precisely, we conjecture that fiscal policies should be more successful in stimulating output in regimes where the financial accelerator leads to “tight” credit conditions, which increase the difficulties of firms to finance their investment and production activities forcing them to curb their employment. We estimate a two credit-market regime TVAR model in first differences on US quarterly data for the period 1984-2010. In our analyses, we proxy non-linearities resulting from credit conditions using as threshold variable the spread between the BAA-rated corporate bond yield and the 10-year treasury constant maturity rate. We find that the responses of output to fiscal policies significantly change according to the state of credit markets. Whenever the economy is in the “tight” credit regime, the GIRFs display a strong and persistent reaction of output to fiscal policy shocks. On the contrary, the response of GDP to fiscal policies is much milder when the economy experiences ``normal'' credit conditions. The different patterns exhibited by the GIRFs in the two credit regimes are reinforced by the computation of fiscal multipliers. When firms face increasing financing costs, multipliers are much higher than one at different time horizons. Conversely, the multipliers are much weaker --- usually lower than one --- when the external finance premium is reducing. A battery of t-tests applied on the bootstrapped distributions of GIRFs confirm that fiscal multipliers are significantly different in the two credit regimes. We test the robustness of our results to four potential issues concerning: i) the specification of the model (first differences vs. levels); ii) the presence of expectations about fiscal policies not already absorbed by the model (i.e., fiscal foresight); iii) the adoption of a different threshold variables; iv) alternative measures of output, fiscal and monetary variables and different sample periods. In all cases, we find that our main results robustly hold

A TVAR Approach ∗ †

In the present work we investigate how the state of credit markets non-linearly affects the impact of fiscal policies. We estimate a Threshold Vector Autoregression (TVAR) model on U.S quarterly data for the period 1984-2010. We employ the spread between BAA-rated corporate bond yield and 10-year treasury constant maturity rate as a proxy for credit conditions. We find that the response of output to fiscal policy shocks are stronger and more persistent when the economy is in the “tight ” credit regime. The fiscal multipliers are abundantly and persistently higher than one when firms face increasing financing costs, whereas they are feebler and often lower than one in the “normal ” credit regime. On the normative side, our results suggest policy makers to carefully plan fiscal policy measures according t

Macroeconomic Regimes, Technological Shocks and Employment Dynamics

Ferraresi T, Roventini A, Semmler W. Macroeconomic Regimes, Technological Shocks and Employment Dynamics. JAHRBUCHER FUR NATIONALOKONOMIE UND STATISTIK. 2019;239(4):599-625.The debate about the impact of technology on employment has always had a central role in economic theory. At the same time, the nexus of technological progress and employment might depend on macroeconomic regimes. In this work we investigate the interrelations among technology, output and employment in the U.S. economy in growth recessions vs. growth expansions. More precisely, using U.S. data we estimate different threshold vector autoregressions (TVARs) with TFP, hours, and GDP, employing the latter as threshold variable, and assess the generalized impulse responses of GDP and hours as to TFP shocks. For our entire period of observation, 1957Q1-2011Q4, positive technology shocks, while spurring GDP growth, by and large, display a negative effect on hours worked in growth recessions, but they are not significantly different from zero in good times. Yet, since the mid eighties (1984Q1-2011Q4) productivity shocks increase hours worked in low growth periods. The results are mainly driven by the response of labor along the extensive margin (number of employees), and remain persistent so in the face of a battery of robustness checks

Macroeconomic Regimes, Technological Shocks and Employment Dynamics

In this work we investigate the interrelations among technology, output and employment in the different states of the U.S. economy (recessions vs. expansions). More precisely, we estimate different threshold vector autoregression (TVAR) models with TFP, hours, and GDP, employing the latter as threshold variable, and we assess the ensuing generalized impulse responses of GDP and hours as to TFP shocks. We find that positive productivity shocks, while spurring GDP growth, display a negative effect on hours worked at least on impact, independently of the state of the economy. In the 1957-2011 period, the effects of productivity shocks on employment are abundantly negative in downturns, but they are not significantly different from zero in good times. However, the impact of TFP shocks in different business cycle regimes depends on the chosen sample: after the mid eighties (1984-2011), productivity shocks increase hours during recessions. Finally, we express and test some conjectures that might have caused the changes in the responses in different time periods

Assessing the Economic Impact of Lockdowns in Italy: A Computational Input-Output Approach

We build a novel interregional computational input-output model to assess the economic impact of lockdowns in Italy. Lockdowns are modeled as shocks to labor supply, calibrated on regional and sectoral employment data coupled with the prescriptions of government decrees. When estimated on data from the first lockdown, our model closely reproduces the observed economic dynamics during spring 2020. We also show that the model delivers a good out-of-sample performance during fall and winter 2020 and demonstrate that it can be used to analyze counterfactual scenarios

A prognostic score from a multicentric retrospective analysis of patients affected by sarcoma with metachronous lung metastases undergoing metastasectomy

BackgroundDespite the lack of evidence-based on prospective randomized studies, surgery has become the cornerstone of the treatment in patients with pulmonary oligometastatic sarcomas. Our study aimed to construct a composite prognostic score for metachronous oligometastatic sarcoma patients. MethodsA retrospective analysis was performed on data patients who underwent radical surgery for metachronous metastases in six research institutes from January 2010 to December 2018. The log-hazard ratio (HR) obtained from the Cox model was used to derive weighting factors for a continuous prognostic index designed to identify differential outcome risks. ResultsA total of 251 patients were enrolled in the study. In the multivariate analysis, a longer disease-free interval (DFI) and a lower neutrophil-to-lymphocytes ratio (NLR) were predictive of a better overall survival (OS) and disease-free survival (DFS). A prognostic score was developed based on DFI and NLR data, identifying 2 risk class groups for DFS (3-years DFS 20.2% for the high-risk group [HRG]and 46.4% for the low-risk group [LRG] [<0.0001]) and 3 risk groups for OS (3 years OS 53.9% for the HRG vs. 76.9% for the intermediate-risk group and 100% of the LRG (p < 0.0001)). ConclusionThe proposed prognostic score effectively predicts outcomes for patients with lung metachronous oligo-metastases from the surgically treated sarcoma