XXV ENCONTRO BRASILEIRO DE ECONOMETRIA Porto Seguro - BA, 2003 REVISITING DISTRIBUTED LAG MODELS THROUGH A BAYESIAN PERSPECTIVE

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1 XXV ENCONTRO BRASILEIRO DE ECONOMETRIA Porto Seguro - BA, 2003 REVISITING DISTRIBUTED LAG MODELS THROUGH A BAYESIAN PERSPECTIVE Romy R. Ravines, Alexandra M. Schmidt and Helio S. Migon 1 Instituto de Matemática Universidade Federal do Rio de Janeiro. 1 This work is part of the first author s PhD studies, who is being supervised by H.S. Migon and A.M. Schmidt. R.R. Ravines is grateful to CAPES for the the financial support during her PhD studies.

2 Outline Review some distributed lag models Write them into a particular class of Bayesian Dynamic Models, the transfer functions models. Perform inference following the Bayesian paradigm. Make use of Markov chain Monte Carlo (MCMC) methods. Computation is made by the use of the software WinBugs. An example: the Koyck s consumption function is analyzed using two different approaches, the Koyck s and the Solow s distributed lag models.

3 Distributed-lag Models First Prev Next Last Go Back Full Screen Close Quit

4 Distributed-lag Models The general form of a linear distributed-lag model is Y t = β i X t i + ɛ t (1) i=0 where any change in X t will affect E[Y t ] in all the later periods. The term β i in (1) is the ith reaction coefficient, and it is usually assumed that lim i β i = 0 and i=0 β i = β <.

5 Distributed-lag Models The general form of a linear distributed-lag model is Y t = β i X t i + ɛ t (1) i=0 where any change in X t will affect E[Y t ] in all the later periods. The term β i in (1) is the ith reaction coefficient, and it is usually assumed that lim i β i = 0 and i=0 β i = β <. One important aspect to be considered is the number of parameters involved in these distributed lag models. In order to be parsimonious, it is assumed that the coefficients of lagged variables are functionally related.

6 The Koyck Distributed Lag β i = αλ i, i, with 0 < λ < 1. (2) Then, Y t = αx t + αλ 1 X t 1 + αλ 2 X t ɛ t. (3)

7 The Koyck Distributed Lag Then, β i = αλ i, i, with 0 < λ < 1. (2) Y t = αx t + αλ 1 X t 1 + αλ 2 X t ɛ t. (3) The Solow Distributed Lags ( ) r + i 1 β i = α (1 λ) r λ, 0 < λ < 1, i, r > 0. (4) i

8 The Koyck Distributed Lag Then, β i = αλ i, i, with 0 < λ < 1. (2) Y t = αx t + αλ 1 X t 1 + αλ 2 X t ɛ t. (3) The Solow Distributed Lags ( ) r + i 1 β i = α (1 λ) r λ, 0 < λ < 1, i, r > 0. (4) i The Almon Distributed Lags β i = α 0 + α 1 i + α 2 i α p i p = p α k i k. (5) k=0

9 (a) Koyck (b) Solow (c) Almon Figure 1: Examples of different Distributed Lag Models

10 Bayesian Dynamic Models First Prev Next Last Go Back Full Screen Close Quit

11 Bayesian Dynamic Models The general dynamic model is defined by {F (.), G(.), V, W } t. F (.) and G(.) are general smooth functions defining the mean of the response variable and the state parameters evolution. V and W represent the variances. For each t, the univariate Dynamic Linear Model (DLM) is Observation equation: Y t =F tθ t + ɛ t, ɛ t N(0, V t ) System equation: θ t =G t θ t 1 + ω t, ω t N(0, W t ) (6) Initial information: (θ 0 D 0 ) N[m 0, C 0 ] Extensions for non-linear (unknown G) and non-normal Bayesian models are easily introduced. First Prev Next Last Go Back Full Screen Close Quit

12 Bayesian Dynamic Models The general dynamic model is defined by {F (.), G(.), V, W } t. F (.) and G(.) are general smooth functions defining the mean of the response variable and the state parameters evolution. V and W represent the variances. For each t, the univariate Dynamic Linear Model (DLM) is Observation equation: Y t =F tθ t + ɛ t, ɛ t N(0, V t ) System equation: θ t =G t θ t 1 + ω t, ω t N(0, W t ) (6) Initial information: (θ 0 D 0 ) N[m 0, C 0 ] Extensions for non-linear (unknown G) and non-normal Bayesian models are easily introduced. The distributed lagged models can be seen as particular cases of DLM. Hence, they can be expressed in the form of (6). First Prev Next Last Go Back Full Screen Close Quit

13 Form-free transfer functions m E(Y t θ t ) = β i X t i = β 0 X t + β 1 X t β m X t m, (7) i=0 The transfer response function of X is { β i X i = 0, 1,..., m; 0 i > m.

14 Form-free transfer functions m E(Y t θ t ) = β i X t i = β 0 X t + β 1 X t β m X t m, (7) i=0 The transfer response function of X is { β i X i = 0, 1,..., m; 0 i > m. Functional form transfer functions Y t = F θ t + ɛ t (8a) θ t = Gθ t 1 + ψ t X t + θ t (8b) ψ t = ψ t 1 + ψ t (8c) The transfer function model (8) can be written as the standard DLM form: Y t = F θt + ɛ t, ɛ t N(0, σɛ 2 ) θ t = G t θt 1 + ω t, ω t N(0, σωi). 2 (9)

15 Bayesian Dynamic Models and Distributed Lag Models First Prev Next Last Go Back Full Screen Close Quit

16 Bayesian Dynamic Models and Distributed Lag Models The Koyck distributed lag model, can be rewritten as Y t = E t + ɛ t (10a) E t = λe t 1 + αx t (10b) where E t = αx t + αλ 1 X t 1 + αλ 2 X t and 0 < λ < 1. In this model, the transfer response function of X is simply αλ i X. Here, in the representation (8), we have n = 1, θ t = E t, the effect variable, ψ t = α, the current effect for all t, F = 1, G = λ and the noise term θ t is assumed to be zero. First Prev Next Last Go Back Full Screen Close Quit

17 Bayesian Dynamic Models and Distributed Lag Models The Solow s distributed lag model can be expressed in the form of (9). In this case we have (1 λl) r E t = α(1 λ) r X t. An evolution equation can be assigned for λ and r.

18 Bayesian Dynamic Models and Distributed Lag Models The Solow s distributed lag model can be expressed in the form of (9). In this case we have (1 λl) r E t = α(1 λ) r X t. An evolution equation can be assigned for λ and r. Also, the Almon s model can be rewritten in a free form transfer function (7) since it is a regression on a fixed and finite number of lagged variables. By using (5) to define the coefficients of the lagged variables, the transfer response function of X t i in the mean of Y t will be { p k=0 α ki k X t i i = 0, 1,..., m; 0 i > m.

19 Inference Procedure First Prev Next Last Go Back Full Screen Close Quit

20 Inference Procedure From Bayes theorem: π(θ x) l(x θ)π(θ) Bayesian inference has experienced a great development since the early 90 s due to the introduction of Markov Chain Monte Carlo (MCMC) methods. One of the most popular methods and easy to implement is the Gibbs Sampling. For highly structured (complex) models, we usually have to write our own program.

21 Inference Procedure From Bayes theorem: π(θ x) l(x θ)π(θ) Bayesian inference has experienced a great development since the early 90 s due to the introduction of Markov Chain Monte Carlo (MCMC) methods. One of the most popular methods and easy to implement is the Gibbs Sampling. For highly structured (complex) models, we usually have to write our own program. This task has been simplified after the introduction of the software BUGS (Bayesian Analysis using Gibbs Sampling). BUGS was developed in the MRC Biostatistics Unit and is available free of charge from

22 Application: Consumption Function Estimation First Prev Next Last Go Back Full Screen Close Quit

23 Application: Consumption Function Estimation Zellner and Geisel (1970) proposed this consumption function: Y t = kx t + ɛ t (11) where, for the tth period, t = 1, 2,..., T, Y t is measured real consumption, Xt is normal real income, k is a parameter whose value is unknown, and ɛ t is an error term or transitory consumption. Assuming that the normal income satisfies X t X t 1 = (1 λ)(x t X t 1) where the parameter λ is such that 0 < λ < 1, the model becomes: Y t = k(1 λ)(x t + λx t 1 + λ 2 X t ) + ɛ t (12)

24 The transfer function model is: Y t = E t + ɛ t E t = λe t 1 + ψx t (13) where ψ = k(1 λ) and E t = ψx t + λψx t 1 + λ 2 ψx t

25 The transfer function model is: Y t = E t + ɛ t E t = λe t 1 + ψx t (13) where ψ = k(1 λ) and E t = ψx t + λψx t 1 + λ 2 ψx t Several assumptions can be made about the serial correlation properties of the residual ɛ t in (13): Model I: ɛ t ind N(0, σ2 ); Model II: ɛ t = ρɛ t 1 + ν t. with ν t ind N(0, σ2 )

26 The transfer function model is: Y t = E t + ɛ t E t = λe t 1 + ψx t (13) where ψ = k(1 λ) and E t = ψx t + λψx t 1 + λ 2 ψx t Several assumptions can be made about the serial correlation properties of the residual ɛ t in (13): Model I: ɛ t ind N(0, σ2 ); Model II: ɛ t = ρɛ t 1 + ν t. with ν t ind N(0, σ2 ) A priori we set λ Be(1, 1) and k Be(1, 1), σ 2 IG(0.0001, ) and, for Model II, ρ N(0, 100). First Prev Next Last Go Back Full Screen Close Quit

27 The transfer function model is: Y t = E t + ɛ t E t = λe t 1 + ψx t (13) where ψ = k(1 λ) and E t = ψx t + λψx t 1 + λ 2 ψx t Several assumptions can be made about the serial correlation properties of the residual ɛ t in (13): Model I: ɛ t ind N(0, σ2 ); Model II: ɛ t = ρɛ t 1 + ν t. with ν t ind N(0, σ2 ) A priori we set λ Be(1, 1) and k Be(1, 1), σ 2 IG(0.0001, ) and, for Model II, ρ N(0, 100). Similarly to Zellner and Geisel (1970), we use the U.S. quarterly price-deflated, seasonally adjusted data on personal disposable income and personal consumption expenditure,1947.i-1960.iv First Prev Next Last Go Back Full Screen Close Quit

28 Example of a code used in WinBUGS model #(Y=consumption, X=income) { for(t in 2:T) { mean.y[t] <- lambda*y[t-1] + k*(1-lambda)*x[t] Y[t] dnorm(mean.ct[t], tau.v) } # Prior lambda dbeta(1,1) k dbeta(1,1) tau.v dgamma(0.0001,0.0001) var.v <- 1/tau.v }

29 Figure 2: Interface do WinBUGS First Prev Next Last Go Back Full Screen Close Quit

30 Table 1: Posterior summaries associated with parameters in Model I k λ mean modes sd mean modes sd ZG KT T F ZG = Zellner & Geisel, 2 KT = Koyck s transformation, 3 TF = Transfer function (a) k (b) λ Figure 3: Posterior densities of k and λ in Model I, using TF

31 Table 2: Posterior summaries associated with parameters in Model II k λ ρ mean median sd mean median sd mean median sd ZG KT TF ZG = Zellner & Geisel, 2 KT = Koyck s transformation, 3 TF = Transfer function (a) k (b) λ Figure 4: Posterior densities of k and λ in Model II, using TF First Prev Next Last Go Back Full Screen Close Quit

32 Comparing Koyck s models First Prev Next Last Go Back Full Screen Close Quit

33 Comparing Koyck s models The Deviance Information Criterion (DIC) can be used to assess model complexity and compare different models. DIC is given by DIC = D + p D = D( θ) + 2p D (14) The WinBUGS version 1.4 computes the DIC automatically. The model with the smallest DIC is considered to be the best one.

34 Comparing Koyck s models The Deviance Information Criterion (DIC) can be used to assess model complexity and compare different models. DIC is given by DIC = D + p D = D( θ) + 2p D (14) The WinBUGS version 1.4 computes the DIC automatically. The model with the smallest DIC is considered to be the best one. Table 3: Model comparison through DIC Deviance DIC Model I Koyck s transformation Transfer function Model II Koyck s transformation Transfer function

35 We use the same dataset to estimate some particular Solow s models First Prev Next Last Go Back Full Screen Close Quit

36 We use the same dataset to estimate some particular Solow s models First, we adjusted three Solow s models for fixed r = 1, 2, 3 in (4), assuming that ɛ t ind N(0, σ2 ) in (11) Then, we allowed r to vary in the discrete set {1, 2, 3}. We used a Categorical priori distribution for r, that is, r Categorial(p) with p = (0.2, 0.3, 0.5), and started the chain with r 0 = 3. First Prev Next Last Go Back Full Screen Close Quit

37 We use the same dataset to estimate some particular Solow s models First, we adjusted three Solow s models for fixed r = 1, 2, 3 in (4), assuming that ɛ t ind N(0, σ2 ) in (11) Then, we allowed r to vary in the discrete set {1, 2, 3}. We used a Categorical priori distribution for r, that is, r Categorial(p) with p = (0.2, 0.3, 0.5), and started the chain with r 0 = 3. Table 4: Posterior summaries and DIC associated with the Solow s models k λ DIC mean modes sd mean modes sd r= r= r= r=1,2, First Prev Next Last Go Back Full Screen Close Quit

38 (a) k (b) λ (c) Prob(r = 1 y) (d) Prob(r = 2 y) (e) Prob(r = 3 y) Figure 5: Posterior distribution of the parameters in Solow s Model with a categorical prior for r

39 Discussion and Future work First Prev Next Last Go Back Full Screen Close Quit

40 Discussion and Future work We discussed here the inference of distributed lag models under a Bayesian framework.

41 Discussion and Future work We discussed here the inference of distributed lag models under a Bayesian framework. Inference of distributed lag models can be performed using MCMC algorithms. Use of the software WinBUGS

42 Discussion and Future work We discussed here the inference of distributed lag models under a Bayesian framework. Inference of distributed lag models can be performed using MCMC algorithms. Use of the software WinBUGS More complex models can be analyzed under the Bayesian framework: Migon (1985), Alves, Gamerman and Ferreira (2003).

43 Discussion and Future work We discussed here the inference of distributed lag models under a Bayesian framework. Inference of distributed lag models can be performed using MCMC algorithms. Use of the software WinBUGS More complex models can be analyzed under the Bayesian framework: Migon (1985), Alves, Gamerman and Ferreira (2003). An important theoretical issue, which is currently under research, is how to choose the form of the transfer function.

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