Geoadditive Models for Regional Count Data: An Application to Industrial Location
Davide Castellani
ERSA conference papers,
2012
Abstract
We propose a geoadditive negative binomial model (Geo-NB-GAM) for regional count data which allows us to simultaneously address some important methodological issues, such as spatial clustering, nonlinearities and overdispersion. We apply this model to study location determinants of inward greenfield investments occurred over the 2003-2007 period in 249 European regions. The inclusion of a geoadditive component (a smooth spatial trend surface) permits us to control for spatial unobserved heterogeneity which induces spatial clustering. Allowing for nonlinearities reveals, in line with theoretical predictions, that the positive effect of agglomeration economies fades as the density of economic activities reaches some limit value. However, no matter how dense the economic activity becomes, our results suggest that congestion costs would never overcome positive agglomeration externalities.
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Macroeconomic Factors and Micro-Level Bank Risk
Claudia M. Buch
Bundesbank Discussion Paper 20/2010,
2010
Abstract
The interplay between banks and the macroeconomy is of key importance for financial and economic stability. We analyze this link using a factor-augmented vector autoregressive model (FAVAR) which extends a standard VAR for the U.S. macroeconomy. The model includes GDP growth, inflation, the Federal Funds rate, house price inflation, and a set of factors summarizing conditions in the banking sector. We use data of more than 1,500 commercial banks from the U.S. call reports to address the following questions. How are macroeconomic shocks transmitted to bank risk and other banking variables? What are the sources of bank heterogeneity, and what explains differences in individual banks’ responses to macroeconomic shocks? Our paper has two main findings: (i) Average bank risk declines, and average bank lending increases following expansionary shocks. (ii) The heterogeneity of banks is characterized by idiosyncratic shocks and the asymmetric transmission of common shocks. Risk of about 1/3 of all banks rises in response to a monetary loosening. The lending response of small, illiquid, and domestic banks is relatively large, and risk of banks with a low degree of capitalization and a high exposure to real estate loans decreases relatively strongly after expansionary monetary policy shocks. Also, lending of larger banks increases less while risk of riskier and domestic banks reacts more in response to house price shocks.
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Does It Pay to Have Friends? Social Ties and Executive Appointments in Banking
Allen N. Berger, Thomas Kick, Michael Koetter, Klaus Schaeck
Journal of Banking and Finance,
No. 6,
2013
Abstract
We exploit a unique sample to analyze how homophily (affinity for similar others) and social ties affect career outcomes in banking. We test if these factors increase the probability that the appointee to an executive board is an outsider without previous employment at the bank compared to being an insider. Homophily based on age and gender increase the chances of the outsider appointments. Similar educational backgrounds, in contrast, reduce the chance that the appointee is an outsider. Greater social ties also increase the probability of an outside appointment. Results from a duration model show that larger age differences shorten tenure significantly, whereas gender similarities barely affect tenure. Differences in educational backgrounds affect tenure differently across the banking sectors. Maintaining more contacts to the executive board reduces tenure. We also find weak evidence that social ties are associated with reduced profitability, consistent with cronyism in banking.
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Effects of Incorrect Specification on the Finite Sample Properties of Full and Limited Information Estimators in DSGE Models
Sebastian Giesen, Rolf Scheufele
Abstract
In this paper we analyze the small sample properties of full information and limited information estimators in a potentially misspecified DSGE model. Therefore, we conduct a simulation study based on a standard New Keynesian model including price and wage rigidities. We then study the effects of omitted variable problems on the structural parameters estimates of the model. We find that FIML performs superior when the model is correctly specified. In cases where some of the model characteristics are omitted, the performance of FIML is highly unreliable, whereas GMM estimates remain approximately unbiased and significance tests are mostly reliable.
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Money and Inflation: Consequences of the Recent Monetary Policy
Makram El-Shagi, Sebastian Giesen
Journal of Policy Modeling,
No. 4,
2013
Abstract
We use a multivariate state space framework to analyze the short run impact of money on prices in the United States. The key contribution of this approach is that it allows to identify the impact of money growth on inflation without having to model money demand explicitly.
Using our results, that provide evidence for a substantial impact of money on prices in the US, we analyze the consequences of the Fed's response to the financial crisis. Our results indicate a raise of US inflation above 5% for more than a decade. Alternative exit strategies that we simulate cannot fully compensate for the monetary pressure without risking serious repercussions on the real economy. Further simulations of a double dip in the United States indicate that a repetition of the unusually expansive monetary policy – in addition to increased inflation – might cause growth losses exceeding the contemporary easing of the crisis.
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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows retracing the driving forces of the forecast and hence enables the interpretability of the forecast outcome.
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Financial Factors in Macroeconometric Models
Sebastian Giesen
Volkswirtschaft, Ökonomie, Shaker Verlag GmbH, Aachen,
2013
Abstract
The important role of credit has long been identified as a key factor for economic development (see e.g. Wicksell (1898), Keynes (1931), Fisher (1933) and Minsky (1957, 1964)). Even before the financial crisis most researchers and policy makers agreed that financial frictions play an important role for business cycles and that financial turmoils can result in severe economic downturns (see e.g. Mishkin (1978), Bernanke (1981, 1983), Diamond (1984), Calomiris (1993) and Bernanke and Gertler (1995)). However, in practice researchers and policy makers mostly used simplified models for forecasting and simulation purposes. They often neglected the impact of financial frictions and emphasized other non financial market frictions when analyzing business cycle fluctuations (prominent exceptions include Kiyotaki and Moore (1997), Bernanke, Gertler, and Gilchrist (1999) and Christiano, Motto, and Rostagno (2010)). This has been due to the fact that most economic downturns did not seem to be closely related to financial market failures (see Eichenbaum (2011)). The outbreak of the subprime crises ― which caused panic in financial markets and led to the default of Lehman Brothers in September 2008 ― then led to a reconsideration of such macroeconomic frameworks (see Caballero (2010) and Trichet (2011)). To address the economic debate from a new perspective, it is therefore necessary to integrate the relevant frictions which help to explain what we have experienced during recent years.
In this thesis, I analyze different ways to incorporate relevant frictions and financial variables in macroeconometric models. I discuss the potential consequences for standard statistical inference and macroeconomic policy. I cover three different aspects in this work. Each aspect presents an idea in a self-contained unit. The following paragraphs present more detail on the main topics covered.
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Natural-resource or Market-seeking FDI in Russia? An Empirical Study of Locational Factors Affecting the Regional Distribution of FDI Entries
K. Gonchar, Philipp Marek
HSE Working Papers, Series: Economics, WP BRP 26/EC/2013,
2013
Abstract
This paper analyzes the spatial distribution of foreign direct investment (FDI) across regions in Russia. Our analysis employs data on Russian firms with a foreign investor during the 2000-2009 period and links regional statistics in the conditional logit model. The main findings are threefold. First, we conclude that market-related factors and the availability of natural resources are important factors in attracting FDI. Second, existing agglomeration economies encourage foreign investors. Third, the findings imply that service-oriented FDI co-locates with extraction industries in resource-endowed regions.
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Towards a Theory of Climate Innovation - A Model Framework for Analyzing Drivers and Determinants
Wilfried Ehrenfeld
Journal of Evolutionary Economics,
2013
Abstract
In this article, we describe the results of a multiple case study on the indirect corporate innovation impact of climate change in the Central German chemical industry. We investigate the demands imposed on enterprises in this context as well as the sources, outcomes and determining factors in the innovative process at the corporate level. We argue that climate change drives corporate innovations through various channels. A main finding is that rising energy prices were a key driver for incremental energy efficiency innovations in the enterprises’ production processes. For product innovation, customer requests were a main driver, though often these requests are not directly related to climate issues. The introduction or extension of environmental and energy management systems as well as the certification of these are the most common forms of organizational innovations. For marketing purposes, the topic of climate change was hardly utilized so far. As the most important determinants for corporate climate innovations, corporate structure and flexibility of the product portfolio, political asymmetry regarding environmental regulation and governmental funding were identified.
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The GVAR Handbook: Structure and Applications of a Macro Model of the Global Economy for Policy Analysis
Filippo di Mauro, M. Hashem Pesaran
Oxford University Press,
2013
Abstract
The recent crisis has shown yet again how the world economies are globally interlinked, via a complex net of transmission channels. When it comes, however, to build econometric frameworks aimed at analysing such linkages, modellers are faced with what is called the "curse of dimensionality": there far too many parameters to be estimated with respect to the available observations. The GVAR, a VAR based model of the global economy, offers a solution to this problem. The basic model is composed of a large number of country specific models, comprising domestic, foreign and purely global variables. The foreign variables, however, are treated as weakly exogenous. This assumption, which is typically held when empirically tested for virtually all economies - with the notable exception of the US which is treated differently - allows to estimate first the individual country models separately. Only in a second stage country-specific models are simultaneously solved, thus allowing global interactions.This volume presents - for a first time in a compact and rather easy to read format - principles and structure of the basic GVAR model and a number of its many applications and extensions developed in the last few years by a growing literature. Its main objective is to show how powerful the model can be as a tool for forecasting and scenario analysis. The clear modelling structure of the GVAR appeals to policy makers and practitioners as shown by its growing use among major institutions, as well as by econometricians, as shown by the main extensions and applications.
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