SERIEs(2010)1:51–65
DOI10.1007/s13209-010-0020-9
ORIGINAL ARTICLE
DSGE models and their use at the ECB
Frank Smets·Kai Christoffel·Günter Coenen·
Roberto Motto·Massimo Rostagno
Received:16October2009/Accepted:09December2009/Published online:23February2010
?The Author(s)2010.This article is published with open access at https://www.doczj.com/doc/6210352838.html,
Abstract Bayesian dynamic stochastic general equilibrium(DSGE)models com-bine microeconomic behavioural foundations with a full-system Bayesian likelihood estimation approach using key macro-economic variables.Because of the usefulness of this class of models for addressing questions regarding the impact and consequences of alternative monetary policies they are nowadays widely used for forecasting and policy analysis at central banks and other institutions.In this paper we provide a brief description of the two main aggregate euro area models at the ECB.Both models share a common core but their detailed speci?cation differs re?ecting their speci?c focus and use.The New Area Wide Model(NAWM)has a more elaborate interna-tional block,which is useful for conditioning the euro area projections on assumptions about foreign economic activity,prices and interest rates and to widen the scope for scenario analysis.The Christiano,Motto and Rostagno(CMR)model instead has a more developed?nancial sector,which allows it to be used for monetary and?nancial scenarios and for cross checking.Based on the comparison of two models we?nd a broad agreement on the qualitative predictions they make,although,in quantitative terms,there are some differences.However,the perspectives provided by the two models are often complementary,rather than con?icting.
Keywords DSGE models·Central banks·Monetary policy
JEL Classi?cation B4·C5·E32·E50
Prepared for a special issue of the Spanish Economic Review.The opinions expressed are our own and should not be attributed to the European Central Bank or its Governing Council.
F.Smets(B)·K.Christoffel·
G.Coenen·R.Motto·M.Rostagno
European Central Bank,Frankfurt,Germany
e-mail:Frank.Smets@ecb.int
1Introduction
Central banks need a wide range of macro-econometric models and tools for fore-casting and monetary policy analysis.Over the past5years,an increasing number of institutions have introduced Bayesian dynamic stochastic general equilibrium(DSGE) models in their tool kit for policy analysis.1These models combine a consistent,micro-founded DSGE structure,characterised by the derivation of behavioural relationships from the optimising behaviour of agents subject to technological and budget con-straints and the speci?cation of a well-de?ned general equilibrium,with a full-system Bayesian likelihood estimation using key macro-economic variables.
As argued by Smets and Wouters(2003,2004),the DSGE approach is particularly suitable for addressing questions regarding the impact and consequences of alternative monetary policies.The general equilibrium structure lends itself to telling economi-cally coherent stories and structuring forecast-related discussions around it.Off-model information about“deep”structural parameters can be used to calibrate and/or esti-mate the model,which is particularly useful when time series are short.The model structure also helps to identify parameters and the type of shocks hitting the economy and to reduce the risk of over-?tting.Finally,the structural nature of the model and, in particular,its explicit account for the role of expectations makes the analysis less subject to the Lucas critique and more suitable for policy analysis.DSGE models put a premium on the important role of expectations in assessing alternative policy actions. They also give a better feel for which parameters are likely to be policy invariant and which ones are not.
The empirical strategy,full-system Bayesian likelihood estimation,also has a num-ber of advantages over the more traditional equation-by-equation estimation of large macro models.First,it formalises the use of prior information and helps identi?ca-tion,thereby making the estimation algorithm of the highly restricted and non-linear model much more stable.As shown by Canova(this issue),the likelihood function of DSGE models is often?at and irregular in a number of the parameters.Prior information helps overcoming such identi?cation issues.Secondly,the Bayesian like-lihood approach delivers a full characterisation of the parameter and shock uncertainty, allowing easily constructing probability distributions for unobserved variables(e.g. the output gap)and derived functions(e.g.forecasts).The state-space formulation underlying the approach,whereby the state equation captures the dynamics of the state variables in the model economy and the observation equations links those state variables to observable macro-economic time series,is a very?exible tool that can deal explicitly with measurement error,unobservable state variables,large data sets and different sources of information.Finally,the Bayesian estimation methodology provides a natural framework for decision making under uncertainty.As new data and new models appear,decision makers can update their posterior probabilities and appropriately weigh them when making decisions.
The attractive combination of rigorous economic reasoning and data coherence that characterises Bayesian DSGE models implies that they can be used successfully for
1Examples are the Sveriges Riksbank,the Norges Bank,the Czech National Bank,the Federal Reserve Board,the IMF and the European Commission.
scenario analysis and monetary policy design without compromising on their forecast-ing performance,which is comparable to the one of purely statistical and data-driven methods.In this paper,we provide a brief description of the two main aggregate euro area DSGE models that are being used in the context of the ECB’s quarterly projec-tion rounds and the associated cross-checking in the context of the ECB’s monetary analysis.Both models have a core which is similar to that of Christiano et al.(2005) and Smets and Wouters(2003).However,the New Area Wide Model(NAWM)has a more elaborate international block,which is useful for conditioning the euro area pro-jections on assumptions about foreign economic activity,prices and interest rates.The Christiano,Motto and Rostagno(CMR)model instead has a more developed?nancial sector,which allows it to be used in cross checking.In addition to the two estimated euro area DSGE models,a number of other DSGE models have been developed to address speci?c issues such as the effects of?scal policy,the international interac-tions between the euro area and the rest of the world and country-speci?c developments within the euro area.2These are not discussed in this paper.
The rest of the paper is structured as follows.Section2gives a brief description of the main features of those two models and their use at the ECB.Section3compares some of the empirical and structural features of the two models.First,it discusses and compares the monetary policy transmission mechanism in both models.Secondly,it uses the models to interpret the sources of recent developments in economic activity in the euro area.Finally,it provides an example of the counterfactual policy simula-tions that can be done using the two models.More details and examples of this type of empirical,quantitative policy analysis can be found in the various papers describ-ing the two models and their applications.Finally,the last section(Sect.4)brie?y addresses some of the recent criticism of modern DSGE models that has arisen in the light of the current?nancial crisis and provides some conclusions.
2Estimated euro area DSGE models at the ECB
Many models are in use at the ECB ranging from purely statistical models to fully ?edged structural(DSGE)models.Within the class of DSGE models,two models are routinely used in the policy process.The NAWM has been developed for forecasting purposes and for policy analysis.The CMR model has been developed to support the monetary analysis of the ECB and its two-pillar strategy,and to conduct monetary and?nancial scenarios.The need to serve for different purposes has driven also the modelling choices in developing these models.The NAWM includes a detailed inter-national block,whereas the CMR includes a detailed modelling of the monetary and ?nancial side of the economy.
The two models share similar long-run properties and a similar“core block”.This lends itself to interpreting the speci?cities of the two models in terms of additional transmission channels with respect to their common“core block”.The core model
2These models include the version of the NAWM used for globalization studies(Jacquinot and Straub 2008),the NAWM with a?nancial block(McAdam and Lombardo(2009)),a DSGE model to analyse macroeconomic interdependencies in the euro area(Gomes et al.2009)and the rational expectation multi country model for the euro area countries used for forecasting and policy analysis(Dieppe et al.2009).
Households - consumption/saving decisions - labour supply
Production - Combine labour and Markets: imperfect
competition & Monetary Government Fig.1Structure of the Models.Note:The boxes with the solid frame denote the core model that is nested in both models.The boxes with dashed frame at the bottom of the ?gure show the open economy features of the NAWM whereas the box with dash-dotted frame on the right-hand side of the ?gure describes the ?nancial dimension modelled in the CMR
includes features introduced in Christiano et al.(2005)and Smets and Wouters (2003),which have been shown to help explaining the data well both in the US and the euro area.Such features have become standard in DSGE modelling.They include habit persistence in consumption,adjustment costs in the ?ow of investment,imperfect competition,sticky prices and sticky wages,and the inclusion of a large number of structural shocks that act as shifters of the structural relationships.Figure 1provides a diagrammatic representation of the main sectors of the economy included in the “core block”(boxes with solid frame in the middle of the ?gure)and of the additional sectors that are included in the NAWM (boxes with dashed frame at the bottom of the ?gure)and the CMR model (box shaded with dashed-dotted frame on the right-hand side of the ?gure).
In the NAWM the common core block is embedded into an international environ-ment.3This has wide-ranging consequences for the model dynamics both regarding the in?uence of foreign developments on the domestic economy as well as by directly in?uencing the decision process of domestic households and ?rms.
Households are trading both domestic and foreign bonds.The effective return on the risk-less domestic bonds depends on a ?nancial intermediation premium,which drives a wedge between the interest rate controlled by the monetary authority and the return required by the household.Similarly,when taking a position in the international bond market,the household encounters an external ?nancial intermediation premium which depends on the economy-wide (net)holdings of internationally traded foreign bonds expressed in domestic currency relative to domestic nominal output.That is,if the domestic economy is a net debtor,households have to pay an increasing external 3See Christoffel et al.(2008)for a full description of the NAWM model.
intermediation premium on their international debt.The uncovered interest parity condition determines the dynamics of the exchange rate.
Turning to the production side there are four different kinds of?rms.In the pro-duction sector of the tradable,intermediate good there are domestic?rms producing for the domestic market,domestic?rms producing for the foreign market and for-eign?rms producing for the domestic market.The intermediate good?rms engage in Calvo price setting where both import as well as export goods are priced in the domestic currency.4The representative?rm producing the non-tradable?nal invest-ment and private consumption goods,combines purchases of a bundle of domestically produced intermediate goods,with purchases of a bundle of imported foreign inter-mediate goods.The relative demand for the goods is determined by the relative prices, the price elasticities and the degree of home bias.The resulting trade?ows are affected by domestic and foreign demand,relative prices and by the exchange rate,accounting for limited exchange-rate pass-through in the short to medium run.
The design of the NAWM for use in the ECB/Eurosystem staff projections has been guided by two important considerations,namely(i)to provide a comprehensive set of core projection variables and(ii)to allow conditioning on monetary,?scal and external developments which,in the form of technical assumptions,are an important element of the projections.As a consequence,the scale of the model—compared with a typical DSGE model—is relatively large.Employing Bayesian methods,it is estimated on18 key macroeconomic variables.In addition to standard national account data,data for the nominal effective exchange rate,euro area foreign demand,euro area competitors’export prices as well as oil prices are used,which are deemed important variables in the projections capturing the in?uence of external developments.
Turning to the CMR model,it extends the“core block”by explicitly modelling the monetary and?nancial dimension of the economy.Portfolio and?nancing deci-sions are non-recursive in the model,but they directly affect consumption,investment and prices.The model is a variant of the model with?nancial frictions developed in Christiano et al.(2003,2007),which in turn includes the?nancial accelerator of Bernanke et al.(1999)and the banking system of Chari et al.(1995).The main fea-tures of?nancial intermediation in the CMR are the following.First,the presence of many types of assets in the economy that differ in their degree of liquidity and maturity gives rise to portfolio decisions.Second,investment in physical capital is leveraged,giving rise to the need for external?nancing.There is risk of default; and the presence of asymmetric information in credit markets implies that the incen-tives of borrowers and lenders are not aligned.This leads the lender to charge a premium which is a function of the equity of the borrower.As the value of equity is mainly driven by asset price?uctuations,there is a direct link between asset prices and real economic activity.Third,in the model part of the working capital has to be ?nanced prior to the time in which revenues from selling current production become available.Fourth,in the model savers and lenders do not interact directly but via ?nancial intermediaries.Intermediaries have their own balance sheet with liabili-ties,mainly different type of deposits,so that it is possible to construct aggregates 4The alternative setting,assuming local currency pricing both for exports and imports,leads to counter-factual production price dynamics in the eurozone and a deteriorated?t of the model in the estimation.
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NAWM CMR NAWM CMR NAWM CMR Fig.2Monetary policy transmission mechanism.Note:The dashed and dash-doted lines show the response of the NAWM and the CMR model to an unanticipated 50basis point increase in interest rates,for output annualized in?ation and interest rates
such as M1and M3,and assets,mainly,different types of loans.The production of deposits requires resources in terms of capital,labour and excess reserves.The pres-ence of excess reserves captures the intermediaries’need for maintaining a liquidity buffer to accommodate unexpected withdrawals.In the model intermediaries cannot default.Finally,in the model ?nancial contracts are denominated in nominal terms.As borrowers and lenders are ultimately interested in the real value of their claims,shifts in the price level unforeseen at the time the ?nancial contract is signed bring about real effects.This is a way to include the “Fisher debt-de?ation”channel in the model.
The parameter values underlying the quantitative results presented in the remaining of the paper are obtained in two ways.Some structural parameters are calibrated in order to match great-ratios and long-run averages.In the calibration it is ensured that the long-term properties of the two models coincide to the largest possible extent.The remaining structural parameters and the parameters governing the stochastic pro-cesses are estimated by using a Bayesian version of the standard maximum likelihood approach.
3A comparison of the NA WM and the CMR models
3.1Monetary policy transmission mechanism
In this section we assess the dynamic implications of the models by comparing the transmission mechanism of monetary policy shocks.Figure 2shows the response to an unexpected increase in the nominal interest rate of 50basis points.
As the two models share some of the transmission channels,we focus on the role played by the channels which are speci?c to each of the models.
In line with common wisdom regarding the transmission of monetary policy,in the NAWM the increase in the real rate leads to temporarily curtailed demand,with investment falling more strongly than consumption.The responses re?ect also the open economy dimension and the endogenous response of the exchange rate adding additional transmission channels affecting both the real and nominal side.
On the real side we?nd that the positive interest rate differential between foreign and domestic bonds implies an increased demand for domestic bonds.This implies an appreciation of the domestic currency via the uncovered interest parity assump-tion.Since intermediate imports are priced in the local currency the exchange rate movement implies a gradual,but noticeable drop in import prices and an improve-ment of the terms of trade.The improved terms of trade lead to expenditure-switching from domestic towards foreign goods in the composition of the?nal consumption and investment goods.As a consequence,imports fall by less than domestic demand. Exports are priced in producer currency implying a stronger price increase and a more pronounced reduction in exports.
Continuing with the price side reaction to a monetary policy shock we?nd two additional effects of the open economy dimension.First and as discussed above,the open economy features imply an ampli?cation of the decline in aggregate demand and consequently in factor demand.Firms cut back their demand for labour and,therefore, employment falls.Via its impact on?rms’marginal cost,the resulting decline in wages puts downward pressure on domestic prices.Second,consumption prices will decline more strongly than domestic prices because of the drop in import prices due to the appreciation of the domestic currency.In sum,the peak absolute effect on economic activity and in?ation is reached after about1year,whereas the implied price level effects are gradual and long-lasting.
Turning to the CMR model,the asset side of the?nancial sector’s balance sheet introduces three main propagation mechanisms relative to the“core block”.First, there is the?nancial accelerator effect.The deterioration in economic activity brought about by the initial monetary policy tightening leads to a shortfall in earnings and to capital losses,both of which erode the value of borrowers’net worth.The associate rise in the bankruptcy rate leads to a rise in the external?nance premium charged over and above the risk-free rate controlled by the central bank.This channel tends to magnify the contractionary impact of a policy tightening.The second propagation mechanism,the“Fisher debt-de?ation”channel reinforces the standard accelerator effect described above.The debt-de?ation effect implies that the decline in the price level brought about by the unexpected increase in the policy rate—thus by construc-tion also the decline in the price level is unforeseen at the time the?nancial contract was signed—increases the real value of the debt outstanding.This redistribution of wealth between borrowers and lenders has real effects on the economy.Note that the Fisher and accelerator mechanisms reinforce each other only in the case of shocks that move the price level and output in the same direction,such as monetary policy shocks,whereas they tend to cancel each other out in the wake of shocks that move the price level and output in opposite directions,such as transitory technology shocks. The third propagation mechanism in the model is given by the need to?nance part of the working capital in advance.The additional propagation mechanisms included in the model should not be taken as necessarily implying stronger downward pressure on
in?ation.On the one hand,higher?nancing costs lead to lower in?ationary pressure via their depressing effect on economic activity.On the other hand,they represent additional costs,putting upward pressure on price developments.
The model makes also predictions for the liability side of banks’balance sheet. Given the importance of monetary and credit developments in the ECB monetary pol-icy strategy,understanding the path followed by narrow and broad monetary aggregates in response to a policy tightening is of particular interest.According to the model,a policy tightening triggers an initial positive reaction of the stock of nominal M3.This is driven by the speculative demand for money,captured by the M3components outside M1.This is explained by the fact that the interest rate earned on instruments included in M3–M1is closely linked to market rates.The model suggests that the own rate on M3–M1goes up approximately by the full50basis points rise in the policy rate.The interest rate earned on the liquid component of M3is instead less reactive.According to the model,after the initial rise,M3undergoes a significant and permanent decline. This“perverse”initial decline of M3following a policy tightening found in the model is consistent with evidence obtained on the basis of identi?ed V ARs.
Overall,taking into account that the two models share only some of the transmission channels,the quantitative differences between the NAWM and the CMR responses to
a policy shock appear to be rather small.
3.2Structural interpretation of GDP growth
The models can be used to interpret recent macroeconomic developments in terms of the structural shocks that have hit the economy.The results of this are displayed in Figs.3and4,which provide an additive decomposition of annual GDP growth. The contribution of the different categories of shocks is shown by means of bars of different colour.Looking at the different phases of the business cycle since the start of the European Monetary Union(EMU)in1999,the main?ndings are the following.
First,both models suggest that the expansion of1999–early2000is mainly explained in terms of forces that can be probably associated with the convergence process in interest rates in the run-up to EMU(see light blue bars with diagonal lines), and in terms of increased degree of competition,possibly driven by globalisation forces(see green bars,color?gure online).Demand shocks are found to be lagging (see red bars with diamonds).
Second,the interpretation of the slowdown of2000–2001and the following phase of weak growth until2005provided by the two models is less clear cut.The NAWM suggests that the downturn starting in the second half of2000was triggered by adverse in?uences from abroad.For instance,the emergence of new competitors in euro area export markets eventually led to losses in export market shares.Moreover,the sharp deceleration of economic activity in the United States and its spillovers to the rest of the world caused a pronounced fall in euro area foreign demand from2001onwards. Within the NAWM,these developments are captured by negative export preference and negative foreign demand shocks,respectively.Furthermore we can observe a neg-ative contribution of the technology shock group in the2001slowdown.Looking at the further decomposition we can attribute this mainly to the transitory technology
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1999200020012002200320042005200620072008Markups Policy International Demand Technology Output Growth Fig.3Structural interpretation of GDP growth in NAWM.Note:GDP growth,year-on-year %change,in deviation from https://www.doczj.com/doc/6210352838.html,st observation is 2009q2
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Fig.4Structural Interpretation of GDP growth in CMR.Note:GDP growth,year-on-year %change,in deviation from https://www.doczj.com/doc/6210352838.html,st observation is 2009q2
shock which is related to the increased degree of labour hoarding in this episode.The subdued growth of real GDP over the period 2002–2005is then largely explained by negative demand shocks,notably domestic risk premium shocks,that entailed a protracted slump in domestic spending.Since 2003the overall contribution of the foreign shocks has been rather modest.This masks the fact that in 2003the adverse impact of external risk premium shocks (accounting for the marked appreciation of
the euro)was largely offset by the unwinding of the previous shocks to export pref-erences.In contrast,the favourable developments in euro area foreign demand during the2004–2006period have been largely compensated by the continued apprecia-tion of the euro and a renewed deterioration of foreign preferences for euro area exports.
The CMR model suggests a different interpretation of the rapid slowdown of2000–2001.It does not support the occurrence of adverse technological shocks and explains this episode in terms of negative?nancial factors associated with the stock market collapse.Financial shocks mainly capture the sudden re-appraisal of pro?t prospects, which led to the stock market collapse,which in turn aggravated the already weak balance-sheet position of the corporate sector at the time.The associated rise in the riskiness of lending activity led to a sharp rise in the external?nance premium.This prompted a cut in investment plans and a painful process aimed at repairing bal-ance sheets.According to the model,?nancial shocks exerted a progressively stronger downward pull on output until the end of2002and then they started receding.As of2002,the poor economic performance is explained by the CMR also in terms of unfavourable productivity shocks which exerted a downward pull on economic activ-ity and upward pressure on in?ation.This countercyclical behaviour of productivity shocks had clear monetary policy implications,as it prevented in?ation from quickly receding,notwithstanding the decline in resource utilisation caused by the economic slowdown.
Third,both models(although the CMR to a larger extent)?nd that monetary policy was instrumental in preventing the unfavourable forces that hit the euro area over the period from2001to2006from causing a more severe slowdown.
Fourth,both models?nd that the recovery initiated at the end of2005is different in nature to the earlier recovery in1999,and is mainly a demand driven phenomenon.
Turning to the recent recession and to the impact of the?nancial crisis on GDP growth the NAWM explains the downturn in terms of three mayor components.First, the?nancial turmoil led to an increase in perceived?nancial risks and higher risk premia in terms of the spread between market interest rates and the rates controlled by the monetary authorities.The increased risk premium depressed domestic demand components as captured in the red bars with diamonds.Second,the deterioration of the supply side led to a downward revision of production capabilities as measured in the negative contribution from the technology shock group.Third and most importantly, the global dimension of the crisis implied a substantial reduction in world trade and a corresponding plummeting of exports in the euro area.This effect is particularly important because the reduction in world trade was accompanied by losses in market shares owing to the high proportion of investment goods in euro area exports.The negative contribution of the monetary policy group includes the shock to the sim-ple,empirical monetary policy rule,but not the non-conventional monetary policy measures installed in response to the crisis.
According to the CMR,the most important driver of the recent crisis can be traced down to shocks emanating from?nancial intermediation(see yellow bars with hori-zontal lines),in particular the sudden re-appreciation of credit risk.The second most important contribution to the recent free fall in real economic activity has been the sharp drop in domestic and international demand(see red bars with diamonds).In
Table1Counterfactual scenario
200520062007
NAWM
In?ation03052 GDP growth32011 CMR
In?ation01734 GDP growth2168 Average difference between counterfactual outcome and historical data over a given year.In?ation is mea-sured in terms of the GDP de?ator,and expressed in year-on-year terms.GDP growth is in quarter-on-quarter terms
Difference with respect to historical data(in basis points)
addition,according to the model,the situation has been aggravated by the deteriora-tion of the supply side of the economy(see blue bars with vertical lines).The only positive support to the economy has come from the exceptional compression of mark-ups,and from monetary policy(see light blue bars with diagonal lines)associated with the unprecedented slashing of policy rates.
3.3A policy counterfactual
As an example for a policy counterfactual,we assess the macroeconomic impact of delaying the policy tightening cycle that started in2005.At that time the policy rate was maintained constant at2%since2001,due to protracted weak economic activity and subdued in?ation.On the basis of its assessment of the in?ation outlook,in December 2005the ECB decided to start removing policy accommodation with a series of inter-est rate hikes.This decision was criticised at the time by several commentators and politicians on the basis that it started too early and the tightening cycle should have been postponed by a few quarters.The models can be used to answer the question, what would have happened to economic activity and in?ation if the interest-rate cycle was delayed by,say,two quarters?
As shown in Table1,delaying the interest rate cycle would have shifted in?ation upward significantly,reaching the peak effect almost2years later.In2007actual in?a-tion turned out to be2.4%,considerably above the ECB definition of price stability of close but below2%.Had the ECB delayed its tightening cycle,in2007in?ation would have been34basis points higher according to the counterfactual simulation carried out with the CMR model,and52basis points higher according to the NAWM.Table1 shows that the impact on economic activity arising from delaying the tightening cycle would have been felt especially in2006,with GDP growth higher by16basis points according to the CMR and20basis points higher according to the NAWM.
4Recent criticism of the DSGE approach
The introduction of Bayesian DSGE models in policy institutions has also been accom-panied by increasing criticism of some of the elements and assumptions underlying this approach.In this section,we brie?y address three of those criticisms.
First,it has been argued that both econometrically and economically some of the shocks and frictions in common DSGE models are not well identi?ed.The most force-ful illustration of these identi?cation problems in standard New Keynesian models has been provided by Canova and Sala(2009).These authors and Canova(this issue)show that the likelihood function often shows little curvature in key parameters of the model. Moreover,because of the highly non-linear nature,it is not always obvious where the identi?cation problems lie,making correct inference dif?cult.Clearly,acknowledging these identi?cation problems must be an important element of any empirical analysis. However,as argued above,the Bayesian approach allows using prior information to address some of these identi?cation problems.For example,Mackowiak and Smets (2009)discuss how the wealth of new micro information on price setting can be used in the speci?cation and estimation of macro models.Clearly,there is never an unam-biguous,one-to-one mapping between micro features such as the frequency of price changes and the simpli?ed structural macro model.However,confronting micro infor-mation with its macro-implications is a useful and informative exercise which can help reduce identi?cation problems.It can also point to de?ciencies in the speci?cation of the model.Similarly,as shown above it is standard practice to calibrate some of the key parameters by using,for example,information on the great macro-economic ratios. The analysis of Canova and Sala(2009)does highlight that it is important to check the informativeness of the data by comparing the prior distribution with the posterior dis-tribution.One of the criticisms of Chari et al.(2008)of the Smets and Wouters(2007) model is that the economic interpretation of some of the shocks is not clear.For exam-ple,the so-called wage mark-up shocks affecting the labour supply equation could be due to a variety of factors such as shifts in labour supply coming from changing preferences or participation rates,shifts in the bargaining power of unions or changing taxes.The welfare and policy implications of these different sources of wage mark-up variations can be quite different.Also in this case,using additional information may help solving this identi?cation problem.For example,Gali(2009)shows that simply adding the unemployment rate to the observable variables may allow distinguishing between the?rst two sources of mark-up?uctuations.
Second,the assumption of rational(or model-consistent)expectations and perfect information,which underlies most of the DSGE models,is obviously an extreme assumption.As argued above,it is a useful and consistent benchmark,in particular when analysing the steady-state effects of changes in the policy regime.By bringing out expectations explicitly,their impact can be discussed directly.At the same time,it is unreasonable to assume that in an uncertain world and taking into account that the model is an abstraction of reality agents use the model to form their expectations in a model-consistent way.A number of avenues have been pursued to include learning and imperfect information in DSGE models.First,it is fair to say that addressing infor-mation problems at the micro level and analysing its implications for the aggregate macro economy is still at an early stage and is only feasible in small highly stylised models.Second,a number of authors have introduced learning about shocks in the model.This will typically help explaining some of the persistence in the response of the economy to shocks.For example,Collard et al.(2009)show that models with such signal extraction problems better?t the data.Third,an alternative is to endow the agents with forecasting regressions that are regularly https://www.doczj.com/doc/6210352838.html,ani(2006,2008)
and Slobodyan and Wouters(2009)?nd that DSGE models with learning mechanisms of this sort?t the macro-economic variables better than rational-expectations models and can also explain some of the time variation in the estimated persistence of in?ation and variances.
A third criticism has become loud and clear since the outbreak of the?nancial crisis. Most DSGE models do not explicitly model a?nancial intermediation sector and rely on perfect arbitrage equations to model asset prices.As a result,there is only a lim-ited role for?nancial phenomena such as agency problems arising from asymmetric information giving rise to debt constraints and the possibility of default.As discussed above,one of the models used at the ECB,the CMR model,does have an explicit bank-ing sector and includes an agency problem with respect to the?nancing of investment by?rms.As in most other DSGE models,the banking sector itself is,however,not subject to asymmetric information problems and costly?nancing constraints.As one of the major propagation mechanisms of the current?nancial crisis has been tensions in the interbank market,it is not surprising that a lot of current research focuses on modelling a more explicit banking sector.Recent examples are Gertler and Karadi (2009),Christensen and Dib(2009)and Gerali et al.(2009).It remains to be seen whether such extensions can capture the slow build-up of?nancial imbalances and associated credit and asset price booms that we have witnessed over the past decade and the sudden collapse in2007and2008.Moving away from models that are linear-ised around a steady state is likely to be one condition for capturing such non-linear phenomena.Another feature that is often missing from DSGE models used in policy institutions is a well-developed housing market.Historical experience,as well as the current crisis,has highlighted the important role that overextended real estate markets play in many?nancial crisis.The work by Iacoviello(2004),which itself is based on Kiyotaki and Moore(1997)is one way of introducing?nancial frictions in real estate ?nance.
5Conclusion
Bayesian DSGE models have become a useful tool in forecasting and policy analysis. They complement the many other tools such as BV ARs,dynamic factor models,partial equilibrium models,small-scale indicator models that are frequently used in policy institutions.As these models are regularly used,there is a tendency to extend them to address more and more questions.This raises the issue of their optimal size.A single large model provides a common language that provides a benchmark for discussion amongst various economists.However,a large model may also be more dif?cult to understand and to handle and it may lack robustness.A suite of models on the other hand ensures robustness and?exibility in addressing the multitude of questions that typically come up in actual policy discussions.One of the practical challenges is to ?nd a?ne balance between those two approaches.
In this paper,we have compared the two main estimated DSGE models for the euro area used at the ECB:the NAWM and the CMR model.From a qualitative point of view,there is a broad agreement on the predictions they make,although,in quantitative terms,there are some differences.However,the perspectives provided by the two mod-
els are often complementary,rather than con?icting.The role played by money,credit and,in general,?nancial factors in CMR complements the international dimension and the role of the exchange rate in the NAWM.This analysis shows the usefulness of carrying out policy exercises with alternative models in order to assess the robustness of the results.
Open Access This article is distributed under the terms of the Creative Commons Attribution Noncom-mercial License which permits any noncommercial use,distribution,and reproduction in any medium, provided the original author(s)and source are credited.
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結 構方程程模型型及其應 新增資 應用 資料
目錄 內容 頁數 引言 2 I. 第9.1版的改動 3 - 4 II. 章節內的新增資料 第一章 5 第三章 6 – 8 第十二章 9 – 10 第十四章 11 – 17 III. 附录內的新增資料 19 1
引言 自2005，為方便普通話及廣東話的學生，修習香港中文大學我所任教的結構方程課程，我製做了一個含有2種方言的網上課程，其後我亦將整個課程放在個人網頁(https://www.doczj.com/doc/6210352838.html,)免費讓公眾使用。 網上課程更精簡地解釋重點，尤其是對本書最艱深的部份（第三、四章），幫助最大。學員先看綱上課程，再參考書本內容，必感事半功倍。 主要参考文獻： du Toit, S., du Toit, M., Mels, G., & Cheng, Y. (n.d.). LISREL for Windows: SIMPLIS syntax files. Lincolnwood, IL: Scientific Software International, Inc. (available https://www.doczj.com/doc/6210352838.html,/lisrel/techdocs/SIMPLISSyntax.pdf) J?reskog, K.G. & S?rbom, D. (1999). LISREL 8: User’s Reference Guide. Lincolnwood, IL: Scientific Software International, Inc. J?reskog, K.G. & S?rbom, D. (1999). Structural Equation Modeling with the SIMPLIS Command Language. Lincolnwood, IL: Scientific Software International, Inc. Scientific Software International (SSI) (2012). LISREL 9.1 Release Notes. Lincolnwood, IL: The Author. (available from https://www.doczj.com/doc/6210352838.html,/lisrel/LISREL_9.1_Release_Notes.pdf) 2
大连海事大学 实验报告 《系统工程》 2014～2015学年第一学期 实验名称：基于解释模型在大学生睡眠质量问题的研究学号姓名：马洁茹姚有琳 指导教师：贾红雨 报告时间： 2014年9月24日
《系统工程》课程上机实验要求 实验一解释结构模型在大学生睡眠质量问题中的研究 实验名称：基于MATLAB软件或C/Java/其他语言ISM算法程序设计(一) 实验目的 系统工程课程介绍了系统结构建模与分析方法——解释结构模型法（Inter pretative Structural Modeling ·ISM）是现代系统工程中广泛应用的一种分析方法，能够利用系统要素之间已知的零乱关系，用于分析复杂系统要素间关联结构，揭示出系统内部结构。ISM方法具有在矩阵的基础上再进一步运算、推导来解释系统结构的特点，对于高维多阶矩阵的运算依靠手工运算速度慢、易错，甚至几乎不可能。 本次实验的目的是应用计算机应用软件或者是基于某种语言的程序设计快速实现解释结构模型(ISM)方法的算法，使学生对系统工程解决社会经济等复杂性、系统性问题需要计算机的支持获得深刻的理解。学会运用ISM分析实际问题。 (二) 实验要求与内容： 1.问题的选择 根据对解释结构模型ISM知识的掌握，以及参考所给的教学案例论文，决定选择与我们生活有关的——大学生睡眠质量问题。 2．问题背景 睡眠与我们的生活息息相关，当每天的身体机制在不断运行的过程中身体负荷不断变大，到了夜间就需要休息。但是同一寝室的同学大多休息时段不同，有些习惯早睡，有些会由于许多原因晚睡。有些睡眠较沉不会轻易被打扰，有些睡眠较轻容易被鼾声或者其他声响惊醒。学习得知，解释系统模型是通过对表面分离、凌乱关系的研究，揭示系统内部结构的方法。因此，我想尝试通过解释模型来对该问题进行研究分析。 3.用画框图的形式画出ISM的建模步骤。
结构方程模型的应用及分析策略 侯杰泰成子娟 (香港中文大学教育学院东北师范大学教育学院，130024) 摘要:差不多所有心理、教育、社会等概念，均难以直接准确测量，结构方程(SEM，Structural Equation Modelling)提供一个处理测量误差的方法，采用多个指标去反映潜在变量，也令估计整个模型因子间关系，较传统回归方法更为准确合理。本文主要用一系列有关学习动机的虚拟例子，指出每个问题的主要分析策略，以展示SEM在教育及心理学可以应用的研究范畴。文内探讨的方法包括：验证性因素、高阶因子、路径及因果分析、多时段(multiwave)设计、单形模型(Simple Model)、及多组比较等。 关键词结构方程验证性因素分析路径及因果分析高阶因子多组比较 结构方程(SEM,Structural Equation Modelling)、协方差结构模型(Covariance Structure Modelling、LISREL)等类似名词已渐流行，并成为一种十分重要的数据分析技巧；在大学高等学位研究课程，它是多变量分析(multivariate analysis)的重要课题；比较重要的社会、教育、心理期刊，也早已特开专栏介绍(如：候，1994；Connell & Tanaka,1987;Joreskog & Sorbom,1982)；可见SEM在统计学中所建立的声望及崇高地位是无容置疑的。本文主要用一系列有关学习动机的虚拟例子，来指出每个问题的主要分析策略，以展示结构方程模型在教育及心理学可以应用的研究范畴。 一、结构方程：优点及拟合概念 1.数学模式 很多社会、心理等变项，均不能准确地及直接地量度，这包括智力、社会阶层、学习动机等，我们只好退而求其次，用一些外项指标(observable indicators)，去反映这些潜伏变项。例如：我们以学生父母教育程度、父母职业及其收入(共六个变项)，作为学生家庭社经地位(潜伏变项)的指标，我们又以学生中、英、数三科成绩(外显变项)，作为学业成就(潜伏变项)的指标。 简单来说SEM可分测量(measurement)及潜伏变项(latent variable)两部分。测量部分就是求出六个社经指标与社经地位(或三科成绩与学业成就)(即外显指标与潜伏变项之间)的关系：而潜伏变项部分则指社经地位与学业成就(即潜伏变项与潜伏变项间)的关系。 指标(外显变项)含有随机(或系统)性的量度上误差，但潜伏变项则不含这些部份。SEM可用以下矩阵方程表示(Bollen,1989;Joreskog & Sorbom,1993)： η=βη+Γξ+ζ
《结构方程模型及其应用》 内容简介 侯杰泰，香港中文大学教育心理系教授、系主任。主要研究方向为学习动机，应用统计和香港语文政策。曾多次在北京、上海、南京、长春、广州等地举办的地区或全国性结构方程分析研习班上讲学。 在社会、心理、教育、经济、管理、市场等研究的数据分析中，当今称得上前沿的几个统计方法中，应用最广、研究最多的恐怕非结构方程分析莫属。它包含了方差分析、回归分析、路径分析和因子分析，弥补了传统回归分析和因子分析的不足，可以分析多因多果的联系、潜变量的关系，还可以处理多水平数据和纵向数据，是非常重要的多元数据分析工具。 本书是国内第一本系统介绍结构方程模型和LISREL的著作。阐述了结构方程分析（包括验证性因子分析）的基本概念、统计原理、在社会科学研究中的应用、常用模型及其LISREL程序、输出结果的解释和模型评价。《结构方程模型及其应用》还讨论了一些与结构方程模型有关的专题，是一本由初级至中上程度的结构方程分析著作，可作为有关专业高年级本科生和研究生的教科书及应用工作者的参考书。 目录 序 第一部分结构方程模型入门 第一章引言
一、描述数据 二、具体例子展示准确与简洁的考虑 三、探索性与验证性因子分析比较 第二章结构方程模型简介 一、结构方程模型的重要性 二、结构方程模型的结构 三、结构方程模型的优点 四、结构方程模型包含的统计方法 五、路径图的图标规则 六、结构方程分析软件包 七、LISIREL操作入门 第二部分结构方程模型应用 第三章应用示范I：验证性因子分析和全模型 一、验证性因子分析 二、多质多法模型 三、全模型 四、高阶因子分析 第四章应用示范II：单纯形和多组模型 一、单纯形模型 二、多组验证性因子分析 三、多组分析：均值结构模型 四、回归模型
海事大学 实验报告 《系统工程》 2014～2015学年第一学期 实验名称：基于解释模型在大学生睡眠质量问题的研究学号：马洁茹有琳 指导教师：贾红雨 报告时间： 2014年9月24日
《系统工程》课程上机实验要求 实验一解释结构模型在大学生睡眠质量问题中的研究 实验名称：基于MATLAB软件或C/Java/其他语言ISM算法程序设计(一) 实验目的 系统工程课程介绍了系统结构建模与分析方法——解释结构模型法（Inter pretative Structural Modeling ·ISM）是现代系统工程中广泛应用的一种分析方法，能够利用系统要素之间已知的零乱关系，用于分析复杂系统要素间关联结构，揭示出系统部结构。ISM方法具有在矩阵的基础上再进一步运算、推导来解释系统结构的特点，对于高维多阶矩阵的运算依靠手工运算速度慢、易错，甚至几乎不可能。 本次实验的目的是应用计算机应用软件或者是基于某种语言的程序设计快速实现解释结构模型(ISM)方法的算法，使学生对系统工程解决社会经济等复杂性、系统性问题需要计算机的支持获得深刻的理解。学会运用ISM分析实际问题。 (二) 实验要求与容： 1.问题的选择 根据对解释结构模型ISM知识的掌握，以及参考所给的教学案例论文，决定选择与我们生活有关的——大学生睡眠质量问题。 2．问题背景
睡眠与我们的生活息息相关，当每天的身体机制在不断运行的过程中身体负荷不断变大，到了夜间就需要休息。但是同一寝室的同学大多休息时段不同，有些习惯早睡，有些会由于许多原因晚睡。有些睡眠较沉不会轻易被打扰，有些睡眠较轻容易被鼾声或者其他声响惊醒。学习得知，解释系统模型是通过对表面分离、凌乱关系的研究，揭示系统部结构的方法。 因此，我想尝试通过解释模型来对该问题进行研究分析。 3.用画框图的形式画出ISM的建模步骤。
大连海事大学实验报告 《系统工程》 2014?2015学年第一学期 实验名 称：基于解释模型在大学生睡眠质量问题的研究 学号姓 名： 马洁茹姚有琳指导教 师： 贾红雨 报告时 间： 2014 年9月24日
《系统工程》课程上机实验要求 实验一解释结构模型在大学生睡眠质量问题中的研究 实验名称：基于MATLAB^件或C/Java/其他语言ISM算法程序设计 （一）实验目的 系统工程课程介绍了系统结构建模与分析方法一一解释结构模型法（In ter pretative Structural Modeling ? ISM）是现代系统工程中广泛应用的一种分 析方法，能够利用系统要素之间已知的零乱关系，用于分析复杂系统要素间关联结构，揭示出系统内部结构。ISM方法具有在矩阵的基础上再进一步运算、推导来解释系统结构的特点，对于高维多阶矩阵的运算依靠手工运算速度慢、易错，甚至几乎不可能。 本次实验的目的是应用计算机应用软件或者是基于某种语言的程序设计快速实现解释结构模型（ISM）方法的算法，使学生对系统工程解决社会经济等复杂性、系统性问题需要计算机的支持获得深刻的理解。学会运用ISM分析实际问题。 （二）实验要求与内容： 1?问题的选择 根据对解释结构模型ISM知识的掌握，以及参考所给的教学案例论文，决定选择与我们生活 有关的一一大学生睡眠质量问题。 2 ?问题背景 睡眠与我们的生活息息相关，当每天的身体机制在不断运行的过程中身体负荷不断变大，到了夜间就需要休息。但是同一寝室的同学大多休息时段不同，有些习惯早睡，有些会由于许多原因晚睡。有些睡眠较沉不会轻易被打扰，有些睡眠较轻容易被鼾声或者其他声响惊醒。学习得知，解释系统模型是通过对表面分离、凌乱关系的研究，揭示系统内部结构的方法。因此，我想尝试通过解释模型来对该问题进行研究分析。 3.用画框图的形式画出ISM的建模步骤
Amos模型设定操作 在使用AMOS进行模型设定之前，建议事先在纸上绘制出基本理论模型和变量影响关系路径图，并确定潜变量与可测变量的名称，以避免不必要的返工。 1.绘制潜变量 使用建模区域绘制模型中的潜变量，在潜变量上点击右键选择Object Properties，为潜变量命名。 2.为潜变量设置可测变量及相应的残差变量 使用绘制。在可测变量上点击右键选择Object Properties为可测变量命名。其中Variable Name 对应的是数据的变量名，在残差变量上右键选择Object Properties为残差变量命名。
3.配置数据文件，读入数据 , File——Data Files——File Name——OK。 4.模型拟合 View——Analysis Properties——Estimation——Maximum Likelihood。 5.标准化系数 Analysis Properties——Output——Standardized Estimates——因子载荷标准化系数。
6.参数估计结果 @ Analyze——Calculate Estimates。红色框架部分是模型运算基本结果信息，点击View the Output Path Diagram查看参数估计结果图。 7.模型评价 点击查看AMOS路径系数或载荷系数以及拟合指标评价。 路径系数/载荷系数的显著性 模型评价首先需要对路径系数或载荷系数进行统计显著性检验。 模型拟合指数 模型拟合指数是考察理论结构模型对数据拟合程度的统计指标。拟合指数的作用是考察理论模型与数据的适配程度，并不能作为判断模型是否成立的唯一依据。拟合优度高的模型只能作为参考，还
3.2解释结构模型 系统是由许多具有一定功能的要素（如设备、事件、子系统等）所组成的， 各要素之间总是存在着相互支持或相互制约的逻辑关系。在这些关系中，又可以分为直接关系和间接关系等。为此，开发或改造一个系统时，首先要了解系统中 各要素间存在怎样的关系，是直接的还是间接的关系，只有这样才能更好地完成 开发或改造系统的任务。要了解系统中各要素之间的关系，也就是要了解和掌握系统的结构，建立系统的结构模型。 结构模型化技术目前已有许多种方法可供应用，其中尤以解释结构模型法（InterpretativeStructuralModeling,简称ISM）最为常用。 3.2.1结构模型概述 一、解释结构模型的概念 解释结构模型（ISM）是美国华费尔特教授于1973年作为分析复杂的社会 经济系统有关问题的一种方法而开发的。其特点是把复杂的系统分解为若干子系 统（要素），利用人们的实践经验和知识，以及电子计算机的帮助，最终将系统 构造成一个多级递阶的结构模型。 ISM属于概念模型，它可以把模糊不清的思想、看法转化为直观的具有良好 结构关系的模型，应用面十分广泛。从能源问题等国际性问题到地区经济开发、 企事业甚至个人范围的问题等，都可应用ISM来建立结构模型，并据此进行系 统分析。它特别适用于变量众多、关系复杂且结构不清晰的系统分析，也可用于方案的排序等。 所谓结构模型，就是应用有向连接图来描述系统各要素间的关系，以表示一个作为要素集合体的系统的模型，图3-1所示即为两种不同形式的结构模型。
图3-1两种不同形式的结构模型 结构模型一般具有以下基本性质： （1）结构模型是一种几何模型。结构模型是由节点和有向边构成的图或树 图来描述一个系统的结构。节点用来表示系统的要素，有向边则表示要素间所存 在的关系。这种关系随着系统的不同和所分析问题的不同，可理解为“影响”、“取决于”、“先于”、“需要”、“导致”或其他含义。 （2）结构模型是一种以定性分析为主的模型。通过结构模型，可以分析系统的要素 选择是否合理，还可以分析系统要素及其相互关系变化对系统总体的影响等问题。 （3）结构模型除了可以用有向连接图描述外，还可以用矩阵形式来描述。 矩阵可以通过逻辑演算用数学方法进行处理。因此，如果要进一步研究各要素之间关系，可以 通过矩阵形式的演算使定性分析和定量分析相结合。这样，结构模型的用途就更为广泛，从而 使系统的评价、决策、规划、目标确定等过去只能凭个人的经验、直觉或灵感进行的定性分析，能够依靠结构模型来进行定量分析。 （4）结构模型作为对系统进行描述的一种形式，正好处在自然科学领域所用的数学 模型形式和社会科学领域所用的以文章表现的逻辑分析形式之间。因此，它适合用来处理 处于以社会科学为对象的复杂系统中和比较简单的以自然科学为对象的系统中存在的问题， 结构模型都可以处理。 总之，由于结构模型具有上述这些基本性质，通过结构模型对复杂系统进行分析往往能够 抓住问题的本质，并找到解决问题的有效对策。同时，还能使由不同专业人员组成的系统开发 小组易于进行内部相互交流和沟通。
应用解释结构模型（ISM）分析大学生就业的问题09工业工程周浩吕超宇 摘要： 关键词：解释结构模型大学生就业原因及对策 背景： 据人力资源和社会保障部公布的数据，2009年我国将有2400万劳动力需要安排就业，其中将有超过700万大学毕业生需要解决就业问题。数据显示,2009年高校毕业生规模达到611万,比2008年增长52万；而据预测,2011年这一数字将达到峰值758万。与此同时,国际金融危机的影响进一步显现,可以预见,在未来相当长时期内大学生就业压力不会减弱。如何帮助大学生走出就业难的困境将成为政府与社会长期而艰臣的任务。 大学生就业难是一个现实问题，更是一个社会问题。总体来说,大学毕业生具有较高的人力资本水平,是劳动力市场上的优势群体。但随着全球化的发展与知识经济的冲击,青年初次与持续就业所需的能力门坎逐年提高,大学生必须具备能够满足新经济要求的核心就业能力才能成功发展,但现有教育培训体系缺乏必要的就业市场需求导向,缺乏对创业行为的深入研究,高等教育培养出来的大学生在知识和技能结构上与人才市场的需求存在脱节,大学生就业的结构性矛盾日益突出。 （https://www.doczj.com/doc/6210352838.html,/xiaobao/news_view.asp?newsid=663）
应用解释结构模型分析问题： 1.1成立ISM 小组 小组成员主要由来自09工业工程的周浩和吕超宇组成； 1.2确定关键问题与确定因素，列举各导致因素的相关性 根据当今大学生的就业现状，我们小组应用头脑风暴法在小组内经过激烈讨论，并在网上查阅大量的资料，基本上确定影响当今大学生就业的因素大致为以下12种原因，小组成员又经过多次探讨分析确定他们之间的关系并按照下面的影响关系填写表2所示的框图。 （1）j S i S 对有影响，填1；j S i S 对无影响，填0；（i ，j=0,1，……12） （2）对于有相互影响的因素，取你认为影响大一方为影响关系，即有影响； 表1导致因素 关键问题：大学生就业问题 0S 导致因素 1 专业设置与社会需求脱节 1S 2 就业政策不完善 2S 3 竞争压力较大（ 3S 4 教育机制存在弊端 4S 5 海归的竞争 5S 6 大学生就业观念 6S 7 缺乏工作经验 7S 8 知识陈旧，转化率低 8S
1什么是结构方程模型？ 结构方程模型是应用线性方程表示观测变量与潜变量之间，以及潜在变量之间关系的一种多元统计方法，其实质是一种广义的一般线性模型。 ?結構方程模式(Structural Equation Models，簡稱SEM)，早期稱為線性結構方程模式(Linear Structural Relationships，簡稱LISREL)或稱為共變數結構分析(Covariance Structure Analysis)。 ?主要目的在於考驗潛在變項(Latent variables)與外顯變項(Manifest variable, 又稱觀察變項)之關係，此種關係猶如古典測驗理論中真分數(true score)與實得分數(observed score)之關係。它結合了因素分析(factor analysis)與路徑分析(path analysis)，包涵測量與結構模式。 ? 1.1介绍潜在变量与观察变量的概念 ?（1）很多社会、心理研究中所涉及到的变量，都不能准确、直接地测量，这种变量称为潜变量，如工作自主权、工作满意度等。 ?（2）这时，只能退而求其次，用一些外显指标，去间接测量这些潜变量。如用工作方式选择、工作目标调整作为工作自主权（潜变量）的指标，以目前工作满意度、工作兴趣、工作乐趣、工作厌恶程度（外显指标）作为工作满意度的指标。 ?（3）传统的统计分析方法不能妥善处理这些潜变量，而结构方程模型则能同时处理潜变量及其指标。 （4）书上第7页 观测变量：能够观测到的变量(路径图中以长方形表示) 潜在变量：难以直接观测到的抽象概念，由测量变量推估出来的变量（路 径图中以椭圆形表示） 内生变量：模型总会受到任何一个其他变量影响的变量（因变量；路径图 会受到任何一个其他变量以单箭头指涉的变量 外生变量：模型中不受任何其他变量影响但影响其他变量的变量（自变量； 路径图中会指向任何一个其他变量，但不受任何变量以单箭头指涉的变量） 中介变量：当内生变量同时做因变量和自变量时，表示该变量不仅被其他 变量影响，还可能对其他变量产生影响。 内生潜在变量：潜变量作为内生变量 外生观测变量：外生潜在变量的观测变量 外生潜在变量：潜变量作为外生变量 外生观测变量：外生潜在变量的观测变量 中介潜变量：潜变量作为中介变量 中介观测变量：中介潜在变量的观测变量 1.2介绍测量模型与结构模型的概念（书上第9页）
第一章绪论 1.1 模型表述 1.1.1 测量模型 1.1.2 结构模型 1.1.3 模型表达方程 1.2 模型识别 1.3 模型估计 1.4 模型评估 1.5 模型修正 附录1.1 将总体方差／协方差表达为模型参数的函数附录1.2 结构方程模型的最大似然函数 第二章验证性因子分析模型 2.1 验证性因子分析模型基础知识 2.2 连续观察标识的验证性因子分析模型 2.3 非正态与删截连续观察标识的验证性因子分析模型2. 3.1 非正态性检验 2.3.2 非正态数据的验证性因子分析模型 2.3.3 删截标识的验证性生因子分析模型 2.4 分类观察标识的验证性因子分析模型 2.5 高阶验证性因子分析模型 附录2.1 BSI-18量表 附录2.2 条目可靠度 附录2.3 Cronbacha系数 附录2.4 分类结局测量的连接函数和概率计算 第三章结构方程模型 3.1 MIMIC模型 3.2 结构方程模型 3.3 单标识变量中测量误差的校正 3.4 检验涉及潜变量的交互作用
附录3.1 测量误差的影响 第四章潜发展模型 4.1 线性潜发展模型 4.2 非线性潜发展模型 4.3 多结局测量发展过程的线性潜发展模型 4.4 两部式潜发展模型 4.5 分类结局测量的潜发展模型 第五章多组模型 5.1 多组验证性因子分析模型 5.1.1 多组一阶验证性因子分析模型 5.1.2 多组二阶验证性因子分析模型 5.2 多组结构方程模型 5.3 多组潜发展模型 第六章结构方程建模的样本量估计 6.1 结构方程模型样本量估计的经验法则 6.2 satorra-Saris法估计样本量 6.2.1 应用satorra-Saris法估计CFA模型的样本量 6.2.2 应用satorra-Saris法估计LGM模型的样本量 6.3 蒙特卡罗模拟法估计样本量 6.3.1 蒙特卡罗模拟法估计CFA模型的样本量 6.3.2 蒙特卡罗模拟法估计LGM模型的样本量 6.3.3 蒙特卡罗模拟法估计具有协变量的LGM模型样本量 6.3.4 蒙特卡罗模拟法估计具有协变量和缺失值的LGM模型样本量6.4 基于模型拟合统计量／指标的SEM样本量估计 参考文献
结构方程模型的研究进展与应用 引言 从大量事件样本进行统计分析，由事件的表象获得本质性的事件规律，是科研人员特别是管理工作者常见的研究工作方法，也是很有效的科学研究方法。 统计分析方法众多，深浅不一，效果各异。对于复杂事件而言，其牵涉的层面复杂，影响和制约因子众多，这些影响或制约因子往往又非孤立，而是相互牵涉、相互影响。故需要剖析事件的内在层面结构关系，分析事件的影响显在因子，并构建一定的结构方程模型，进而挖掘出事件的影响潜在因子，综合分析并构建一个或若干个事件发展的判断指标，且设定某一程度的判断标准，判断事件的发展动态。这样的统计分析方法就是结构方程模型。结构方程模型因其优越性得到飞快的发展和广泛的应用。 1 结构方程模型的研究 1.1 基本概念、思想及本质阐述 人们对于结构方程模型（StructuralEquationModeling简称为SEM）的概念的阐述也是变化的，有从含糊到明确、由片面到全面、由肤浅到不断深入、由定格到扩充和发展的过程。 20 世纪二三十年代，结构方程模型思想刚刚起源、萌芽时，起初确定为由Sewll Wright[1]最初提出了路径分析的概念，这种路径分析当时还没有定义为结构方程模型。之后的数十年中，对于路径分
析的方法和内涵不断扩充与展开。直到20 世纪70 年代，一些学者以Joreskog 和Wiley 为代表，将因子分析和路径分析等统计方法加以整合，明确提出结构方程模型的概念[2],结构方程模型的概念明确提出后，立即得到迅猛发展，内容进一步充实，方法扩充，针对实际研究对象的具体模式不断涌现，应用的范围迅速扩展。早期的结构方程模型跟数学中的数理统计方法不是很融合，结合不大，也没有注重数理统计方法的重要性和运用的实效性。结构方程模型所包含的内容也很少，结构较为简单，方法较为单一，所列出的影响因子较少，全为显性因子，对于潜在因子的重视和提出要求是在21 世纪初的事情了。 进入21 世纪后，人们对于结构方程模型的内在本质进一步明确，对其内涵进一步加以扩充，其模型结构图的构建越来越复杂，因子越列越多，潜在因子被明确提出并作为结构方程模型必须要求的内容。如今明确阐述结构方程模型为当代行为和社会领域量化研究的重要统计方法，是传统数理统计方法与一定的计算机技术相结合的产物（这一点对于现代和未来的结构方程模型的发展来说更为确切）。 当今学者[3,4]强调结构方程模型中包含显性指标、潜在变量、干扰或误差变量间的关系，进而获得自变量对因变量的直接效果、间接效果或总效果。其基本上是一种验证性方法，通常必须有理论或经验法则的支持，在理论引导的前提下才能构建模型结构图，并进行后续工作。即便是对于模型的修正，也必须依据相关理论进行，强调理论的合理性，故结构方程模型是较为严谨的一种统计分析方法和理论。