Growth with heterogenous interdependence

We present a growth model with spatial interdependencies in the heterogeneous technological progress and the stock of knowledge that, under certain conditions, yields agrowth-initial equation that can be taken to the data. We then use data on EU-NUTS2 regions and a correlated random e ects specication to estimate the resulting spatial Durbin dynamic panel model with spatially weighted individual e ects. QML estimatessupport our model against simpler alternatives that impose a homogeneous technology. Also, our results indicate that rich regions tend to have higher (unobserved) productivityand are likely to stay rich because of the strong time and spatial dependence of the GDP per capita. Poor regions, on the other hand, tend to enjoy productivity spillovers but arelikely to stay poor unless they increase their saving rates.This research was funded by grants ECO2014-55553-P and ECO2016-78652 (Ministerio de Econom a y Competitividad), ECO2018-88888-P (AEI/FEDER, UE) and 2014FI B00301 (Agaur, Generalitat de Catalunya). We thank participants at the 59th ERSA Congress (Lyon), 21st INFER Annual Conference (Vrije Universiteit Brussel), UCLouvain Saint-Louis and the 6th Workshop in Industrial and Public Economics (Universitat Rovira i Virgili) for useful comments. Usual caveats apply

Factor extraction using Kalman filter and smoothing: this is not just another survey

Dynamic Factor Models, which assume the existence of a small number of unobservedlatent factors that capture the comovements in a system of variables, are the main "bigdata" tool used by empirical macroeconomists during the last 30 years. One importanttool to extract the factors is based on Kalman lter and smoothing procedures that cancope with missing data, mixed frequency data, time-varying parameters, non-linearities,non-stationarity and many other characteristics often observed in real systems of economicvariables. This paper surveys the literature on latent common factors extracted using Kalmanfilter and smoothing procedures in the context of Dynamic Factor Models. Signal extractionand parameter estimation issues are separately analyzed. Identi cation issues are also tackledin both stationary and non-stationary models. Finally, empirical applications are surveyedin both cases

Dynamic factor models: Does the specification matter?

Dynamic factor models (DFMs), which assume the existence of a small number of unobserved underlying factors common to a large number of variables, are very popular among empirical macroeconomists. Factors can be extracted using either nonparametric principal components or parametric Kalman filter and smoothing procedures, with the former being computationally simpler and robust against misspecification and the latter coping in a natural way with missing and mixed-frequency data, time-varying parameters, nonlinearities and non-stationarity, among many other stylized facts often observed in real systems of economic variables. This paper analyses the empirical consequences on factor estimation, in-sample predictions and out-of-sample forecasting of using alternative estimators of the DFM under various sources of potential misspecification. In particular, we consider factor extraction when assuming different number of factors and different factor dynamics. The factors are extracted from a popular data base of US macroeconomic variables, widely analyzed in the literature without consensus about the most appropriate model specification. We show that this lack of consensus is only marginally crucial when it comes to factor extraction, but it matters when the objective is out-of-sample forecasting.This study was funded by the Spanish National Research Agency, Ministry of Science and Technology (Grant Nos. PID2019-108079GB-C21/AIE/10.13039/501100011033 and PID2019-108079GBC22/AIE/10.13039/501100011033

Dynamic factor models: does the specification matter?

Dynamic Factor Models (DFMs), which assume the existence of a small number of unobserved underlying factors capturing the comovements in large systems of variables, are very popular among empirical macroeconomists to reduce dimension and to extract factors with an economic interpretation. Factors can be extracted using either non-parametric Principal Components (PC) or parametric Kalman filter and smoothing (KFS) procedures, with the former being computationally simpler and robust against misspecification and the latter being efficient if the specification is correct and coping in a natural way with missing and mixed-frequency data, time-varying parameters, non-linearities and non-stationarity among many other stylized facts often observed in real systems of economic variables. This paper analyses the empirical consequences on factor estimation and forecasting of using alternative extraction procedures and estimators of the DFM parameters under various sources of potential misspecification. In particular, we consider factor extraction when assuming different number of factors and different factor dynamics. The factors are extracted from a popular data base of US macroeconomic variables that has been widely analyzed in the literature without consensus about the most appropriate model speciffication. We show that this lack of consensus is ony marginally cruzial when it comes to factor extraction but it matters when the objective is forecasting