Jaime Martinez-Martin

Real-time forecasting US GDP from small-scale factor models

We show that the single-index dynamic factor model developed by Aruoba and Diebold (Am Econ Rev, 100:20-24, 2010) to construct an index of US business cycle conditions is also very useful for forecasting US GDP growth in real time. In addition, we adapt the model to include survey data and financial indicators. We find that our extension is unequivocally the preferred alternative for computing backcasts. In nowcasting and forecasting, our model is able to forecast growth as well as AD and better than several baseline alternatives. Finally, we show that our extension could also be used to infer US business cycles with great accuracy.

Breaking Down World Trade Elasticities: A Panel ECM Approach

This paper exhaustively analyses the recent decline of international trade elasticities to output growth. We extend an empirical model of import demand functions to account not only for transitory factors, such as relative prices and import intensity-adjusted measures of demand (I-O Tables), but also for habitually neglected permanent factors such as protectionism, vertical integration (i.e. Global Value Chains) and foreign direct investment (FDI). Dealing with a non-stationary heteregenous panel of 27 countries, we estimate a panel Error Correction Model from 1960 to 2015 in order to break down world trade elasticities. Our main fi ndings evidence: i) the presence of panel (cointegrating) structural changes in the trade-to-GDP relationship in 2000 and 2009, private consumption being a source of disruption; ii) although investment and exports are the most sensitive, import-intensive components of demand, this is far from being transitory, which is clearly weighing on the current slowdown; iii) the relevant contribution of GVCs shows a procyclical pattern, questioning the permanent nature of the current levelling-off of vertical integration processes. The lack of progress in reducing import tariffs and the usual discarded, complementary relationship between FDI and imports have a residual role. All in all, our results have substantial policy implications, as they reinforce the idea of a historical break towards a new ‘normal’ trading phase.

Understanding the Weakness in Global Trade - What is the New Normal?

Global trade has been exceptionally weak over the past four years. While global trade grew at approximately twice the rate of GDP prior to the Great Recession, the ratio of global trade to GDP growth has declined to about unity since 2012. This paper assesses to what extent the change in the relationship between global trade and global economic activity is a temporary phenomenon or constitutes a lasting change. It finds that global trade growth has been primarily dampened by two factors. First, compositional factors, including geographical shifts in economic activity and changes in the composition of aggregate demand, have weighed on the sensitivity of trade to economic activity. Second, structural developments, such as waning growth in global value chains, a rise in non-tariff protectionist measures and a declining marginal impact of financial deepening, are dampening the support from factors that boosted global trade in the past. Notwithstanding the particularly pronounced weakness in 2015 that is assessed to be mostly a temporary phenomenon owing to a number of country-specific adverse shocks, the upside potential for trade over the medium term appears to be limited. The JEL Classification: F10, F13, F14, F15

What are the Drivers of TFP Growth? An Empirical Assessment

This chapter builds upon the related research that grapples with determinants of TFP, as the driving force of potential growth. In particular, we empirically estimate, in a homogenous and systematic manner, cross-country contributions of cyclical and structural determinants of aggregate TFP growth. Under a growth accounting framework, we compute TFP growth estimates for 41 economies over the 1992–2014 period. After selecting its main drivers by means of a Bayesian Model Averaging (BMA) approach, we exploit panel estimates to conclude that a substantial share of the growth underperformance in recent years was related to cyclical factors, mainly the output gap, but also: (i) over-indebtedness for advanced economies; and (ii) the decline in commodity prices for commodity exporters. In addition, the growth of IT capital and the convergence towards the technological frontier appear to be significant structural drivers of TFP productivity growth in emerging market economies.

TFP Growth and Commodity Prices in Emerging Economies

In this paper we aim at empirically testing cross-country impacts of commodity price shocks to aggregate TFP growth for a sample of emerging economies. Under a growth accounting framework, we estimate country-specific TFP growth (1992-2014) and select the attendant robust determinants by means of a Bayesian Model Averaging (BMA) approach. To identify the effects of structural shocks, we propose a Bayesian panel VAR model and calculate cyclically adjusted TFP growth net of demand shocks (i.e. output gap) and commodity prices. Our results suggest that: i) the relationship of commodity prices to TFP growth has been very high in small commodity-exporting economies (i.e. an increase of 10% in commodity prices is associated with a sizable expansion of TFP growth in a year for an average commodity exporter); ii) although our evidence is not suf?cient to empirically distinguish among theoretical explanations, our results favour an interpretation that weights short-term effects of commodity prices on productivity, either through transitional dynamics to the manufacturing sector or through mismeasurement of TFP; and iii) cyclically adjusted TFP growth highlights the importance of negative supply shocks in commodity-exporting countries. All in all, much of the increase in TFP growth in the last decade was related to a favourable cyclical environment, a result with potentially significant policy implications for commodity-dependent economies.

Monitoring the World Business Cycle

We propose a Markov-switching dynamic factor model to construct an index of global business cycle conditions, to perform short-term forecasts of world GDP quarterly growth in real time and to compute real-time business cycle probabilities. To overcome the real-time forecasting challenges, the model accounts for mixed frequencies, for asynchronous data publication and for leading indicators. Our pseudo real-time results show that this approach provides reliable and timely inferences of the world quarterly growth and of the world state of the business cycle on a monthly basis.