[雙語(yǔ)翻譯]消費(fèi)信貸外文翻譯--金融自由化時(shí)代的消費(fèi)信貸--對(duì)被壓抑需求的過(guò)度反應(yīng)（英文）

1、This article was downloaded by: [Computing Giannone et al., 2010) and structural models (structural VAR, e.g. Chrystal and Mizen, 2005 or the financial block of a national macro-econometric model, see e.g. Fase et al.,

2、1992; Jeanfils, 2000, presenting central bank models for Belgium and the Netherlands, respectively). Alternatively, approaches based on micro-data entail theestimation of single equations that use information from bank l

3、ending surveys (see e.g. De Bondt et al., 2010; Hempell and Sorensen, 2010, both of which use panel data), survey data from samples of individual households (Fernandez-Corugedo and Muellbauer, 2006) or pub- lished data f

4、rom a panel of individual banks (Kashyap and Stein, 2000). Finally, micro-founded dynamic sto- chastic general equilibrium (DSGE) models have been developed (e.g. Darracq Paries et al., 2010; Rubaszek and Serwa, 2012) th

5、at model the banking sector with credit frictions. Most of the empirical work encountered in the literature investigates the determinants of credit to the private sector as a whole. Furthermore, due to the differences in

6、 behaviour and in financing constraints among the different sectors, one strand of the credit litera- ture focuses on explaining loans to businesses (e.g. Sorensen et al., 2009) separately from loans to households (e.g.

7、Fitzer and Reiss, 2008; Rubaszek and Serwa, 2012). The development of credit to the private sector is explained both by demand and supply-related variables. On the demand side, loan decisions by firms and house- holds ar

8、e based on their own balance sheet condition and available sources of external funds. The key factors in all demand specifications include the cost of credit (loan interest rate) and a measure of the level of transaction

9、s of households and/or businesses, captured by an eco- nomic activity variable, normally real GDP. On the sup- ply side, the ability and willingness of banks to extend loans is related to factors that influence their own

10、 funding conditions (relevant variables include bank equity, total assets, deposits and the cost of external financing), their capital position, the cost of alternative bank portfolio choices (e.g. the spread between the

11、 loan rate and the T-bill rate), competition from other banks and their per- ceptions of risk (macroeconomic variables, nonperform- ing loans). In the empirical literature, loans to the private sector are conventionally

12、modelled as a demand function (e.g. Hofmann, 2001; Calza et al., 2003). The simulta- neous estimation of a separate supply curve for loans is not deemed necessary in most studies when demand effects are likely, or simply

13、 assumed, to dominate supply effects. More importantly, the identification of a separate demand and supply curve is not always feasible, depend- ing on data availability, the choice of variables and the theoretical model

14、. Kakes (2000), Hulsewig et al. (2004) and Sorensen et al. (2009) are all studies which impose theoretical restrictions on a VECM in order to identify a loan demand and a loan supply function for the Netherlands, Germany

15、 and the euro area, respectively. The determination of separate loan supply and/or loan demand curves supports the existence of a bank lending channel, notably as stated in the Bernanke–Blinder model (1998). The Bernanke

16、–Blinder framework sug- gests that the channel operates well in economies in which market frictions exist (e.g. emerging markets),140 S. N. Brissimis et al.Downloaded by [Computing & Library Services, University of H