/* 
 *  gretl -- Gnu Regression, Econometrics and Time-series Library
 *  Copyright (C) 2001 Allin Cottrell and Riccardo "Jack" Lucchetti
 * 
 *  This program is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 * 
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 * 
 *  You should have received a copy of the GNU General Public License
 *  along with this program.  If not, see <http://www.gnu.org/licenses/>.
 * 
 */

/* tsls.c - Two-Stage Least Squares for gretl */

#include "libgretl.h"
#include "qr_estimate.h"
#include "libset.h"
#include "estim_private.h"
#include "missing_private.h"
#include "matrix_extra.h"
#include "tsls.h"

#define TDEBUG 0

static void tsls_omitzero (int *list, const DATASET *dset,
			   const char *mask)
{
    int i, v, t, allzero;

    for (i=list[0]; i>1; i--) {
        v = list[i];
	allzero = 1;
	for (t=dset->t1; t<=dset->t2; t++) {
	    if (mask != NULL && mask[t - dset->t1]) {
		continue;
	    }
	    if (dset->Z[v][t] != 0.0) {
		allzero = 0;
		break;
	    }
	}
        if (allzero) {
	    gretl_list_delete_at_pos(list, i);
	}
    }
}

/**
 * tsls_free_data:
 * @pmod: model to operate on.
 *
 * Frees the first-stage data attached to a two-stage least squares 
 * model that was estimated using %OPT_E (part of a system).
 */

void tsls_free_data (const MODEL *pmod)
{
    const char *endog = gretl_model_get_data(pmod, "endog");
    double **X = gretl_model_get_data(pmod, "tslsX");
    int i, m = 0;

    if (endog != NULL && X != NULL) {
	for (i=0; i<pmod->ncoeff; i++) {
	    if (endog[i]) m++;
	}
	for (i=0; i<m; i++) {
	    free(X[i]);
	}
    }
}

/* when tsls is called for initial estimation of an equation
   within a system of equations, save the first-stage fitted
   values on the model under the key "tslsX".
*/

static int 
tsls_save_data (MODEL *pmod, const int *hatlist, const int *exolist,
		DATASET *dset)
{
    double **X = NULL;
    char *endog = NULL;
    int i, v, err = 0;
    size_t esize, Xsize;
    size_t xs_old, es_old;
    int recycle_X = 0;
    int recycle_e = 0;

    esize = pmod->ncoeff * sizeof *endog;
    Xsize = hatlist[0] * sizeof *X;

    /* re-use old pointers if applicable */

    X = gretl_model_get_data_full(pmod, "tslsX", NULL, &xs_old);
    if (X != NULL) {
	if (Xsize == xs_old) {
	    recycle_X = 1;
	} else {
	    tsls_free_data(pmod);
	    gretl_model_detach_data_item(pmod, "tslsX");
	    free(X);
	    X = NULL;
	} 
    }

    if (!recycle_X && Xsize > 0) {
	X = malloc(Xsize);
	if (X == NULL) {
	    return E_ALLOC;
	}
    }

    endog = gretl_model_get_data_full(pmod, "endog", NULL, &es_old);
    if (endog != NULL) {
	if (esize == es_old) {
	    recycle_e = 1;
	} else {
	    gretl_model_detach_data_item(pmod, "endog");
	    free(endog);
	    endog = NULL;
	}
    }

    if (!recycle_e && esize > 0) {
	endog = malloc(esize);
	if (endog == NULL) {
	    free(X);
	    return E_ALLOC;
	}
    }

    /* steal the appropriate columns from Z */
    for (i=1; i<=hatlist[0]; i++) {
	v = hatlist[i];
	X[i-1] = dset->Z[v];
	dset->Z[v] = NULL;
    }

    for (i=0; i<pmod->ncoeff; i++) {
	v = pmod->list[i+2];
	endog[i] = !in_gretl_list(exolist, v);
    }

     /* now attach X and endog to the model */
    if (!recycle_X && X != NULL) {
	gretl_model_set_data(pmod, "tslsX", X, GRETL_TYPE_DOUBLE_ARRAY,
			     Xsize);
    }
    if (!recycle_e && endog != NULL) {
	gretl_model_set_data(pmod, "endog", endog, GRETL_TYPE_CHAR_ARRAY,
			     esize);
    }

    return err;
}

static void add_tsls_var (int *list, int v, gretlopt opt)
{
    int pos = gretl_list_separator_position(list);
    int i;

    if (opt & OPT_T) {
	/* add as instrument */
	list[0] += 1;
	list[list[0]] = v;
    } else if (opt & OPT_B) {
	/* add as exogenous regressor */
	list[0] += 2;
	for (i=list[0]-1; i>pos; i--) {
	    list[i] = list[i-1];
	}
	list[pos] = v;
	list[list[0]] = v;
    } else {
	/* add as endogenous regressor */
	list[0] += 1;
	for (i=list[0]; i>pos; i--) {
	    list[i] = list[i-1];
	}
	list[pos] = v;
    }
}

static void delete_tsls_var (int *list, int v, gretlopt opt)
{
    int pos = gretl_list_separator_position(list);
    int i, j;

    if (opt & OPT_T) {
	/* delete as instrument */
	for (i=pos+1; i<=list[0]; i++) {
	    if (list[i] == v) {
		for (j=i; j<list[0]; j++) {
		    list[j] = list[j + 1]; 
		}
		list[0] -= 1;
		break;
	    }
	}
    } else if (opt & OPT_B) {	
	/* delete from both sub-lists */
	for (i=2; i<=list[0]; i++) {
	    if (list[i] == v) {
		for (j=i; j<list[0]; j++) {
		    list[j] = list[j + 1]; 
		}
		list[0] -= 1;
	    }
	}
    } else {
	/* delete from regressor list */
	for (i=2; i<pos; i++) {
	    if (list[i] == v) {
		for (j=i; j<list[0]; j++) {
		    list[j] = list[j + 1]; 
		}
		list[0] -= 1;
		break;
	    }
	}
    }
}

static int in_ivreg_list (const int *list, int v, gretlopt opt)
{
    int pos = gretl_list_separator_position(list);
    int i, imin, imax;

    if (pos == 0) {
	return 0;
    }

    if (opt & OPT_T) {
	imin = pos + 1;
	imax = list[0];
    } else if (opt & OPT_B) {
	imin = 2;
	imax = list[0];
    } else {
	imin = 2;
	imax = pos - 1;
    }

    for (i=imin; i<=imax; i++) {
	if (list[i] == v) {
	    return i;
	}
    }

    return 0;
}

/**
 * ivreg_list_omit:
 * @orig: list specifying original IV model.
 * @drop: list of variables to be omitted.
 * @opt: may include %OPT_T (omit variable only as instrument)
 * or %OPT_B (omit both as regressor and instrument).  The default
 * is to omit (only) as regressor.
 * @err: pointer to receive error code.
 *
 * Creates a new IVREG specification list, by first copying @orig
 * then deleting from the copy the variables found in @drop.
 *
 * Returns: the new, reduced list or %NULL on error.
 */

int *
ivreg_list_omit (const int *orig, const int *drop, gretlopt opt, int *err)
{
    int *newlist;
    int i;

    *err = incompatible_options(opt, OPT_T | OPT_B);
    if (*err) {
	return NULL;
    }

    newlist = gretl_list_copy(orig);

    for (i=1; i<=drop[0]; i++) {
	if (in_ivreg_list(orig, drop[i], opt)) {
	    delete_tsls_var(newlist, drop[i], opt);
	} else {
	    *err = E_UNSPEC;
	}
    }

    if (*err) {
	free(newlist);
	newlist = NULL;
    }

    return newlist;
}

/**
 * ivreg_list_add:
 * @orig: list specifying original IV model.
 * @add: list of variables to be added.
 * @opt: may include %OPT_T (add variable only as instrument)
 * or %OPT_B (add both as regressor and instrument).  The default
 * is to add as an endogenous regressor.
 * @err: pointer to receive error code.
 *
 * Creates a new IVREG specification list, by copying @orig
 * and adding the variables found in @add.
 *
 * Returns: the new, augmented list or %NULL on error.
 */

int *
ivreg_list_add (const int *orig, const int *add, gretlopt opt, int *err)
{
    int norig = orig[0];
    int nadd = add[0];
    int *newlist;
    int i;

    *err = incompatible_options(opt, OPT_T | OPT_B);
    if (*err) {
	return NULL;
    }

    if (opt & OPT_B) {
	nadd *= 2;
    }

    newlist = gretl_list_new(norig + nadd);

    for (i=0; i<=norig; i++) {
	newlist[i] = orig[i];
    }

    for (i=1; i<=add[0]; i++) {
	if (in_ivreg_list(orig, add[i], opt)) {
	    *err = E_ADDDUP;
	} else {
	    add_tsls_var(newlist, add[i], opt);
	} 
    }

    if (*err) {
	free(newlist);
	newlist = NULL;
    }

    return newlist;
}

/**
 * tsls_make_endolist:
 * @reglist: regression specification.
 * @instlist: predetermined and exogenous variables.
 * @addconst: location to receive notification of whether
 * the constant was added to the content of @instlist,
 * or NULL.
 * @err: location to receive error code.
 *
 * Determines which variables in @reglist, when checked against the
 * predetermined and exogenous vars in @instlist, need to be 
 * instrumented, and populates the returned list accordingly.
 *
 * If @addconst is non-NULL and the constant is found in @reglist
 * but not in @instlist, then it is added to @instlist and this
 * is flagged by writing 1 into @addconst.
 *
 * Returns: allocated list of variables to be instrumented,
 * or NULL if there are no such variables.
 */

int *tsls_make_endolist (const int *reglist, int **instlist, 
			 int *addconst, int *err)
{
    int *hatlist = NULL;
    int i, vi;

    for (i=2; i<=reglist[0]; i++) {
	vi = reglist[i];
	if (!in_gretl_list(*instlist, vi)) {
	    if (vi == 0) {
		/* found const in reglist but not instlist: 
		   needs fixing -- or is this debatable? */
		if (addconst != NULL) {
		    *addconst = 1;
		}
	    } else {
		hatlist = gretl_list_append_term(&hatlist, vi);
		if (hatlist == NULL) {
		    *err = E_ALLOC;
		    return NULL;
		}
	    }
	} 
    }

    if (addconst != NULL && *addconst) {
	/* add constant to list of instruments */
	int clist[2] = {1, 0};

	*err = gretl_list_insert_list(instlist, clist, 1);
    }

    return hatlist;
}

/* fill the residuals matrix for tsls likelihood calculation */

static int fill_E_matrix (gretl_matrix *E, MODEL *pmod, 
			  const int *reglist, const int *instlist,
			  DATASET *dset)
{
    MODEL emod;
    int *elist;
    double uit;
    int nx = instlist[0];
    int i, vi, s, t, j = 0;
    int T = E->rows;
    int err = 0;

    elist = gretl_list_new(nx + 1);
    if (elist == NULL) {
	return E_ALLOC;
    }

    for (i=2; i<=elist[0]; i++) {
	elist[i] = instlist[i-1];
    }

    for (i=1; i<=reglist[0] && !err; i++) {
	vi = reglist[i];

	if (in_gretl_list(instlist, vi)) {
	    continue;
	}

	elist[1] = vi;

	if (model_has_missing_obs(pmod)) {
	    set_reference_missmask_from_model(pmod);
	}

	/* regress the given endogenous var on all the instruments */
	emod = lsq(elist, dset, OLS, OPT_A);
	if ((err = emod.errcode)) {
	    clear_model(&emod);
	    break;
	}

	/* put residuals into appropriate column of E and
	   increment the column: we should get exactly the same
	   count of non-NA values as for the original model
	*/
	s = 0;
	for (t=pmod->t1; t<=pmod->t2; t++) {
	    uit = emod.uhat[t];
	    if (!na(uit)) {
		if (s < T) {
		    gretl_matrix_set(E, s, j, uit);
		}
		s++;
	    }
	}
	j++;

	if (s != T) {
	    err = E_DATA;
	}

	clear_model(&emod);
    }

    free(elist);

    return err;
}

/* calculate the maximized log-likelihood for a just-identified
   model */

static int tsls_loglik (MODEL *pmod, 
			int nendo,
			const int *reglist,
			const int *instlist,
			DATASET *dset)
{
    gretl_matrix *E, *W;
    int T = pmod->nobs;
    int k = 1 + nendo;
    int err = 0;

    pmod->lnL = NADBL;

    E = gretl_matrix_alloc(T, k);
    W = gretl_matrix_alloc(k, k); 

    if (E == NULL || W == NULL) {
	err = E_ALLOC;
    }

    if (!err) {
	err = fill_E_matrix(E, pmod, reglist, instlist, dset);
    }

    if (!err) {
	err = gretl_matrix_multiply_mod(E, GRETL_MOD_TRANSPOSE,
					E, GRETL_MOD_NONE,
					W, GRETL_MOD_NONE);
    }

    if (!err) {
	double ldet = gretl_matrix_log_determinant(W, &err);

	if (err) {
	    pmod->lnL = NADBL;
	} else {
	    /* Davidson and MacKinnon, ETM, p. 538 */
	    pmod->lnL = -(T / 2.0) * (LN_2_PI + ldet);
	} 
    }
    
    mle_criteria(pmod, 0);

    gretl_matrix_free(E);
    gretl_matrix_free(W);

    return err;
}

/* perform the Sargan overidentification test for an IV model */

static int 
ivreg_sargan_test (MODEL *pmod, int Orank, int *instlist, 
		   DATASET *dset)
{
    int t1 = pmod->t1;
    int t2 = pmod->t2;
    int ninst = instlist[0];
    int i, t, err = 0;

    MODEL smod;
    int *OT_list = NULL;
    int nv = dset->v;

    if (Orank == 0) {
	return 0;
    }

    if (pmod->list[0] == 2 && pmod->list[2] == 0) {
	/* degenerate model with const only */
	return 0;
    }

    err = dataset_add_series(1, dset);
    if (err) {
	return err;
    }

    for (t=t1; t<=t2; t++) {
	dset->Z[nv][t] = pmod->uhat[t];
    }

    OT_list = gretl_list_new(ninst + 1);
    if (OT_list == NULL) {
	dataset_drop_last_variables(1, dset);
	return E_ALLOC;
    }

    OT_list[1] = nv;
    for (i=2; i<=OT_list[0]; i++) {
	OT_list[i] = instlist[i-1];
    }

#if TDEBUG
    fprintf(stderr, "running regression for Sargan test\n");
#endif

    smod = lsq(OT_list, dset, OLS, OPT_A);

    if (smod.errcode) {
	fprintf(stderr, "ivreg_sargan_test: smod.errcode = %d\n", smod.errcode);
	err = smod.errcode;
    } else {
	ModelTest *test = model_test_new(GRETL_TEST_SARGAN);
	double OTest = smod.rsq * smod.nobs;

	if (test != NULL) {
	    model_test_set_teststat(test, GRETL_STAT_LM);
	    model_test_set_dfn(test, Orank);
	    model_test_set_value(test, OTest);
	    model_test_set_pvalue(test, chisq_cdf_comp(Orank, OTest));
	    maybe_add_test_to_model(pmod, test);
	}	
    }

    clear_model(&smod);
    dataset_drop_last_variables(1, dset);
    free(OT_list);

    return err;
}

#define HTDBG 0

/* Hausman test via the regression method */

static int 
tsls_hausman_test (MODEL *tmod, int *reglist, int *hatlist,
		   DATASET *dset)
{
    MODEL hmod;
    double URSS;
    int *HT_list = NULL;
    int t, df;
    int nv = dset->v;
    int ku = 0;
    int err = 0;

#if HTDBG
    int nobs1;
    PRN *dbgprn = NULL;
    dbgprn = gretl_print_new(GRETL_PRINT_STDOUT, NULL);
#endif

    HT_list = gretl_list_add(reglist, hatlist, &err);
    if (err) {
	if (err == E_NOADD) {
	    /* more of a no-op than an error */
	    err = 0;
	} else {
	    fprintf(stderr, "tsls_hausman_test: gretl_list_add: err = %d\n", err);
	}
	return err;
    } 

#if TDEBUG
    fprintf(stderr, "running regression for Hausman test, 1\n");
#endif

    /* estimate the unrestricted model */
    hmod = lsq(HT_list, dset, OLS, OPT_A);
    if (hmod.errcode) {
	fprintf(stderr, "tsls_hausman_test: hmod.errcode (U) = %d\n", hmod.errcode);
	err = hmod.errcode;
	goto bailout;
    } 

#if HTDBG
    pprintf(dbgprn, "Hausman: unrestricted model (df = %d)\n", hmod.dfd);
    printmodel(&hmod, dset, OPT_NONE, dbgprn);
    nobs1 = hmod.nobs;
#endif

    if (hmod.dfd == 0) {
	/* perfect fit, can't do test */
	goto bailout;
    }

    URSS = hmod.ess;
    ku = hmod.ncoeff;

    /* add fitted values from unrestricted model to dataset */
    err = dataset_add_series(1, dset);
    if (err) {
	goto bailout;
    } 

    for (t=hmod.t1; t<=hmod.t2; t++) {
	dset->Z[nv][t] = hmod.yhat[t];
    }

    clear_model(&hmod);

    free(HT_list);
    HT_list = gretl_list_copy(reglist);
    HT_list[1] = nv;

#if TDEBUG
    fprintf(stderr, "running regression for Hausman test, 2\n");
#endif

    /* regress the U fitted values on the regressors of the restricted 
       model (so the sum of squares equals RRSS-URSS)
    */
    hmod = lsq(HT_list, dset, OLS, OPT_A);

#if HTDBG
    strcpy(dset->varname[dset->v - 1], "Ufitvals");
    pprintf(dbgprn, "Hausman: aux regression\n", hmod.dfd);
    printmodel(&hmod, dset, OPT_NONE, dbgprn);
    pprintf(dbgprn, "smpl1 = %d, smpl2 = %d\n", nobs1, hmod.nobs);
    pprintf(dbgprn, "k1 = %d, k2 = %d\n", hmod.ncoeff, ku);
#endif

    if (hmod.errcode) {
	fprintf(stderr, "tsls_hausman_test: hmod.errcode (D) = %d\n", hmod.errcode);
	err = hmod.errcode;
    } else if ((df = ku - hmod.ncoeff) > 0) {
	/* Degrees of freedom for the Hausman test need not be equal to the
	   number of added regressors, since some of them may not really be 
	   endogenous.
	*/
	double DRSS = hmod.ess;
	double HTest = (DRSS/URSS) * hmod.nobs;
	ModelTest *test = model_test_new(GRETL_TEST_IV_HAUSMAN);

#if HTDBG
	pprintf(dbgprn, "(RRSS - URSS) = %g, URSS = %g\n", DRSS, URSS);
	pprintf(dbgprn, "Htest = %g [%.4f]\n", HTest, chisq_cdf_comp(df, HTest));
#endif

	if (test != NULL) {
	    model_test_set_teststat(test, GRETL_STAT_WALD_CHISQ);
	    model_test_set_dfn(test, df);
	    model_test_set_value(test, HTest);
	    model_test_set_pvalue(test, chisq_cdf_comp(df, HTest));
	    maybe_add_test_to_model(tmod, test);
	}
    }

 bailout:

#if HTDBG
    gretl_print_destroy(dbgprn);
#endif

    dataset_drop_last_variables(dset->v - nv, dset);
    clear_model(&hmod);
    free(HT_list);

    return err;
}

/* form matrix of instruments and perform QR decomposition
   of this matrix; return Q */

static gretl_matrix *tsls_Q (int *instlist, int **pdlist,
			     const DATASET *dset, char *mask, 
			     int *err)
{
    gretl_matrix *Q = NULL;
    gretl_matrix *R = NULL;
    int rank, ndrop = 0;
    int *droplist = NULL;
    double test;
    int i, j, k;

    Q = gretl_matrix_data_subset_masked(instlist, dset, 
					dset->t1, dset->t2, 
					mask, err);
    if (*err) {
	return NULL;
    }

    k = gretl_matrix_cols(Q);

    if (k > Q->rows) {
	/* can't do QR decomp! */
	gretl_errmsg_sprintf(_("Number of instruments (%d) exceeds the "
			       "number of observations (%d)"),
			     k, Q->rows);
	*err = E_DF;
	goto bailout;
    }

    R = gretl_matrix_alloc(k, k);
    if (R == NULL) {
	*err = E_ALLOC;
	goto bailout;
    } 

    *err = gretl_matrix_QR_decomp(Q, R);
    if (*err) {
	goto bailout;
    }

    rank = gretl_check_QR_rank(R, err, NULL);

#if TDEBUG
    fprintf(stderr, "tsls_Q: k = Q->cols = %d, rank = %d\n", k, rank);
    if (rank == 0) {
	gretl_matrix_print(R, "R");
    }
#endif

    if (*err) {
	goto bailout;
    } else if (rank < k) {
	fprintf(stderr, "tsls_Q: k = %d, rank = %d\n", k, rank);
	ndrop = k - rank;
    }

    if (ndrop > 0) {
	droplist = gretl_list_new(ndrop);
	if (droplist != NULL) {
	    droplist[0] = 0;
	}

	j = 1;
	for (i=0; i<k; i++) {
	    test = gretl_matrix_get(R, i, i);
	    if (fabs(test) < R_DIAG_MIN) {
		if (droplist != NULL) {
		    droplist[0] += 1;
		    droplist[droplist[0]] = instlist[j];
		}	    
		fprintf(stderr, "tsls_Q: dropping redundant instrument %d (%s)\n", 
			instlist[j], dset->varname[instlist[j]]);
		gretl_list_delete_at_pos(instlist, j--);
	    }
	    j++;
	}

	k = instlist[0];
	gretl_matrix_free(Q);
	Q = gretl_matrix_data_subset_masked(instlist, dset, 
					    dset->t1, dset->t2, 
					    mask, err);
	if (!*err) {
	    R = gretl_matrix_reuse(R, k, k);
	    *err = gretl_matrix_QR_decomp(Q, R);
	}
    }

 bailout:

    gretl_matrix_free(R);

    if (*err) {
	free(droplist);
	gretl_matrix_free(Q);
	Q = NULL;
    } else {
	*pdlist = droplist;
    }

    return Q;
}

static int tsls_form_xhat (gretl_matrix *Q, gretl_matrix *r,
			   DATASET *dset, int v0, int v1, 
			   const char *mask)
{
    const double *x = dset->Z[v0];
    double *xhat = dset->Z[v1];
    int k = gretl_matrix_cols(Q);
    int allzero = 1;
    int i, t, s = 0;
    int err = 0;

#if TDEBUG > 1
    fprintf(stderr, "tsls_form_xhat: v0=%d, v1=%d, t1=%d, t2=%d, mask=%p\n",
	    v0, v1, dset->t1, dset->t2, (void *) mask);
#endif

    /* form r = Q'y */
    for (i=0; i<k; i++) {
	r->val[i] = 0.0;
	s = 0;
	for (t=dset->t1; t<=dset->t2; t++) {
	    if (mask != NULL && mask[t - dset->t1]) {
		continue;
	    }
	    r->val[i] += gretl_matrix_get(Q, s++, i) * x[t];
	}
    }

    /* form Qr = QQ'y */
    s = 0;
    for (t=dset->t1; t<=dset->t2; t++) {
	if (mask != NULL && mask[t - dset->t1]) {
	    xhat[t] = NADBL;
	    continue;
	}
	xhat[t] = 0.0;
	for (i=0; i<k; i++) {
	    xhat[t] += gretl_matrix_get(Q, s, i) * r->val[i];
	}
	if (xhat[t] != 0) {
	    allzero = 0;
	}
	s++;
    }

    if (allzero) {
	gretl_errmsg_sprintf(_("The first-stage fitted values for %s are all zero"),
			     dset->varname[v0]);
	err = E_DATA;
    } else {
	/* name the fitted series according to the original */
	strcpy(dset->varname[v1], "h_");
	strncat(dset->varname[v1], dset->varname[v0], VNAMELEN - 3);
    }

    return err;
}

static void tsls_residuals (MODEL *pmod, const int *reglist, 
			    const DATASET *dset,
			    gretlopt opt)
{
    int yno = reglist[1];
    double yh, sigma0 = pmod->sigma;
    int i, t;

    pmod->ess = 0.0;

    for (t=pmod->t1; t<=pmod->t2; t++) {
	if (model_missing(pmod, t)) {
	    continue;
	}
	yh = 0.0;
	for (i=0; i<pmod->ncoeff; i++) {
	    yh += pmod->coeff[i] * dset->Z[reglist[i+2]][t];
	}
	pmod->yhat[t] = yh; 
	pmod->uhat[t] = dset->Z[yno][t] - yh;
	pmod->ess += pmod->uhat[t] * pmod->uhat[t];
    }

    if (pmod->ess <= 0.0) {
	pmod->sigma = 0.0;
    } else {
	int den = (opt & OPT_N)? pmod->nobs : pmod->dfd;

	pmod->sigma = sqrt(pmod->ess / den);
    }

    if (sigma0 > 0.0) {
	double corr = pmod->sigma / sigma0;

	for (i=0; i<pmod->ncoeff; i++) {
	    pmod->sderr[i] *= corr;
	}
    }
}

#define TSLS_CORR_RSQ 1

static void tsls_extra_stats (MODEL *pmod, int yno, int overid, 
			      const DATASET *dset)
{
    double r;

#if TSLS_CORR_RSQ
    pmod->rsq = gretl_corr_rsq(pmod->t1, pmod->t2, dset->Z[yno], pmod->yhat);
#else
    pmod->rsq = 1 - pmod->ess / pmod->tss;
#endif

    r = 1 - pmod->rsq;
    pmod->adjrsq = 1.0 - (r * (pmod->nobs - 1) / pmod->dfd);

    pmod->fstt = pmod->chisq = NADBL;

    if (pmod->ncoeff > 1) {
	if (overid && pmod->ncoeff == 2) {
	    pmod->chisq = wald_omit_chisq(NULL, pmod);
	} else {	
	    pmod->fstt = wald_omit_F(NULL, pmod);
	}
    } 

    /* tsls is not an ML estimator unless it's exactly identified, 
       and in that case we require a special calculation */
    pmod->lnL = NADBL;
    pmod->criterion[C_AIC] = NADBL;
    pmod->criterion[C_BIC] = NADBL;
    pmod->criterion[C_HQC] = NADBL;
    
    if (dataset_is_time_series(dset) && !model_has_missing_obs(pmod)) {
	/* time series, no missing obs within sample range */
	pmod->rho = rhohat(1, pmod->t1, pmod->t2, pmod->uhat);
	pmod->dw = dwstat(1, pmod, (const double **) dset->Z);
    } else {
	pmod->rho = pmod->dw = NADBL;
    }
}

static void tsls_recreate_full_list (MODEL *pmod, const int *reglist,
				     const int *instlist)
{
    int *full_list;

    full_list = gretl_lists_join_with_separator(reglist, instlist);

    if (full_list == NULL) {
	pmod->errcode = E_ALLOC;
    } else {
	free(pmod->list);
	pmod->list = full_list;
    }
}

static void replace_list_element (int *list, int targ, int repl)
{
    int i;

    for (i=1; i<=list[0]; i++) {
	if (list[i] == targ) {
	    list[i] = repl;
	    break;
	}
    }
}

static int reglist_remove_redundant_vars (const MODEL *tmod, 
					  int *s2list, 
					  int *reglist, 
					  int *endolist,
					  int *hatlist)
{
    int *dlist = gretl_model_get_data(tmod, "droplist");
    int i, pos;

    if (dlist == NULL) {
	return 1;
    }

    for (i=1; i<=dlist[0]; i++) {
	pos = in_gretl_list(s2list, dlist[i]);
	if (pos > 1) {
	    gretl_list_delete_at_pos(reglist, pos);
	}
	pos = in_gretl_list(hatlist, dlist[i]);
	if (pos > 1) {
	    /* First replace dlist[i] with the ID of the original
	       regressor from @endolist, in place of the "hatlist"
	       variable (which will be deleted when the model is
	       returned), so that the printout of regressors that are
	       dropped due to exact collinearity will show the
	       appropriate names.

	       Second, delete the redundant term from both endolist
	       and hatlist, so that subsequent calculations will not
	       get messed up.
	    */	    
	    dlist[i] = endolist[pos];
	    gretl_list_delete_at_pos(endolist, pos);
	    gretl_list_delete_at_pos(hatlist, pos);
	}
    }

    return 0;
}

/* Stock-Yogo: compute test statistic for weakness of instruments.
   This is the Cragg-Donald statistic: the minimum eigenvalue of the
   matrix defined below (which reduces to the first-stage F-statistic
   when there is just one endogenous regressor).  See Stock and Yogo,
   "Testing for Weak Instruments in Linear IV Regressions" (originally
   NBER Technical Working Paper 284; revised February 2003), at
   http://ksghome.harvard.edu/~JStock/pdf/rfa_6.pdf
*/

static int 
compute_stock_yogo (MODEL *pmod, const int *endolist, 
		    const int *instlist, const int *hatlist,
		    const DATASET *dset)
{
    const double **dZ = (const double **) dset->Z;
    gretl_matrix_block *B = NULL;
    gretl_matrix_block *B2 = NULL;
    gretl_matrix *G, *S, *Y;
    gretl_matrix *Ya, *YPY;
    gretl_matrix *X, *Z, *E = NULL;
    int T = pmod->nobs;
    int n = endolist[0];
    int K1 = pmod->ncoeff - n;
    int K2 = instlist[0] - K1;
    int i, s, vi, vj, t;
    int err = 0;

    B = gretl_matrix_block_new(&G, n, n,
			       &S, n, n,
			       &Y, T, n,
			       &Ya, T, n,
			       &YPY, n, n,
			       NULL);
    if (B == NULL) {
	return E_ALLOC;
    }

#if TDEBUG
    fprintf(stderr, "stock_yogo: pmod->ncoeff=%d, n=%d, K1=%d, K2=%d\n",
	    pmod->ncoeff, n, K1, K2);
    printlist(endolist, "endolist, in stock-yogo");
#endif

    if (K1 > 0) {
	/* we have some exogenous regressors */
	gretl_matrix *M;
	int j, ix = 0, iz = 0;

	B2 = gretl_matrix_block_new(&X, T, K1,
				    &Z, T, K2,
				    &E, T, K2,
				    NULL);
	if (B2 == NULL) {
	    gretl_matrix_block_destroy(B);
	    return E_ALLOC;
	}

	/* form the X and Z matrices */
	for (i=1; i<=instlist[0]; i++) {
	    vi = instlist[i];
	    if (in_gretl_list(pmod->list, vi)) {
		/* included exogenous var -> X */
		M = X;
		j = ix++;
	    } else {
		/* excluded exogenous var -> Z */
		M = Z;
		j = iz++;
	    }
	    s = 0;
	    for (t=pmod->t1; t<=pmod->t2; t++) {
		if (!na(pmod->uhat[t])) {
		    gretl_matrix_set(M, s++, j, dZ[vi][t]);
		}
	    }
	}
    }	

    /* form Y matrix */
    for (i=0; i<n; i++) {
	vi = endolist[i+1];
	s = 0;
	for (t=pmod->t1; t<=pmod->t2; t++) {
	    if (!na(pmod->uhat[t])) {
		gretl_matrix_set(Y, s++, i, dZ[vi][t]);
	    }
	}
    }

    if (K1 == 0) {
	/* form Y-hat matrix in Ya */
	for (i=0; i<n; i++) {
	    vi = hatlist[i+1];
	    s = 0;
	    for (t=pmod->t1; t<=pmod->t2; t++) {
		if (!na(pmod->uhat[t])) {
		    gretl_matrix_set(Ya, s++, i, dZ[vi][t]);
		}
	    }
	}
    } else {
	int bdim = (K1 > K2)? K1 : K2;
	gretl_matrix *b;

	/* the leading dimension of b is the number of regressors */
	b = gretl_matrix_alloc(bdim, K2);
	if (b == NULL) {
	    err = E_ALLOC;
	}

	if (!err) {
	    /* partial X out of Y: Y <- M_x Y */
	    gretl_matrix_reuse(b, K1, n);
	    gretl_matrix_reuse(E, -1, n);
	    err = gretl_matrix_multi_ols(Y, X, b, E, NULL);
	    gretl_matrix_copy_values(Y, E);
	}

	if (!err) {
	    /* partial X out of Z: Z <- M_x Z */
	    gretl_matrix_reuse(b, K1, K2);
	    gretl_matrix_reuse(E, -1, K2);
	    err = gretl_matrix_multi_ols(Z, X, b, E, NULL);
	    gretl_matrix_copy_values(Z, E);
	}

	if (!err) {
	    /* form projection P_z Y, in Ya */
	    gretl_matrix_reuse(b, K2, n);
	    gretl_matrix_reuse(E, -1, n);
	    err = gretl_matrix_multi_ols(Y, Z, b, E, NULL);
	    gretl_matrix_copy_values(Ya, Y);
	    gretl_matrix_subtract_from(Ya, E);
	}

	gretl_matrix_free(b);
    } 

    if (!err) {
	/* form Y' P_z Y */
	err = gretl_matrix_multiply_mod(Y, GRETL_MOD_TRANSPOSE,
					Ya, GRETL_MOD_NONE,
					YPY, GRETL_MOD_NONE);
    }

    /* now write first-stage residuals into Ya */
    for (i=0; i<n && !err; i++) {
	vi = endolist[i+1];
	vj = hatlist[i+1];
	s = 0;
	for (t=pmod->t1; t<=pmod->t2; t++) {
	    if (!na(pmod->uhat[t])) {
		gretl_matrix_set(Ya, s++, i, dZ[vi][t] - dZ[vj][t]);
	    }
	}
    }    

    if (!err) {
	/* form S = \hat{\Sigma}_{vv} */
	err = gretl_matrix_multiply_mod(Y, GRETL_MOD_TRANSPOSE,
					Ya, GRETL_MOD_NONE,
					S, GRETL_MOD_NONE);
	if (!err) {
	    gretl_matrix_xtr_symmetric(S);
	    gretl_matrix_divide_by_scalar(S, T - K1 - K2);
	    for (i=0; i<S->rows; i++) {
		if (gretl_matrix_get(S, i, i) <= 0) {
		    err = E_SINGULAR;
		    break;
		}
	    }
	}
    }

    if (!err) {
	double rc = gretl_symmetric_matrix_rcond(S, &err);

	if (!err && (na(rc) || rc < 1.0e-7)) { /* note: was 1.0e-6 */
#if 0
	    fprintf(stderr, "Stock-Yogo: rcond(S) = %g\n", rc);
#endif
	    err = E_SINGULAR;
	}
    }

    if (!err) {
	/* S^{-1/2}: invert S and Cholesky-decompose */
	err = gretl_invert_symmetric_matrix(S);
	if (!err) {
	    err = gretl_matrix_cholesky_decomp(S);
	} 
    }

    if (!err) {
	/* finally, form big sandwich */
	err = gretl_matrix_qform(S, GRETL_MOD_TRANSPOSE,
				 YPY, G, GRETL_MOD_NONE);
	if (!err) {
	    gretl_matrix_divide_by_scalar(G, K2);
	}
    }

    if (!err) {
	double gmin = gretl_symm_matrix_lambda_min(G, &err); 

	if (!err) {
	    gretl_model_set_double(pmod, "gmin", gmin);
	    gretl_model_set_int(pmod, "n", n);
	    gretl_model_set_int(pmod, "K2", K2);
	}	    
    }

    gretl_matrix_block_destroy(B);
    gretl_matrix_block_destroy(B2);

#if 0
    if (err) {
	fprintf(stderr, "compute_stock_yogo: err = %d\n", err);
    }
#endif

    return err;
}

/* Calculate first-stage F-test as diagnostic for weak instruments.
   This variant can only handle one endogenous regressor, but it
   supports robust estimation if the model in question uses a robust
   covariance estimator.
*/

static int compute_first_stage_F (MODEL *pmod, 
				  const int *endolist, 
				  const int *reglist, 
				  const int *instlist, 
				  DATASET *dset, 
				  gretlopt opt)
{
    MODEL mod1;
    int *list1 = NULL;
    int *flist = NULL;
    double F;
    int i, ev, vi;
    int err = 0;

    gretl_model_init(&mod1);
    ev = endolist[1];

    /* The regression list, list1, has endogenous regressor ev as the
       dependent variable and includes all instruments; the list for
       omission, flist, includes all the excluded instruments.
    */

    list1 = gretl_list_append_term(&list1, ev);
    if (list1 == NULL) {
	return E_ALLOC;
    }

    for (i=1; i<=instlist[0]; i++) {
	vi = instlist[i];
	list1 = gretl_list_append_term(&list1, vi);
	if (list1 == NULL) {
	    err = E_ALLOC;
	    break;
	}
	if (!in_gretl_list(reglist, vi)) {
	    flist = gretl_list_append_term(&flist, vi);
	    if (flist == NULL) {
		err = E_ALLOC;
		break;
	    }
	}
    }

    if (!err) {
	gretlopt myopt = (opt & OPT_R)? OPT_R : OPT_NONE;

	mod1 = lsq(list1, dset, OLS, myopt | OPT_A);
	err = mod1.errcode;
	if (err) {
	    fprintf(stderr, "compute_first_stage F: lsq failed\n");
	}
    }

    if (!err) {
	F = wald_omit_F(flist, &mod1);
	if (na(F)) {
	    err = 1;
	}
    }

    if (!err) {
	gretl_model_set_double(pmod, "stage1-F", F);
	gretl_model_set_int(pmod, "stage1-dfn", flist[0]);
	gretl_model_set_int(pmod, "stage1-dfd", mod1.dfd);
	if (!(opt & OPT_R)) {
	    /* flag lookup of Stock-Yogo critical values */
	    gretl_model_set_double(pmod, "gmin", F);
	}	
    }
    
    clear_model(&mod1);
    free(list1);
    free(flist);

    return err;
}

static int tsls_adjust_sample (const int *list, DATASET *dset,
			       char **pmask)
{
    int i, t, t1min = dset->t1, t2max = dset->t2;
    char *mask = NULL;
    int T, vi, missobs;
    int err = 0;

    /* advance start of sample range to skip missing obs? */
    for (t=t1min; t<t2max; t++) {
	missobs = 0;
	for (i=1; i<=list[0]; i++) {
	    vi = list[i];
	    if (vi == 0 || vi == LISTSEP) {
		continue;
	    }
	    if (na(dset->Z[vi][t])) {
		missobs = 1;
		break;
	    }
	}
	if (missobs) {
	    t1min++;
	} else {
	    break;
	}
    }

    /* retard end of sample range to skip missing obs? */
    for (t=t2max; t>t1min; t--) {
	missobs = 0;
	for (i=1; i<=list[0]; i++) {
	    vi = list[i];
	    if (vi == 0 || vi == LISTSEP) {
		continue;
	    }
	    if (na(dset->Z[vi][t])) {
		missobs = 1;
		break;
	    }
	}
	if (missobs) {
	    t2max--;
	} else {
	    break;
	}	
    }

    /* count missing values in mid-range of data */
    missobs = 0;
    for (t=t1min; t<=t2max; t++) {
	for (i=1; i<=list[0]; i++) {
	    vi = list[i];
	    if (vi == 0 || vi == LISTSEP) {
		continue;
	    }
	    if (na(dset->Z[vi][t])) {
		missobs++;
		break;
	    }
	}
    }
    
    T = t2max - t1min + 1;

    if (missobs == T) {
	err = E_MISSDATA;
    } else if (missobs > 0) {
	mask = calloc(T, 1); /* all NUL bytes */
	if (mask == NULL) {
	    err = E_ALLOC;
	} else {
	    for (t=t1min; t<=t2max; t++) {
		for (i=1; i<=list[0]; i++) {
		    vi = list[i];
		    if (vi == 0 || vi == LISTSEP) {
			continue;
		    }
		    if (na(dset->Z[vi][t])) {
			mask[t - t1min] = 1;
			break;
		    }
		}
	    }
	}
    }

#if TDEBUG
    fprintf(stderr, "tsls_adjust_sample: t1=%d, t2=%d, missobs=%d, ok obs=%d\n", 
	    t1min, t2max, missobs, t2max - t1min + 1 - missobs);
#endif

    if (!err) {
	dset->t1 = t1min; 
	dset->t2 = t2max;
    }

    *pmask = mask;

    return err;
}

/**
 * ivreg_process_lists:
 * @list: original specification list.
 * @reglist: location to receive regression list.
 * @instlist: location to receive list of instruments.
 *
 * Split the incoming list into its two components and perform
 * some basic checks; if the checks fail the two created
 * lists are destroyed.
 *
 * Returns: 0, on success, non-zero error code on failure.
 */

int ivreg_process_lists (const int *list, int **reglist, int **instlist)
{
    int *rlist, *zlist;
    int i, err, oid;

    err = gretl_list_split_on_separator(list, reglist, instlist);
    if (err) {
	fprintf(stderr, "gretl_list_split_on_separator: failed\n");
	return err;
    }

    rlist = *reglist;
    zlist = *instlist;

    if (rlist[0] < 2 || zlist == NULL || zlist[0] < 1) {
	err = E_ARGS;
    } else {
	for (i=1; i<=zlist[0]; i++) {
	    if (zlist[i] == list[1]) {
		gretl_errmsg_set(_("You can't use the dependent variable as an instrument"));
		err = E_DATA;
		break;
	    }
	}
    }
    
    if (!err) {
	oid = zlist[0] - rlist[0] + 1;
	if (oid < 0) {
	    gretl_errmsg_sprintf(_("The order condition for identification is not satisfied.\n"
				   "At least %d more instruments are needed."), -oid);
	    fprintf(stderr, "zlist[0] = %d, rlist[0] = %d\n", zlist[0], rlist[0]);
	    err = E_DATA; 
	}
    }

    if (err) {
	free(*reglist);
	free(*instlist);
	*reglist = NULL;
	*instlist = NULL;
    }

    return err;
}

/**
 * tsls:
 * @list: dependent variable plus list of regressors.
 * @dset: dataset struct.
 * @opt: may contain %OPT_R for robust VCV, %OPT_N for no df correction, 
 * %OPT_A if this is an auxiliary reression, %OPT_E if we're
 * estimating one equation within a system, %OPT_H to
 * add "hatlist" to model even under %OPT_E, %OPT_X to skip
 * the set of tests that are usually calculated.
 *
 * Estimate the model given in @list by means of Two-Stage Least
 * Squares.  If %OPT_E is given, fitted values from the first-stage
 * regressions are saved with the model.
 * 
 * Returns: a #MODEL struct, containing the estimates.
 */

MODEL tsls (const int *list, DATASET *dset, gretlopt opt)
{
    MODEL tsls;
    gretl_matrix *Q = NULL, *r = NULL;
    char *missmask = NULL;
    int orig_t1 = dset->t1, orig_t2 = dset->t2;
    int *reglist = NULL, *instlist = NULL;
    int *hatlist = NULL, *s2list = NULL;
    int *exolist = NULL, *endolist = NULL;
    int *idroplist = NULL;
    int addconst = 0;
    int nendo, nreg = 0;
    int orig_nvar = dset->v;
    int sysest = (opt & OPT_E);
    int no_tests = (opt & OPT_X);
    int OverIdRank = 0;
    int i, err = 0;

#if TDEBUG
    printlist(list, "tsls: incoming list");
#endif

    /* initialize model in case we bail out early on error */
    gretl_model_init(&tsls);

    gretl_error_clear();

    /* reglist: dependent var plus list of regressors
       instlist: list of instruments
    */
    tsls.errcode = ivreg_process_lists(list, &reglist, &instlist);
    if (tsls.errcode) {
	return tsls;
    }

    /* adjust sample range for missing observations */
    err = tsls_adjust_sample(list, dset, &missmask);

    if (!err) {
	/* allocate second stage regression list */
	s2list = gretl_list_copy(reglist);
	if (s2list == NULL) {
	    err = E_ALLOC;
	}
    }

    if (err) {
	goto bailout;
    }  

    /* in case we drop any instruments as redundant */
    if (sysest) {
	exolist = gretl_list_copy(instlist);
    }

    /* drop any vars that are all zero, and reshuffle the constant
       into first position among the independent vars 
    */
    tsls_omitzero(reglist, dset, missmask);
    reglist_check_for_const(reglist, dset);
    tsls_omitzero(instlist, dset, missmask);

#if TDEBUG
    printlist(reglist, "reglist");
    printlist(instlist, "instlist");
#endif

    /* Determine the list of variables (endolist) for which we need to
       obtain fitted values in the first stage.  Note that we accept
       the condition where there are no such variables (and hence
       TSLS is in fact redundant); this may sometimes be required
       when tsls is called in the context of system estimation.
    */
    endolist = tsls_make_endolist(reglist, &instlist, &addconst, &err);
    if (err) {
	goto bailout;
    }

#if TDEBUG
    printlist(endolist, "endolist");
#endif

    if (endolist != NULL) {
	nendo = endolist[0];
	hatlist = gretl_list_copy(endolist);
	if (hatlist == NULL) {
	    err = E_ALLOC;
	}
    } else {
	/* no endogenous regressors */
	nendo = 0;
    }

    if (!err) {
	Q = tsls_Q(instlist, &idroplist, dset, missmask, &err);
    }

    if (err) {
	goto bailout;
    } 

    /* check (again) for order condition: we do this after tsls_Q in case
       any instruments get dropped at that stage
    */
    nreg = reglist[0] - 1;
    OverIdRank = instlist[0] - nreg;
    if (OverIdRank < 0) {
        gretl_errmsg_sprintf(_("The order condition for identification is not satisfied.\n"
			       "At least %d more instruments are needed."), -OverIdRank);
	err = E_DATA; 
	goto bailout;
    }

#if TDEBUG
    printlist(endolist, "endolist, after tsls_Q");
    fprintf(stderr, "nreg = %d, OverIdRank = %d\n", nreg, OverIdRank);
#endif

    /* allocate storage for fitted vars (etc.), if needed */
    if (nendo > 0) {
	err = dataset_add_series(nendo, dset);
	if (!err) {
	    r = gretl_vector_alloc(Q->cols);
	    if (r == NULL) {
		err = E_ALLOC;
	    }
	}
	if (err) {
	    goto bailout;
	}
    }

    /* 
       Deal with the variables for which instruments are needed: loop
       across the list of variables to be instrumented (endolist),
       form the fitted values as QQ'x_i, and add these fitted values
       into the data array, dset->Z.
    */

    for (i=0; i<nendo; i++) {
	int v0 = endolist[i+1];
	int v1 = orig_nvar + i;
	
	/* form xhat_i = QQ'x_i */
	err = tsls_form_xhat(Q, r, dset, v0, v1, missmask);
	if (err) {
	    goto bailout;
	}

	/* substitute v1 into the right place in the second-stage
	   regression list */
	replace_list_element(s2list, v0, v1);

	/* and update hatlist */
	hatlist[i+1] = v1;
    }

    /* second-stage regression */
    tsls = lsq(s2list, dset, OLS, (sysest)? OPT_Z : OPT_NONE);
    if (tsls.errcode) {
	fprintf(stderr, "tsls, stage 2: tsls.errcode = %d\n", tsls.errcode);
	goto bailout;
    }

#if TDEBUG > 1
    PRN *prn = gretl_print_new(GRETL_PRINT_STDERR, &err);

    printmodel(&tsls, dset, OPT_S, prn);
    gretl_print_destroy(prn);
#endif

    if (tsls.list[0] < s2list[0]) {
	/* Were collinear regressors dropped? If so, adjustments are needed */
	OverIdRank += s2list[0] - tsls.list[0];
	reglist_remove_redundant_vars(&tsls, s2list, reglist, endolist,
				      hatlist);
	nendo = endolist[0];
#if TDEBUG
	fprintf(stderr, "tsls: dropped collinear vars\n");
#endif
    }

    /* record the number of instruments used */
    gretl_model_set_int(&tsls, "ninst", instlist[0]);

    if (!no_tests) {
	if (!sysest || (opt & OPT_H)) {
	    if (nendo == 1) {
		/* handles robust estimation, for single endogenous regressor */
		compute_first_stage_F(&tsls, endolist, reglist, instlist, 
				      dset, opt);
	    } else if (!(opt & OPT_R) && nendo > 0) {
		/* at present, only handles case of i.i.d. errors */
		compute_stock_yogo(&tsls, endolist, instlist, hatlist, dset);
	    }
	}
    } 

    if (nendo > 0) {
	/* special: we need to use the original RHS vars to compute
	   residuals and associated statistics */
	tsls_residuals(&tsls, reglist, dset, opt);
    }

    if (opt & OPT_R) {
	/* robust standard errors called for */
	qr_tsls_vcv(&tsls, dset, opt);
	if (tsls.errcode) {
	    goto bailout;
	}	
    } 

    if (nendo > 0) {
	/* compute additional statistics (R^2, F, etc.) */
	tsls_extra_stats(&tsls, reglist[1], OverIdRank, dset);
    }

    if (!sysest && !no_tests && nendo > 0) {
	if (hatlist != NULL) {
	    tsls_hausman_test(&tsls, reglist, hatlist, dset);
	}
	if (OverIdRank > 0) {
	    ivreg_sargan_test(&tsls, OverIdRank, instlist, dset);
	} else {
	    tsls_loglik(&tsls, nendo, reglist, instlist, dset);
	}
    }

    /* set command code on the model */
    tsls.ci = IVREG;

    /* write the full 2SLS list (dep. var. and regressors, followed by
       instruments, possibly purged of redundant elements) into the model
       for future reference
    */
    tsls_recreate_full_list(&tsls, reglist, instlist);

    if (idroplist != NULL) {
	gretl_model_set_list_as_data(&tsls, "inst_droplist", idroplist); 
    }

#if 0
    if (addconst) {
	gretl_model_set_int(&tsls, "addconst", addconst);
    }
#endif

 bailout:

    gretl_matrix_free(Q);
    gretl_matrix_free(r);
    free(missmask);

    if (err && !tsls.errcode) {
	tsls.errcode = err;
    }

    if (!tsls.errcode && endolist != NULL) {
	if (sysest) {
	    /* save first-stage fitted values */
	    tsls_save_data(&tsls, hatlist, exolist, dset);
	}
	if (!sysest || (opt & OPT_H)) {
	    /* save list of endogenous regressors on model */
	    gretl_model_set_list_as_data(&tsls, "endolist", endolist);
	    endolist = NULL; /* model takes ownership */
	}
    } 

    /* delete any first-stage fitted values from dataset */
    dataset_drop_last_variables(dset->v - orig_nvar, dset);

    free(reglist); 
    free(instlist);
    free(hatlist);
    free(exolist);
    free(endolist);
    free(s2list);

    if ((opt & OPT_A) || tsls.errcode) {
	model_count_minus();
    }

    /* restore original sample range */
    dset->t1 = orig_t1;
    dset->t2 = orig_t2;

    return tsls;
}


